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Recently, the use of uncrewed aerial vehicles (UAVs) in joint sensing and communication applications has received a lot of attention. However, integrating UAVs in current cellular systems presents major challenges related to trajectory optimization and interference management among others. This paper considers a multi-cell network including a UAV, which senses and forwards the sensory data from different events to the central base station. Particularly, the current manuscript covers how to design the UAV's ({i} ) 3D trajectory, (ii) power allocation, and (iii) sensing scheduling such that (a) a set of events are sensed, (b) interference to neighboring cells is kept at bay, and (c) the amount of energy required by the UAV is minimized. The resulting nonconvex optimization problem is tackled through a combination of ({i} ) low-complexity binary optimization, (ii) successive convex approximation, and (iii) the Lagrangian method. Simulation results over a range of various key parameters have shown the merits of our approach, which consumes 33%-200% less energy compared to different benchmarks.

IMT-2020 (International Mobile Telecommunications-2020) is the prevailing mobile communication technology at the moment, significantly affecting societal progress. Nevertheless, the roll-out of the IMT-2020 system has introduced numerous interferences to existing services. The coexistence with fixed satellite services has become a topical issue currently under consideration. This paper discusses the compatibility and interference issues between IMT-2020 and the 14 GHz FSS (fixed-satellite service) uplink, as well as the spectrum access issue solved by artificial intelligence methods. The study shows that the interference from IMT-2020 macro-base stations to FSS space stations exceeds the ITU standard by approximately 10 dB. To control the interference, a partition-based power control algorithm is proposed, which divides ground base stations into multiple areas and virtualizes each area's base stations into a single large base station then applies power control to maximize the total transmission power of the base stations within the area. Furthermore, three intra-partition power control algorithms are introduced: average power allocation, power allocation based on channel gain, andna power allocation method based on the maximum intra-partition sum rate. Additionally, under the assumption that dynamic satellite nodes are available in the system for ground user access, a spectrum access algorithm utilizing deep reinforcement learning is designed. Simulation results confirm the effectiveness of the proposed scheme, which can reduce the interference from the IMT-2020 system to the FSS service below the threshold, ensuring harmonious coexistence of the two services. [ABSTRACT FROM AUTHOR]

The field of wireless communication has undergone revolutionary changes driven by technological advancements in recent years. Central to this evolution is wireless ad hoc networks, which are characterized by their decentralized nature and have introduced numerous possibilities and challenges for researchers. Moreover, most of the existing Internet of Things (IoT) networks are based on ad hoc networks. This study focuses on the exploration of interference management and Medium Access Control (MAC) schemes. Through statistical derivations and systematic simulations, we evaluate the efficacy of guard zone-based MAC protocols under Rayleigh fading channel conditions. By establishing a link between network parameters, interference patterns, and MAC effectiveness, this work contributes to optimizing network performance. A key aspect of this study is the investigation of optimal guard zone parameters, which are crucial for interference mitigation. The adaptive guard zone scheme demonstrates superior performance compared to the widely recognized Carrier Sense Multiple Access (CSMA) and the system-wide fixed guard zone protocol under fading channel conditions that mimic real-world scenarios. Additionally, simulations reveal the interactions between network variables such as node density, path loss exponent, outage probability, and spreading gain, providing insights into their impact on aggregated interference and guard zone effectiveness. [ABSTRACT FROM AUTHOR]

In wireless communication networks, mobile users in overlapping areas may experience severe interference, therefore, designing effective Interference Management (IM) methods is crucial to improving network performance. However, when managing multiple disturbances from the same source, it may not be feasible to use existing IM methods such as Interference Alignment (IA) and Interference Steering (IS) exclusively. It is because with IA, the aligned interference becomes indistinguishable at its desired Receiver (Rx) under the cost constraint of Degrees-of-Freedom (DoF), while with IS, more transmit power will be consumed in the direct and repeated application of IS to each interference. To remedy these deficiencies, Interference Alignment Steering (IAS) is proposed by incorporating IA and IS and exploiting their advantages in IM. With IAS, the interfering Transmitter (Tx) first aligns one interference incurred by the transmission of one data stream to a one-dimensional subspace orthogonal to the desired transmission at the interfered Rx, and then the remaining interferences are treated as a whole and steered to the same subspace as the aligned interference. Moreover, two improved versions of IAS, i.e., IAS with Full Adjustment at the Interfering Tx (IAS-FAIT) and Interference Steering and Alignment (ISA), are presented. The former considers the influence of IA on the interfering user-pair's performance. The orthogonality between the desired signals at the interfered Rx can be maintained by adjusting the spatial characteristics of all interferences and the aligned interference components, thus ensuring the Spectral Efficiency (SE) of the interfering communication pairs. Under ISA, the power cost for IS at the interfered Tx is minimized, hence improving SE performance of the interfered communication-pairs. Since the proposed methods are realized at the interfering and interfered Txs cooperatively, the expenses of IM are shared by both communication-pairs. Our in-depth simulation results show that joint use of IA and IS can effectively manage multiple disturbances from the same source and improve the system's SE.

