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Channel and delay coefficient are two essential parameters for the characterization of a multipath propagation environment. It is crucial to generate realistic channel and delay coefficient in order to study the channel characteristics that involves signals propagating through environments with severe multipath effects. While many deterministic channel models, such as ray-tracing (RT), face challenges like high computational complexity, data requirements for geometrical information, and inapplicability for space-ground links, and nongeometry-based stochastic channel models (NGSCMs) might lack spatial consistency and offer lower accuracy, we present a scalable tutorial for the channel modeling of dual mobile space-ground links in urban areas, utilizing the Quasi Deterministic Radio Channel Generator (QuaDRiGa), which adopts a geometry-based stochastic channel model (GSCM), in conjunction with an International Telecommunication Union (ITU) provided state duration model. This tutorial allows for the generation of realistic channel and delay coefficients in a multipath environment for dual mobile space-ground links. We validate the accuracy of the work by analyzing the generated channel and delay coefficient from several aspects, such as received signal power and amplitude, multipath delay distribution, delay spread and Doppler spectrum.

A novel analytical approach to optimal base station (BS) location problem is proposed. It is based on the widely used system and propagation path models but, unlike known studies, makes use a convex optimization formulation to minimize the total transmit power subject to quality-of-service (QoS, rate) constraints. In contrast to the previously-proposed approaches, the sufficient Karush-Kuhn-Tucker (KKT) conditions are used here to characterize a globally (rather than locally)-optimum point as a convex combination of user locations, where convex weights depend on user parameters, path loss exponent and overall geometry of the problem. Based on this characterization, a number of novel closed-form solutions are obtained. In particular, the optimum BS location is shown to be the average of user locations in the case of unobstructed line-of-sight (LOS) propagation (the path loss exponent equals 2) and identical user parameters but not in general. If the user set is symmetric, the optimal BS location is independent of the pathloss exponent, which is not the case in general. The analytical results show the impact of propagation conditions (e.g. clear/obstructed LOS) as well as system and user parameters (bandwidth, rate demand, etc.) on optimal BS location: the higher the path loss exponent, the heavier the impact of distant users; users with higher rate demands have more impact. The obtained analytical results facilitate insights, which are unavailable from purely numerical studies and which can be used to develop design guidelines. Based on these results, an iterative algorithm is proposed and its convergence is proved. The single BS results are further extended to multi-BS scenarios (e.g. a cell cluster) using the K-means algorithm with proper modifications, so that the total (sum) BS power in a cell cluster is locally minimized, subject to user rate constrains. Numerical experiments validate the analytical solutions and show the effectiveness of the proposed algorithms. Overall, the emphasis is on an analytical framework, solutions and insights rather than on numerical algorithms.

Universal Subscriber Identity Module (USIM) is an essential part of the mobile network mainly for providing identification and authentication of the subscriber. The activation and deactivation of USIMs are the two most critical services that must be supported by Mobile Network Operators (MNOs). The current solutions suffer from several limitations such as the lack of round-the-clock services and the presence of a single point of failure. In this paper, we propose a blockchain-based scheme for USIM management. Each MNO creates its own smart contract and publishes its address to subscribers. Subscribers can then directly submit their requests by registering a transaction that invokes a specific function of the smart contract. The proposed scheme provides an anytime-anywhere service while at the same time it leverages the benefits of blockchain technology, such as a decentralized architecture that prevents Denial-of-Service (DoS) attacks, as well as a secure auditable log and payment using cryptocurrency. Moreover, we provide a security proof for the scheme through formal verification. Our results demonstrate that our scheme ensures subscriber privacy while providing mutual authentication among participants. Finally, our evaluation on the Ethereum blockchain confirms the efficiency of the scheme in terms of both transaction and execution costs.

In free-space optical satellite networks (FSOSNs), satellites can have different laser inter-satellite link (LISL) ranges for connectivity. As the LISL range increases, the number of satellites from among all the satellites in the constellation that will be needed on the shortest path between a source and a destination ground station decrease, and thereby the number of the LISLs on the shortest path decreases. Greater LISL ranges can reduce network latency of the path but can also result in an increase in transmission power for satellites on the path. Consequently, this tradeoff between satellite transmission power and network latency should be investigated, and in this work we examine it in FSOSNs drawing on the Starlink Phase 1 Version 3 (i.e., the latest version of Starlink's Phase 1) and Kuiper Shell 2 (i.e., Kuiper's biggest shell) constellations for different LISL ranges and different inter-continental connections. We use appropriate system models for calculating the average satellite transmission power (i.e., the average of the transmission power of all satellites on the shortest path) and network latency (i.e., the end-to-end latency of the shortest path). The results show that the mean network latency (i.e., the mean of network latency over all time slots) decreases and mean average satellite transmission power (i.e., the mean of average satellite transmission power over all time slots) increases with an increase in LISL range. For the Toronto–Sydney inter-continental connection in an FSOSN with Starlink's Phase 1 Version 3 constellation, when the LISL range is approximately 2,900 km, the mean network latency and mean average satellite transmission power intersect are approximately 135 ms and 380 mW, respectively. For an FSOSN with the Kuiper Shell 2 constellation in this inter-continental connection, this LISL range is around 3,800 km, and the two parameters are approximately 120 ms and 700 mW, respectively. For the Toronto–Istanbul and Toronto–London inter-continental connections, the LISL ranges at the intersection are different and vary from 2,600 km to 3,400 km. Furthermore, we analyze outage probability performance of optical uplink/downlink due to atmosphere attenuation and turbulence.

We present an automatic signal modulation classification model using combinatorial deep learning technique. Our proposed deep learning model increase accuracy for low Signal-to-Noise Ratio (SNR) and maintain a high classification accuracy for high SNR signals. Using a hybrid deep learning model combining both ConvLSTM with Transformer-block neural networks, the proposed modulation classifier architecture can learn the signal for both low and high SNR and get better accuracy for signals with high noise. The proposed deep learning modulation classification technique achieves improved classification accuracy of 66% for low SNR signals and 93.5% at high SNR showing that our model is robust under noisy signal modulation. Thus, getting better accuracy in lower SNR signals without sacrifice accuracy for higher SNR signals. An adaptive weighted focal loss function is proposed as an optimized loss function for efficient classification which can be used to control the outliers within a class imbalance and avoid underflow issues. Our deep learning radio modulation classification model works using raw signal without the need of denoising the noisy signal.

Anomaly detection for the Internet of Things (IoT) is a major intelligent service required by many fields, including intrusion detection, state monitoring, device-activity analysis, and security supervision. However, the heterogeneous distribution of data and resource-constrained end nodes in ubiquitous IoT systems present challenges for existing anomaly detection models. Due to the advantages of flexible deployment and multi-dimensional resources, high altitude platform stations (HAPSs) and unmanned aerial vehicles (UAVs), which are important components of vertical heterogeneous networks (VHetNets), have significant potential for sensing, computing, storage, and communication applications in ubiquitous IoT systems. In this paper, we propose a novel VHetNet-enabled asynchronous federated learning (AFL) framework by adopting the compound-action actor-critic (CA2C) algorithm for UAV selection, which enables decentralized UAVs to collaboratively train a global anomaly detection model based on their local sensory data from IoT devices. In the VHetNet-enabled AFL framework, the UAV selection process aims to prevent UAVs with low local model quality and large energy consumption from affecting the learning efficiency and model accuracy. Due to the wide coverage as well as strong storage and computation capabilities, a HAPS operates as a central aerial server for aggregating local models of UAVs asynchronously and making decisions intelligently. Moreover, we propose a CA2C-based joint device association, UAV selection, and UAV placement algorithm to further enhance the overall federated execution efficiency and detection model accuracy under UAV energy constraints. Extensive experimental evaluation on real-world datasets demonstrates that the proposed algorithm can achieve high detection accuracy with short federated execution time and low energy consumption.

