Deep learning has been widely adopted for WiFi CSI-based human activity recognition (HAR) due to its ability to learn spatio-temporal features in a privacy-preserving and cost-effective manner. However, DL-based models generalize poorly across environments, a challenge that is amplified in multi-user settings where overlapping activities cause CSI entanglement and domain shifts. Moreover, practical deployments often limit access to labeled source data due to privacy constraints, motivating source-free adaptation using only unlabeled target-domain CSI and a pretrained source model. In this paper, we propose MU-SHOT-Fi, a source-free unsupervised domain adaptation framework for both single-user and multi-user Wi-Fi sensing scenarios. MU-SHOT-Fi employs permutation-invariant set prediction with Hungarian matching during source training, followed by frozen-classifier backbone adaptation in the target domain. To enable stable adaptation under domain shifts without labels, we introduce occupancy-weighted information maximization that prevents model collapse by focusing diversity regularization on likely-occupied slots while excluding the dominant class from marginal entropy. Additionally, we employ binary rotation prediction as spatial self-supervision that exploits CSI frequency-time structure to learn domain-invariant features. For single-user scenarios, we introduce SU-SHOT-Fi by customizing MU-SHOT-Fi through replacing occupancy weighting with standard information maximization and incorporating contrastive predictive coding to exploit temporal consistency. Extensive experiments are conducted on the multi-user WiMANS dataset and single-user Widar 3.0 dataset across cross-environment, cross-frequency, cross-orientation, and combined domain shifts. The results demonstrate that MU-SHOT-Fi effectively recovers multi-user exact-activity classification performance under large domain shifts while maintaining accurate occupancy estimation and preventing collapse toward dominant classes. The source code is publicly available at https://github.com/AhmedRadwan02/mu-shot-fi.

This paper develops a comprehensive framework to investigate and optimize the downlink performance of cooperativemulti-band networks (MBNs)operating on upper mid-band (UMB) and terahertz (THz) frequencies, where base stations (BSs) in each band cooperatively serve users. The framework captures sophisticated features such as near-field channel modeling, fully and partially connected antenna architectures, and users’ mobility. First, we consider joint user association and hybrid beamforming optimization to maximize the system sum-rate, subject to power constraints, maximum cluster size of cooperating BSs, and users’ quality-of-service (QoS) constraints. By leveraging fractional programming FP and majorization-minimization techniques, an iterative algorithm is proposed to solve the non-convex optimization problem. We then consider handover (HO)-aware resource allocation formovingusers in a cooperative UMB/THz MBN. Two HO-aware resource allocation methods are proposed. The first method focuses on maximizing the HO-aware system sum-rate subject to HO-aware QoS constraints. Using Jensen’s inequality and properties of logarithmic functions, the non-convex optimization problem is tightly approximated with a convex one and solved. The second method addresses a multi-objective optimization problem to maximize the system sum-rate, while minimizing the total number of HOs. Numerical results demonstrate the efficacy of the proposed algorithms, cooperative UMB/THz MBN over stand-alone THz networks, as well as the critical importance of accurate near-field modeling in extremely large antenna arrays. Moreover, the proposed HO-aware resource allocation methods effectively mitigate the impact of HOs, enhancing performance in the considered system.

In this paper, we propose a robust design for overt-the-air federated edge learning (OTA-FEEL) that leverages sensing capabilities at the parameter server (PS) to mitigate the impact of target echoes on the analog model aggregation. We derive novel expressions for the Cramér-Rao bound of the target response and mean squared error (MSE) of the estimated global model to measure sensing and aggregation quality. We then develop a joint scheduling and beamforming framework that optimizes the OTA-FEEL performance while maintaining desired sensing and communication quality. The resulting scheduling problem reduces to a combinatorial mixed-integer nonlinear programming problem (MINLP). We develop a low-complexity hierarchical method based on the matching pursuit algorithm that uses a step-wise strategy to omit the least effective devices in each iteration based on a metric that captures both the aggregation and sensing quality. Numerical results show that accurate sensing effectively suppresses target echoes on the uplink, preserving model aggregation quality despite interference.

Efficient resource allocation is essential for optimizing various tasks in wireless networks, which are usually formulated as generalized assignment problems (GAP). GAP, as a generalized version of the linear sum assignment problem, involves both equality and inequality constraints that add computational challenges. In this work, we present a novel Conditional Value at Risk (CVaR)-based Variational Quantum Eigensolver (VQE) framework to address GAP in vehicular networks (VNets). Our approach leverages a hybrid quantum-classical structure, integrating a tailored cost function that balances both objective and constraint-specific penalties to improve solution quality and stability. Using the CVaR-VQE model, we handle the GAP efficiently by focusing optimization on the lower tail of the solution space, enhancing both convergence and resilience on noisy intermediate-scale quantum (NISQ) devices. We apply this framework to a user-association problem in VNets, where our method achieves 23.5% improvement compared to the deep neural network (DNN) approach.

This paper focuses on the energy and spectral efficient design of an indoor communication system that leverages terahertz (THz) and visible light communication (VLC). We first optimize THz access points (APs) deployment in an indoor environment equipped with VLC APs such that uniform data rate can be guaranteed for all arbitrarily located users in the room. We discretize the placement problem and then apply fractional programming techniques and a Majorization-Minimization (MM) approach to solve it. Then, we develop a novel mobility-aware resource allocation framework to optimize user-AP assignment, subchannel allocation (SA), and power allocation (PA) to maximize the handoff (HO)-aware sum rate and energy efficiency (EE) of hybrid THz/VLC networks. The HO-aware sum rate and EE adapts according to the HOs experienced by users. The proposed framework constrains users’ quality-of-service demands, transmit power budgets, molecular absorption loss thresholds, illumination requirements, and minimum electromagnetic field exposure. This joint problem is a mixed integer nonlinear programming problem which is generally intractable and mostly solved by decomposing into multiple sub-problems via alternating optimization. Different from the traditional approach, we cast this problem as a multi-objective optimization problem and obtain a solution that jointly optimizes all variables using quadratic optimization and MM approach. Computational complexity analysis is presented for both solutions. The proposed placement solution is much faster than the optimal solution. Moreover, the time complexity does not increase with augmenting the number of THz APs. Also, the proposed mobility-aware joint resource allocation solution significantly outperforms the existing benchmarks.

Employing wireless systems with dual sensing and communications functionalities is becoming critical in next generation of wireless networks. In this paper, we propose a robust design for over-the-air federated edge learning (OTA-FEEL) that leverages sensing capabilities at the parameter server (PS) to mitigate the impact of target echoes on the analog model aggregation. We first derive novel expressions for the Cramér-Rao bound of the target response and mean squared error (MSE) of the estimated global model to measure radar sensing and model aggregation quality, respectively. Then, we develop a joint scheduling and beamforming framework that optimizes the OTA-FEEL performance while keeping the sensing and communication quality, determined respectively in terms of Cramér-Rao bound and achievable downlink rate, in a desired range. The resulting scheduling problem reduces to a combinatorial mixed-integer nonlinear programming problem (MINLP). We develop a low-complexity hierarchical method based on the matching pursuit algorithm used widely for sparse recovery in the literature of compressed sensing. The proposed algorithm uses a step-wise strategy to omit the least effective devices in each iteration based on a metric that captures both the aggregation and sensing quality of the system. It further invokes alternating optimization scheme to iteratively update the downlink beamforming and uplink post-processing by marginally optimizing them in each iteration. Convergence and complexity analysis of the proposed algorithm is presented. Numerical evaluations on MNIST and CIFAR-10 datasets demonstrate the effectiveness of our proposed algorithm. The results show that by leveraging accurate sensing, the target echoes on the uplink signal can be effectively suppressed, ensuring the quality of model aggregation to remain intact despite the interference.

Deep learning (DL)-based Semantic Communications (SemCom) is becoming critical to maximize overall efficiency of communication networks. Nevertheless, SemCom is sensitive to wireless channel uncertainties, source outliers, and suffer from poor generalization bottlenecks. To address the mentioned challenges, this paper develops a latent diffusion model-enabled SemCom system with three key contributions, i.e., 1) to handle potential outliers in the source data, semantic errors obtained by projected gradient descent based on the vulnerabilities of DL models, are utilized to update the parameters and obtain an outlier-robust encoder, 2) a lightweight single-layer latent space transformation adapter completes one-shot learning at the transmitter and is placed before the decoder at the receiver, enabling adaptation for out-of-distribution data and enhancing human-perceptual quality, and 3) an end-to-end consistency distillation (EECD) strategy is used to distill the diffusion models trained in latent space, enabling deterministic single or few-step low-latency denoising in various noisy channels while maintaining high semantic quality. Extensive numerical experiments across different datasets demonstrate the superiority of the proposed SemCom system, consistently proving its robustness to outliers, the capability to transmit data with unknown distributions, and the ability to perform real-time channel denoising tasks while preserving high human perceptual quality, outperforming the existing denoising approaches in semantic metrics such as multi-scale structural similarity index measure (MS-SSIM) and learned perceptual image path similarity (LPIPS).