Wireless communication technologies have profoundly impacted the interconnectivity of mobile users and terminals. Nevertheless, the exponential increase in the number of users poses significant challenges, particularly in interference management, which is a major concern in wireless communication. Machine learning (ML) approaches have emerged as powerful tools for solving various problems in this domain. However, existing studies have not fully addressed the problem of interference management for wireless communication using ML techniques. In this paper, we explore the application of recurrent neural network (RNN) approaches to address co-channel interference in wireless communication. Specifically, we investigate the effectiveness of long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and gated recurrent unit (GRU) network architectures in two different network settings. The first network comprises 10 connected devices, while the second network involves 20 devices. Our experimental results demonstrate that Bi-LSTM outperforms LSTM and GRU in terms of mean squared error, normalized mean squared error, and sum rate. While LSTM and GRU produce similar results, LSTM exhibits a marginal advantage over GRU. In addition, a combined RNN approach is also studied, and it can provide better results in dense networks. [ABSTRACT FROM AUTHOR]

In recent years the increase of mobile devices and users of the internet has led to an increase in the burden on the network, low connectivity, low bandwidth, and less throughput. With many advantages of the 5G cellular network, this network suffers from signal interference which causes a massive problem for the network. There are many challenges in the cellular network related to energy consumption, and one of the leading and critical issues in 5G is interference management. There is poor voice quality during indoor communication, so interference management provides Quality of Service (QoS) to improve this communication. In indoor communication, there is low power compared to outdoor communication because there are many users, and due to users, there are low data rates and overhead on the base station. From this viewpoint, modulation schema and coding schemes achieve better channel conditions, a significant convergence area, and better QoS. This research proposes a scheme named multiple input multiple output (MIMO) technology for interference management in a 5G network. The evaluation of this scheme with a relay strategy has also been carried out to avoid interference and enhance the strength of the propagated signal. The simulation has revealed the performance of the proposed interference management scheme with a relay strategy based on performance evaluation parameters such as end-to-end delay, throughput, path loss, and energy consumption. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is an anticipated technology for fifth-generation networks for increasing mass connectivity, spectrum efficiency, user-fairness, and higher capacity. NOMA allows end-clients to share indistinguishable radio resources such as spreading code, subcarrier, and time slots simultaneously. Thus, the main challenge involved in conceptualizing effective NOMA design is selection of resource allocation (i.e., user clustering, power allocation, and quality-of-service (QoS) assurance) algorithms. NOMA can be easily integrated with current fifth-generation multi-access methodologies. In this survey paper, the NOMA methodologies are discussed, and provide an overview of the methodologies and algorithms designed for optimizing power allocation, interference management, and network selection management in the heterogenous multiple carrier NOMA. The survey highlights the current limitation of the existing resource provisioning framework and presents a solution to overcome the current limitation. [ABSTRACT FROM AUTHOR]

In the context of wireless systems going forward, particularly in the Beyond 5G (B5G) era, where high data rates and low latency are critical, D2D communication is a pivotal technology that has many advantages. In D2D communication, the allocation of resources plays a critical role in achieving higher throughput while ensuring interference management, improved spectrum and energy efficiency, and system fairness. Conventional resource allocation methodologies encounter challenges in dynamically changing and diverse communication environments. To deal with the dynamic and unpredictable character of channel characteristics, we utilize a distributed iterative resource allocation technique based on the reinforcement learning (RL) approach that empowers the system to learn and adapt to the wireless environment autonomously. In this paper, we formulate a distributed Q learning-based RL method as its real-time learning capabilities, reduced communication overhead, and exploration efficiency make it well-suited for adapting to dynamic D2D environments compared to other RL methods. By applying the Q-learning method, the D2D devices act as learning agents striving to maximize the cumulative rewards. Through interactions with the environment and continuous learning from feedback, these agents adapt to real-time resource allocation decisions over time. Comparing our proposed Q-learning method to the state-of-the-art RL techniques, simulation results show improvements in energy and spectrum efficiency, latency, an increase in Jain's fairness index, and improvements in overall system throughput of about 6 %–8 %. The scalability is found to be 1.69 interpreting that Q-learning exhibits a good scalability as the throughput does not abruptly decrease for an increasing number of devices.

We propose a novel power control technique called PC-ECGNN, which uses edge convolution to optimize power allocation in wireless IoT networks. PC-ECGNN leverages interference link distances as edge features and desired link channel gains as initial vertex features, iteratively updating vertex features based on neighbors and edge features. PC-ECGNN is the first technique to incorporate edge convolution into power control and has been customized for the considered scenario, optimizing the neural network structure to provide fast convergence and high performance simultaneously. Experimental results show that PC-ECGNN outperformed the state-of-the-art PC-MPGNN, achieving a 4% increase in average spectral efficiency and a 4dBm reduction in average transmit power compared to PC-MPGNN. Furthermore, our technique demonstrates advantages over existing methods in dynamic environmental changes. The proposed model, trained in a fixed environment, showed minimal performance degradation across various test environments different from the training setting, outperforming traditional models trained in individual environments. When applying meta-learning, the proposed model achieved better performance in each test environment after additional fine-tuning with only 1% of the pre-training epochs, compared to models trained with the full number of epochs in each individual test environment.

An important part of designing non-geostationary satellite orbit (NGSO) constellations is to ensure that they can co-exist with the legacy geostationary satellite orbit (GSO) systems in terms of interference levels. According to the current radio regulations defined by the International Telecommunication Union (ITU), NGSO systems shall not cause unacceptable interference to GSO systems. However, the traditional interference avoidance methods considered for the NGSO systems impact the coverage and overall service quality of their users. Embracing the trends of equipping NGSO satellites with phased array antennas, this paper investigates downlink beamforming strategies to improve NGSO users’ service and mitigate the interference caused at GSO systems, ensuring seamless co-channel operation for NGSO and GSO network systems. Our proposed aggregate interference-constrained (AIC) beamforming optimization, limits the co-channel interference at GSO ground stations while minimizing the NGSO satellites’ transmitted power. The AIC beamformer is designed under average channel state information (aCSI) to consider a practical scenario in NGSO satellite communications where acquiring perfect instantaneous channel information is challenging due to satellite mobility and long propagation roundtrip. Furthermore, for a more general scenario, this study considers robust AIC beamforming to account for the uncertainty in GSO ground stations’ location when suppressing interference. Numerical results evidence the benefits of our proposed method ensuring service quality and providing the lowest probability for unaccepted interference levels at GSO ground stations among other benchmarks.