Federated Learning (FL) is a decentralized machine learning (ML) technique that allows a number of participants to train an ML model collaboratively without having to share their private local datasets with others. When participants are unmanned aerial vehicles (UAVs), UAV-enabled FL would experience heterogeneity due to the majorly skewed (non-independent and identically distributed -IID) collected data. In addition, UAVs may demonstrate unintentional misbehavior in which the latter may fail to send updates to the FL server due, for instance, to UAVs' disconnectivity from the FL system caused by high mobility, unavailability, or battery depletion. Such challenges may significantly affect the convergence of the FL model. A recent way to tackle these challenges is client selection, based on customized criteria that consider UAV computing power and energy consumption. However, most existing client selection schemes neglected the participants' reliability. Indeed, FL can be targeted by poisoning attacks, in which malicious UAVs upload poisonous local models to the FL server, by either providing targeted false predictions for specifically chosen inputs or by compromising the global model's accuracy through tampering with the local model. Hence, we propose in this article a novel client selection scheme that enhances convergence by prioritizing fast UAVs with high-reliability scores, while eliminating malicious UAVs from training. Through experiments, we assess the effectiveness of our scheme in resisting different attack scenarios, in terms of convergence and achieved model accuracy. Finally, we demonstrate the performance superiority of the proposed approach compared to baseline methods.

In free-space optical satellite networks (FSOSNs), satellites connected via laser inter-satellite links (LISLs), latency is a critical factor, especially for long-distance inter-continental connections. Since satellites depend on solar panels for power supply, power consumption is also a vital factor. We investigate the minimization of total network latency (i.e., the sum of the network latencies of all inter-continental connections in a time slot) in a realistic model of a FSOSN, the latest version of the Starlink Phase 1 Version 3 constellation. We develop mathematical formulations of the total network latency over different LISL ranges and different satellite transmission power constraints for multiple simultaneous inter-continental connections. We use practical system models for calculating network latency and satellite optical link transmission power, and we formulate the problem as a binary integer linear program. The results reveal that, for satellite transmission power limits set at 0.5 W, 0.3 W, and 0.1 W, the average total network latency for all five inter-continental connections studied in this work levels off at 339 ms, 361 ms, and 542 ms, respectively. Furthermore, the corresponding LISL ranges required to achieve these average total network latency values are 4500 km, 3000 km, and 1731 km, respectively. Different limitations on satellite transmission power exhibit varying effects on average total network latency (over 100 time slots), and they also induce differing changes in the corresponding LISL ranges. In the absence of satellite transmission power constraints, as the LISL range extends from the minimum feasible range of 1575 km to the maximum feasible range of 5016 km, the average total network latency decreases from 589 ms to 311 ms.

Passive Intermodulation (PIM) is a well-known phenomenon that poses a significant challenge to modern wireless communication systems. Although efforts have been made to reduce or mitigate the PIM through hardware design, frequency planning, and/or band separation techniques, the impact of PIM on radio performance remains significant. This is especially true for radio transceivers operating in the frequency division duplex mode, which are known to be susceptible to PIM interference. Moreover, with the adoption of carrier aggregation and advanced multi-antenna technology, PIM is increasingly becoming a major problem in multi-band multi-standard radio systems. Therefore, innovative approaches that exploit PIM detection, avoidance, and cancellation techniques are required to effectively mitigate or reduce its impact. In response to these challenges, this study highlights various PIM interference mechanisms and their interactions which cause potential receiver sensitivity degradation. The discussion further explores PIM mitigation strategies, and underscores the potential of digital PIM cancellation solutions to offer a hardware-independent resolution for reducing PIM impact in multi-band radios, in the 5G era and beyond in radio systems.

Non-orthogonal multiple access (NOMA) has emerged as a promising solution for enabling massive connectivity in future wireless networks. A great deal of research has extensively considered the implementation of the NOMA with other technologies such as multi-antenna and cooperative communications. Most of the previous studies focused on investigating the downlink NOMA networks, while the uplink papers received relatively less attention. Besides, the majority of the current studies on uplink NOMA schemes have neglected practical limitations. Motivated by this, we investigate the uplink single-input multiple-output cooperative NOMA (SIMO-CNOMA) performance in the presence of hardware impairment (HWI), imperfect channel state information (ipCSI), and imperfect successive interference cancellation (ipSIC). We expand the scope of the proposed system to encompass multiple relay schemes, within which the selection relay technique is executed. We derive the outage probability (OP) and the system throughput of the considered system over the Rayleigh fading channels. In this respect, we provide performance analysis for different combinations of relays and antennas. The OP and system throughput analytical expressions are validated through computer simulations. In addition, the effects of HWI, ipCSI, and ipSIC on the performance of the uplink SIMO-CNOMA are discussed. The results show that the system’s performance can be improved with an increase in the relays and antenna numbers. Also, there is an error floor at the high signal-to-noise ratio (SNR) in ideal conditions (i.e., in the absence of HWI, ipCSI, and ipSIC), and the error floor is increased in non-ideal conditions (i.e., in the presence of HWI, ipCSI, and ipSIC). The arbitrary number of users in the presence of non-ideal conditions increases the error and reduces the system’s performance. Finally, although the performance gains obtained through the use of multiple antennas and relays, the adverse effects of HWI, ipCSI, and ipSIC on system performance cannot be avoided.

Non-orthogonal multiple access (NOMA) was identified as a technology enabler for the fifth generation and beyond networks, due to the prominent advantages achieved when integrating NOMA with other wireless technologies. In this article, we investigate the interplay between NOMA and generalized space shift keying (GSSK) in a hybrid NOMA-GSSK (N-GSSK) network. Specifically, we provide a comprehensive analytical framework and propose a novel energy-based N-GSSK detector for the reliable realization of N-GSSK systems. The proposed receiver is energy-efficient and enjoys low complexity, as it exploits the energy of the received signals and does not require the knowledge of NOMA signals. To quantify its efficiency, we further investigate the performance of the proposed detector in terms of pairwise error probability, bit error rate union bound, and achievable rate. The accuracy of the developed mathematical framework is corroborated through Monte-Carlo simulations, which show that N-GSSK outperforms conventional NOMA and GSSK, particularly in terms of spectral efficiency.

Since the outbreak of the COVID-19 crisis, the transition to remote education presented several challenges to educational institutions. Unlike face-to-face classes where educators can modify and keep track of the lessons and content according to the students’ observed emotions and participation, such activities are difficult to complete in online learning environments. To address this issue, we propose here a novel and comprehensive framework that leverages advanced computer vision and analysis techniques to detect students’ emotions during online learning and assess their state of mind regarding the taught content. Our framework is composed of three modules. The first module uses a novel lightweight machine learning method, called convolutional neural network-random forest (CNN-RF), to efficiently detect the students’ basic emotions, e.g., sad, happy, etc., during the online course. Our approach surpasses existing benchmarks in terms of accuracy (over 71%) on the FER-2013 dataset, while being less complex (i.e., using a smaller number of parameters). The second module consists of mapping the basic emotions to an education-aware state of mind, e.g., interest, boredom, distraction, etc. Unlike the few works that proposed simplistic mapping, we propose here a Plutchik wheel’s inspired mapping system, which is more precise and reflects better the relationship between combinations of basic emotions and the resulting education-aware state of mind. Thus, our understanding of the students’ cognitive and affective experiences during online learning can be enhanced. The third module is a visualization dashboard that offers clear and intuitive real-time representations of basic emotions and states of mind. This tool provides educators with invaluable insights into students’ emotional dynamics, enabling them to identify learning difficulties with high precision and make informed recommendations for improvements in course content and online teaching methods. In summary, the proposed framework presents a novel and powerful tool that addresses the challenges related to online learning. By accurately detecting the students’ emotions, assessing their states of mind, and providing real-time visualization, our approach represents a significant advancement toward the optimization of online education, which is critically needed in rural and remote areas of the globe.

