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One of the most advanced waveforms available in the beyond-fifth-generation radio (B5GR) framework is nonorthogonal multiple access (NOMA). The power amplifier (PA) of the NOMA structure performs less efficiently when the signal is strong and the peak-to-average power ratio (PAPR) is high. This study applies a hybrid algorithm to the NOMA structure, combining fractal partial transmit sequence (PTS), A-law companding, and the bacterial foraging algorithm (BFA). Fractals are known for their self-repeating structures at different scales, allowing for efficient coverage and exploration of space. Fractals enhance the bacteria foraging algorithm (BFA) by improving search efficiency, enabling better exploration of complex, multi-dimensional optimization landscapes. Similarly, BFA balances exploring the search in promising areas. We utilized BFA to achieve optimal phase factors for the PTS algorithm and applied A-Law companding to the NOMA symbols to further enhance the structure’s performance. The intermediate computational complexity in the Rician and Rayleigh channels improves PAPR, bit error rate (BER), and power spectral density performance. Simulation results reveal that the performance of the proposed hybrid method is superior to that of existing PAPR reduction algorithms and substantially enhances the efficiency of NOMA. [ABSTRACT FROM AUTHOR]

This paper introduces a new method for estimating the maximum Doppler shift using decision feedback channel estimation (DFCE). In highly mobile environments, which are expected to be covered beyond 5G and 6G systems, the relative movement between the transmitter and receiver causes Doppler shifts. This leads to inter-carrier interference (ICI), significantly degrading communication quality. To mitigate this effect, systems that estimate the maximum Doppler shift and adaptively adjust communication parameters have been extensively studied. One of the most promising methods for maximum Doppler shift estimation involves inserting pilot signals at both the beginning and end of the packet. Although this method achieves high estimation accuracy, it introduces significant latency due to the insertion of the pilot signal at the packet's end. To address this issue, this paper proposes a new method for rapid estimation using DFCE. The proposed approach compensates for faded signals using channel state information obtained from decision feedback. By treating the compensated signal as a reference, the Doppler shift can be accurately estimated without the need for pilot signals at the end of the packet. This method not only maintains high estimation accuracy but also significantly reduces the latency associated with conventional techniques, making it well-suited for the requirements of next-generation communication systems. [ABSTRACT FROM AUTHOR]

With the centralization of the RAN control functionality, a very large number of near real-time network optimization use cases have emerged. Until now, each of them has been implemented and validated as isolated network management functions, named xApps in Open RAN terminology. To be able to progress towards realistic, commercially deployable functionality, comprehensive conflict mitigation is needed, because the complete isolation of these xApps would result in conflicting decisions and instability. In this article, we study how the majority of these use cases can be implemented harmoniously, to create an enhanced RAN control plane, underlining the functionality required from the central units of the RAN, the grouping, and the interaction of the network management decision. Analyzing the gap existing in the O-RAN architecture, we identify the functionality needed and propose a management framework. Furthermore, we present an implementation roadmap for the development of such functionality as part of the Fraunhofer FOKUS Open5GCore toolkit, as a reference for how such functionality can be prototyped, validated, and integrated with external algorithms easily, to benefit from the large body of academic research. [ABSTRACT FROM AUTHOR]

This study proposes the integration of a beamforming neural network (BFNN) and multiple-input single-output based non-orthogonal multiple access (MISO-NOMA) with hybrid beamforming (HBF) for cell edge users (CEU) in a millimeter wave (mmWave)-based beyond 5G cellular communication system. This system is referred to as MISO-NOMA-HBFBFNN. The proposed scheme has been implemented to support multiple users simultaneously and also to considerably enhance and significantly improve the overall the sum channel capacity (SC) and user channel capacities. Additionally, the simulation results demonstrate the superiority of the proposed MISO-NOMA-HBF-BFNN scheme over the existing MISONOMA with HBF and MISO-OMA with HBFBFNN based schemes in terms of user capacities and SC. [ABSTRACT FROM AUTHOR]

Massive MIMO (Multiple-input-multiple output), crucial for 5 G and Beyond 5 G (B5G) networks, faces challenges with terahertz frequencies in B5G as the communication shifts from far-field to near-field. This shift disrupts traditional Massive MIMO channel estimation, leading to increased pilot overhead and limited performance. The current study used the hybrid-field orthogonal matching pursuit (HF-OMP) algorithm from the compressive sensing (CS) framework to estimate the channel with scatters from both far-field and near-field. The method, however, needs more flexibility regarding scatter location in the field of interest. Also, the usage of HF-OMP under a single measurement vector (SMV) scheme limits the overall performance of the existing method. To address these, we have proposed an adaptive hybrid-field simultaneous-OMP algorithm under multiple measurement vector (MMV) of the CS framework which selects multiple atoms having common support within a single iteration. This innovative approach tackles channel estimation for both far-field and near-field scatters while adapting to their varying distributions. Compared to classical methods, simulations show the new algorithm achieves up to a 14 % improvement in normalized mean square error within a 0 dB to 10 dB signal-to-noise ratio range. This translates to significant reductions in pilot overhead and enhanced channel reliability, paving the way for more efficient and robust wireless networks in the future.

