This article introduces a new approach to address the spectrum scarcity challenge in 6G networks by implementing the enhanced licensed shared access (ELSA) framework. Our proposed auction mechanism aims to ensure fairness in spectrum allocation to mobile network operators (MNOs) through a novel weighted auction called the fair Vickery-Clarke-Groves (FVCG) mechanism. Through comparison with traditional methods, the study demonstrates that the proposed auction method improves fairness significantly. The enhancement of the efficiency of the LSA system is suggested through the utilization of spectrum sensing and the integration of UAV-based networks. This research employs two methods to solve the problem. Firstly, a novel greedy algorithm, named Market Share-Based Weighted Greedy Algorithm (MSWGA), is proposed to achieve better fairness compared to traditional auction methods. Secondly, Deep Reinforcement Learning (DRL) algorithms are exploited to optimize the auction policy and demonstrate its superiority over other methods. Simulation results show that the deep deterministic policy gradient (DDPG) method performs superior to soft actor critic (SAC), MSWGA, and greedy methods. Moreover, a significant improvement is observed in fairness index compared to the traditional greedy auction methods. This improvement is as high as about 27% and 35% when deploying the MSWGA and DDPG methods, respectively.

This paper presents an analysis of the Age of Information (AoI) in a wireless sensor network consisting of multiple IoT sensors. This network consists of three nodes: a wireless power source (WPS), sensors, and an access point (AP). We exploit hybrid visible light communication/radio frequency (VLC/RF) for the sensors with the orthogonal frequency bands. Power domain non-orthogonal multiple access (PD-NOMA) and successive interference cancellation (SIC) are also adopted for the sensors and the AP. Finally, we present two main optimization problems for average AoI with the sensor's transmit power constraint. We analyze the average AoI in a hybrid VLC/RF network, and we obtain the global optimum value for the average AoI of each sensor by considering the probability of sensors being charged, the probability of choosing the links, calculating the probability of successful decoding. According to the optimization results based on the Particle Swarm Optimization (PSO) method, the average AoI for each sensor and the total average AoI in the proposed system are reduced by 10% and 15%, respectively.

In this paper, fresh Basic Safety Messages (BSM) (e.g., vehicle’s position and speed) are used to control the Connected Automated Vehicles (CAVs) to reduce Time to Collision (TTC) error which leads to decrease in Collision Risk (CR). In contrast to exiting works, a novel Safety-aware Age of Information (S-AoI) metric is proposed that in addition to AoI, takes into account the risk assessment of CAVs to design an efficient transmission protocol for BSMs. We also deploy user-centric Cell-free-massive-MIMO (CFmMIMO) to improve the communication coverage, accessibility, and reliability, where each CAV is served by a cluster of nearby Access Points (APs). Unlike previous works, a two time-scale distributed deterministic policy gradients algorithm is adopted which greatly reduces the signal processing complexity, system load as well as signaling overhead while maintaining the performance. Simulation results show that the proposed framework, i.e, user-centric CFmMIMO technology together with S-AoI metric, can reduce average TTC error between 24%-35% across different lane change probabilities compared to the baseline scenario in which we use small cell mMIMO with AoI metric. Such a reduction in TTC error results in significant decrease (as high as 75%) in CR ratio.

This paper proposes the use of unmanned aerial vehicles (UAVs) with intelligent reflecting surfaces (IRS) to reflect signals from the industrial internet of things (IIoT) to the destination, where power-domain non-orthogonal multiple access (PD-NOMA) is used in the uplink. The objective of our paper is to minimize the average age of information (AAoI) of users affected by transmit power constraint, and UAV movement restrictions. By optimizing transmit power, sub-carriers, trajectory, and phase shift matrix elements, UAV-IRS on IIoT networks can improve the freshness of the data collected from IIoT devices. The nonlinear integer optimization problem leads to an NP-hard problem, which is practically difficult to solve. We exploit the powerful reinforcement learning algorithm, i.e., the proximal policy optimization (PPO). The numerical results illustrate the benefits of IRS-enabled UAV communication systems. By using IRSs and the PPO algorithm, UAVs can achieve better performance than other methods that consider a fixed IRS, random deployment, other RL methods(A2C), and the impact of UAV jitter.