Non-orthogonal multiple access (NOMA) is currently one of the promising techniques for the 6th generation (6G) wireless mobile networks, which can be combined with different technologies, such as cooperative communications and radio frequency (RF) wireless power transfer. RF can transmit energy over a wireless medium and has been seen as an essential application of systems. On the other hand, due to mismatched components and poor circuit fabrications, the transceiver suffers from RF front-end effects in actual situations, such as in-phase and quadrature-phase imbalance (IQI) which degrade the performance of the system. In this paper, we investigate the harvest-then-cooperate assisted NOMA for a wireless-powered cooperative communication network (HTC-NOMA-WPCCN) with practical constraints such as IQI and imperfect successive interference cancellation (ISIC). We thereafter extend the analysis to the multi-helper-user scheme to improve the performance of our considered system. The linear and non-linear EH are considered in our proposed system. We analyze the outage probability (OP), ergodic capacity (EC) and throughput. We discuss the effect of the IQI, ISIC, image rejection ratio and power allocation on the proposed HTC-NOMA-WPCCN. Our theoretical analysis is validated by Monte Carlo simulations. The simulation results demonstrate that the IQI and ISIC can significantly degrade the OP, EC and throughput performances of HTC-NOMA-WPCCN. The linear EH achieves better performance gain than the non-linear EH. Furthermore, this latter is influenced more by IQI and SIC imperfections, which closely approach a practical transmission. On the other hand, we compare the proposed system with UL NOMA without EH, and the results clearly showed the superiority of the proposed system. Finally, the system performance is influenced by changes in image rejection ratio, power allocation and WPCCN parameters which effects the IQI and SIC on the RF impairments.

In this paper, we present the energy efficiency (EE) analysis of a multi-tier cellular network, where intra- and inter-tier dependence in the base station (BS) locations are captured via point processes, i.e., three variants of hard-core point processes (HCPPs) and the Poisson hole process (PHP). The analytical expression for EE is derived by approximating both the average power consumption of BSs and the coverage probability using an approximate signal-to-interference ratio analysis based on the Poisson point process (PPP). It is demonstrated that the proposed Matérn HCPP Type-I-PHP model provides better EE results compared to the other HCPP-PHP and PPP models.

This paper proposes a computationally efficient two-stage machine learning based approach using neural networks to solve the cluster assignment problem in a millimeter wave-non orthogonal multiple access (mmWave-NOMA) system where each user’s individual successive interference cancellation (SIC) decoding capabilities are taken into consideration. The artificial neural network (ANN) is applied in real time to assign users to clusters taking each user’s instantaneous channel state information (CSI) and SIC decoding capabilities as inputs. The algorithm is trained offline on cloud resources, i.e., not using the base station (BS) compute resources. This training is done using a dataset obtained by offline computation of input parameters using the optimization algorithms called NOMA-minimum exact cover (NOMA-MEC) and NOMA-best beam (NOMA-BB) from our earlier work in this area. As a result, we term the proposed algorithms in this paper as ANN-NOMA-MEC and ANN-NOMA-BB, respectively. The problem with applying optimization techniques, even low-complexity heuristics, in live networks for user clustering is that they require a very large number of computation steps to make a clustering decision. If these clustering decisions are based on the instantaneous channel of hundreds of users, it becomes prohibitively complex to implement in practical systems on a millisecond granularity as required by beyond 5G (B5G) systems. Instead, our proposed approach takes all this complexity offline and even off the BS compute resources and instead only applies a trained neural network to make such clustering decisions at a microsecond granularity on hundreds of users. Simulation results show the effectiveness of the ANN-NOMA-MEC and ANN-NOMA-BB schemes as the neural network trained on offline simulation data performs comparably with the NOMA-MEC and NOMA-BB heuristics that is applying computationally intensive algorithms to make every clustering decision in a live network.

In this work, a novel energy efficient multi-objective resource allocation algorithm for heterogeneous cloud radio access networks (H-CRANs) is proposed where the trade-off between increasing throughput and decreasing operation cost is considered. H-CRANs serve groups of users through femto-cell access points (FAPs) and remote radio heads (RRHs) equipped with massive multiple input multiple output (MIMO) connected to the base-band unit (BBU) pool via front-haul links with limited capacity. We formulate an energy-efficient multi-objective optimization (MOO) problem with a novel utility function. Our proposed utility function simultaneously improves two conflicting goals as total system throughput and operation cost. With this MOO, we jointly assign the sub-carrier, transmit power, access point (AP)(RRH/FAP), RRH, front-haul link, and BBU. To address the conflicting objectives, we convert the MOO problem into a single-object optimization problem using an elastic-constraint scalarization method. With this approach, we flexibly adjust trade-off parameters to choose between two objective functions. To propose an efficient algorithm, we deploy successive convex approximation (SCA) and complementary geometric programming (CGP) approaches. Finally, via simulation results we discuss how to select the values of trade-off parameters, and we study their effects on conflicting objective functions (i.e., throughput and operation cost in MOO problem). Simulation results also show that our proposed approach can offload traffic from C-RANs to FAPs with low transmit power and thereby reduce operation costs by switching off the under-utilized RRHs and BBUs. It can be observed from the simulation results that the proposed approach outperforms the traditional approach in which each user is associated to the AP (RRHs/FAPs) with the largest average value of signal strength. The proposed approach reduces operation costs by 30 % and increases throughput index by 25 % which in turn leads to greater energy efficiency (EE).

This paper studies the application of neural networks to Viterbi detection of FTN signals in an intersymbol interference (ISI) channel. The main contribution of this paper is to propose a receiver structure for detecting FTN signals in unknown static ISI channel. In particular, we propose a novel low-complexity neural network structure for calculating the branch metrics, and we explore its suitability for FTN signalling with channel uncertainty. We compare the proposed network, which we call the Metric Net (MetNet), to a benchmark neural network-based technique for metric calculation, the ViterbiNet, which was originally designed for ISI channels. The simulation results confirm that the MetNet outperforms the ViterbiNet, with two orders of magnitude lower complexity, and is much more resilient to channel uncertainty than the traditional Viterbi detector, which uses Euclidean distance for metric calculations. We further show that the MetNet exhibits robustness to being trained at mismatched SNR values and FTN pulse acceleration factors, meaning that the number of trained models required can be significantly reduced. Additionally, the results show that the proposed MetNet remains a favorable alternative at much higher levels of channel uncertainties. The results also reflect that we can generalize the MetNet to work with different channel models defined by different decaying factors. Finally, we show that we succeed in achieving a bandwidth efficiency gain of 33% due to FTN by using the MetNet in the presence of channel uncertainty.

Significant efforts are being made to integrate satellite and terrestrial networks into a unified wireless network. One major aspect of such an integration is the use of unified user terminals (UTs), which work for both networks and can switch seamlessly between them. However, supporting broadband connectivity for handheld UTs directly from low Earth orbit (LEO) satellite networks is very challenging due to link budget reasons. This paper proposes using distributed massive multiple-input multiple-output (DM-MIMO) techniques to improve the data rates of handheld devices with a view to supporting their broadband connectivity by exploiting the ultra-dense deployment of LEO satellites and high-speed inter-satellite links. In this regard, we discuss DM-MIMO-based satellite networks from different perspectives, including the channel model, network management, and architecture. In addition, we evaluate the performance of such networks theoretically by deriving closed-form expressions for spectral efficiency and using extensive simulations based on actual data from a Starlink constellation. The performance is compared with that of collocated massive MIMO connectivity (CMMC) and single-satellite connectivity (SSC) scenarios. The simulation results validate the analytical results and show the superior performance of DM-MIMO-based techniques compared to CMMC and SSC modes for improving the data rates of individual users.