Abstract The future generations of communication technologies envision the transmission of signals across the millimeter wave and sub-THz spectrums. However, the characteristics of the propagation channel at such high frequencies differ from what is observed in the conventional low-frequency spectrum with for instance, the apparition of stronger phase noise (PN) induced by the Radio Frequency (RF) transceivers and more especially by the oscillators. That is why there is growing interest in evaluating and adapting the 5G new radio (5G-NR) physical layer to the presence of PN. This article is dedicated to the study of discrete Fourier transform-spread-Orthogonal Frequency Division Multiplexing (DFT-s-OFDM) under uncorrelated Gaussian PN (GPN) impairments. We show that the presence of GPN induces two distortions: (i) a frequency-dependent random rotation of data, namely the subcarrier phase error (SPE) and (ii) a frequency-dependent intercarrier interference (ICI) that are analytically expressed. Then, we investigate the design of the adapted and optimal detection criterion according to the baseband model we derived in this paper. We demonstrate that (i) the proposed polar detector outperforms the conventional Euclidean detector and (ii) contrary to legacy OFDM, DFT-s-OFDM is a promising solution when strong GPN is involved.

In this study, we propose a novel wavelength shift keying (WSK) technique that is combined with the conventional intensity modulation scheme for the transmission of point-to-point bidirectional data at the rate of 10 Gbps in each direction. We observe that WSK technique has not been investigated for implementation in point-to-point free space optical (FSO) links. Therefore, to the best of our knowledge, our study is the first one to perform this investigation. Our proposed link uses WSK in the downlink direction while we re-use the optical carriers for the transmission of uplink data. The use of WSK in the downlink direction enables us to perform balanced detection at the receiver, resulting in 3 dB improvement in receiver sensitivity compared to simple direct detection. We present bit error rate (BER) results for the signals transmitted in both the directions under different turbulence conditions and FSO link lengths. It was observed that the downlink signals generally perform better compared to the uplink due to the use of balanced detection and higher intensity fluctuations induced over the re-used optical carrier transmitted in the uplink direction.Please check and confirm that all the author names (given name, family name) and initials are correctly identified. The author names and initials are CORRECT. [ABSTRACT FROM AUTHOR]

The future generations of communication technologies envision the transmission of signals across the millimeter wave and sub-THz spectrums. However, the characteristics of the propagation channel at such high frequencies differ from what is observed in the conventional low-frequency spectrum with for instance, the apparition of stronger phase noise (PN) induced by the Radio Frequency (RF) transceivers and more especially by the oscillators. That is why there is growing interest in evaluating and adapting the 5G new radio (5G-NR) physical layer to the presence of PN. This article is dedicated to the study of discrete Fourier transform-spread-Orthogonal Frequency Division Multiplexing (DFT-s-OFDM) under uncorrelated Gaussian PN (GPN) impairments. We show that the presence of GPN induces two distortions: (i) a frequency-dependent random rotation of data, namely the subcarrier phase error (SPE) and (ii) a frequency-dependent intercarrier interference (ICI) that are analytically expressed. Then, we investigate the design of the adapted and optimal detection criterion according to the baseband model we derived in this paper. We demonstrate that (i) the proposed polar detector outperforms the conventional Euclidean detector and (ii) contrary to legacy OFDM, DFT-s-OFDM is a promising solution when strong GPN is involved. [ABSTRACT FROM AUTHOR]

The expected huge number of connected devices in Internet of Things (IoT) applications characterizes the massive machine-type communication (mMTC) scenario, one prominent use case of beyond fifth-generation (B5G) systems. To meet mMTC connectivity requirements, grant-free (GF) random access (RA) protocols are seen as a promising solution due to the small amount of data that MTC devices usually transmit. In this paper, we propose a GF RA protocol based on a multi-agent reinforcement learning approach, applied to aid IoT devices in selecting the least congested RA pilots. The rewards obtained by the devices in collision cases resemble the congestion level of the chosen pilot. To enable the operation of the proposed method in a realistic B5G network scenario and aiming to reduce signaling overheads and centralized processing, the rewards in our proposed method are computed by the devices taking advantage of a large number of base station antennas. Numerical results demonstrate the superior performance of the proposed method in terms of latency, network throughput, and per-device throughput compared with other protocols. [ABSTRACT FROM AUTHOR]