WiFi-enabled Internet-of- Things (IoT) devices are evolving from mere communication devices to sensing instru-ments, leveraging Channel State Information (CSI) extraction capabilities. Nevertheless, resource-constrained IoT devices and the intricacies of deep neural networks necessitate transmitting CSI to cloud servers for sensing. Although feasible, this leads to considerable communication overhead. In this context, this paper develops a novel Real-time Sensing and Compression Network (RSCNet) which enables sensing with compressed CSI; thereby reducing the communication overheads. RSCNet facilitates op-timization across CSI windows composed of a few CSI frames. Once transmitted to cloud servers, it employs Long Short-Term Memory (LSTM) units to harness data from prior windows, thus bolstering both the sensing accuracy and CSI reconstruction. RSCNet adeptly balances the trade-off between CSI compression and sensing precision, thus streamlining real-time cloud-based WiFi sensing with reduced communication costs. Numerical findings demonstrate the gains of RSCNet over the existing benchmarks like SenseFi, showcasing a sensing accuracy of 97.4 % with minimal CSI reconstruction error. Numerical results also show a computational analysis of the proposed RSCNet as a function of the number of CSI frames.

In order to facilitate massive connectivity and connecting the unconnected, aerial communications are becoming increasingly essential as a complement to terrestrial infrastructure. The integrated aerial-terrestrial network (IATN) offers both line-of-sight (LoS) and non-LoS (NLoS) connectivity and flexible deployment. This paper introduces a framework designed to optimize the cooperation between aerial and terrestrial networks, with the goal of maximizing the deployment cost efficiency (DCE) of the network (i.e., the ratio of the network's total data transmission rate to the combined deployment and energy costs). The cooperation among unmanned aerial vehicles (UAVs) and terrestrial base-station (BSs) is supported with clustered cell-free massive MIMO (C-CF-M-MIMO). Specifically, we formulate a problem focused on maximizing the DCE while adhering to power constraints and zero intra-cell pilot contamination. Subsequently, we propose a pilot-contamination aware user clustering, and a distributed coalition formation game for BSs and UAVs clustering in C-CF-M-MIMO-enabled IATN. Our numerical findings demonstrate the efficacy of the proposed algorithm when compared to conventional benchmark methods. Furthermore, the C-CF-M-MIMO-enabled IATN outperforms BSs-only and UAVs-only network equipped with typical cell-free configurations, such as (i) traditional CF-MIMO and (ii) user-centric CF-MIMO.

As fast-moving low Earth orbit (LEO) satellite communication systems gain increasing prominence, the significance of analytical performance models that account for mobility becomes more crucial than ever. Additionally, while considerable progress has been made in modeling the coverage performance of single-antenna LEO satellites, there is a noticeable gap when it comes to considering multi-antenna satellites. This paper presents a novel stochastic geometry framework to characterize the user coverage probability in a downlink LEO satellite network in the presence of multi-antenna satellites, handoffs (HOs), and the Shadowed-Rician fading model. We first determine the distribution of the desired and interfering channel power gains under zero-forcing beamforming. Then, we characterize the HO probability per unit time referred to as the HO rate under distance-based association. Next, we derive the handoff-aware coverage probability expression, and we validate our findings through numerical results obtained from Monte-Carlo simulations, offering insights into the effects of HO and multi-antenna processing on user coverage probability.

Next-generation Virtual Reality (VR) technology enables full-user immersion and support for multiuser Virtual Experiences (VEs). Given the low-cost and passive nature of intelligent reflecting surfaces (IRSs), this paper investigates the optimal design of a multi-user IRS-assisted VR network, where an IRS is optimally deployed in a confined space as a function of VR fully-immersed users' trajectory. In particular, we consider sum-rate maximization of all VR users and optimize the Access Point's (AP) active beamforming, and the IRS's placement, phase shifts, and radiation patterns in a confined indoor environment operating in millimeter Wave (mmWave) frequencies. We introduce the Alternating Optimization (AO) algorithm, decompose the problem into distinct sub-problems, and solve each problem optimally. That is, maximum-ratio transmission (MRT) is applied for optimal beamforming at the AP, optimal closed-from IRS phase shifts are determined using quadratic transformation, global optimization is conducted to determine the ideal locations for the IRS elements, and the monotonic optimal radiation pattern has been analyzed. Our findings highlight that strategically allocating the IRS's resources at optimal physical locations enhances signal stability and maximizes per-user throughput.

This paper explores the evolution of wireless communication networks from utilizing the sub-6 GHz spectrum and the millimeter wave frequency band to incorporating extremely high frequencies like optical and terahertz for 6G and beyond. While these higher frequencies offer broader bandwidths and extreme data rate capabilities, the transition from single-band and heterogeneous networks to multi-band networks (MBNs), where various frequency bands coexist introduces novel challenges in channel modeling, transceiver and antenna design, programmable simulation platforms, standardization, and resource allocation. This paper provides a tutorial overview from the communication design perspective of the various frequency bands, elaborating on the above issues. Then, we introduce and examine typical MBN architectures for future networks and provide a detailed overview of state-of-the-art resource allocation problems for existing MBNs that typically operate on two frequency bands. The considered resource allocation optimization problems and solution techniques are discussed comprehensively. We then identify key performance metrics and constraint sets that should be considered for resource allocation optimization in future MBNs and provide numerical results to depict how various system parameters and user behaviors can influence their performance. Finally, we present several potential research issues as future work for the design and performance optimization of MBNs.

Information aging has gained prominence in characterizing communication protocols for timely remote estimation and control applications. This work proposes an Age of Information (AoI)-aware threshold-based dynamic frame slotted ALOHA (T-DFSA) for contention resolution in random access machine-type communication networks. Unlike conventional DFSA that maximizes the throughput in each frame, the frame length and age-gain threshold in T-DFSA are determined to minimize the normalized average AoI reduction of the network in each frame. At the start of each frame in the proposed protocol, the common Access Point (AP) stores an estimate of the age-gain distribution of a typical node. Depending on the observedstatus of the slots, age-gains of successful nodes, and maximum available AoI, the AP adjusts its estimation in each frame. The maximum available AoI is exploited to derive the maximum possible age-gain at each frame and thus, to avoid overestimating the age-gain threshold, which may render T-DFSA unstable. Numerical results validate our theoretical analysis and demonstrate the effectiveness of the proposed T-DFSA compared to the existing optimal frame slotted ALOHA, threshold-ALOHA, and age-based thinning protocols in a considerable range of update generation rates.

This paper considers minimizing the age-of-information (AoI) and transmit power consumption in a vehicular network, where a roadside unit (RSU) provides timely updates about a set of physical processes to vehicles. We consider non-orthogonal multi-modal information dissemination, which is based on superposed message transmission from RSU and successive interference cancellation (SIC) at vehicles. The formulated problem is a multi-objective mixed-integer nonlinear programming problem; thus, a Pareto-optimal front is very challenging to obtain. First, we leverage the weighted-sum approach to decompose the multi-objective problem into a set of multiple single-objective sub-problems corresponding to each predefined objective preference weight. Then, we develop a hybrid deep Q-network (DQN)-deep deterministic policy gradient (DDPG) model to solve each optimization sub-problem respective to predefined objective-preference weight. The DQN optimizes the decoding order, while the DDPG solves the continuous power allocation. The model needs to be retrained for each sub-problem. We then present a two-stage meta-multi-objective reinforcement learning solution to estimate the Pareto front with a few fine-tuning update steps without retraining the model for each sub-problem. Simulation results illustrate the efficacy of the proposed solutions compared to the existing benchmarks and that the meta-multi-objective reinforcement learning model estimates a high-quality Pareto frontier with reduced training time.

To enable massive connectivity and connecting the unconnected, aerial communications are becoming critical to complement with the terrestrial infrastructure. Integrated aerial-terrestrial network (IATN) offers both line-of-sight (LoS) and non-LoS (NLoS) connectivity and deployment flexibility. This paper presents a framework to optimize the deployment of aerial network and cooperation among aerial-terrestrial network such that the network deployment cost efficiency (i.e. the ratio of network sum-rate and deployment-plus-energy-cost) is maximized. The cooperation among unmanned aerial vehicles (UAVs) and terrestrial base-station (BSs) is supported with clustered cell-free massive MIMO (C-CF-M-MIMO). Specifically, we first formulate a Deployment Cost Efficiency (DCE) maximization problem subject to power budget, zero intra-cell pilot contamination, and UAV location constraints. We then propose a grid-based joint UAV density and location optimization, a pilot-contamination aware user clustering, and a distributed coalition game approach for clustering in C-CF-M-MIMO-enabled IATN. Complexity and convergence of the proposed algorithm are presented. Our numerical results show the efficacy of the proposed algorithm compared to conventional benchmarks. The proposed C-CF-M-MIMO-enabled IATN also outperforms terrestrial-only and aerial-only networks enabled with typical cell-free configurations, namely, (i) traditional CF-MIMO, and (ii) user-centric CF-MIMO.