6G and beyond networks tend towards fully intelligent and adaptive design in order to provide better operational agility in maintaining universal wireless access and supporting a wide range of services and use cases while dealing with network complexity efficiently. Such enhanced network agility will require developing a self-evolving capability in designing both the network architecture and resource management to intelligently utilize resources, reduce operational costs, and achieve the coveted quality of service (QoS). To enable this capability, the necessity of considering an integrated vertical heterogeneous network (VHetNet) architecture appears to be inevitable due to its high inherent agility. Moreover, employing an intelligent framework is another crucial requirement for self-evolving networks to deal with real-time network optimization problems. Hence, in this work, to provide a better insight into network architecture design in support of self-evolving networks, we highlight the merits of integrated VHetNet architecture while proposing an intelligent framework for self-evolving integrated vertical heterogeneous networks (SEI-VHetNets). The impact of the challenges associated with SEI-VHetNet architecture, on network management is also studied considering a generalized network model. Furthermore, the current literature on network management of integrated VHetNets along with the recent advancements in artificial intelligence (AI)/machine learning (ML) solutions are discussed. Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are identified. Finally, the potential future research directions for advancing the autonomous and self-evolving capabilities of SEI-VHetNets are discussed.

As the services and requirements of next-generation wireless networks become increasingly diversified, it is estimated that the current frequency bands of mobile network operators (MNOs) will be unable to cope with the immensity of anticipated demands. Due to spectrum scarcity, there has been a growing trend among stakeholders toward identifying practical solutions to make the most productive use of the exclusively allocated bands on a shared basis through spectrum sharing mechanisms. However, due to the technical complexities of these mechanisms, their design presents challenges, as it requires coordination among multiple entities. To address this challenge, in this paper, we begin with a detailed review of the recent literature on spectrum sharing methods, classifying them on the basis of their operational frequency regime—that is, whether they are implemented to operate in licensed bands (e.g., licensed shared access (LSA), spectrum access system (SAS), and dynamic spectrum sharing (DSS)) or unlicensed bands (e.g., LTE-unlicensed (LTE-U), licensed assisted access (LAA), MulteFire, and new radio-unlicensed (NR-U)). Then, in order to narrow the gap between the standardization and vendor-specific implementations, we provide a detailed review of the potential implementation scenarios and necessary amendments to legacy cellular networks from the perspective of telecommunication vendors and regulatory bodies. Next, we analyze applications of artificial intelligence (AI) and machine learning (ML) techniques for facilitating spectrum sharing mechanisms and leveraging the full potential of autonomous sharing scenarios. Finally, we conclude the paper by presenting open research challenges, which aim to provide insights into prospective research endeavors.

The global averaged civilian positioning accuracy is still at meter level for all existing Global Navigation Satellite Systems (GNSSs), and the performance is even worse in urban areas. At lower altitudes than satellites, high altitude platform stations (HAPS) offer several benefits, such as lower latency, less pathloss, and likely smaller overall estimation error for the parameters associated in the pseudorange equation. HAPS can support GNSSs in many ways, and in this paper we treat the HAPS as another type of ranging source. In so doing, we examine the positioning performance of a HAPS-aided GPS system in an urban area using both a simulation and physical experiment. The HAPS measurements are unavailable today; therefore, they are modeled in a rather simple but logical manner in both the simulation and physical experiment. We show that the HAPS can improve the horizontal dilution of precision (HDOP), the vertical dilution of precision (VDOP), and the 3D positioning accuracy of GPS in both suburban and dense urban areas. We also demonstrate the applicability of a RAIM algorithm for the HAPS-aided GPS system, especially in the dense urban area.

In the next generation (6G) wireless networks, the integration of terrestrial and non-terrestrial networks is essential to provide flawless connectivity over the globe. The vital element of this architecture is the high altitude platform station (HAPS) systems, which can provide reliable and ubiquitous connectivity among satellites, unmanned aerial vehicles (UAVs), and terrestrial users. Motivated by the importance of HAPS systems, in this paper, we provide three different use-cases for HAPS eavesdropping scenarios and investigate their physical layer security (PLS) performances. To quantify the PLS performance of the proposed setups, we perform secrecy outage probability (SOP), probability of positive secrecy (PPSC), average secrecy capacity (ASC), and secrecy throughput (ST) analyses. Furthermore, we also provide important design guidelines that can be beneficial for secure HAPS systems.

Non-orthogonal multiple access (NOMA) has been a strong candidate to support massive connectivity in future wireless networks. In this regard, its implementation into cooperative relaying, named cooperative-NOMA (CNOMA), has received tremendous attention from researchers. However, most of the existing CNOMA studies have failed to address practical constraints since they assume ideal conditions. Particularly, the error performance of CNOMA schemes with imperfections has not been investigated yet. In this paper, we provide an analytical framework for error and outage performance of CNOMA schemes under practical assumptions where we take into account imperfect successive interference canceler (ipSIC), imperfect channel state information (ipCSI), and hardware impairments (HWI) at the transceivers. We derive analytical expressions of bit error rate (BER) expressions in CNOMA schemes whether the direct links between source and users exist or not which is, to the best of the authors’ knowledge, the first study in the open literature. We also derive the outage probability (OP) expressions for CNOMA schemes with and without direct links under practical assumptions. For comparisons, we provide BER and OP expressions for downlink NOMA with practical constraints which also have not been given in the literature, yet. The theoretical BER and OP expressions are validated with computer simulations where the perfect match is observed. Finally, we discuss the effects of the system parameters (e.g., power allocation, HWI level, ipCSI factor) on the performance of CNOMA schemes to reveal fruitful insights for society. The results demonstrate that the HWI, ipCSI and ipSIC have a significant effect on the performance of the systems.

In this paper, we develop a low-cost cellular internet of medical things (IoMT)-based electrocardiogram (ECG) recorder for monitoring heart conditions and used in practical cases. In order to remove noise from signals recorded by these non-clinical devices, we propose a cloud-based denoising approach that focuses on utilizing deep neural network techniques in the time-frequency domain through the two stages. Accordingly, we exploit the fractional Stockwell transform (FrST) to transfer the ECG signal into the time-frequency domain and apply the deep robust two-stage network (DeepRTSNet) for noise cancellation. Due to the practical use case, the various heart physiologies and noise levels in different amplitudes and frequencies are needed to be robust against wide-range noises in actual conditions. We utilize the MIT-BIH Apnea-ECG database (APNEA-ECG) with several different heart physiologies. Next, the different noises consisting of muscle artifacts (MA), baseline wander (BW), and electrode motion (EM) from the MIT-BIH Noise Stress Test Database (NSTDB) and random noise, are added to the signals. The main focus of the noise generation part is the fast Fourier transformation (FFT) of the simulated noisy signal and the practical noisy signal has a maximum cross-correlation to gain a better morphological resemblance between realistic signals and the prepared datasets. Based on the results, DeepRTSNet outperforms prior learning-based methods and conventional non-learning approaches in terms of signal-to-noise ratio (SNR), root mean square error (RMSE), and percent root mean square difference (PRD). Moreover, outcomes reveal that DeepRTSNet has an extraordinary performance with a certain amount of further complexity than others.

Massive swarms of low Earth orbit (LEO) satellites are poising to serve for high-speed and low-latency ubiquitous connectivity with almost global coverage. Broadband inter-satellite communication is one of the key elements of satellite communication systems that orchestrate massive satellite swarms in cooperation. Thanks to technological advancements in microelectronics and micro-systems, the terahertz (THz) band has emerged as a strong candidate for inter-satellite links (ISLs) due to its promise of wideband communication. Especially, multi-antenna systems can improve the system performance along with the wideband supported by the THz band. However, multi-antenna systems should be reconsidered due to their size, weight, and price/power (SWaP) constraints. On the other hand, as a state-of-art multi-antenna technology, reconfigurable intelligent surface (RIS) is able to relax SWaP constraints because of its passive component-based structures. However, as similar reflection characteristic throughout wideband is difficult to meet, it is possible to observe beam misalignment. In this work, we first provide an assessment of the use of the THz band for ISLs and quantify the impact of misalignment fading on error performance. Then, in order to compensate for the high path loss associated with high carrier frequencies, and to further improve the signal-to-noise ratio (SNR), we propose the use of RISs mounted on neighboring satellites to enable signal propagation. Based on a mathematical analysis of the problem, we present the error rate expressions for RIS-assisted ISLs with misalignment fading. Also, numerical results show that RIS can leverage the error rate performance and achievable capacity of THz ISLs.