Summary: Ultra‐accurate indoor localization/positioning technology is rapidly gaining interest in 5G and beyond (B5G) communications. Achieving 0.1 m level of precise indoor localization is a significant challenge that can be resolved using channel state information (CSI) of wireless signals from nearby access points. The complex indoor propagation issues like shadowing, blockage effects, and multi‐path fading diminish CSI, affecting precise position/location accuracy. Thus, to achieve ultra‐accurate localization, a novel MOIL framework is proposed using a deep learning BiLSTM model and CSI features in an efficient and optimized manner. The proposed MOIL integrates two functional blocks, SecLOP and CordNet. SecLOP conceals multi‐path effects and noise to optimize the input information using sequential responses of signals. CordNet minimizes precise position/location errors by taking all combinations of optimized signal features from the SecLOP block. Extensive simulation experiments were conducted to validate MOIL in different indoor scenarios, such as line‐of‐sight (LOS) and non‐line‐of‐sight (NLOS). Moreover, through a substantial ablation study, the efficiency of the proposed framework is validated/verified. The results outperformed existing works by providing cm‐level accuracy of 1 cm in LOS and 1.5 cm in NLOS scenarios. The proposed work outperformed the literature by 31.3% in LOS and 25.30% in NLOS scenarios. [ABSTRACT FROM AUTHOR]

The beyond 5G millimeter-wave cellular network system is expected to provide high-quality service in indoor areas. Due to the high density of obstacles, cooperative communication techniques are employed to improve the user's desired signal power by finding more than one appropriate station to serve that user. While the conventional system utilizes additional equipment such as Reconfigurable Intelligent Surfaces (RIS) and relays to enable cooperative features, the paper introduces a new network paradigm that utilizes the second nearest Base Station (BS) of the typical user as the Decode and Forward (DF) relay. Thus, depending on the success of decoding the message from the user's serving BS or the second nearest BS, the typical user can work with or without assistance from the relay, whose operation follows the discipline of the power-domain NOMA technique. In the case of relay assistance, the Maximum Ratio Combining technique is utilized by the typical user to combine the desired signals. To examine the performance of the proposed system, the Nakagamim and the newly developed path loss model, which considers the density of walls and their properties, are adopted to derive the coverage probability of the user with and without relay assistance. The closedform expressions of this performance metric are derived using Gauss quadrature and Welch-Satterthwaite approximation. Through analytical and simulation results, it is seen that the proposed system can improve the user coverage probability by up to 10%. [ABSTRACT FROM AUTHOR]

We review dielectric resonator antenna (DRA) designs. This review examines recent advancements across several categories, specifically focusing on their applicability in array configurations for millimeter-wave (mmW) bands, particularly in the context of 5G and beyond 5G applications. Notably, the off-chip DRA designs, including in-substrate and compact DRAs, have gained prominence in recent years. This surge in popularity can be attributed to the rapid development of cost-effective multilayer laminate manufacturing techniques, such as printed circuit boards (PCBs) and low-temperature co-fired ceramic (LTCC). Furthermore, there is a growing demand for DRAs with beam-steering, dual-band functions, and on-chip alignment availability, as they offer versatile alternatives to traditional lossy printed antennas. DRAs exhibit distinct advantages of lower conductive losses and greater flexibility in shapes and materials. We discuss and compare the performances of different DRA designs, considering their material usage, manufacturing feasibility, overall performance, and applications. By exploring the pros and cons of these diverse DRA designs, this review provides valuable insights for researchers in the field. [ABSTRACT FROM AUTHOR]

The development of communication technology will promote the application of Internet of Things, and Beyond 5G will become a new technology promoter. At the same time, Beyond 5G will become one of the important supports for the development of edge computing technology. This paper proposes a communication task allocation algorithm based on deep reinforcement learning for vehicle-to-pedestrian communication scenarios in edge computing. Through trial and error learning of agent, the optimal spectrum and power can be determined for transmission without global information, so as to balance the communication between vehicle-to-pedestrian and vehicle-to-infrastructure. The results show that the agent can effectively improve vehicle-to-infrastructure communication rate as well as meeting the delay constraints on the vehicle-to-pedestrian link.

Here we propose a new signal set design for physical cell identification (PCI) in sub-THz OFDM systems. We also derive useful properties of proposed signal set, which can be employed to assign PCI and reduce interference. The proposed signal set is easy to generate because it is designed using binary m-sequences. These sequences exhibit the relationship of being preferred pairs to each other. The performance of the proposed signal set is similar to that of the secondary synchronization signal (SSS) for 5G NR, but the possible number of signal set is much larger than that of SSS for 5G NR. Furthermore, the proposed signal set contains SSS for 5G NR and a binary Gold sequence set as subsets. The proposed signal set can provide approximately 100 times more signals than the SSS of 5G NR. The signal to noise ratio (SNR) loss of the proposed signal is less than 1 dB for 4 times more sequences relative SSS of 5G NR and less than 2 dB for 25 times more sequences than the SSS of 5G NR.