Over-the-air federated learning (OTA-FL) is an emerging technique to reduce the computation and communication overload caused by the orthogonal transmissions of the model updates in conventional federated learning (FL). This reduction is achieved at the expense of introducing aggregation error that can be efficiently suppressed by means of receive beamforming via large array-antennas. This paper studies OTA-FL in massive multiple-input multiple-output (MIMO) systems with limited number of radio frequency (RF)-chains. For this setting, the beamforming for over-the-air model aggregation needs to be addressed jointly with antenna selection. This leads to an NP-hard problem due to its combinatorial nature. We develop three different algorithms to solve the problem. First, we use the penalty dual decomposition (PDD) technique and propose a two-tier algorithm for joint antenna selection and beamforming. The second algorithm interprets the antenna selection task as a sparse recovery problem and invokes the least absolute shrinkage and selection operator (Lasso) algorithm to approximate the sparse solution. The third algorithm invokes the same sparse recovery based interpretation, but employs the low-complexity method of fast iterative soft-thresholding to find a sparse solution. Convergence and complexity analysis is presented for all the algorithms. The numerical investigations depict that the two algorithms based on the sparse recovery interpretation outperform the PDD-based algorithm, when the number of RF-chains at the edge server is much smaller than its array size. However, as the number of RF-chains increases, the PDD-based algorithm outperforms. Our simulations further depict that learning performance with all the antennas being active at the parameter server (PS) can be closely tracked by selecting less than 20% of the antennas at the PS.

This paper develops a unified framework to maximize the network sum-rate in a multi-user, multi-BS downlink terahertz (THz) network by optimizing user associations, number and bandwidth of sub-bands in a THz transmission window (TW), bandwidth of leading and trailing edge-bands in a TW, sub-band assignment, and power allocations. The proposed framework incorporates multi-connectivity and captures the impact of molecular absorption coefficient variations in a TW, beam-squint, molecular absorption noise, and link blockages. To make the problem tractable, we first propose a convex approximation of the molecular absorption coefficient using curve fitting in a TW, determine the feasible bandwidths of the leading and trailing edge-bands, and then derive closed-form optimal solution for the number of sub-bands considering beam-squint constraints. We then decompose joint user associations, sub-band assignment, and power allocation problem into two sub-problems: 1) joint user association and sub-band assignment, and 2) power allocation. To solve the former problem, we analytically prove the unimodularity of the constraint matrix which enables us to relax the integer constraint without loss of optimality. To solve power allocation sub-problem, a fractional programming (FP)-based centralized solution as well as an alternating direction method of multipliers (ADMM)-based light-weight distributed solution is proposed. The overall problem is then solved using alternating optimization until convergence. Complexity analysis of the algorithms and numerical convergence are presented. Numerical findings validate the effectiveness of the proposed algorithms and extract useful insights about the interplay of the density of base stations (BSs), Average order of multi-connectivity (AOM), molecular absorption, hardware impairment, imperfect CSI, and link blockages.

In this paper, we first develop a tractable mathematical model of the handoff (HO)-aware rate outage experienced by a typical connected and autonomous vehicle (CAV) in a given THz vehicular network. The derived model captures the impact of line-of-sight (LOS) Nakagami-m fading channels, interference, and molecular absorption effects. We first derive the statistics of the interference-plus-molecular absorption noise ratio and demonstrate that it can be approximated by Gamma distribution using Welch-Satterthwaite approximation. Then, we show that the distribution of signal-to-interference-plus-molecular absorption noise ratio (SINR) follows a generalized Beta prime distribution. Based on this, a closed-form HO-aware rate outage expression is derived. Finally, we formulate and solve a CAVs' traffic flow maximization problem to optimize the base-stations (BSs) density and speed of CAVs with collision avoidance, rate outage, and CAVs' minimum traffic flow constraint. The CAVs' traffic flow is modeled using Log-Normal distribution. Our numerical results validate the accuracy of the derived expressions using Monte-Carlo simulations and discuss useful insights related to optimal BS density and CAVs' speed as a function of crash intensity level, THz molecular absorption effects, minimum road-traffic flow and rate requirements, and maximum speed and rate outage limits.

This paper develops a deep reinforcement learning solution to simultaneously optimize the multi-UAV cell-association decisions and their moving velocity decisions on a given 3D aerial highway. The objective is to improve both the transportation and communication performances, e.g., collisions, connectivity, and HOs. We cast this problem as a Markov decision process (MDP) where the UAVs' states are defined based on their velocities and communication data rates. We have a 2D transportation-communication action space with decisions like UAV acceleration/deceleration, lane-changes, and UAV-base station (BS) assignments for a given UAV's state. To deal with the multi-dimensional action space, we propose a neural architecture having a shared decision module with multiple network branches, one for each action dimension. A linear increase of the number of network outputs with the number of degrees of freedom can be achieved by allowing a level of independence for each individual action dimension. To illustrate the approach, we develop Branching Dueling Q-Network (BDQ) and Branching Dueling Double Deep Q-Network (Dueling DDQN). Simulation results demonstrate the efficacy of the proposed approach, i.e., 18.32% improvement compared to the existing benchmarks.

In fully automated traffic streams, speed optimization of connected and autonomous vehicles (CAVs) is a fundamental challenge. However, while increasing the CAVs' speed improves traffic flow, it increases communication handoffs as the CAVs switch from one base station (BS) to another, thus reducing communication data rates. Therefore, a trade-off exists between the communication data rates and CAV traffic flow. In this paper, we develop a novel framework to analyze and maximize the macroscopic traffic flow by optimizing the speed of CAVs and network deployment such that the CAVs' data rate requirements can be satisfied. We first characterize a closed form expression of the macroscopic traffic flow by considering exponential distribution of the spacing between CAVs. The derived expression is used to jointly optimize the deployment of BSs and speed of CAVs while maximizing the CAVs' traffic flow with collision avoidance and handoff-aware data rate constraints. Closed-form optimal solutions are then presented for the CAV's speed and the number of BSs deployed along the corridor considering a high signal-to-noise ratio (SNR) regime. Numerical results validate the accuracy of the derived expressions. Our results show that increasing the BS density or lowering the data rate requirements of CAVs enhances the data rates which increases CAV speeds and in turn the traffic flow.

In this paper, we consider a simultaneous transmitting and reflecting (STAR)-reconfigurable intelligent surface (RIS)-assisted multi mobile-edge-computing (MEC) system, where servers can be placed on both sides of the STAR-RIS and each device offloads a part of its computational tasks to the MEC servers. Specifically, we formulate a weighted-sum computing and communication latency minimization problem to jointly optimize the offloading data volume, edge computing resource of servers, multi-user detection (MUD) matrices, as well as energy splitting coefficients and phase-shifts of the STAR-RIS in the presence of coupling between transmission and reflection phase shifts. Using block coordinate descent (BCD), we decompose the computing and communication problems and solve them in an iterative manner through alternating optimization. We show that the optimal offloading volume can be given by establishing the equivalence of the local computing latency and edge computing latency of servers. Also, we proved that the edge resource allocation problem is jointly convex in both the transmit and reflect MEC resources. Therefore, the optimal MEC resources can be found using KKT conditions and the bisection search method. Numerical results demonstrate the effectiveness of the proposed STAR-RIS-enabled multi-MEC system in terms of obtained latency and convergence compared to the conventional benchmarks.

Rate splitting multiple access (RSMA) and non-orthogonal multiple access (NOMA) are the key enabling multiple access techniques to enable massive connectivity. However, it is unclear whether RSMA would consistently outperform NOMA from a system sum-rate perspective, users’ fairness, as well as convergence and feasibility of the resource allocation solutions. This paper investigates the weighted sum-rate maximization problem to optimize power and rate allocations in a hybrid RSMA-NOMA network. In the hybrid RSMA-NOMA, by optimally allocating the maximum power budget to each scheme, the BS operates on NOMA and RSMA in two orthogonal channels, allowing users to simultaneously receive signals on both RSMA and NOMA. Based on the successive convex approximation (SCA) approach, we jointly optimize the power allocation of users in NOMA and RSMA, the rate allocation of users in RSMA, and the power budget allocation for NOMA and RSMA considering successive interference cancellation (SIC) constraints. Numerical results demonstrate the trade-offs that hybrid RSMA-NOMA access offers in terms of system sum rate, fairness, convergence, and feasibility of the solutions.

Deep neural networks (DNNs) are emerging as a potential solution to solve NP-hard wireless resource allocation problems. However, in the presence of intricate constraints, e.g., users’ quality-of-service (QoS) constraints, guaranteeing constraint satisfaction becomes a fundamental challenge. In this paper, we propose a novel unsupervised learning framework to solve the classical power control problem in a multi-user interference channel, where the objective is to maximize the network sum-rate under users’ minimum data rate or QoS requirements and power budget constraints. Utilizing a differentiable projection function, two novel deep learning (DL) solutions are pursued. The first is called Deep Implicit Projection Network (DIPNet), and the second is called Deep Explicit Projection Network (DEPNet). DIPNet utilizes a differentiable convex optimization layer to implicitly define a projection function. On the other hand, DEPNet uses an explicitly-defined projection function, which has an iterative nature and relies on a differentiable correction process. DIPNet requires convex constraints; whereas, the DEPNet does not require convexity and has a reduced computational complexity. To enhance the sum-rate performance of the proposed models even further, Frank-Wolfe algorithm (FW) has been applied to the output of the proposed models. Extensive simulations depict that the proposed DNN solutions not only improve the achievable data rate but also achieve zero constraint violation probability, compared to the existing DNNs. The proposed solutions outperform the classic optimization methods in terms of computation time complexity.