The integration of visible light communications (VLC) in future generation of wireless communications leads to consider the deployment of multiple access points (APs) transmitting in the optical domain. Since each optical AP generates a small and confined coverage footprint, scenarios comprising multiple optical APs are subject to intercell interference. In this context, angle diversity receivers (ADRs) composed of multiple photodiodes pointing to distinct orientations each, have been proposed for mitigating the interference and blocking effects. The design of ADRs typically assumes that the field-of-view (FoV) generated by each photodiode does not overlap with the FoV of all other photodiodes. In this work, we propose the derivation of the theoretical expressions of the probability distribution function (PDF) and the cumulative distribution function (CDF) of the signal-to-interference plus noise ratio (SINR) in multicell scenarios for ADRs in which the FoV generated by each photodiode may overlap with the FoV of the other photodiodes. Several geometrical conditions are proposed in order to derive the statistical characterization of photodiode combining schemes such as select best combining (SBC), equal gain combining (EGC) and maximum ratio combining (MRC). It is shown that the derived closed-form expressions obtain a similar performance as the results obtained through Monte Carlo simulations. Moreover, the SINR enhancement due to the use of the proposed ADR in comparison with single photodiode receivers is highlighted.

Efficient and timely sharing of critical information is crucial for Public Safety (PS) communications, which can be fulfilled using one of the cutting-edge technologies, Device-to-device (D2D) communication. During an emergency, the PS applications should be prioritized over other applications, ensuring the emergency messages reach the first responders in time. Due to its inherent characteristics, the evolved Node Base station will not prioritize or categorize the D2D communication based on its application type, thus treating all applications equally. Further, D2D communication introduces significant interference to cellular users and vice-versa while sharing resources, and it is vital to reduce the impact of these interferences to ensure the Quality of Service for all users in the network. Hence, this article proposes a novel interference management approach to increase the overall sum rate of the system. In addition, the proposed approach also allows more D2D communication in general, particularly PS application-based D2D communication, to be active in the network. As the formulated problem is a Mixed-Integer Non-Linear Programming (MINLP) type of problem, it is split into two sub-problems, namely, Iterative Resource Allocation and Sharing and Iterative Power Optimization to achieve a polynomial time complexity. The theoretical proofs adequately explain the algorithm’s time complexity and convergence property. The simulation results show that the proposed system enhances the overall sum rate by allowing more active PS D2D applications in the network.

Mutual interference between NOMA cellular users (CUs) and D2D links poses a challenge in extending NOMA’s resource allocation schemes to NOMA-D2D models. In this paper, we develop a novel resource allocation algorithm for NOMA-D2D systems and utilize it for comparing NOMA-D2D and OFDMA-D2D models. The proposed scheme aims to optimize the system sum rate and ensure a minimal rate for CUs. We formulate this as a mixed integer non-linear programming (MINLP) problem encompassing resource block (RB) allocation and power control. We propose a heuristic sub-optimal solution to lower the computational complexity. Our solution has two stages. In the first stage, D2D pairs and half of the CUs are admitted to the system to maximize the system sum rate. The rest of the CUs join the system in the next stage, while the upper bound for their achievable signal-to-interference-plus-noise-ratio (SINR) is maximized. Our first stage involves resource allocation for an OFDMA-D2D system. Employing an equal power allocation policy, we decompose the resource allocation problem for D2D pairs and CUs into two assignment problems. In the next step, the RB allocation scheme for CUs, which complies with the NOMA principle, is formulated as another assignment problem, and the power control strategy is updated. This two-stage heuristic solution allows a comparison of NOMA-D2D and OFDMA-D2D performances with identical power budgets, while the number of cellular connections in the NOMA-D2D system is twice that of the OFDMA-D2D system. Our results indicate that OFDMA-D2D achieves a higher sum rate and individual rates over NOMA-D2D.

With the exponential growth of data demand and the advent of 5G networks, the need for efficient resource allocation algorithms has become paramount. This study presents a dynamic resource allocation algorithmic framework aimed at optimizing efficiency and performance in 5G networks. The framework focuses on frequency reuse at the edges while employing fractional pilots for enhanced spectrum utilization. 5G networks promise unprecedented speeds and low latency, enabling a wide array of applications from IoT to augmented reality. However, the efficient allocation of resources remains a challenge, especially at the network edges where interference is high. Traditional static resource allocation schemes fail to adapt to dynamic network conditions, leading to suboptimal performance. The main challenge lies in effectively managing resources to meet the diverse demands of various applications while mitigating interference and maximizing spectral efficiency. The proposed framework employs a dynamic resource allocation algorithm that adapts to changing network conditions in real-time. Leveraging fractional pilots, the algorithm optimizes frequency reuse at the network edges, thereby enhancing spectral efficiency. The framework integrates stochastic learning for predictive analytics to anticipate resource demands and interference patterns. Simulation results demonstrate significant improvements in spectral efficiency and network performance compared to traditional static allocation methods. The utilization of fractional pilots effectively reduces interference, enabling higher throughput and lower latency, especially at the network edges. The dynamic nature of the algorithm ensures adaptability to varying traffic loads, leading to enhanced overall network efficiency. [ABSTRACT FROM AUTHOR]