This paper demonstrated high-output-power and high-efficiency power amplifier (PA) monolithic microwave-integrated circuit (MMIC) at 300-GHz band (252–296 GHz) with the use of InPbased metal–oxide–semiconductor high-electron-mobility transistors (HEMTs) with composite-channel (CC) and double-side-doping (DD) techniques. The CC-DD structure obtained high output current and low channel resistance due to the improved carrier density and mobility. W-band load-pull measurement revealed the drastically improved output power density of CC-DD structure compared with that of singlechannel DD structure. The 2-stage cascaded, 4-way, and 16-way PA-MMICs were designed based on stacked common-gate transistors with current reuse topology. The cascaded PA-MMIC exhibited a poweradded efficiency (PAE) of 7.8%, and the 16-way PA-MMIC exhibited an output power of 16.9 dBm. These values are the highest among all the values reported for the 300-GHz band PA-MMICs. The 4-way PA-MMIC achieved a high output power of 13.6–14.6 dBm and high PAE of 4.8%–6.3% simultaneously at the entire 300-GHz band.

We propose a wideband model-embedding delta-sigma radio-over-fiber (DS-RoF) design for keeping the adjacent channel leakage low. This RoF design is derived on the basis of the DS-RoF embedding a pulse-distortion model (PDM). To enable wideband modulation with low adjacent channel leakage, we also propose an optimization method using an objective function simulating the adjacent channel leakage. By refining the digital down-converter from previous work, we additionally propose an extended PDM that generalizes its parameters from real numbers to complex numbers, which allow for fine-tuned delay adjustments in the PDM. A RoF demonstration using commercially available small form-factor pluggable (SFP) modules and a 10-km single mode fiber (SMF) with a 400-MHz-bandwidth orthogonal frequency division multiplexing (OFDM) signal, including a calibration path for modeling pulse distortion, revealed that the proposed design with our extended complex-valued PDM improved the signal-to-noise ratio (SNR) by 0.8 dB, normalized mean-squared error (NMSE) by 0.3 dB and adjacent channel leakage ratio (ACLR) by 0.4 dB compared with a RoF design with the original real-valued PDM. These results indicate that the complex-valued PDM, which enables fine-tuned delay adjustments, increases modeling accuracy, leading to enhanced compensation performance. Moreover, the proposed DS-RoF design with our complex-valued PDM also achieved an ACLR below -31 dBc. This result indicates that, to the best of our knowledge, we are the first to realize a 400-MHz-bandwidth modulation required for 5G millimeter wave as a DS-RoF system.

Cell-free massive multiple-input-multiple-output (CF-mMIMO) networks incorporate a cell-free architecture with distributed antennas in a geographical area, and aim to deliver high data rates and support large numbers of users. It is crucial that such networks operate in an energy-efficient manner within the available spectrum. Thus, we focus on maximizing the energy efficiency (EE) of a CF-mMIMO network, which coexists with a collocated primary network in underlay mode. The EE maximization is a non-convex problem and to compute a power allocation policy efficiently, we propose a weighted minimum-mean-square-error (WMMSE) based Dinkelbach’s algorithm. Besides this, we also provide a simplified algorithm for maximum-ratio precoding in which we approximate the non-convex EE objective function with a lower bound, transforming the non-convex EE problem into a convex problem. Subsequently, we propose a policy for downlink power allocation that maximizes the EE of the secondary CF-mMIMO network while adhering to power constraints at each access point and interference constraints at each primary user. We also compare with some heuristic power allocation policies. The results demonstrate that the proposed WMMSE based power allocation scheme outperforms the heuristic power allocation schemes to a significant degree.

The concept of sustainability has been recently extended to cover economic and social factors besides the traditional environmental ones. This paper reflects on the potential of mobile communication standards towards achieving sustainable mobility, with focus on vehicular communications and use cases in smart cities scenarios. In this context, intelligent transportation systems, including connected and autonomous vehicles, will be key for developing affordable and sustainable infrastructures and services. We start by identifying three current technology trends, namely, towards climate neutral; cloudification and edge computing; and Big Data and artificial intelligence, and then we examine their capability to enable sustainable Vehicle-to-Everything (V2X) communication systems in beyond 5G and 6G networks. In the second part of the paper, a set of selected use case categories involving connected and autonomous vehicles is presented, showcasing the potential impact of the selected technology trends. Finally, a review of the estimates of the quantitative savings that could be achieved in environmental-related parameters such as energy/fuel consumption and greenhouse gas emissions is provided.

Modeling the probability of blocking User Equipment (UE) sessions within a cell is a crucial aspect within the management of 5G services with Guaranteed Bit Rate (GBR) requirements, especially in the process of planning in advance the deployment of such services. The complexity of modeling the UE blocking probability arises from the dependency of this performance indicator on multiple factors, including the UE channel quality within the cell, the MAC scheduling discipline, the statistical distributions of the traffic demand and session duration, and the GBR requirements of the corresponding service. In this vein, we propose an analytical model to assess the UE blocking probability for a GBR service. The proposed model is based on a Markov chain which is insensitive to the holding time distribution of the UE data sessions. Furthermore, it may consider as input any continuous distribution for the average Signal-to-Interference-plus-Noise Ratio (SINR) within the cell. The simulation results demonstrate the execution time of the proposed model is on the order of tens of milliseconds, which makes it suitable for testing multiple network configurations in a short term, training ML models or detecting traffic anomalies in real time. Additionally, the results show that our model exhibits an estimation error for the UE blocking probability below 2.6%.