Integrating the multitude of emerging internet of things (IoT) applications with diverse requirements in beyond fifth generation (B5G) networks necessitates the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services. However, bandwidth limited and congested sub-6GHz bands are incapable of fulfilling this coexistence. In this paper, we consider a reconfigurable intelligent surface (RIS)-aided wideband terahertz (THz) communication system to this end. In specific, we formulate a resource management problem, aiming at jointly optimizing the reflection coefficient of the RIS elements and the transmit power of the base station, as well as the wideband THz resource block allocation. To solve this problem, we adopt a supervised learning approach relying on optimization, deep learning and ensemble learning methods. Simulation results show that for an RIS of size$11\times 11$, up to 49% spectral efficiency gain is achieved for the eMBB service compared to the counterparts, while ensuring the reliability and latency requirements of the URLLC service. Further, the ensemble learning model can perform real-time resource management at the expense of up to 1% performance loss, compared to the optimization approach.

In this paper, the downlink transmission of a two-tier heterogeneous network (HetNet) is considered in which a macro base station (MBS) serves the macro users using orthogonal frequency division multiple access (OFDMA) and small base stations (SBSs) serve the small-cell users through multi-carrier non-orthogonal multiple access (MC-NOMA) and joint transmission (JT). In particular, assuming the subcarriers are already allocated to macro users, the problem of scheduling (i.e., joint user association and subcarrier allocation) and power control is studied with the goal of maximizing the total users’ perceived quality-of -experience (QoE) for small-cell users, while a minimum data rate for macro users is guaranteed. To solve the joint optimization problem, a near-optimal and computationally efficient two-phase solution approach is proposed based on the tools from optimization and machine learning (ML). In the first phase, the optimization problem is solved to obtain the scheduling decisions and transmit power variables. In the second phase, the optimized scheduling decisions and transmit power variables serve as training samples for an AlexNet classifier and support vector regressor (SVR), respectively. Simulation results reveal that the integration of JT into MC-NOMA, outperforms the conventional MC-NOMA scheme by up to 24%, 19%, and 21% for the web, video and audio multimedia services, respectively. Compared to a conventional convolutional neural network, our results demonstrate that for the web, video, and audio-services, AlexNet increases the scheduling prediction accuracy up to 14%, 11%, and 17%, while SVR increases the power prediction accuracy up to 8%, 7%, and 12%, respectively.

Vehicle-to-Infrastructure (V2I) communication is becoming critical for the enhanced reliability of autonomous vehicles (AVs). However, the uncertainties in the road-traffic and AVs' wireless connections can severely impair timely decision-making. It is thus critical to simultaneously optimize the AVs' network selection and driving policies in order to minimize road collisions while maximizing the communication data rates. In this paper, we develop a reinforcement learning (RL) framework to characterize efficient network selection and autonomous driving policies in a multi-band vehicular network (VNet) operating on conventional sub-6GHz spectrum and Terahertz (THz) frequencies. The proposed framework is designed to (i) maximize the traffic flow and minimize collisions by controlling the vehicle's motion dynamics (i.e., speed and acceleration) from autonomous driving perspective, and (ii) maximize the data rates and minimize handoffs by jointly controlling the vehicle's motion dynamics and network selection from telecommunication perspective. We cast this problem as a Markov Decision Process (MDP) and develop a deep Q-learning based solution to optimize the actions such as acceleration, deceleration, lane-changes, and AV-base station assignments for a given AV's state. The AV's state is defined based on the velocities and communication channel states of AVs. Numerical results demonstrate interesting insights related to the inter-dependency of vehicle's motion dynamics, handoffs, and the communication data rate. The proposed policies enable AVs to adopt safe driving behaviors with improved connectivity.

Passive beamforming in reconfigurable intelligent surfaces (RISs) enables a feasible and efficient way of communication when the RIS reflection coefficients are precisely adjusted. In this paper, we present a framework to track the RIS reflection coefficients with the aid of deep learning from a time-series prediction perspective in a terahertz (THz) communication system. The proposed framework achieves a two-step enhancement over the similar learning-driven counterparts. Specifically, in the first step, we train a liquid state machine (LSM) to track the historical RIS reflection coefficients at prior time steps (known as a time-series sequence) and predict their upcoming time steps. We also fine-tune the trained LSM through Xavier initialization technique to decrease the prediction variance, thus resulting in a higher prediction accuracy. In the second step, we use ensemble learning technique which leverages on the prediction power of multiple LSMs to minimize the prediction variance and improve the precision of the first step. It is numerically demonstrated that, in the first step, employing the Xavier initialization technique to fine-tune the LSM results in at most 26% lower LSM prediction variance and as much as 46% achievable spectral efficiency (SE) improvement over the existing counterparts, when an RIS of size 11×11 is deployed. In the second step, under the same computational complexity of training a single LSM, the ensemble learning with multiple LSMs degrades the prediction variance of a single LSM up to 66% and improves the system achievable SE at most 54%.

Using tools from stochastic geometry, this paper develops a tractable framework to analyze the performance of a mobile user in a two-tier wireless network operating on sub-6GHz and terahertz (THz) transmission frequencies. Specifically, using an equivalence distance approach, we characterize the overall handoff (HO) probability in terms of the horizontal and vertical HO and mobility-aware coverage probability. In addition, we characterize novel coverage probability expressions for THz network in the presence of molecular absorption noise and highlight its significant impact on the users’ performance. Specifically, we derive a novel closed-form expression for the Laplace Transform of the cumulative molecular noise and interference observed by a mobile user in a hybrid RF-THz network. Furthermore, we provide a novel approximation to derive the conditional distance distributions of a typical user in a hybrid RF-THz network. Finally, using the overall HO probability and coverage probability expressions, the mobility-aware probability of coverage has been derived in a hybrid RF-THz network. Our mathematical results validate the correctness of the derived expressions using Monte-Carlo simulations. The results offer insights into the adverse impact of users’ mobility and molecular noise in THz transmissions on the probability of coverage of mobile users. Our results demonstrate that a small increase in the intensity of terahertz base-stations (TBSs) (about 5 times) can increase the HO probability much more compared to the case when the intensity of RF BSs (RBSs) is increased by 100 times. Furthermore, we note that high molecular absorption can be beneficial (in terms of minimizing interference and molecular noise) for specific deployment intensity of TBSs and the benefits can outweigh the drawbacks of signal degradation due to molecular absorption.

Using stochastic geometry tools, we develop a comprehensive framework to analyze the downlink performance of various types of users (e.g., users served by direct base station (BS) transmissions and indirect intelligent reflecting surface (IRS)-assisted transmissions) in a cellular network with multiple BSs and IRSs. For the proposed users, we provide the approximate expressions for the performance in terms of coverage probability, ergodic capacity, and energy efficiency (EE). The proposed stochastic geometry framework can capture the impact of channel fading, locations of BSs and IRSs, arbitrary phase-shifts and interference experienced by a typical user supported by direct transmission and/or IRS-assisted transmission. For IRS-assisted transmissions, we first model approximate the desired signal power from the nearest IRS as a sum of scaled generalized gamma (GG) random variables whose parameters are functions of the IRS phase shifts. Then, we derive the Laplace Transform (LT) of the received signal power in a closed form. Also, we approximate the aggregate interference from multiple IRSs as the sum of normal random variables. Then, we derive the LT of the aggregate interference from all IRSs and BSs. The derived LT expressions are used to calculate coverage probability, ergodic capacity, and EE for users served by direct BS transmissions as well as users served by IRS-assisted transmissions. Finally, we derive the overall network coverage probability, ergodic capacity, and EE based on the fraction of direct and IRS-assisted users, which is defined as a function of the deployment density of IRSs, as well as blockage probability of direct transmission links. Numerical results validate the derived analytical expressions and extract useful insights related to the number of IRS elements, large-scale deployment of IRSs and BSs, and the impact of IRS interference on direct transmissions.

This paper presents a theoretical framework to analyze the performance of an integrated unmanned aerial vehicle (UAV)-intelligent reflecting surface (IRS) relaying system in which the IRS provides an additional degree of freedom combined with the flexible deployment of full-duplex UAV to enhance communication between ground nodes. Our framework considers three different transmission modes: (i) UAV-only mode, (ii) IRS-only mode, and (iii) integrated UAV-IRS mode to achieve spectral and energy-efficient relaying. For the proposed modes, we provide exact and approximate expressions for the end-to-end outage probability, ergodic capacity, and energy efficiency (EE) in closed-form. We use the derived expressions to optimize key system parameters such as the UAV altitude and the number of elements on the IRS considering different modes. We formulate the problems in the form of fractional programming (e.g. single ratio, sum of multiple ratios or maximization-minimization of ratios) and devise optimal algorithms using quadratic transformations. Furthermore, we derive an analytic criterion to optimally select different transmission modes to maximize ergodic capacity and EE for a given number of IRS elements. Numerical results validate the derived expressions. The solutions obtained from the proposed optimization algorithms are compared with those obtained through exhaustive search. Insights are drawn related to the different communication modes, optimal number of IRS elements, and optimal UAV height.