In this paper, we design a new flexible smart software-defined radio access network (Soft-RAN) architecture with traffic awareness for sixth generation (6G) wireless networks. In particular, we consider a hierarchical resource allocation model for the proposed smart soft-RAN model where the software-defined network (SDN) controller is the first and foremost layer of the framework. This unit dynamically monitors the network to select a network operation type on the basis of distributed or centralized resource allocation procedures to intelligently perform decision-making. In this paper, our aim is to make the network more scalable and more flexible in terms of conflicting performance indicators such as achievable data rate, overhead, and complexity indicators. To this end, we introduce a new metric, i.e, throughput-overhead-complexity (TOC), for the proposed machine learning-based algorithm, which supports a trade-off between these performance indicators. In particular, the decision making based on TOC is solved via deep reinforcement learning (DRL) which determines an appropriate resource allocation policy. Furthermore, for the selected algorithm, we employ the soft actor-critic (SAC) method which is more accurate, scalable, and robust than other learning methods. Simulation results demonstrate that the proposed smart network achieves better performance in terms of TOC compared to fixed centralized or distributed resource management schemes that lack dynamism. Moreover, our proposed algorithm outperforms conventional learning methods employed in recent state-of-the-art network designs.

Network slicing (NwS) is one of the main technologies in the fifth-generation of mobile communication and beyond (5G+). One of the important challenges in the NwS is information uncertainty which mainly involves demand and channel state information (CSI). Demand uncertainty is divided into three types: number of users requests, amount of bandwidth, and requested virtual network functions workloads. Moreover, the CSI uncertainty is modeled by three methods: worst-case, probabilistic, and hybrid. In this paper, our goal is to maximize the utility of the infrastructure provider by exploiting deep reinforcement learning (DRL) algorithms in end-to-end NwS resource allocation under demand and CSI uncertainties. Enhanced mobile broadband (eMBB) requires high data rates. The uncertainties we argued above have a direct negative impact on the data rate and our objective function. Therefore, we focus primarily on eMBB. Additionally, we also consider ultra-reliable low latency communications (uRLLC) and massive machine-type communication (mMTC). The proposed formulation is a non-convex mixed-integer non-linear programming problem. To perform resource allocation in problems that involve uncertainty, we need a history of previous information. To this end, we use a recurrent deterministic policy gradient (RDPG) algorithm, a recurrent and memory-based approach in DRL. Then, we compare the RDPG method in different scenarios with soft actor-critic (SAC), deep deterministic policy gradient (DDPG), distributed, and greedy algorithms. The simulation results show that the SAC method is better than the DDPG, distributed, and greedy methods, respectively. Moreover, the RDPG method out performs the SAC approach on average by 70%.

Abstract In this paper, a new resource allocation framework for unmanned aerial vehicle (UAV) assisted multicast wireless networks is proposed in which the network users according to their request are divided into several multicast groups. Power domain non‐orthogonal multiple access is adopted as the transmission technology using which the dedicated signals of multicast groups are superimposed and transmitted simultaneously as the UAV passes over the communication area for fixed and mobile users. The proposed scenarios are discussed from two perspectives, offline and online modes. In offline mode, the problem is implemented for fixed and mobile users whose locations are predictable (the location of users, over the communication time, is known at the beginning of the communication time) and in online mode for mobile users whose locations are unpredictable (the location of users, over the communication time, is unknown at the beginning of the communication time). Also, a scenario is proposed in which the online mode the number of mobile users can grow in each time slot. The problem of joint power allocation and UAV trajectory design as an optimisation problem that is non‐linear and non‐convex for two proposed scenarios is formulated. To solve the problem, an alternate search method, successive convex approximation, and geometric programming are adopted. Using simulation results, the performance of the proposed scheme is evaluated for different values of the network parameters.