As societal needs evolve, especially with the advent of demanding applications like remote surgeries in Smart Health, current communication systems struggle to meet the required latency and reliability for such use cases. Similarly, enhancing existing applications, such as increasing transmission rates in mobile networks to provide Quality of Service (QoS) and improved user experiences, presents complex challenges. The pursuit of higher data rates has been realized by 5G wireless communication networks, which have already been commercially deployed. Nevertheless, the proliferation of smart communication devices and the emergence of IoE (Internet of Everything) applications present challenges to existing 5G networks in meeting the escalating requirements for ultra-reliable, low-latency communication. To address these limitations, researchers are focusing on the development of the sixth generation (6G) of cellular systems. Consequently, the selection of enabling technologies is crucial for designing a suitable 6G architecture that can address these evolving demands. Herein the main aim is to provide insights into the significant technologies for the future 6G, including their operation principles, potential applications, current research status, and associated technical challenges. This article proposes a methodology to analyze the relevance of enabling technologies and leverage it for designing an optimal 6G architecture. The evaluation results offer a unique perspective on 6G enablers, pinpointing issues and stimulating research for future mobile architectures. Additionally, the obtained insights provide researchers with essential information to stay updated on emerging enabling technologies and their suitability for crafting new and optimized 6G architectures that are engineered with adaptability and flexibility in mind, highlighting cell-free massive MIMO, hierarchical cell structures, and network slicing to facilitate ultra-reliable, low-latency communication and cater to various applications, all while prioritizing energy efficiency and sustainability.

Wireless communications, encompassing mobile communications, satellite communications, wireless local area networks (LANs), and fixed wireless systems, play a major role in communication networks. In preparation for the World Radiocommunication Conference 2023 (WRC-23) that was held in November 2023, the International Telecommunication Union - Radiocommunication Sector (ITU-R), a de jure standards organization, conducted studies and discussions broader than those limited to Beyond 5thgeneration mobile communication system (5G) and 6G. The 3rd Generation Partnership Project (3GPP), a private standardization organization, has also started to study specifications after Release 18 under the name "5G-Advanced," as a further evolved version of 5G. The IEEE 802.11 Working Group (WG), a WG of the Standards Association under the Institute of Electrical and Electronics Engineers (IEEE), is studying next-generation wireless LANs. This article introduces these standardization efforts as well as the NTT Group's activities for standardization of satellite communications, fixed wireless communications, and radio-wave propagation. [ABSTRACT FROM AUTHOR]

With the expansion of Information and Communication Technology, it is important to develop a communication network that can provide high-capacity ubiquitous connectivity. This work proposes an energy-efficient passive optical network (PON) using orthogonal frequency division multiple access (OFDMA) and wavelength division multiplexing (WDM) to facilitate the dense deployment of radio units (RUs) in a beyond 5G (B5G) communication network. High-speed connectivity is ensured by employing a hybrid PON architecture that includes a combination of free space optics (FSO) links and optical fiber (OF) media to carry OFDM and WDM multiplexed traffic. Furthermore, an optical frequency comb generator (OFCG) is utilized at the transmitter module to generate and leverage the spectrum for transmitting information from baseband units (BBUs) to the RUs situated near the end users. The proposed system is analyzed through (i) simulation analysis using Optisystem for transmission capacity computations and (ii) mathematical analysis to determine the total savings in energy. The simulation analysis shows that the given architecture can carry data across 3 km of FSO medium using 512 subcarriers per BBU transmitting at 10 Gbps of data with QPSK-modulated bit sequence. Additionally, energy efficiency shows that the use of an OFCG cuts the total energy usage by 22% at the transmitter module without negatively impacting the system's high cardinality and transmission capacity. [ABSTRACT FROM AUTHOR]

The expected huge number of connected devices in Internet of Things (IoT) applications characterizes the massive machine-type communication (mMTC) scenario, one prominent use case of beyond fifth-generation (B5G) systems. To meet mMTC connectivity requirements, grant-free (GF) random access (RA) protocols are seen as a promising solution due to the small amount of data that MTC devices usually transmit. In this paper, we propose a GF RA protocol based on a multi-agent reinforcement learning approach, applied to aid IoT devices in selecting the least congested RA pilots. The rewards obtained by the devices in collision cases resemble the congestion level of the chosen pilot. To enable the operation of the proposed method in a realistic B5G network scenario and aiming to reduce signaling overheads and centralized processing, the rewards in our proposed method are computed by the devices taking advantage of a large number of base station antennas. Numerical results demonstrate the superior performance of the proposed method in terms of latency, network throughput, and per-device throughput compared with other protocols.