With the continuous advancement of the trend of economic globalisation and the in-depth development of personalised services, the manufacturing mode has begun to change to service-oriented manufacturing, and the focus of enterprises has gradually shifted from the industrial chain to the supply chain. However, at present, accidents often occur in the supply chain around products, such as changes in orders, lack of resources in a short period of time, etc. These interference events are difficult to control and cause great damage to the normal operation and economic interests of enterprises for a long time. Therefore, it is necessary to study the optimisation methods of enterprise supply chain. Therefore, it is necessary to study the optimisation methods of enterprise supply chain. The study uses system dynamics to analyses employee counterproductive behaviour, develops a disturbance management model incorporating employee behavioural factors, and solves it with an improved particle swarm optimisation (PSO) algorithm. The experimental results show that the maximum number of noninferior solutions obtained by the improved PSO algorithm is 14 and 12, respectively. Compared with the GA_TOM (Genetic Algorithm_TOM), the improved algorithm is closer to the ideal pareto front. In the MS index, the average and minimum values obtained by the improved PSO algorithm are 0.57 and 0.609, respectively, which can cover more ideal pareto fronts. It shows that the algorithm effectively improves the stability and security of the supply chain, and provides a practical reference for the supply chain optimisation of manufacturing enterprises. [ABSTRACT FROM AUTHOR]

In ultra dense networks (UDNs), a large number of small-cell base stations (SBSs) are densely deployed in hotspots, e.g., office buildings, residential apartments, shopping malls, etc. Thus, SBSs have more serious interference between them in UDNs than they have in heterogeneous networks (HetNets) and it is a crucial issue to assign subchannels with consideration of the interference to satisfy the demands of small-cell user equipments (SUEs) for UDNs. In this paper, I propose a novel dynamic channel assignment (DCA) scheme named fairness based dynamic channel assignment (FDCA) to improve the system capacity of SUEs with guaranteeing the fairness for the downlink of in-building UDNs based on orthogonal frequency division multiple access-frequency division duplex. In the proposed FDCA scheme, a network controller unit first generates an interference matrix using channel state information from SUEs in UDNs and then it fairly assigns different numbers of subchannels to SUEs through their serving SBSs based on the interference matrix and optimal values of the signal to interference plus noise ratio threshold. Through simulation results, I show that the proposed FDCA scheme outperforms other DCA schemes using graph coloring algorithm in terms of the fairness with the reduced mean SUE capacity slightly. That is, the mean SUE capacity of the proposed FDCA scheme decreases 1 ∼ 5% than that of other schemes while the fairness index of it increases 16 ∼ 21% when the numbers of SBSs and SUEs are 25 and 100, respectively. [ABSTRACT FROM AUTHOR]

One of solutions to address the increasing demands for data traffic by mobile users is to implement ultra dense network (UDN). UDN consists of many femtocells that are densely deployed on macrocellular communication networks. Since femtocells radius is very short and they are tightly packed, it faces interferences problem i.e., co-tier and cross-tier interferences. This paper proposes interference management technique based on clustering method for UDN multicellular communication networks at downlink transmission. It is purposed to reduce interference effects. To evaluate the proposed clustering method, two simulation scenarios have been designed; namely baseline system and system with the proposed clustering method. The scenarios applied is to randomly distribute femtocells following a uniform distribution in three macrocells areas. Through a clustering algorithm, adjacent femtocells are grouped into one cluster and assigned different frequency channels for each femtocell in that cluster. The same cluster pattern is repeated for all femtocells. Thus, interference effect is canceled within one cluster and reduced among clusters by widening the distance among femtocells that use the same frequency channels. Through simulation experiment, the proposed clustering method is evaluated and compared to the baseline system. The simulation results show that Signal to Interference plus Noise Ratio (SINR) of femtocells increases 0.44 dB, throughput increases 1.67%, Bit Error Rate (BER) reduces, and other parameters improve as well which include spectral efficiency, network energy, and average network energy. The proposed clustering method increases performances of the networks and provides better solution for transmission data speed in densely femtocell network.

The application of drones, also known as unmanned aerial vehicles deployed as unmanned aerial base stations (UABSs), has received extensive interest for public safety communications (PSC) to fill the coverage gaps and establish ubiquitous connectivity. In this article, we design a PSC LTE-Advanced air–ground-based HetNet (AG-HetNet) that is a scenario representation of a geographical area during and after a disaster. As part of the AG-HetNet infrastructure, we have UABSs and ground user equipment (GUE) flocking together in clusters at safe places or evacuation shelters. AG-HetNet uses cell range expansion (CRE), intercell interference coordination (ICIC), and 3D beamforming techniques to ensure ubiquitous connectivity. Through system-level simulations and using a brute-force technique, we evaluate the performance of the AG-HetNet in terms of fifth-percentile spectral efficiency (5pSE) and coverage probability. We compare system-wide 5pSE and coverage probability when UABSs are deployed on a hexagonal grid and for different clustering distributions of GUEs. The results show that reduced power subframes (FeICIC) defined in 3GPP Release-11 can provide practical gains in 5pSE and coverage probability than the 3GPP Release-10 with almost blank subframes (eICIC).