Intelligent vehicular communication is fundamental to manage vehicle-to-grid (V2G) interaction, where electric vehicles (EVs) provide energy to balance demand of critical loads (CLs). We propose resource efficiency (RE) to exploit the tradeoff between spectral efficiency (SE) and cost efficiency (CE) of EVs in a V2G communication network. The CE is the data rate of the V2G channel between EVs and base station (BS) over the operating cost of EVs to supply energy to CLs. We consider maximizing the RE in the downlink of a V2G communication network, where EVs are served by a BS and associated with CLs, while satisfying energy demand and charging station constraints. As the proposed RE problem is inherently non-convex and known to be NP-hard, we develop a suboptimal scheme based on a two-phase algorithm. Phase 1 derives optimum EV-CL association using a heuristic approach, while phase 2 finds optimum power allocation using geometric programming. We then derive upper and lower bounds to the optimal RE as a benchmark to study the performance gap of the suboptimal scheme. Simulation results demonstrate that the proposed suboptimal scheme is close to the optimal solution, while its complexity is relatively low, making it promising for V2G applications.

We develop novel statistical models of the harvested energy from renewable energy sources considering harvest-store-consume (HSC) architecture. We consider three renewable energy harvesting scenarios, i.e., (i) harvesting from the solar power, (ii) harvesting from the wind power, and (iii) hybrid solar and wind power. In this context, we first derive the closed-form expressions for the density functions and moments of the harvested power solar and wind power. Then, we calculate the probability of energy outage at UAVs and signal-to-noise ratio (SNR) outage at ground cellular users. The energy outage occurs when the UAV is unable to support the flight consumption and transmission consumption from its battery power and the harvested power. Due to the intricate distribution of the hybrid solar and wind power, we derive novel closed-form expressions for the moment generating function (MGF) of the harvested solar power and wind power. Then, we apply Gil-Pelaez inversion to evaluate the energy outage at the UAV and SNR outage at the ground users. In addition, we formulate the SNR outage minimization problem and obtain closed-form solutions for the transmit power and flight time of the UAV. Furthermore, we demonstrate the application of moments in computing novel metrics such as the probability of charging the UAV battery within the flight time, average UAV battery charging time, probability of energy outage at UAVs, and the probability of eventual energy outage (i.e., the probability of energy outage in a finite duration of time) at UAVs. Numerical results validate the analytical expressions and reveal interesting insights related to the optimal flight time and transmit power of the UAV as a function of the harvested energy.

In this paper, we develop a resource allocation framework to optimize the downlink transmission of a backhaul-aware multi-cell cognitive radio network (CRN) which is enabled with multi-carrier non-orthogonal multiple access (MC-NOMA). The considered CRN is composed of a single macro base station (MBS) and multiple small BSs (SBSs) that are referred to as the primary and secondary tiers, respectively. For the primary tier, we consider orthogonal frequency division multiple access (OFDMA) scheme and also Quality of Service (QoS) to evaluate the user satisfaction. On the other hand in secondary tier, MCNOMA is employed and the user satisfaction for web, video and audio as popular multimedia services is evaluated by Quality-of-Experience (QoE). Furthermore, each user in secondary tier can be served simultaneously by multiple SBSs over a subcarrier via Joint Transmission (JT). In particular, we formulate a joint optimization problem of power control and scheduling (i.e., user association and subcarrier allocation) in secondary tier to maximize total achievable QoE for the secondary users. An efficient resource allocation mechanism has been developed to handle the non-linear form interference and to overcome the non-convexity of QoE serving functions. The scheduling and power control policy leverage on Augmented Lagrangian Method (ALM). Simulation results reveal that proposed solution approach can control the interference and JT-NOMA improves total perceived QoE compared to the existing schemes.

We develop a comprehensive framework to characterize and optimize the performance of a unmanned aerial vehicle (UAV)-assisted D2D network, where D2D transmissions underlay cellular transmissions. Different from conventional non-line-of-sight (NLoS) terrestrial transmissions, aerial transmissions are highly likely to experience line-of-sight (LoS). As such, characterizing the performance of mixed aerial-terrestrial networks with accurate fading models is critical to precise network performance characterization and resource optimization. We first characterize closed-form expressions for a variety of performance metrics such as frame decoding error probability (referred to as reliability), outage probability, and ergodic capacity of users. The terrestrial and aerial transmissions may experience either LoS Rician fading or NLoS Nakagami-m fading with a certain probability. Based on the derived expressions, we formulate a hierarchical bi-objective mixed-integer-nonlinear-programming (MINLP) problem to minimize the total transmit power of all users and maximize the aggregate throughput of D2D users subject to quality-of-service (QoS) measures (i.e., reliability and ergodic capacity) of cellular users. We model the proposed problem as a bi-partite one-to-many matching game. To solve this problem, we first obtain the optimal closed-form power allocations for each D2D and cellular user on any possible subchannel, and then incorporate them to devise efficient subchannel and power allocation algorithms. Complexity analysis of the proposed algorithms is presented. Numerical results verify the accuracy of our derived expressions and reveal the significance of aerial relays compared to ground relays in increasing the throughput of D2D pairs especially for distant D2D pairs.

We analyze the end-to-end performance of an unmanned-aerial-vehicle (UAV)-assisted data ferrying network where the UAV serves as a data ferry between the source base station (BS) and multiple destination receivers. We evaluate the end-to-end reliability both with and without packet retransmission technique called automatic repeat request (ARQ), bit error probability (BEP), energy-efficiency, and transmission outage probability. We consider line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions in both the data loading and delivering links and model them with the Rician and Rayleigh fading channels, respectively. We derive tractable approximations for the derived SNR outage results and demonstrate their application in optimizing the distance that a UAV should travel in order to balance the energy-coverage trade-offs. We formulate two different optimization problems and convexify them to solve for the optimal ferrying distance, i.e. (i) outage-constrained energy minimization and (ii) energy-constrained SNR outage minimization. Closed-form optimal solutions are obtained for the second problem. In addition, we formulate a bi-objective optimization problem in order to minimize SNR outage and energy consumption with desired SNR outage probability constraints. The objective function is then reformulated using difference of convex functions (DC) and solved using a DC algorithm. Numerical results validate the derived expressions and show a comparison of the obtained solutions with the solutions obtained from exhaustive search. Insights related to the impact of LoS Rician and NLoS Rayleigh fading channels as well as the optimal ferrying distance are obtained considering a variety of objective functions.

This paper characterizes the meta distribution of the downlink signal-to-interference ratio (SIR) attained at a typical Internet-of-Things (IoT) device in a dual-hop IoT network. The IoT device associates with either a serving macro base station (MBS) for direct transmissions or associates with a decode and forward (DF) relay for dual-hop transmissions, depending on the biased received signal power criterion. In contrast to the conventional success probability, the meta distribution is the distribution of the conditional success probability (CSP), which is conditioned on the locations of the wireless transmitters. The meta distribution is a fine-grained performance metric that captures important network performance metrics such as the coverage probability and the mean local delay as its special cases. Specifically, we derive the moments of the CSP in order to calculate analytic expressions for the meta distribution. Further, we derive mathematical expressions for special cases such as the mean local delay, variance of the CSP, and success probability of a typical IoT device and typical relay with different offloading biases. We take in consideration in our analysis the association probabilities of IoT devices. Finally, we investigate the impact of increasing the relay density on the mean local delay using numerical results.

We develop an analytical framework to derive the meta distribution and moments of the conditional success probability (CSP), which is defined as success probability for a given realization of the transmitters, in large-scale co-channel uplink and downlink non-orthogonal multiple access (NOMA) networks with one NOMA cluster per cell. The moments of CSP translate to various network performance metrics such as the standard success or signal-to-interference ratio (SIR) coverage probability (which is the 1-st moment), the mean local delay (which is the −1st moment in a static network setting), and the meta distribution (which is the complementary cumulative distribution function of the success or SIR coverage probability and can be approximated by using the 1st and 2nd moments). For the uplink NOMA network, to make the framework tractable, we propose two point process models for the spatial locations of the inter-cell interferers by utilizing the base station (BS)/user pair correlation function. We validate the proposed models by comparing the second moment measure of each model with that of the actual point process for the inter-cluster (or inter-cell) interferers obtained via simulations. For downlink NOMA, we derive closed-form solutions for the moments of the CSP, success (or coverage) probability, mean local delay, and meta distribution for the users. As an application of the developed analytical framework, we use the closed-form expressions to optimize the power allocations for downlink NOMA users in order to maximize the success probability of a given NOMA user with and without latency constraints. Closed-form optimal solutions for the transmit powers are obtained for two-user NOMA scenario. We note that maximizing the success probability with latency constraints can significantly impact the optimal power solutions for low SIR thresholds and favor orthogonal multiple access.