The ultra-dense deployment of interconnected satellites will characterize future low Earth orbit (LEO) mega-constellations. Exploiting this towards a more efficient satellite network (SatNet), this paper proposes a novel LEO SatNet architecture based on distributed massive multiple-input multipleoutput (DM-MIMO) technology allowing ground user terminals to be connected to a cluster of satellites. To this end, we investigate various aspects of DM-MIMO-based satellite network design, the benefits of using this architecture, the associated challenges, and the potential solutions. In addition, we propose a distributed joint power allocation and handover management (D-JPAHM) technique that jointly optimizes the power allocation and handover management processes in a cross-layer manner. This framework aims to maximize the network throughput and minimize the handover rate while considering the quality-of-service (QoS) demands of user terminals and the power capabilities of the satellites. Moreover, we devise an artificial intelligence (AI)-based solution to efficiently implement the proposed D-JPAHM framework in a manner suitable for real-time operation and the dynamic SatNet environment. To the best of our knowledge, this is the first work to introduce and study DM-MIMO technology in LEO SatNets. Extensive simulation results reveal the superiority of the proposed architecture and solutions compared to conventional approaches in the literature.

The undeniable potential of computation offloading has been attracting attention from researchers for more than a decade. With advances in multi-access edge computing (MEC), computation offloading has become a more critical issue because of the heterogeneity in the computational power of edge devices and the elevated importance of extending their lifespan. Due to the apparent advantages, the use of MEC in 6G networks, where a vertical heterogeneous network composed of space, air, and ground networks is only natural. The non-terrestrial networking elements constitute effective computational resources. However, recent research investigating the potential of computational offloading in 6G networks has involved models that do not adequately reflect the complexity of the underlying processes. In this study, we propose a realistic computation model for 6G networks that considers crucial properties of the offloaded job, including the inter-dependency of the job tasks and the decomposability of the job. Our model is based on the mature application domain of MEC, where proven solutions are already studied. We also investigate the potential of a high altitude platform station (HAPS)-aided MEC platform using this model. The proposed model allows us to design offloading strategies to enable adaptive computational offloading. Through numerical analyses, we show that the proposed model provides sufficient insight to reduce the total processing time significantly.

Cell outage compensation enables a network to react to a catastrophic cell failure quickly and serve users in the outage zone uninterruptedly. Utilizing the promising benefits of non-orthogonal multiple access (NOMA) for improving the throughput of cell edge users, we propose a newly NOMA-based cell outage compensation scheme. In this scheme, the compensation is formulated as a mixed integer non-linear program (MINLP) where outage zone users are associated to neighboring cells and their power are allocated with the objective of maximizing spectral efficiency, subject to maintaining the quality of service for the rest of the users. Owing to the importance of immediate management of cell outage and handling the computational complexity, we develop a low-complexity suboptimal solution for this problem in which the user association scheme is determined by a newly heuristic algorithm, and power allocation is set by applying an innovative deep neural network (DNN). The complexityof our proposed method is in the order of polynomial basis, which is much less than the exponential complexity of finding an optimal solution. Simulation results demonstrate thatthe proposed method approaches the optimal solution. Moreover, the developed scheme greatly improves fairness and increases the number of served users.

In this paper, we investigate a resource allocation problem for a Cellular Vehicle to Everything (C-V2X) network to improve energy efficiency of the system. To address this problem, self-organizing mechanisms are proposed for joint and disjoint subcarrier and power allocation procedures which are performed in a fully distributed manner. A multi-agent Q-learning algorithm is proposed for the joint power and subcarrier allocation. In addition, for the sake of simplicity, it is decoupled into two sub-problems: a subcarrier allocation sub-problem and a power allocation sub-problem. First, to allocate the subcarrier among users, a distributed Q-learning method is proposed. Then, given the optimal subcarriers, a dynamic power allocation mechanism is proposed where the problem is modeled as a non-cooperative game. To solve the problem, a no-regret learning algorithm is utilized. To evaluate the performance of the proposed approaches, other learning mechanisms are used which are presented in Fig. 8. Simulation results show the multi-agent joint Q-learning algorithm yields significant performance gains of up to about 11% and 18% in terms of energy efficiency compared to proposed disjoint mechanism and the third disjoint Q-learning mechanism for allocating the power and subcarrier to each user; however, the multi-agent joint Q-learning algorithm uses more memory than disjoint methods.

We consider the problem of designing a low-complexity decoder for antipodal uniquely decodable (UD) /errorless code sets for overloaded synchronous code-division multiple access (CDMA) systems, where the number of signals $K_{\mathrm{max}}^{a}$ is the largest known for the given code length $L$ . In our complexity analysis, we illustrate that compared to maximum-likelihood (ML) decoder, which has an exponential computational complexity for even moderate code lengths, the proposed decoder has a quasi-quadratic computational complexity. Simulation results in terms of bit-error-rate (BER) demonstrate that the performance of the proposed decoder has only a $1-2$ dB degradation in signal-to-noise ratio (SNR) at a BER of 10−3 when compared to ML. Moreover, we derive the proof of the minimum Manhattan distance of such UD codes and we provide the proofs for the propositions; these proofs constitute the foundation of the formal proof for the maximum number users $K_{\mathrm{max}}^{a}$ for $L=8$ .

Communications are realized as a result of successive decisions at the physical layer, from modulation selection to multi-antenna strategy, and each decision affects the performance of the communication systems. Future communication systems must include extensive capabilities as they will encompass a wide variety of devices and applications. Conventional physical layer decision mechanisms may not meet these requirements, as they are often based on impractical and oversimplifying assumptions that result in a trade-off between complexity and efficiency. By leveraging past experiences, learning-driven designs are promising solutions to present a resilient decision mechanism and enable rapid response even under exceptional circumstances. The corresponding design solutions should evolve following the lines of learning-driven paradigms that offer more autonomy and robustness. This evolution must take place by considering the facts of real-world systems and without restraining assumptions. In this paper, the common assumptions in the physical layer are presented to highlight their discrepancies with practical systems. As a solution, learning algorithms are examined by considering the implementation steps and challenges. Furthermore, these issues are discussed through a real-time case study using software-defined radio nodes to demonstrate the potential performance improvement. A cyber-physical framework is presented to incorporate future remedies.

Future integrated terrestrial, aerial, and space networks will involve thousands of low-Earth-orbit (LEO) satellites, which will form a network of mega-constellations. These mega-constellations will play a significant role in providing communication and Internet services anywhere, at any time, and for everything. Due to the large scale and highly dynamic nature of future LEO satellite networks (SatNets), their management will be a complicated process, especially the aspect of mobility management and its two components: location management and handover management. In this article, we present a comprehensive and critical review of the state-of-the-art research in location management for LEO SatNets. First, we give an overview of the Internet Engineering Task Force (IETF) mobility management standards (e.g., Mobile IPv6 and Proxy Mobile IPv6) and discuss the limitations of their location management techniques for future LEO SatNets. We highlight the mobility characteristics of future LEO SatNets and their challenging features, and we describe two unprecedented future location management scenarios. A taxonomy of existing location management solutions for LEO SatNets is also presented with solutions classified according to three approaches. The “Issues to consider” section draws attention to critical points related to each of the reviewed approaches that should be considered in future LEO SatNets location management. To identify the research gaps, the current state of LEO SatNets location management is summarized. Noteworthy future research directions are recommended. The article provides a road map for researchers and industry to shape the future of location management for LEO SatNets.