The field of wireless communication has undergone revolutionary changes driven by technological advancements in recent years. Central to this evolution is wireless ad hoc networks, which are characterized by their decentralized nature and have introduced numerous possibilities and challenges for researchers. Moreover, most of the existing Internet of Things (IoT) networks are based on ad hoc networks. This study focuses on the exploration of interference management and Medium Access Control (MAC) schemes. Through statistical derivations and systematic simulations, we evaluate the efficacy of guard zone-based MAC protocols under Rayleigh fading channel conditions. By establishing a link between network parameters, interference patterns, and MAC effectiveness, this work contributes to optimizing network performance. A key aspect of this study is the investigation of optimal guard zone parameters, which are crucial for interference mitigation. The adaptive guard zone scheme demonstrates superior performance compared to the widely recognized Carrier Sense Multiple Access (CSMA) and the system-wide fixed guard zone protocol under fading channel conditions that mimic real-world scenarios. Additionally, simulations reveal the interactions between network variables such as node density, path loss exponent, outage probability, and spreading gain, providing insights into their impact on aggregated interference and guard zone effectiveness.

The application of drones, also known as unmanned aerial vehicles deployed as unmanned aerial base stations (UABSs), has received extensive interest for public safety communications (PSC) to fill the coverage gaps and establish ubiquitous connectivity. In this article, we design a PSC LTE-Advanced air–ground-based HetNet (AG-HetNet) that is a scenario representation of a geographical area during and after a disaster. As part of the AG-HetNet infrastructure, we have UABSs and ground user equipment (GUE) flocking together in clusters at safe places or evacuation shelters. AG-HetNet uses cell range expansion (CRE), intercell interference coordination (ICIC), and 3D beamforming techniques to ensure ubiquitous connectivity. Through system-level simulations and using a brute-force technique, we evaluate the performance of the AG-HetNet in terms of fifth-percentile spectral efficiency (5pSE) and coverage probability. We compare system-wide 5pSE and coverage probability when UABSs are deployed on a hexagonal grid and for different clustering distributions of GUEs. The results show that reduced power subframes (FeICIC) defined in 3GPP Release-11 can provide practical gains in 5pSE and coverage probability than the 3GPP Release-10 with almost blank subframes (eICIC). [ABSTRACT FROM AUTHOR]

Due to the broadcast nature of wireless communications, users’ data transmitted wirelessly is susceptible to security/privacy threats. Meanwhile, as a result of the limitation of spectrum resources, massive wireless connections will incur serious interference, which may damage the efficiency of data transmission. Therefore, improving both efficiency and secrecy of data transmission is of research significance. In this paper, we propose a wireless transmission scheme by taking both Secure Communication (SC) and Interference Management (IM) into account, namely SCIM. With this scheme, an SCIM signal is generated by the legitimate transmitter (Tx) and sent along with the desired signal, so that the SCIM signal can interact with and suppress the environmental interference at the legitimate receiver (Rx). Meanwhile, the SCIM signal may interfere with the eavesdropper in the coverage of legitimate transmission so as to deteriorate the eavesdropping performance. Therefore, the secrecy of desired transmission is improved. In this way, both the transmission efficiency and privacy are enhanced. Then, by taking various transmission preferences into account, we develop different implementations of SCIM, including Interference Suppression First SCIM (ISF-SCIM), Data Transmission First SCIM (DTF-SCIM), Anti-Eavesdropping First SCIM (AEF-SCIM), and Secrecy Rate Maximization SCIM (SRM-SCIM). Our in-depth simulation results have shown the proposed methods to effectively improve the efficiency and secrecy of the legitimate transmission.

Wireless communication technologies have profoundly impacted the interconnectivity of mobile users and terminals. Nevertheless, the exponential increase in the number of users poses significant challenges, particularly in interference management, which is a major concern in wireless communication. Machine learning (ML) approaches have emerged as powerful tools for solving various problems in this domain. However, existing studies have not fully addressed the problem of interference management for wireless communication using ML techniques. In this paper, we explore the application of recurrent neural network (RNN) approaches to address co-channel interference in wireless communication. Specifically, we investigate the effectiveness of long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and gated recurrent unit (GRU) network architectures in two different network settings. The first network comprises 10 connected devices, while the second network involves 20 devices. Our experimental results demonstrate that Bi-LSTM outperforms LSTM and GRU in terms of mean squared error, normalized mean squared error, and sum rate. While LSTM and GRU produce similar results, LSTM exhibits a marginal advantage over GRU. In addition, a combined RNN approach is also studied, and it can provide better results in dense networks.

The symbiosis of millimeter wave (mmWave) transmission and Device-to-Device (D2D) communication has become a a promising candidate for temporary or spontaneous short-distance communications. Although the use of directional antennas in mmWave communications can effectively reduce the interference between users, as the network becomes much denser, the mmWave D2D communication network tends to be interference-limited rather than noise-limited. Significantly, the inter-user interference from the line-of-sight path should be carefully circumvented to boost the achievable rate further. We first identify that beamwidth, channel state, and resource allocation jointly affect the interference level and achievable rate. To effectively manage the interference, a joint beamwidth selection and resource optimization problem are established to maximize the sum rate. We propose a two-stage algorithm to solve this non-convex mixed integer programming with low complexity. First, a feasibility-aware particle swarm optimization based beamwidth selection algorithm is proposed to find the proper beamwidth. Second, a constrained concave-convex procedure based joint power optimization and time slots scheduling algorithm is proposed to facilitate the concurrent transmission and further improve the sum rate. Numerical results demonstrate that the proposed two-stage algorithm can effectively reduce interference and improve the sum rate with low complexity. [ABSTRACT FROM AUTHOR]