In this study, we propose a novel machine learning based algorithm to improve the performance of beyond 5 generation (B5G) wireless communication system that is assisted by Orthogonal Frequency Division Multiplexing (OFDM) and Non-Orthogonal Multiple Access (NOMA) techniques. The non-linear soft margin support vector machine (SVM) problem is used to provide an automatic modulation classifier (AMC) and a signal power to noise and interference ratio (SINR) estimator. The estimation results of AMC and SINR are used to reassign the modulation type, codding rate, and transmit power throughout the frames of eNode B connections. The AMC success rate versus SINR, total power consuming, and sum capacity are evaluated for OFDM-NOMA assisted 5G system. In comparison to recently published methods, our results show that the success rate improves. The suggested method directly senses the physical channel because it computes the SINR and codding rate of received signal just after the signal is detected by successive interference cancellation (SIC). Hence, because of this direct sense, this algorithm can really decrease occupied symbols (overhead signaling) for channel quality information (CQI) in network communication signaling. The results also prove that the proposed algorithm reduces the total power consumption and increases the sum capacity during the eNode B connections. Simulation results compared to other algorithms show more successful AMC, efficient SINR estimator, easier practical implantation, less overhead signaling, less power consumption, and more capacity achievement. [ABSTRACT FROM AUTHOR]

Generally, mobility management (MM) solutions are centralized, which means only one entity controls the mobility-related tasks of mobile nodes connected to the network. However, the massive number of users, rapid growth in the network traffic, and the recent developments regarding the architecture and capability of 5G networks forced multiple entities to perform the task of MM. Similarly, to fulfill the demand for the applications at the time of handoff, we have on-demand MM solutions. These solutions are designed to be adoptable to various service delivery use cases supported by future networks. In addition, they also have power-saving features that turn them off automatically, when all your devices are fully charged. Owing to stated features, we need to explore on-demand MM solutions and these solutions can provide divergent types of services according to the user’s demand. Therefore, in this paper, the importance and comparison of on-demand MM solutions with legacy mechanisms are presented, by keeping in view the merits and constraints of their contributions. Furthermore, the significance of the MM as a service (MMaaS) model is discussed in detail, which is used for the on-demand MM mechanism. In this model, the substantial characteristics of the granularity of services are also elaborated, which are intricately tangled with the adoptability, extensibility, and power-saving requirements of future wireless networks. Finally, we discuss the challenges, future prospects, and application areas of on-demand MM solutions for researchers and application developers.

Handover (HO) management is essential in mobile cellular networks. It ensures seamless connectivity to the User Equipment (UE) while moving from one Base Station (BS) to another within the coverage area. HO optimization refers to the adoption of intelligent and automatic HO techniques in mobile networks. HO optimization is gaining more importance in Fifth-Generation (5G) and Beyond (B5G) systems because of the requirements and specifications that B5G targets. The requirements of B5G include global connectivity, ultra-low latency, big data analytics, extreme data rate transmissions, a massive number of devices in a small area, and new technologies that will support the B5G network, such as Millimeter Wave (mmWave), Terahertz (THz) communication, and Ultra-Dense Networks (UDNs). All these factors cause new HO optimization challenges and require new solutions for HO optimization techniques. This study comprehensively provides HO optimization challenges and solutions in B5G. First, it provides a research background and explanation of HO in legacy. It then investigates the HO optimization challenges in B5G, including future research directions. The paper then discusses the most prominent and recent techniques and technological solutions for HO optimization management in B5G. Finally, we highlight potential techniques for HO optimization in B5G.

Federated Learning (FL) systems orchestrate the cooperative training of a shared Machine Learning (ML) model across connected devices. Recently, decentralized FL architectures driven by consensus have been proposed to enable the devices to share and aggregate the ML model parameters via direct sidelink communications. The approach has the advantage of promoting the federation among the agents even in the absence of a server, but may require an intensive use of communication resources compared to vanilla FL methods. This paper proposes a communication-efficient design of consensus-driven FL optimized for training of Deep Neural Networks (DNNs). Devices independently select fragments of the DNN to be shared with neighbors on each training round. Selection is based on a local optimizer that trades model quality improvement with sidelink communication resource savings. The proposed technique is validated on a vehicular cooperative sensing use case characterized by challenging real-world datasets and complex DNNs typically employed in autonomous driving with up to 40 trainable layers. The impact of layer selection is analyzed under different distributed coordination configurations. The results show that it is better to prioritize the DNN layers possessing few parameters, while the selection policy should optimally balance gradient sorting and randomization. Latency, accuracy and communication tradeoffs are analyzed in detail targeting sustainable federation policies.