In this paper, we study the secrecy performance of mixed radio-frequency (RF) - free space optical (FSO) systems by considering both RF and FSO eavesdropper attacks. More precisely, we shed light into the design of secure mixed RF-FSO relay networks by questioning two critical design problems: 1) What are the main parameters in the design of secure RF-FSO relay networks? 2) How can we improve the confidentiality of the optical information? To do so, we derive the secrecy outage probability and the probability of positive secrecy capacity performance for mixed RF-FSO relaying schemes under RF and FSO eavesdropper attacks, respectively. To justify the problem, we consider two general fading models, Nakagami-m at the RF side and exponentiated Weibull distribution at the FSO side. The results, which are validated with the Monte-Carlo simulations, show that both the location and the size of the photo-aperture is very important in the design of RF-FSO relay networks to enhance the secrecy performance of the overall system.

Low Earth Orbit (LEO) Satellite Network (SatNet) with their mega-constellations are expected to play a key role in providing ubiquitous Internet and communications services in the future. LEO SatNets will provide wide-area coverage and support service availability, continuity, and scalability. To support the integration of SatNets and terrestrial Fifth Generation (5G) networks and beyond, the satellite communication industry has become increasingly involved with the 3rd Generation Partnership Project (3GPP) standardization activities for 5G. In this work, we review the 3GPP standardization activities for the integration of SatNets in 5G and beyond. The 3GPP use cases of SatNets are highlighted and potential requirements to realize them are summarized as well. The impacted areas of New Radio (NR) are discussed with some potential solutions. The foreseen requirements for the management and orchestration of SatNets within 5G are described. Future standardization directions are discussed to support the full integration of SatNets in Sixth Generation (6G) with the goal of ubiquitous global connectivity.

Laser inter-satellite links (LISLs) between satellites in a free-space optical satellite network (FSOSN) can be divided into two classes: permanent LISLs (PLs) and temporary LISLs (TLs). TLs are not desirable in next-generation FSOSNs (NG-FSOSNs) due to high LISL setup time, but they may become feasible in next-next-generation FSOSNs (NNG-FSOSNs). Using the satellite constellation for Phase I of Starlink, we study the impact of TLs on network latency in an NG-FSOSN (which has only PLs) versus an NNG-FSOSN (which has PLs and TLs) under different long-distance inter-continental data communications scenarios, including Sydney–Sao Paulo, Toronto–Istanbul, Madrid–Tokyo, and New York–Jakarta, and different LISL ranges for satellites, including 659.5 km, 1,319 km, 1,500 km, 1,700 km, 2,500 km, 3,500 km, and 5,016 km. It is observed from the results that TLs provide higher satellite connectivity and thereby higher network connectivity, and they lead to lower average network latency for the NNG-FSOSN compared to the NG-FSOSN in all scenarios at all LISL ranges. In comparison with the NG-FSOSN, the improvement in latency with the NNG-FSOSN is significant at LISL ranges of 1,500 km, 1,700 km, and 2,500 km, where the improvement is 16.83 ms, 23.43 ms, and 18.20 ms, respectively, for the Sydney–Sao Paulo inter-continental connection. For the Toronto–Istanbul, Madrid–Tokyo, and New York–Jakarta inter-continental connections, the improvement is 14.58 ms, 23.35 ms, and 23.52 ms, respectively, at the 1,700 km LISL range.

The Release 16 completion unlocks the road to an exciting phase pertain to the sixth generation (6G) era. Meanwhile, to sustain far-reaching applications with unprecedented challenges in terms of latency and reliability, much interest is already getting intensified toward physical layer specifications of 6G. In support of this vision, this work exhibits the forward-looking perception of full-duplex (FD) cooperative relaying in support of upcoming generations and adopts as a mean concern the critical contribution of hybrid automatic repeat request (HARQ) mechanism to ultra-reliable and low-latency communication (URLLC). Indeed, the HARQ roundtrip time (RTT) is known to include basic physical delays that may cause the HARQ abandonment for the 1 ms latency use case of URLLC. Taking up these challenges, this article proposes a hybrid FD amplify-and-forward (AF)-selective decode-and-forward (SDF) relay-based system for URLLC. Over this build system, two HARQ procedures within which the HARQ RTT is shortened, are suggested to face latency and reliability issues, namely, the proposed and the enhanced HARQ procedures. We develop then an analytical framework of this relay based HARQ system within its different procedures. Finally, using Monte-Carlo simulations, we confirm the theoretical results and compare the proposed relay-assisted HARQ procedures to the source-to-destination (S2D) HARQ-based system where no relay assists the communication between the source and the destination.

Recent advances have shown that satellite communication (SatCom) will be an important enabler for next generation terrestrial networks as it can provide numerous advantages, including global coverage, high speed connectivity, reliability, and instant deployment. An ideal alternative for radio frequency (RF) satellites is its free-space optical (FSO) counterpart. FSO or laser SatCom can mitigate the problems occurring in RF SatCom, while providing important advantages, including reduced mass, lower consumption, better throughput, and lower costs. Furthermore, laser SatCom is inherently resistant to jamming, interception, and interference. Owing to these benefits, this paper focuses on downlink laser SatCom, where the best ground station (GS) is selected among numerous candidates to provide reliable connectivity and site diversity. To quantify the performance of the proposed scheme, we derive closed-form outage probability and ergodic capacity expressions for two different practical GS deployment scenarios. Thereafter, asymptotic analysis is conducted to obtain the overall site diversity order, and aperture averaging is studied to illustrate the impact of aperture diameter on the overall performance. Furthermore, we investigate the site diversity order for a constellation of satellites that are communicating with the best GS by using opportunistic scheduling. Finally, important design guidelines that can be useful in the design of practical laser SatComs are outlined.

Orthogonal frequency division multiplexing (OFDM) continues to be deployed in 5G communication systems and is likely to be used in beyond 5G (B5G) communication systems as well, due to its many advantages. However, one major drawback with OFDM systems is its high peak-to-average power ratio (PAPR), especially with large bandwidth transmissions. In this paper, we have provided a regularization optimization based flexible hybrid companding and clipping scheme (ROFHCC) used for PAPR reduction in OFDM systems. To reduce the design complexity, the companding function has two parts. It restrains the signal samples with amplitudes over a given value to a constant value for both peak power reduction and small power compensation. For signals with samples less than a given amplitude, they are expanded by a linear companding function. We build a regularization optimization model to jointly optimize the companding distortion as well as the continuity of the companding function for bit error rate (BER) performance as well as power spectral density (PSD) performance. Simulation results indicate that for the same PAPR performance, the proposed companding scheme has an advantage over the referenced companding schemes. For example, when the average signal power is normalized to be 1, we choose both PAPR for ROFHCC scheme and two-piecewise companding (TPWC) scheme as 4 dB, then we can find that at $BER=10^{-4}$ , the minimum required $E_{b}/{N_{0}}$ for ROFHCC scheme is around 2.3 dB lower than TPWC scheme.