Extended reality (XR) is an emerging technology that has gained significant attention in the context of fifth-generation (5G) and 5G-Advanced cellular networks and beyond. One of the less explored areas for practical XR service deployments is the study of its interaction with the existing traffic such as enhanced mobile broadband (eMBB). This study explores the performance of having both XR and eMBB users simultaneously in a multi-cell network for two different indoor and outdoor deployment scenarios. We show that the main limitation to maximizing XR capacity in the mixed scenario is inter-cell interference (ICI) generated by eMBB users. ICI from eMBB results in a loss of about 80% in XR capacity when an XR source data rate of 45 Mbps and a strict packet delay budget (PDB) of 10 ms is enforced. To mitigate this, we propose new radio resource management enhancements that apply restrictions on eMBB radio resource usage to balance between eMBB and XR simultaneous capacity. With the proposed enhancements, maximum XR capacity can be maintained for the case with an XR source data rate of 45 Mbps and a PDB of 20 ms while restricting eMBB throughput by about 50%. The impact on eMBB throughput performance from adding XR users depends on the XR PDB, deployment environment, and the eMBB radio resource usage restriction. The results demonstrate that the eMBB throughput declines with a factor of 1 to 4 of the XR sum rate.

This paper proposes hybridization of non-orthogonal multiple-access (NOMA) and orthogonal multiple access (OMA) schemes for next-generation cellular networks. Specifically, two schemes that operate NOMA/OMA mode selection as well as channel and power allocation are proposed to improve the resource utilization and bandwidth-efficiency for network capacity maximization. The two proposed hybrid schemes are: (1) single-cell hybrid multiple-access (SC-HMA) and (2) multi-cell hybrid multiple-access (MC-HMA) schemes. The SC-HMA scheme determines the optimal NOMA/OMA mode and user pairs in each resource block by utilizing a matrix representing the capacity outcomes of pairing all possible combinations of users. On the other hand, in the MC-HMA, the NOMA/OMA modes are categorized into intra-cell and inter-cell based on an interference map, where the principal objective is to determine the best mode of operation between the user pairs to improve the overall sum-rate and quality-of-service (QoS). The results show that the proposed HMA schemes provide superior overall network capacity compared to benchmark schemes. In addition, the SC-HMA scheme outperforms the MC-HMA in terms of network capacity at the expense of higher computational complexity.

Any new deployment of networks in the industrial, scientific, and medical (ISM) band, even though it is license-free, has to co-exist with IEEE 802.11 networks. IoT devices are typically deployed in the ISM band, creating a spectrum bottleneck for competing networks. This article investigates the issue of co-existence of wireless networks with WiFi networks. In our scenario, we consider WiFi as the “primary” or higher priority network co-existing with multiple “secondary” networks that may be used for low priority devices, with both networks operating in the ISM band. Towards this end, we first develop an analytical model for a metric called the “received symbol distance” at the primary receiver to obtain a power control parameter for secondary users. This power control parameter is used to scale the power of the secondary user according to the wireless channel between the primary transmitter and primary receiver. The proposed approach is computationally simple and does not require any estimation of channel coefficients. Simulation results show that the proposed technique can be used to effectively increase the spectrum utilization and the probability of a successful transmission by the secondary user, while not having any harmful effect on the primary user.

The number of non-geostationary orbit (NGSO) satellite constellations is growing persistently. Accordingly, co-channel interference with geostationary orbit (GSO) networks will inevitably affect the operation of NGSO and GSO satellites in the years to come. Towards protecting the well-established GSO systems from the interference produced by the newly launched NGSO satellite constellations, the international telecommunication union (ITU) has established regulations and evaluation metrics for the aggregated emitted power flux-densities from NGSO systems. In conformity with these regulations, the NGSO operators should avoid disruptive emissions, which could lead to acute service interruptions. To reduce the downlink interference levels and alleviate harmful service disconnectivity of satellite constellations, we propose a joint power and tilt control strategy to maximize user demand satisfaction. Our proposed method ensures that the equivalent power flux-density (EPFD) from satellite constellation satisfies the ITU regulatory limits while the radio resource management for the user service is optimized via power control and tilting. Simulation results analyzing EPFD levels and geographical dependencies and the performance of optimized LEO transmissions are presented, showing the benefit of the proposed technique in terms of service continuity and spectrum coexistence.

In recent years, with the rapid development of mobile communication, D2D (Device-to-Device, D2D) cooperative communication network has become the main component of future communication network, which greatly improves the spectrum efficiency of the network and the quality of user communication. However, the existing D2D network resource allocation schemes have some problems, such as weak dynamic resource allocation capability and low user communication quality. In view of this challenge, this paper proposes a resource allocation algorithm for D2D cooperative communication networks based on improved Monte Carlo tree search. First, a double-chain deep deciduous Monte Carlo tree search (Dcdd-MCTS) resource allocation model is established, Then, the loss function composed of deciduous MCTS and parallel convolution network is used to update the parameters of the deep neural network model of Dcdd-MCTS. Then, the theory of optimal classification is used to solve the user’s transmit power. Finally, the optimal scheme of dynamic output resource allocation is output. The simulation results show that Dcdd-MCTS has good convergence. In the research on the distance between devices, compared with single-chain deep MCTS and joint optimization algorithm, the proposed algorithm in this paper increases the system throughput by 5%, 2%, respectively, and reduces the outage probability by 33%,18%.