We characterize the accuracy of analyzing the performance of a non-orthogonal multiple access (NOMA) system where users are ranked according to their distances instead of instantaneous channel gains, i.e., product of their distance-based path-loss and fading channel gains. Distance-based ranking of users is analytically tractable and can lead to important insights. However, it may not be appropriate in a multipath fading environment where a near user suffers from severe fading while a far user experiences weak fading. Since the ranking of users (and in turn interferers) in an NOMA system has a direct impact on coverage probability analysis, the impact of the traditional distance-based ranking, as opposed to instantaneous signal power-based ranking, needs to be understood. This will enable us to identify scenarios where distance-based ranking, which is easier to implement compared with instantaneous signal power-based ranking, is acceptable for the system performance analysis. To this end, in this paper, we derive the probability of the event when distance-based ranking yields the same results as instantaneous signal power-based ranking, which is referred to as theaccuracy probability. We characterize the probability of accuracy considering Nakagami-$m$fading channels and three different spatial distribution models of user locations in NOMA, namely, the Poisson point process (PPP), the Matern cluster process (MCP), and the Thomas cluster process (TCP). For all these models of users’ locations, we assume that the spatial locations of the base stations (BSs) follow a homogeneous PPP. We show that the accuracy probability decreases with the increasing number of users and increases with the path-loss exponent. In addition, through examples, we illustrate the impact of accuracy probability on uplink and downlink coverage probabilities. Closed-form expressions are presented for the Rayleigh fading environment. The effects of fading severity and users’ pairing on the accuracy probability are also investigated.

Signal-to-noise-plus-interference ratio (SINR) outage probability is among one of the key performance metrics of a wireless network. In this paper, we propose a semi-analytical method based on the saddle point approximation (SPA) technique to calculate the SINR outage of a wireless system whose SINR can be modeled in the form (Σi=1MXi/(1 + Σi=1NYi)) where Xi denotes the useful signal power and Yidenotes the power of the interference signal. Both M and N can also be random variables. The proposed approach is based on the saddle point approximation to cumulative distribution function as given by Wood-Booth-Butler formula. The approach is applicable whenever the cumulant generating function of the received signal and interference exists, and it allows us to tackle distributions with large skewness and kurtosis with higher accuracy. In this paper, we exploit a four parameter normal-inverse Gaussian (NIG) distribution as a base distribution. Given that the skewness and kurtosis satisfy a specific condition, NIG-based SPA works reliably. When this condition is violated, we recommend SPA based on normal or symmetric NIG distribution, both special cases of NIG distribution, at the expense of reduced accuracy. For the purpose of demonstration, we apply SPA for the SINR outage evaluation of a typical user experiencing a downlink coordinated multi-point transmission from the base stations that are modeled by homogeneous Poisson point process. Numerical results are presented to illustrate the accuracy of the proposed set of approximations.

This paper provides a stochastic geometry framework to perform the coverage and rate analysis of a typical user in co-existing visible light communication (VLC) and radio frequency (RF) networks. The framework can be customized to capture the performance of a typical user in various network configurations such as 1) RF-only, in which only small base-stations (SBSs) are available to provide the coverage to a user; 2) VLC-only, in which only optical BSs (OBSs) are available to provide the coverage to a user; 3) opportunistic RF/VLC, where a user selects the network with maximum received signal power; and 4) hybrid RF/VLC, where a user can simultaneously utilize the available resources from both RF and VLC networks. The developed model for VLC network precisely captures the impact of the field-of-view (FOV) of the photo-detector receiver on the number of optical interferers, distribution of the aggregate interference, association probability, the coverage probability, and average rate of a typical user. A closed-form approximation is presented for special cases and for asymptotic scenarios, such as when the intensity of SBSs becomes very low or the intensity of OBSs becomes very high. The closed-form solutions for network design parameters (such as intensity of OBSs and SBSs, transmit power, and/or FOV) enable network operators to distribute the users among RF and VLC networks according to their choice. Moreover, we also optimize the network parameters in order to prioritize the association of users to VLC network. Finally, simulations are carried out to verify the derived solutions. It is shown that the performance of VLC network depends significantly on the receiver's FOV/intensity of SBSs/OBSs and careful selection of such parameters is crucial to harness the benefits of VLC networks. Important trade-offs between height and intensity of OBSs are highlighted to optimize the performance of VLC networks.

Non-orthogonal multiple access (NOMA) is a promising multiple access technique for 5G wireless technology. In this paper, we first discuss the fundamentals of uplink and downlink NOMA transmissions in a cellular system and outline their key distinctions in terms of implementation complexity, detection and decoding at the SIC receiver(s), and the intra-cell and inter-cell interferences. Later, for both downlink and uplink NOMA, for each individual user in a two-user NOMA cluster, we theoretically derive the NOMA dominant condition, which refers to the condition under which the spectral efficiency gains of NOMA are guaranteed compared to conventional orthogonal multiple access (OMA). The conditions, which are distinct for uplink and downlink as well as for each individual user, provide direct insights into selecting appropriate users in two-user NOMA clusters. Numerical results show the significance of the derived conditions for user selection in uplink/downlink NOMA clusters and provide a comparison to the random user selection. Finally, a brief summary of the recent research investigations is provided which is followed by a discussion on the research challenges and future research directions.

In-band full-duplex (IBFD) backhauling is a potential technique for wireless backhauling of small cells that allows the use of same spectrum for the backhaul and access links of the small cell base stations (SBSs) concurrently, however, at the expense of backhaul interference and self-interference (SI). This paper investigates the problem of optimal access/backhaul spectrum allocation considering IBFD backhauling, out-of-band full-duplex (OBFD) backhauling (in which the access and backhaul transmissions take place on different spectrum), and the SBSs with the provisioning for hybrid IBFD/OBFD backhauling. We first formulate a problem to maximize the minimum achievable rate (i.e., minimum of the rates in the backhaul link and the access link) at the SBSs in a hybrid IBFD/OBFD setting. The solution of the centralized spectrum allocation problem, which serves as a benchmark for any sub-optimal solution, is provided by transforming the original problem into an epigraph form. As a special case of the formulated problem, we derive closed-form optimal solutions for the access/backhaul spectrum allocation of OBFD backhauling as well as IBFD backhauling. We then propose and comparatively analyze the performance of two distributed backhaul spectrum allocation schemes, namely, maximum received signal power (max-RSP) and minimum received signal power (min-RSP) schemes. For these schemes, we theoretically derive the number of allocated backhaul channels, minimum rate coverage probability, and average achievable rate of each SBS given its distance from the centralized wireless backhaul hub (WBH) for both IBFD and OBFD backhauling. Numerical results reveal that the optimal spectrum allocation rules can significantly vary for IBFD and OBFD backhauling. Optimal OBFD backhauling favors more backhaul spectrum for SBSs located far-away from the WBH. With IBFD backhauling, spectrum allocation for SBSs strongly depends on SI. With the reduction in SI, the optimal backhaul spectrum increases/decreases for nearby/farther SBSs. Simulation results comparing the optimal solution with the distributed spectrum allocation solutions based on max-RSP and min-RSP schemes are also presented.

Using the theory of Poisson cluster process (PCP), this paper provides a framework to analyze multi-cell uplink non-orthogonal multiple access (NOMA) systems. Specifically, we characterize the rate coverage probability of an NOMA user who is at rank m (in terms of the distance from its serving base station) among all users in a cell and the mean rate coverage probability of all users in a cell. Since the signal-to-interference-plus-noise ratio of the mth user relies on efficient successive interference cancellation (SIC), we consider three scenarios, i.e., perfect SIC (in which the signals of m - 1 interferers who are stronger than the mth user are decoded successfully), imperfect SIC (in which the signals of m - 1 interferers who are stronger than the mth user may or may not be decoded successfully), and imperfect worst case SIC (in which the decoding of the signal of the mth user is always unsuccessful whenever the decoding of its relative m -1 stronger users is unsuccessful). To derive the rate coverage expressions, we first characterize the Laplace transforms of the intra-cluster interferences in closed-form considering various SIC scenarios. The Laplace transform of the inter-cluster interference is then characterized by exploiting distance distributions from geometric probability. The derived expressions are customized for an equivalent OMA system. Finally, numerical results are presented to validate the derived expressions. The worst case SIC assumption provides remarkable simplifications in the mathematical analysis and is found to be highly accurate for higher user target rate requirements. A comparison of Poisson point process-based and PCP-based modeling is also conducted.