In this paper, we propose a multi-user symbol detection in cooperative-non-orthogonal multiple access (C-NOMA) schemes via deep learning (DL). We use a DL-based detection (DLDet) in both users instead of conventional detectors. Therefore, an iterative detector (i.e., successive interference canceler (SIC)) at the near user (UE1) and a combining plus optimum detector (i.e., maximum ratio combining (MRC) and maximum-likelihood (ML) detector) at the far user (UE2) are not required anymore. The proposed DLDet can detect symbols at the both users simultaneously. Besides, the DLDet does not require an additional channel estimation algorithm to acquire the channel state information (CSI) at the receivers. The multi-user symbol detection is performed based on the received pilot responses simultaneously, thus calling semi-blind. We train the proposed DLDet offline over Rayleigh fading channel and then, we use the offline-trained DLDet as an online detection algorithm. We compare the error performance of the DLDet with two benchmarks: conventional C-NOMA and threshold-based selective C-NOMA (TBS-C-NOMA). With the extensive simulations over Rayleigh fading channels, we reveal that the DLDet outperforms both C-NOMA and TBS-C-NOMA and achieves the full diversity order (i.e., 2). Besides, this performance gain is up to ~10 dB and ~3-7 dB in C-NOMA and TBS-C-NOMA, respectively, which is very promising for the energy-limited networks. Moreover, in order to improve the error performance of the C-NOMA, the TBS-C-NOMA introduces two disadvantages, which are a signaling overhead to obtain the optimum threshold value and a capacity performance decay due the silence of the relay when the threshold is not satisfied. To this end, without using a threshold, outperforming TBS-C-NOMA is essential to revoke these disadvantages. The DLDet accomplish this; hence, the power of the DLDet is unveiled in coping with the error propagation. Then, to reveal the robustness of the DLDet against different fading conditions, we use the offline-trained DLDet as an online detector over different fading channels (i.e., Nakagami- $m$ and Rician). We show that the DLDet outperforms conventional detectors over those fading channels and again outperforms the conventional C-NOMA and TBS-C-NOMA performances, although it is trained over Rayleigh fading channel. Indeed, the DLDet has better performance than the C-NOMA and TBS-C-NOMA even though they are assumed to have perfect CSI whereas the DLDet uses only a single pilot signal. The DLDet again provides the full diversity order (i.e., $2m$ for Nakagami- $m$ and 2 for Rician). This proves the robustness of the DLDet and shows that the DLDet performs well regardless of the fading conditions once it is trained for any fading channel.

Two emerging technologies, cognitive radio (CR) and free-space optical (FSO) communication, have created much interest both in academia and industry recently as they can fully utilize the spectrum while providing cost-efficient secure communication. In this article, motivated by the mounting interest in CR and FSO systems and by their ability to be rapidly deployed for civil and military applications, particularly in emergency situations, we propose a CR enabled radio frequency (RF)/FSO communication model for an aerial relay network. In the proposed model, CR enabled RF communication is employed for a ground-to-air channel to exploit the advantages of CR, including spectrum efficiency, multi-user connectivity, and spatial diversity. For an air-to-air channel, FSO communication is used, since the air-to-air path can provide perfect line-of-sight connectivity, which is vital for FSO systems. Finally, for an air-to-ground channel, a hybrid RF/FSO communication system is employed, where the RF communication functions as a backup for the FSO communication in the presence of adverse weather conditions. The proposed communication model is shown to be capable of fully utilizing the frequency spectrum, while effectively dealing with RF network problems of spectrum mobility and underutilization, especially for emergency conditions when multiple unmanned aerial vehicles (UAVs) are deployed.

Non-terrestrial networks, including Unmanned Aerial Vehicles (UAVs), High Altitude Platform Station (HAPS) nodes and Low Earth Orbiting (LEO) satellites, are expected to have a pivotal role in sixth-generation wireless networks. With inherent features such as flexible placement, wide footprints, and preferred channel conditions, they can tackle several challenges faced by current terrestrial networks. However, their successful and widespread adoption relies on energy-efficient on-board communication systems. In this context, the integration of Reconfigurable Smart Surfaces (RSS) into aerial platforms is envisioned as a key enabler of energy-efficient and cost-effective aerial platform deployments. RSS consist of low-cost reflectors capable of smartly directing signals in a nearly passive way. In this paper, we investigate the link budget of RSS-assisted communications for two RSS reflection paradigms discussed in the literature, namely “specular” and “scattering” paradigms. Specifically, we analyze the characteristics of RSS-equipped aerial platforms and compare their communication performance with that of RSS-assisted terrestrial networks using standardized channel models. In addition, we derive the optimal aerial platform placements for both reflection paradigms. Our results provide important insights for the design of RSS-assisted communications. For instance, given that a HAPS has a large area for RSS, it provides superior link budget performance in most studied scenarios. In contrast, the limited RSS area on UAVs and the large propagation loss in LEO satellite communications make them unfavorable candidates for supporting terrestrial users. Finally, the optimal location of an RSS-equipped platform may depend on the platform's altitude, coverage footprint, and type of environment

Faster-than-Nyquist (FTN) signaling is a promising non-orthogonal pulse modulation technique that can improve the spectral efficiency (SE) of next generation communication systems at the expense of higher detection complexity to remove the introduced inter-symbol interference (ISI). In this paper, we investigate the detection problem of ultra high-order quadrature-amplitude modulation (QAM) FTN signaling where we exploit a mathematical programming technique based on the alternating directions multiplier method (ADMM). The proposed ADMM sequence estimation (ADMMSE) FTN signaling detector demonstrates an excellent trade-off between performance and computational effort enabling successful detection and SE gains for QAM modulation orders as high as 64K (65,536). The complexity of the proposed ADMMSE detector is polynomial in the length of the transmit symbols sequence and its sensitivity to the modulation order increases only logarithmically. Simulation results show that for 16-QAM, the proposed ADMMSE FTN signaling detector achieves comparable SE gains to the generalized approach semidefinite relaxation-based sequence estimation (GASDRSE) FTN signaling detector, but at an experimentally evaluated much lower computational time. Simulation results additionally show SE gains for modulation orders starting from 4-QAM, or quadrature phase shift keying (QPSK), up to and including 64K-QAM when compared to conventional Nyquist signaling. The very low computational effort required makes the proposed ADMMSE detector a practically promising FTN signaling detector for both low order and ultra high-order QAM FTN signaling systems.

Since outdoor events are gradually allowed within the current pandemic situation, a close monitoring of the crowd activity is needed to avoid undesired contact and disease transmission. In this context, unmanned aerial vehicles (UAVs) can be occasionally used to watch these activities, to ensure that health measures are applied, and to trigger alerts when an anomaly is detected. Consequently, we propose in this paper a complete UAV framework for intelligent monitoring of post COVID-19 outdoor activities. Specifically, we propose a three-step approach. In the first, captured images are analyzed using machine learning to detect and locate individuals. The second step consists of a novel coordinates mapping approach to evaluate distances among individuals and cluster them, while the third step provides an energy-efficient and reliable UAV trajectory to further inspect clusters for restrictions violation. Obtained results provide important insights towards the efficient design of the framework: 1) Efficient detection of individuals depends on the angle from which the images were captured, 2) coordinates mapping is very sensitive to estimate errors in individuals’ bounding boxes, and 3) UAV trajectory design algorithm 2-Opt is recommended for practical real-time deployments due to its low-complexity and near-optimal performance.

Hybrid beamforming is used to leverage the benefits of both massive multiple input and multiple output (MIMO) systems and millimeter waves for significantly increasing the capacity of wireless networks. Existing schemes for hybrid beamforming in multiple radio frequency (RF) chains optimize a global measure of performance and ignore quality of service (QoS) per data stream. In this paper, we propose a novel scheme for hybrid beamforming to minimize the transmit power while satisfying QoS defined as the mean square error (MSE) per data stream. We propose a two-stage cascade structure for the baseband precoder and combiner, and obtain their respective matrices in both single- and multi-user systems. We also propose a simplified scheme for designing hybrid beamformers with no nested loops in the alternating optimization method. Simulations show less than 2 dBW optimality gap for the multi-user scenario, with significantly less transmit power as compared to other existing schemes. Our simplified scheme consumes a negligible amount of 0.1 dBW more transmit power than our scheme with nested loops.