5G and beyond 5G networks are going to be an inseparable part of human lives in the future. They will dominate any aspects of everyday activities from smart homes, remote surgery, and smart cities to autonomous driving, which demand high throughput through low latency end-to-end communication. Consequently, employing higher frequencies in millimeter-wave for the coming cellular networks will be inevitable. On the one hand, millimeter-wave can provide massive data rates; on the other hand, it suffers from some shortcomings such as blockage and misalignment that can occur because of the susceptible characteristic of the high frequencies. These flaws can mislead TCP in adjusting its sending rate efficiently and reduce the connection quality that a user discerns. Deploying Multipath TCP (MPTCP) is one of the schemes that can relieve the aforementioned issues and assist in utilizing the new generation mobile networks’ high potential by leveraging its features in exploiting diverse paths in networks such as NR or LTE. A well-designed MPTCP can select the best path among the available ones, so it can enhance the perceived user experience. This paper fills the gap of having a comprehensive overview of MPTCP and its deployment over 5G and beyond 5G networks. It analyses millimeter-wave-based cellular networks, MPTCP procedures and parameters, state-of-the-art MPTCP congestion control mechanisms and schedulers, and the probable solutions that may enhance the protocol’s functionality in cellular communication.

The histrionic growth of mobile subscribers, disruptive ecosystems such as IoT-based applications, and astounding channel capacity requirements to connect trillions of devices are massive challenges of the earlier mobile generations, 5G turned up the key solution. The prime objective of the 5G network is not only to maintain a 1000-fold capacity gain and 10 Giga Bits per second delivered to a single user, but it also assured quality-of-service, higher spectral efficiency, the ultra-reliable and improved battery lifetime of devices and massive machine-type communication (mMTC). The huge traffic load and high amount of resource consumption in 5G applications, augmented reality and virtual reality for magnificent virtual experience, and wireless body area networks will seriously affect the channel capacity of cellular cells and interrupt the admission and service of other users which makes compulsory new means of channel capacity and spectral efficiency enhancement techniques. In this research, we review several key emerging wireless technologies to increase channel capacity and spectral efficiency that will not only lead to improve network performance but also meets the ever-increasing user demands. We investigate various benefits and current research challenges of using these technologies. We analyze massive multi-input multi-output technology (mMIMO) an efficient technique and promising solution for the 5G and Beyond 5G (B5G) networks with several benefits and features. Moreover, this paper will be of vast help to the researchers who will involve advance investigation and also to the wireless network operator industry that is in the search for smooth development of state-of-the-art 5G and B5G networks. [ABSTRACT FROM AUTHOR]

IEEE 802.11ad is the practical networking technology widely used as a 60 GHz millimeter-wave communication method ahead of Beyond 5G and 6G networks. However, compared with the existing Wi-Fi systems that use 2.4/5 GHz, it is difficult to manage the communication quality due to the high carrier frequency, e.g., the signal strength suddenly and significantly degrades due to various factors such as distance and obstacles. For IEEE 802.11ad to be widely adopted, a simple and inexpensive measurement method is needed for non-specialists, including end users, to build and manage a stable IEEE 802.11ad network. In addition, easy-to-understand performance indicators are required. This paper then proposes a multilayer measurement methodology with open-source tools, i.e., iperf3, ping, and iw, for IEEE 802.11ad communication. Our methodology selects indicators necessary to evaluate IEEE 802.11ad communication performance. The tools iperf3 and ping mainly evaluate the IP layer indicators such as throughput, jitter, packet retries, packet loss, and RTT. On the other hand, the RSSI and Tx bitrates of the iw results are used to investigate the wireless link quality. Through empirical results with our measurement methodology in real environments, we show that in addition to traditional indicators such as through-put, the Tx bitrate output by iw can be a new indicator for understanding the IEEE 802.11ad communication quality. [ABSTRACT FROM AUTHOR]

In beyond 5G (B5G) non-terrestrial network (NTN) systems, satellite technologies play an important role. Especially for data collection systems (DCS), low-earth orbit satellites have many advantages. Such as global coverage, low latency, and high efficiency. As a miniaturization technology, CubeSat has attracted extensive attention from a large number of scholars. Satellite constellations can coordinate for distributed tasks. This paper proposes a B5G NTN-based data collection system. A CubeSat constellation achieves global coverage as the basic space platform for DCS. The 5G terrestrial network is used as the data bearer network of the gateway station. A traffic load balance strategy is proposed to optimize the system's efficiency. As a unified hardware platform, software-defined radio (SDR) is compatible with various sensor data models. Finally, the design was verified by a series of experiments. [ABSTRACT FROM AUTHOR]

We review dielectric resonator antenna (DRA) designs. This review examines recent advancements across several categories, specifically focusing on their applicability in array configurations for millimeter-wave (mmW) bands, particularly in the context of 5G and beyond 5G applications. Notably, the off-chip DRA designs, including in-substrate and compact DRAs, have gained prominence in recent years. This surge in popularity can be attributed to the rapid development of cost-effective multilayer laminate manufacturing techniques, such as printed circuit boards (PCBs) and low-temperature co-fired ceramic (LTCC). Furthermore, there is a growing demand for DRAs with beam-steering, dual-band functions, and on-chip alignment availability, as they offer versatile alternatives to traditional lossy printed antennas. DRAs exhibit distinct advantages of lower conductive losses and greater flexibility in shapes and materials. We discuss and compare the performances of different DRA designs, considering their material usage, manufacturing feasibility, overall performance, and applications. By exploring the pros and cons of these diverse DRA designs, this review provides valuable insights for researchers in the field.