In multi-tier cellular networks, user performance in both the downlink (DL) and uplink (UL) transmissions depend on the transmit powers of the base stations (BSs) in different network tiers, users' distances, and non-uniform traffic loads of different BSs. In such a network, decoupled UL-DL user association (DUDe), which allows users to associate with different BSs for UL and DL transmissions, can be used to optimize network performance. Again, in-band full-duplex (FD) communication is considered as a promising technique to improve the spectral efficiency of future multi-tier fifth generation (5G) cellular networks. Nonetheless, due to UL-to-DL and DL-to-UL interferences arising due to FD communications, the performance gains of DUDe in FD multi-tier networks are inconspicuous. To this end, this paper develops a comprehensive framework to analyze the usefulness of DUDe in a full-duplex multi-tier cellular network. We first formulate a joint UL and DL user association problem (with the provisioning for decoupled association) that maximizes the sum-rate for UL and DL transmission of all users. Since the formulated problem is a mixed-integer non-linear programming (MINLP) problem, we invoke approximations and binary constraint relaxations to convert the problem into a Geometric Programming (GP) problem that is solved by using Karush-Kuhn-Tucker (KKT) optimality conditions. Given the centralized nature and complexity of the GP problem, we formulate a distributed two-sided iterative matching game and obtain a solution of the game. In this game, the users and BSs rank one another using preference metrics that are subject to the externalities (i.e., dynamic interference conditions). The solution of the game is guaranteed to converge and provides Pareto-optimal stable associations. Finally, we derive efficient light-weight versions of the iterative matching solution, i.e., non-iterative matching and sequential UL-DL matching algorithms. The performances of the solutions are evaluated in terms of aggregate UL and DL rates of all users, the number of unassociated users, and the number of coupled/decoupled associations. Simulation results demonstrate the efficacy of the proposed algorithms over the centralized GP solution as well as traditional coupled and decoupled user association schemes.

Recent advancements in self-interference (SI) cancellation capability of low-power wireless devices motivate in-band full-duplex (FD) wireless backhauling in small cell networks (SCNs). In-band FD wireless backhauling concurrently allows the use of the same frequency spectrum for the backhaul as well as access links of the small cells. In this paper, using tools from stochastic geometry, we develop a framework to model the downlink rate coverage probability of a user in a given SCN with massive multiple-input-multiple-output (MIMO)-enabled wireless backhauls. The considered SCN is composed of a mixture of small cells that are configured in either in-band or out-of-band backhaul modes with a certain probability. The performance of the user in the considered hierarchical network is limited by several sources of interference, such as the backhaul interference, small cell base station (SBS)-to-SBS interference, and the SI. Moreover, due to the channel hardening effect in massive MIMO, the backhaul links only experience long term channel effects, whereas the access links experience both the long term and the short term channel effects. Consequently, the developed framework is flexible to characterize different sources of interference while capturing the heterogeneity of the access and backhaul channels. In specific scenarios, the framework enables deriving closed-form coverage probability expressions. Under perfect backhaul coverage, the simplified expressions are utilized to optimize the proportion of in-band and out-of-band small cells in the SCN in the closed form. Finally, a few remedial solutions are proposed that can potentially mitigate the backhaul interference and in turn improve the performance of in-band FD wireless backhauling. Numerical results investigate the scenarios in which in-band wireless backhauling is useful and demonstrate that maintaining a correct proportion of in-band and out-of-band FD small cells is crucial in wireless backhauled SCNs.

Recent advancements in the self-interference (SI) cancellation capability of low power wireless devices pave the way of implementing full-duplex (FD) self-backhauling in small-cell networks. FD self-backhauling allows the use of conventional radio access network (RAN) spectrum for backhaul as well as access links concurrently. In this paper, we model and analyze massive MIMO- enabled wireless backhaul networks that are composed of a mixture of small cells, configured either in in-band or out-of-band backhaul mode with a certain probability. We consider a hierarchical network structure to model these networks and characterize the downlink coverage probability of a small cell base station (SBS) for both the in-band and out-of-band backhaul modes. The impact of co-tier and cross-tier backhaul interferences on downlink signal-to-interference ratio (SIR) coverage of small cell users is investigated. Numerical results demonstrate that implementing only either the in-band or out-of-band backhauling solutions may not be useful. Instead, a hybrid system with correct proportion of in-band and out-of-band small cells should be implemented.

The diverse transmit powers of the base-stations (BSs) in a multi-tier cellular network lead to uneven distribution of the traffic loads among different BSs and thus cause underutilization of the available resources at low power BSs. In this context, this paper proposes a channel access-aware (CAA) user association scheme that can simultaneously enhance the system spectral efficiency and balance the traffic loads among different BSs. The CAA scheme is a network-assisted user association scheme that requires the traffic load informations from different BSs in addition to the channel quality indicators. Also, in this paper, we develop a tractable mathematical framework to characterize the spectral efficiency of downlink transmission to a user who associates to a BS using CAA scheme. Numerical results demonstrate the performance gains of CAA scheme over conventional received signal power-based association and biased-received signal power-based association. The derived expressions provide approximate solutions of reasonable accuracy when compared to the results obtained by Monte-Carlo simulations. Moreover, the impact of state-of-the-art almost blank sub-frames (ABS)-based interference coordination scheme on the proposed CAA scheme is also investigated using Monte-Carlo simulations.

Wireless-powered cellular networks (WPCNs) are currently being investigated to ensure the reliability as well as improved battery lifetime of wireless devices. A WPCN leverages on a centralized base station (BS) that takes care of both wireless information and energy transfer. However, the harvested energy and, in turn, the spectral efficiency of uplink transmission of the users may significantly vary depending on the locations of the users and the channels used for energy and information transfer purposes. To this end, this paper theoretically characterizes the signal-to-noise ratio (SNR) outage zones in a WPCN and comparatively analyzes the performance of three useful configurations of dedicated energy sources that can potentially minimize the SNR outage zones. These configurations are: (i) harvesting energy from and information transfer to a full-duplex BS. This is considered as a baseline configuration; (ii) harvesting energy from symmetrically deployed power beacons (PBs) and information transfer to a conventional half-duplex BS; and (iii) harvesting energy from symmetrically deployed PBs that are colocated with the distributed antenna elements (DAEs) of a conventional half-duplex BS. For all the listed cases, we characterize the SNR outage probability and spectral efficiency of an arbitrarily located user within the cellular region. Based on the derived expressions, we also optimize the distance of the PBs from the BS to minimize the SNR outage probability and provide closed-form solutions for special cases. The optimum distance of the PBs is shown to be a function of the number of PBs and the coverage area of the BS. Numerical results validate the accuracy of the derived expressions, provide design insights related to WPCNs, and reveal the significance of the limited number of optimally placed PBs over a large number of randomly deployed PBs.

Energy harvesting in cellular networks is an emerging technique to enhance the sustainability of power-constrained wireless devices. This paper considers the co-channel deployment of a macrocell overlaid with small cells. The small cell base stations (SBSs) harvest energy from environmental sources whereas the macrocell base station (MBS) uses conventional power supply. Given a stochastic energy arrival process for the SBSs, we derive a power control policy for the downlink transmission of both MBS and SBSs such that they can achieve their objectives [e.g., maintain the signal-to-interference-plus-noise ratio (SINR); at an acceptable level] on a given transmission channel. We consider a centralized energy harvesting mechanism for SBSs, i.e., there is a central energy storage (CES) where energy is harvested and then distributed to the SBSs. When the number of SBSs is small, the game between the CES and the MBS is modeled as a single-controller stochastic game and the equilibrium policies are obtained as a solution of a quadratic programming problem. However, when the number of SBSs tends to infinity (i.e., a highly dense network), the centralized scheme becomes infeasible, and therefore, we use a mean field stochastic game to obtain a distributed power control policy for each SBS. By solving a system of partial differential equations, we derive the power control policy of SBSs given the knowledge of mean field distribution and the available harvested energy levels in the batteries of the SBSs.

This paper characterizes the spectral efficiency of an uplink radio frequency (RF)-powered macrocell network considering harvest-then-transmit protocol such that the macrocell users transmit in the uplink while replenishing the energy from their serving base station (BS) in the downlink. Using the theory of order statistics, a tractable mathematical framework is developed to derive the uplink spectral efficiency and the downlink power consumption resulting due to wireless energy transfer. The framework captures the impact of the locations of the users that are selected for uplink transmission, their channel statistics for information and energy transfer, and different user selection schemes. We first analyze the performance of state-of-the-art greedy and round-robin scheduling schemes in RF-powered cellular networks. Closed-form expressions for the minimum power outage probability (i.e., the probability that the selected user is unable to harvest sufficient power for uplink transmission) are also derived. We then develop modified versions of the conventional user selection schemes that improve the spectral efficiency on a given uplink transmission channel with zero power outage probability (i.e., probability of outage due to insufficient amount of harvested power). The developed schemes are shown to outperform the conventional user scheduling schemes in terms of the throughput and energy harvesting time with a trade-off in fairness among users. The accuracy of the expressions is validated via Monte-Carlo simulations. Numerical results highlight the trade-offs associated with the various user selection schemes as a function of network parameters.