Vehicular communications are the key enabler of traffic reduction and road safety improvement referred to as cellular vehicle-to-everything (C-V2X) communications. Considering the numerous transmitting entities in next generation cellular networks, most existing resource allocation algorithms are impractical or non-effective to ensure reliable C-V2X communications which lead to safe intelligent transportation systems. We study a centralized framework to develop a low-complexity, scalable, and practical resource allocation scheme for dense C-V2X communications. The NP-hard sum-rate maximization resource allocation problem is formulated as a mixed-integer non-linear non-convex optimization problem considering both cellular vehicular links (CVLs) and non-cellular VLs (NCVLs) quality-of-service (QoS) constraints. By assuming that multiple NCVLs can simultaneously reuse a single cellular link (CL), we propose two low-complexity sub-optimal matching-based algorithms in four steps. The first two steps provide a channel-gain-based CVL priority and CL assignment followed by an innovative scalable min-max channel-gain-based CVL-NCVL matching. We propose an analytically proven closed-form fast feasibility check theorem as the third step. The objective function is transformed into a difference of convex (DC) form and the power allocation problem is solved optimally using majorization-minimization (MaMi) method and interior point methods as the last step. Numerical results verify that our schemes are scalable and effective for dense C-V2X communications. The low-complexity and practicality of the proposed schemes for dense cellular networks is also shown. Furthermore, it is shown that the proposed schemes outperform other methods up to 6% in terms of overall sum-rate in dense scenarios and have a near optimal performance.

In the travel insurance industry, cancel-for-any-reason insurance, also known as a cancellation protection service (CPS), is a recent attempt to strike a balance between customer satisfaction and service provider (SP) profits. However, some exceptional circumstances, particularly the COVID-19 pandemic, have led to a dramatic decrease in SP revenues, especially for non-refundable tickets purchased early with CPS. This paper begins by presenting a risk group segmentation of customers in an online ticket reservation system. Then, a CPS fee is recommended depending on the different customer risk groups provided by the cluster segmentation via different clustering algorithms such as centroid-based K-means, hierarchical agglomerative, DBSCAN, and artificial neural network-based SOM algorithms. According to the implemented cluster metrics, which include the Silhouette index, Davies-Bouldin index, Entropy index, and DBCV index, the SOM algorithm presents the most appropriate result. After predicting the new customer cluster, a CPS fee will be calculated with the proposed adaptive CPS method based on the cluster segmentation weights. Determining the weight of each cluster is related to the total CPS revenue threshold for all clusters defined by the SP. Therefore, to avoid a loss for SPs, the total CPS revenue will be kept constant with the threshold that the SP has been adjusted. The experimental results based on real-world data show that the risk group segmentation of customers helps to maintain a balance between CPS fees and SP profits. Finally, according to the calculated weights, the proposed model pegs the SP gain/loss variation with a 0.00012 exchange ratio.

Free space optical (FSO) communication has emerged to provide line of sight connectivity and higher throughput over unlicensed optical spectrums. Cognitive radio (CR), on the other hand, can utilize the radio frequency (RF) spectrum and allow a secondary user (SU) to share the same spectrum with the primary user (PU) as long as the SU does not impose interference on the PU. Owing to the potential of these emerging technologies, to provide full spectrum efficiency, this paper focuses on the mixed CR RF-FSO transmission scheme, where RF communication is employed at one hop followed by the FSO transmission on the other hop in a dual-hop decode-and-forward (DF) configuration. To quantify the performance of the proposed scheme, closed form error probability is derived over Rayleigh/Exponentiated Weibull (EW) fading distributions by considering the statistical and instantaneous feedback channel of the primary network. We also employed an asymptotic analysis to illustrate the diversity gain of the overall system. We believe that the proposed scheme can be applicable to the 5G+ networks where an unlicensed university student connects to the home computer with the aid of an FSO path.

Recently, interest on the utilization of unmanned aerial vehicles (UAVs) has aroused. Specifically, UAVs can be used in cellular networks as aerial users for delivery, surveillance, rescue search, or as an aerial base station (aBS) for communication with ground users in remote uncovered areas or in dense environments requiring prompt high capacity. Aiming to satisfy the high requirements of wireless aerial networks, several multiple access techniques have been investigated. In particular, space-division multiple access (SDMA) and power-domain non-orthogonal multiple access (NOMA) present promising multiplexing gains for aerial downlink and uplink. Nevertheless, these gains are limited as they depend on the conditions of the environment. Hence, a generalized scheme has been recently proposed, called rate-splitting multiple access (RSMA), which is capable of achieving better spectral efficiency gains compared to SDMA and NOMA. In this paper, we present a comprehensive survey of key multiple access technologies adopted for aerial networks, where aBSs are deployed to serve ground users. Since there have been only sporadic results reported on the use of RSMA in aerial systems, we aim to extend the discussion on this topic by modelling and analyzing the weighted sum-rate performance of a two-user network served by an RSMA-based aBS. Finally, related open issues and future research directions are exposed.

Unmanned Aerial Vehicle Base Stations (UAV-BSs) are envisioned to be an integral component of the next generation Wireless Communications Networks (WCNs) with a potential to create opportunities for enhancing the capacity of the network by dynamically moving the supply towards the demand while facilitating the services that cannot be provided via other means efficiently. A significant drawback of the state-of-the-art have been designing a WCN in which the service-oriented performance measures (e.g., throughput) are optimized without considering different relevant decisions such as determining the location and allocating the resources, jointly. In this study, we address the UAV-BS location and bandwidth allocation problems together to optimize the total network profit. In particular, a Mixed-Integer Non-Linear Programming (MINLP) formulation is developed, in which the location of a single UAV-BS and bandwidth allocations to users are jointly determined. The objective is to maximize the total profit without exceeding the backhaul and access capacities. The profit gained from a specific user is assumed to be a piecewise-linear function of the provided data rate level, where higher data rate levels would yield higher profit. Due to high complexity of the MINLP, we propose an efficient heuristic algorithm with lower computational complexity. We show that, when the UAV-BS location is determined, the resource allocation problem can be reduced to a Multidimensional Binary Knapsack Problem (MBKP), which can be solved in pseudo-polynomial time. To exploit this structure, the optimal bandwidth allocations are determined by solving several MBKPs in a search algorithm. We test the performance of our algorithm with two heuristics and with the MINLP model solved by a commercial solver. Our numerical results show that the proposed algorithm outperforms the alternative solution approaches and would be a promising tool to improve the total network profit.

This article proposes a millimeter wave-NOMA (mmWave-NOMA) system that takes into account the end-user signal processing capabilities, an important practical consideration. The implementation of NOMA in the downlink (DL) direction requires successive interference cancellation (SIC) to be performed at the user terminals, which comes at the cost of additional complexity. In NOMA, the weakest user only has to decode its own signal, while the strongest user has to decode the signals of all other users in the SIC procedure. Hence, the additional implementation complexity required of the user to perform SIC for DL NOMA depends on its position in the SIC decoding order. Beyond fifth-generation (B5G) communication systems are expected to support a wide variety of end-user devices, each with their own processing capabilities. We envision a system where users report their SIC decoding capability to the base station (BS), i.e., the number of other users signals a user is capable of decoding in the SIC procedure. We investigate the rate maximization problem in such a system, by breaking it down into a user clustering and ordering problem (UCOP), followed by a power allocation problem. We propose a NOMA-minimum exact cover (NOMA-MEC) heuristic algorithm that converts the UCOP into a cluster minimization problem from a derived set of valid cluster combinations after factoring in the SIC decoding capability. The complexity of NOMA-MEC is analyzed for various algorithm and system parameters. For a homogeneous system of users that all have the same decoding capabilities, we show that this equates to a simple maximum number of users per cluster constraint and propose a lower complexity NOMA-best beam (NOMA-BB) algorithm. Simulation results demonstrate the performance superiority in terms of sum rate compared to orthogonal multiple access (OMA) and traditional NOMA clustering schemes that do not incorporate individual users' SIC decoding capability constraints.