In the real-world applications, the overwhelming majority of cases require hashing with relatively short input, say up to 2K bytes. The length of almost all TCP/IP packets is between 40 to 1.5K bytes, and the maximum packet lengths of major protocols, e.g., Zigbee, Bluetooth low energy, and Controller Area Network (CAN) are less than 128 bytes. However, existing schemes are not well optimized for short input. To bridge the gap between real-world needs (in future) and limited performances of state-of-the-art hash functions for short input, we design a family of wide-block permutations Areion that fully leverages the power of AES instructions, which are widely deployed in many devices. As its applications, we propose several hash functions. Areion significantly outperforms existing schemes for short input and even competitive to relatively long message. Indeed, our hash function is surprisingly fast, and its performance is less than 3 cycles/byte in the latest Intel architecture for any message size. Especially, it is about 10 times faster than existing state-of-the-art schemes for short message up to around 100 bytes, which are most widely-used input size in real-world applications, on both the latest CPU architectures (IceLake, Tiger Lake, and Alder Lake) and mobile platforms (Pixel 6 and iPhone 13).

As the technical specifications of the 5th Generation (5G) wireless communication standard are being wrapped up, there are growing efforts amongst researchers, industrialists, and standardisation bodies on the enabling technologies of a 6G standard or the so-called Beyond 5G (B5G) one. Although the 5G standard has presented several benefits, there are still some limitations within it. Such limitations have motivated the setting up of study groups to determine suitable technologies that should operate in the year 2030 and beyond, i.e., after 5G. Consequently, this Special Issue of Future Internet concerning what possibilities lie ahead for a 6G wireless network includes four high-quality research papers (three of which are review papers with over 412 referred sources and one regular research). This editorial piece summarises the major contributions of the articles and the Special Issue, outlining future directions for new research. [ABSTRACT FROM AUTHOR]

The multiple access technique of the current wireless communication system depends on the Orthogonal Multiple Access (OMA) technique. The availability of orthogonal resources is limited in OMA and cannot support the increasing demand for a large number of users and efficient use of the spectrum in the coming years. The Non-Orthogonal Multiple Access (NOMA) is considered in the future release of 5G and Beyond 5G. In NOMA, multiple users can be dealt with in the same resource block at the same time, which allows NOMA to provide large connectivity, low latency, and high spectral efficiency. In this paper, the outage probability expression for the Multiple-Input Single-Output-Non-Orthogonal Multiple Access (MISO-NOMA) downlink system is derived by employing an Indefinite Quadratic Form (IQF) approach. Analytical results are compared with the Monte Carlo simulations which validate our theoretical derivations. Further, the performance of the MISO-NOMA system is assessed with different numbers of users in the system and different Signal-to-Noise Ratio (SNR) values. [ABSTRACT FROM AUTHOR]

The demanding objectives for the future sixth generation (6G) of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity, as well as revolutionary communication and computing paradigms. Among the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces (RISs), which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of functionalities. Incorporating RISs in wireless networks have been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one, intended for various networking objectives, such as coverage extension and capacity boosting, spatiotemporal focusing with benefits in energy efficiency and secrecy, and low electromagnetic field exposure. Motivated by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments, in this paper, we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal propagation. We also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems, which constitute a prerequisite step for the optimized incorporation of RISs in future wireless networks. Finally, we discuss the relevance of the RIS technology in the latest wireless communication standards, and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches.

The introduction of fifth-generation (5G) technology marks a significant milestone in next-generation networks, offering higher data rates and new services. Achieving optimal performance in 5G and beyond 5G (B5G) systems requires addressing key requirements like increased capacity, high efficiency, improved performance, low latency, support for many connections, and quality of service. It is well-known that suboptimal network configuration, hardware impairments, or malfunctioning components can degrade system performance. The physical layer of the radio access network, particularly channel estimation and synchronization, plays a crucial role. Hence, this paper offers an in-depth evaluation of the 5G Physical Downlink Shared Channel (PDSCH), along with its related channel models such as the Clustered Delay Line (CDL) and the Tapped Delay Line (TDL). This work assesses 5G network performance through practical and IA-based channel estimation and synchronization techniques, and anticipates numerologies for B5G networks. Extensive simulations leveraging the Matlab 5G New Radio (NR) toolbox assess standardized channel scenarios in both macro-urban and indoor environments, following configurations set by the 3rd Generation Partnership Project (3GPP). The numerical results offer valuable insights into achieving the maximum achievable throughput across various channel environments, including both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. The throughput comparisons are performed under assumptions of ideal, realistic, and convolutional neural networks (CNN)-based channel estimation with both perfect and realistic synchronization conditions. Importantly, the study pinpoints certain physical layer elements that have a pronounced impact on system performance, providing essential insights for devising effective strategies or refining CNN-based methods for forthcoming mobile B5G networks. [ABSTRACT FROM AUTHOR]