Energy harvesting in cellular networks is an emerging technique to enhance the sustainability of power-constrained wireless devices. This paper considers the co-channel deployment of a macrocell overlaid with small cells. The small cell base stations (SBSs) harvest their energy from environment sources whereas the macro base station (MBS) uses conventional power supply. Given a stochastic energy arrival process, this paper derives a power control policy for the downlink transmission of both MBS and SBSs such that they can obtain an equilibrium of their own objectives on a long-term basis (e.g., maximizing the transmission rate for SBSs while maintaining the target signal-to-interference-plus-noise ratio (SINR) at the macro users) on a given transmission channel. To this end, we propose a single controller stochastic game and develop a power control policy as a solution of a quadratic programming problem. Numerical results demonstrate the significance of the developed optimal power control policy over the conventional fixed and random power control policies.

This paper presents a novel framework to derive the statistics of the interference considering dedicated and shared spectrum access for uplink transmission in two-tier small cell networks such as the macrocell-femtocell networks. The framework exploits the distance distributions from geometric probability theory to characterize the uplink interference while considering a traditional grid-model set-up for macrocells along with the randomly deployed femtocells. The derived expressions capture the impact of path-loss, composite shadowing and fading, uniform and non-uniform traffic loads, spatial distribution of femtocells, and partial and full spectral reuse among femtocells. Considering dedicated spectrum access, first, we derive the statistics of co-tier interference incurred at both femtocell and macrocell base stations (BSs) from a single interferer by approximating generalized-K composite fading distribution with the tractable Gamma distribution. We then derive the distribution of the number of interferers considering partial spectral reuse and moment generating function (MGF) of the cumulative interference for both partial and full spectral reuse scenarios. Next, we derive the statistics of the cross-tier interference at both femtocell and macrocell BSs considering shared spectrum access. Finally, we utilize the derived expressions to analyze the capacity in both dedicated and shared spectrum access scenarios. The derived expressions are validated by the Monte Carlo simulations. Numerical results are generated to assess the feasibility of shared and dedicated spectrum access in femtocells under varying traffic load and spectral reuse scenarios.

Base station (BS) sleeping has emerged as a viable solution to enhance the overall network energy efficiency by inactivating the underutilized BSs. However, it affects the performance of users in sleeping cells depending on their BS association criteria, their channel conditions toward the active BSs, and scheduling criteria and traffic loads at the active BSs. This paper characterizes the performance of cellular systems with BS sleeping by developing a systematic framework to derive the spectral efficiency and outage probability of downlink transmission to the sleeping cell users taking into account the aforementioned factors. In this context, a user association scheme is also developed in which sleeping cell users associate to a BS with maximum mean channel access probability (MMAP). The MMAP-based user association scheme adapts according to traffic load and scheduling criteria at the active BSs. We consider greedy and round-robin schemes at active BSs for scheduling users in a channel. We also derive the analytical results for the conventional maximum received signal power (MRSP)-based user association scheme. Finally, we derive the statistics of the received signal and interference power to evaluate the downlink spectral efficiency of a given sleeping cell user. Numerical results provide a comparison between two user association schemes as a function of system parameters and demonstrate the efficacy of the MMAP-based association scheme in non-uniform traffic load scenarios.

In this paper, we derive a statistical model of the co-tier interference in closed access two tier heterogeneous wireless cellular networks with femtocell deployments. The derived model captures the impact of bounded path loss model, wall penetration loss, user distributions, random locations, and density of the femtocells. Firstly, we derive the analytical expressions for the probability density function (PDF) and moment generating function (MGF) of the co-tier interference considering a single femtocell interferer by exploiting the random disc line picking theory from geometric probability. We then derive the MGF of the cumulative interference from all femtocell interferers considering full spectral reuse in each femtocell. Orthogonal spectrum partitioning is assumed between the macrocell and femtocell networks to avoid any cross-tier interference. Finally, the accuracy of the derived expressions is validated through Monte-Carlo simulations and the expressions are shown to be useful in quantifying important network performance metrics such as ergodic capacity.

Intercell interference (ICI) is a primary cause for performance limitation in emerging wireless cellular systems due to its highly indeterministic nature. In this paper, we derive an analytical statistical model for the uplink ICI in a multiuser multi-carrier cellular network considering the impact of various uncoordinated scheduling schemes on the locations and transmit powers of the interferers. The derived model applies to generic composite fading distributions and provides a useful computational tool to evaluate key performance metrics such as the network ergodic capacity. The derived model is extended to incorporate coordinated scheduling schemes. A study is then presented to quantify the potential performance gains of coordinated over uncoordinated scheduling schemes under various base station coordination scenarios. Numerical results demonstrate that different frequency allocation patterns significantly impact the network performance depending on the coordination among neighboring base stations. The accuracy of the derived analytical expressions is verified via Monte-Carlo simulations.

Uplink power control is in essence an interference mitigation technique that aims at minimizing the inter-cell interference (ICI) in cellular networks by reducing the transmit power levels of the mobile users while maintaining their target received signal quality levels at base stations. Power control mechanisms directly impact the interference dynamics and, thus, affect the overall achievable capacity and consumed power in cellular networks. Due to the stochastic nature of wireless channels and mobile users' locations, it is important to derive theoretical models for ICI that can capture the impact of design alternatives related to power control mechanisms. To this end, we derive and verify a novel statistical model for uplink ICI in Generalized-K composite fading environments as a function of various slow and fast power control mechanisms. The derived expressions are then utilized to quantify numerically key network performance metrics that include average resource fairness, average reduction in power consumption, and ergodic capacity. The accuracy of the derived expressions is validated via Monte-Carlo simulations. Results are generated for multiple network scenarios, and insights are extracted to assess various power control mechanisms as a function of system parameters.

This paper presents a novel framework for modeling the uplink intercell interference (ICI) in a multiuser cellular network. The proposed framework assists in quantifying the impact of various fading channel models and state-of-the-art scheduling schemes on the uplink ICI. Firstly, we derive a semi-analytical expression for the distribution of the location of the scheduled user in a given cell considering a wide range of scheduling schemes. Based on this, we derive the distribution and moment generating function (MGF) of the uplink ICI considering a single interfering cell. Consequently, we determine the MGF of the cumulative ICI observed from all interfering cells and derive explicit MGF expressions for three typical fading models. Finally, we utilize the obtained expressions to evaluate important network performance metrics such as the outage probability, ergodic capacity, and average fairness numerically. Monte-Carlo simulation results are provided to demonstrate the efficacy of the derived analytical expressions.

This paper deals with the statistical modeling of uplink inter-cell interference (ICI) considering greedy scheduling with power adaptation based on channel conditions. The derived model is implicitly generalized for any kind of shadowing and fading environments. More precisely, we develop a generic model for the distribution of ICI based on the locations of the allocated users and their transmit powers. The derived model is utilized to evaluate important network performance metrics such as ergodic capacity, average fairness and average power preservation numerically. Monte-Carlo simulation details are included to support the analysis and show the accuracy of the derived expressions. In parallel to the literature, we show that greedy scheduling with power adaptation reduces the ICI, average power consumption of users, and enhances the average fairness among users, compared to the case without power adaptation.

Resource allocation in orthogonal frequency division multiple access (OFDMA) networks plays an imperative role to guarantee the system performance. However, most of the known resource allocation schemes are focused on maximizing the local throughput of each cell, while ignoring the significant effect of inter-cell interference. This paper investigates the problem of resource allocation (i.e., subcarriers and powers) in the uplink of a multi-cell OFDMA network. The problem has a non-convex combinatorial structure and is known to be NP hard. Firstly, we investigate the upper and lower bounds to the average network throughput due to the inherent complexity of implementing the optimal solution. Later, a centralized sub-optimal resource allocation scheme is developed. We further develop less complex centralized and distributed schemes that are well-suited for practical scenarios. The computational complexity of all schemes has been analyzed and the performance is compared through numerical simulations. Simulation results demonstrate that the distributed scheme achieves comparable performance to the centralized resource allocation scheme in various scenarios.

ABSTRACT In this paper, we consider maximizing the sum rate in the uplink of a multi‐cell orthogonal frequency‐division multiple access network. The problem has a non‐convex combinatorial structure and is known to be NP‐hard. Because of the inherent complexity of implementing the optimal solution, firstly, we derive an upper bound (UB) and a lower bound (LB) to the optimal average network throughput. Moreover, we investigate the performance of a near‐optimal single cell resource allocation scheme in the presence of inter‐cell interference, which leads to another easily computable LB. We then develop a centralized sub‐optimal scheme that is composed of a geometric programming‐based power control phase in conjunction with an iterative subcarrier allocation phase. Although the scheme is computationally complex, it provides an effective benchmark for low complexity schemes even without the power control phase. Finally, we propose less complex centralized and distributed schemes that are well suited for practical scenarios. The computational complexity of all schemes is analyzed, and the performance is compared through simulations. Simulation results demonstrate that the proposed low complexity schemes can achieve comparable performance with that of the centralized sub‐optimal scheme in various scenarios. Moreover, comparisons with the UB and LB provide insight on the performance gap between the proposed schemes and the optimal solution. Copyright © 2011 John Wiley & Sons, Ltd.