We investigate effective interference management for Low Earth Orbit (LEO) satellite networks that provide downlink services to ground users and share the same frequency spectrum range. Since there are multi-group LEO satellites with different constellation orbits, the ground users will experience time-varying interference due to the overlapping of main/side lobes of the satellite beams, which becomes even more challenging when the interfering satellites cannot communicate directly. To address the problem, we consider two LEO satellite groups that provide communication service in the same ground area, while competing for communication resources. We develop solutions that maximize the throughput and manage the time-varying interference under a certain level, without explicit message exchanges between the satellite groups. By exploiting statistical learning and deep reinforcement learning techniques, we develop learning-based resource allocation schemes and evaluate their performance through extensive simulations. We show their effectiveness under different reward settings and different interference managements, and demonstrate that a Deep Q-Network (DQN)-based scheme can achieve the close-to-optimal performance.

Due to the vast rise of users, the variety of application requirements, and the enormous necessity for ubiquitous connectivity anytime and anywhere, integration of new technologies and ideas are required. Therefore, Device to Device (D2D) communication technology has been regarded as a critical concern for Beyond Fifth Generation (B5G) networks. In the D2D technology, devices in close proximity may connect without using a Base Station (BS). Consequently, improving the capacity and performance of traditional networks. The major objectives are to accomplish a high geared data rate, increase the network coverage with low latency, and improve Energy Efficiency (EE) and Spectrum Efficiency (SE). However, the coexistence of D2D technology in the traditional network can pose many issues and challenges, such as power control, interference, spectrum access, security, and mode selection. Consequently, a comprehensive survey that includes an investigation of the current issues in D2D technology is required. In this survey, we present a new categorization of the most important aspects of resource management in B5G D2D communication, such as power allocation, spectrum allocation, interference management, and mode selection. Furthermore, we discuss the limitations and open issues of the current techniques and propose a model approach and future directions for D2D communication in B5G wireless networks. Our survey aims to assist researchers in exploring and improving D2D communication within the framework of forthcoming wireless networks.

Massive multiple-input multiple-output can significantly increase the system capacity by allowing vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) networks to share the radio resource pool, if the inter-network interference is constrained. However, in order to control the inter-network interference, it is necessary to exchange channel state information (CSI) between the V2I and V2V networks, which requires a huge amount of signaling resources. This paper proposes an one-bit signaling-based interference management scheme to reduce the signaling overhead while controlling the interference from the V2V network to the V2I network.

In underground works, one topic that must be carefully studied and analysed is how to deal with interferences induced on buildings by the excavation machine. The paper provides a proposal for the management of the behaviour that must take place in the event of a building falling in a subsidence basin by creating a 4D BIM model able to control the influencing parameters, to visualise subsidence values and to foresee harmful events. To predict the formation of chimneys that could damage buildings structures and façades, the method assumes some predefined design values of the expected subsidence graphically monitoring, over time, different thresholds that can promptly activate safety procedures. The proposed model makes possible, as well, to check the tunnel ring parameters linked to the TBM machine. Dynamo has been used to semi-automatically connect an Excel file with a Revit one to import all the data in the 3D model. As a case study, the paper shows the application of the method to a portion of the central section of the new M4 metro line in Milan, where EPB-type TBM machines have been used. An additional output of the research consists of seven safety guidelines for interference management to be adopted when operational values exceed the assigned threshold. According to all the technicians involved in the M4 metro line site as specialists, the joint use of the 4D BIM model and the set of the safety procedures improve tunnelling management and can be applied with the appropriate changes in similar works. [ABSTRACT FROM AUTHOR]

Several efforts are being made to improve the capacity of 5G networks using emerging technologies of interest. One of the indispensable technologies to fulfill the need is device-to-device (D2D) communication with its untapped associated benefits. Interference is introduced at the base station due to massive traffic congestion. The purpose of this research is to expand the knowledge of interference mitigation in D2D using stochastic geometrical tools which will result in capacity enhancement. This study uses a literature review method based on 5G and other already existing literature on D2D communication. More than one hundred and twenty papers on D2D communications in cellular networks exist but no precise survey paper on interference management to enhance capacity using stochastic geometrical tools exists. The contribution of this survey to theory is that apart from already existing capacity enhancement methods, interference mitigation using stochastic geometrical tools is another technique that can also be used for capacity enhancement in D2D communications.

The emergence of the Industry 4.0 revolution to upgrade the Internet of Things (IoT) standards provides the prominence outcomes for the future wireless communication systems called 5G. The development of 5G green communication systems suffers from the various challenges to fulfill the requirement of higher user capacity, network speed, minimum cost, and reduced resource consumption. The use of 5G standards for Industry 4.0 applications will increase data rate performance and connected device's reliability. Since the arrival of novel Covid-19 disease, there is a higher demand for smart healthcare systems worldwide. However, designing the 5G communication systems has the research challenges like optimum resource utilization, mobility management, cost-efficiency, interference management, spectral efficiency, etc. The rapid development of Artificial Intelligence (AI) across the different formats brings performance enhancement compared to conventional techniques. Therefore, introducing the AI into 5G standards will optimize the performances further considering the various end-user applications. We first present the survey of the terms like 5G standard, Industry 4.0, and some recent works for future wireless communications. The purpose is to explore the current research problems using the 5G technology. We further propose the novel architecture for smart healthcare systems using the 5G and Industry 4.0 standards. We design and implement that proposed model using the Network Simulator (NS2) to investigate the current 5G methods. The simulation results show that current 5G methods for resource management and interference management suffer from the challenges like performance trade-offs. [ABSTRACT FROM AUTHOR]

Device to device (D2D) communication technology is the main component of future communication, which greatly improves the utilization of spectrum resources. However, in the D2D subscriber multiplex communication network, the interference between communication links is serious and the system performance is degraded. Traditional resource allocation schemes need a lot of channel information when dealing with interference problems in the system, and have the problems of weak dynamic resource allocation capability and low system throughput. Aiming at this challenge, this paper proposes a multi-agent D2D communication resource allocation algorithm based on Advantage Actor Critic (A2C). First, a multi-D2D cellular communication system model based on A2C Critic is established, then the parameters of the actor network and the critic network in the system are updated, and finally the resource allocation scheme of D2D users is dynamically and adaptively output. The simulation results show that compared with DQN (deep Q-network) and MAAC (multi-agent actor–critic), the average throughput of the system is improved by 26% and 12.5%, respectively. [ABSTRACT FROM AUTHOR]

In order to achieve the vision of seamless wireless communication coverage, a space–air–ground integrated network is proposed as a key component of the sixth-generation (6G) mobile communication system. However, the spectrum used by aerial networks has become gradually crowded with the increase in wireless devices. Space networks are also in dire need of developing new bands to address spectrum shortages. As an effective way to solve the spectrum shortage problem, spectrum sharing between aerial/space networks and ground networks has been extensively studied. This article summarizes state-of-the-art studies on spectrum sharing between aerial/space networks and ground networks. First, this article provides an overview of aerial networks and space networks and introduces the main application scenarios of aerial networks and space networks. Then, this article summarizes the spectrum sharing techniques between aerial/space networks and ground networks, including existing spectrum utilization rules, spectrum sharing modes and key technologies. Finally, we summarize the challenges of spectrum sharing between aerial/space networks and ground networks. This article provides guidance for spectrum allocation and spectrum sharing of space–air–ground integrated networks. [ABSTRACT FROM AUTHOR]

With the proliferation of femtocell networks, seamless hand-off, and efficient power management are crucial for ensuring undisturbed connectivity and the optimal utilization of resources. Certain limitations of the existing methods include limited adaptability to dynamic network conditions, suboptimal hand-off decision-making, and inefficient power allocation. To tackle the addressed drawbacks of the existing interference management, the Vocal Hunt Optimization-based Power Scheduling BiLSTM (VHO-based PS-BiLSTM) is introduced in the research. This model aims at achieving a seamless transition during the hand-off events while optimizing the power consumption, ultimately enhancing the energy efficiency of the network and maximizing the end-user quality of service. The optimized Bidirectional Long Short-term Memory (BiLSTM) achieves superior performance by acquiring the sequential dependencies in energy consumption data, effectively capturing underlying patterns, and enabling the storage and utilization of data from both past and future time steps. The BiLSTM classifier is optimized with the Vocal Hunt Optimization (VHO) to improve the efficiency of the detection process and in turn, the power scheduling is carried out by the VHO individually based on the detected hand-off. The efficiency of the research is measured in terms of accuracy, sensitivity, specificity, delay, energy, Quality of service (QoS), and throughput which achieve 96.8 %, 95.76 %, 96.25 %, 109.38 ms, 0.999 J, 0.513, and 0.487 respectively. [ABSTRACT FROM AUTHOR]

Future generation of Electric Vehicles (EVs) equipped with modern technologies will impose a significant burden on computation and communication to the network due to the vast extension of onboard infotainment services. To overcome this challenge, multi-access edge computing (MEC) or Fog Computing can be employed. However, the massive adoption of novel infotainment services such as Augmented Reality, Virtual Reality-based services will make the MEC and Fog resources insufficient. To cope with this issue, we propose a system model with onboard computation offloading, where an EV can utilize its neighboring EVs resources that are not resource-constrained to enhance its computing capacity. Then, we propose to solve the problem of computational task offloading by jointly considering the communication, computation, and control in a mobile vehicular network. We formulate a mixed-integer non-linear problem (MINLP) to minimize the trade-off between latency and energy consumption subject to the network resources and the mobility of EVs. The formulated problem is solved via the block coordination descent (BCD) method. In such a way, we decompose the original MINLP problem into three subproblems which are resource block allocation (RBA), power control and interference management (PCP), and offload decision problem (ODP). We then alternatively obtain solutions of RBA and PCP via the duality theory, and the third sub-problem is solvable via the relaxation method and alternating direction Lagrangian multiplier method (ADMM). Numerical results reveal that the proposed solution BCD-based algorithm performs a fast convergence rate.

In the real world of wireless communication systems, one solution to the practical limitation of interference alignment in terms of global channel state information at the transmitter (CSIT) requirement is to consider a linear precoding approach that only uses delayed CSIT known as retrospective interference alignment (RIA). Therefore, in the proposed work, a novel RIA scheme for the cooperative cell edge (CE) users that are more vulnerable to inter-cell interference in a multi-cellular multiple-input multiple-output (MIMO) interference multiple access channels (IMAC) is presented. The core idea of the proposed RIA is characterized by interference refining at the end of the first two stages and interference redirecting at the third stage. The initial stage 1 involves feeding the base station (BS) in cell-1 with the data streams from the cooperative CE users. During stage 2, the BS in cell-2 is fed with data streams from the cooperative CE users in cell-2. In the final stage 3, the overheard equation redirecting is performed. Based on the proposed method, we showed that for a three CE user cooperative multi-cellular MIMO IMAC channel, the achievable degrees of freedom (DoF) is $\frac {24}{7}$ and later extended to a general ${L}$ -CE user cooperative multi-cellular MIMO IMAC channel and obtained the desired DoF as $\frac {{2{L}}({L}+1)}{{2{L}}+1}$ . The simulation result shows that in comparison with the other benchmark schemes considered, the proposed scheme provides a greater DoF and thereby enhances the performance of cooperative CE users.