In this paper, we consider unmanned aerial vehicles (UAVs) equipped with a visible light communication (VLC) access point and coordinated multipoint (CoMP) capability that allows users to connect to more than one UAV. UAVs can move in 3-dimensional (3D) at a constant acceleration, where a central server is responsible for synchronization and cooperation among UAVs. The effect of accelerated movement in UAV is necessary to be considered. Unlike most existing works, we examine the effects of variable speed on kinetics and radio resource allocations. For the proposed system model, we define two different time scales. In the frame, the acceleration of each UAV is specified, and in each slot, radio resources are allocated. Our goal is to formulate a multi-objective optimization problem where the total data rate is maximized, and the total communication power consumption is minimized simultaneously. To handle this multi-objective optimization, we first apply the scalarization method and then apply multi-agent deep deterministic policy gradient (MADDPG). We improve this solution method by adding two critic networks together with two-stage resource allocation.

In this paper, we propose a novel resource management scheme that jointly allocates the transmit power and computational resources in a centralized radio access network architecture. The network comprises a set of computing nodes to which the requested tasks of different users are offloaded. The optimization problem minimizes the energy consumption of task offloading while takes the end-to-end-latency, i.e., the transmission, execution, and propagation latencies of each task, into account. We aim to allocate the transmit power and computational resources such that the maximum acceptable latency of each task is satisfied. Since the optimization problem is non-convex, we divide it into two sub-problems, one for transmit power allocation and another for task placement and computational resource allocation. Transmit power is allocated via the convex-concave procedure. In addition, a heuristic algorithm is proposed to jointly manage computational resources and task placement. We also propose a feasibility analysis that finds a feasible subset of tasks. Furthermore, a disjoint method that separately allocates the transmit power and the computational resources is proposed as the baseline of comparison. A lower bound on the optimal solution of the optimization problem is also derived based on exhaustive search over task placement decisions and utilizing Karush–Kuhn–Tucker conditions. Simulation results show that the joint method outperforms the disjoint method in terms of acceptance ratio. Simulations also show that the optimality gap of the joint method is less than 5%.

In this paper, we propose a novel joint resource allocation and cooperative caching scheme for power-domain non-orthogonal multiple access (PD-NOMA)-based heterogeneous networks (HetNets). In our scheme, the requested content is fetched directly from the edge if it is cached in the storage of one of the base stations (BSs), and otherwise is fetched via the backhaul. Our scheme consists of two phases: 1) Caching phase where the contents are saved in the storage of the BSs; and 2) Delivery phase where the requested contents are delivered to users. We formulate a novel optimization problem over radio resources and content placement variables. We aim to minimize the network cost subject to quality-of-service (QoS), caching, subcarrier assignment, and power allocation constraints. By exploiting advanced optimization methods, such as alternative search method (ASM), Hungarian algorithm, successive convex approximation (SCA), we obtain an efficient sub-optimal solution of the optimization problem. Numerical results illustrate that our ergodic caching policy via the proposed resource management algorithm can achieve a considerable reduction on the total cost on average compared to the most popular caching and random caching policy. Moreover, our cooperative NOMA scheme outperforms orthogonal multiple access (OMA) in terms of the delivery cost in general with an acceptable complexity increase.

In this paper, we study a Deep Reinforcement Learning (DRL) based framework for an online end-user service provisioning in a Network Function Virtualization (NFV)-enabled network. We formulate an optimization problem aiming to minimize the cost of network resource utilization. The main challenge is provisioning the online service requests by fulfilling their Quality of Service (QoS) under limited resource availability. Moreover, fulfilling the stochastic service requests in a large network is another challenge that is evaluated in this paper. To solve the formulated optimization problem in an efficient and intelligent manner, we propose a Deep Q-Network for Adaptive Resource allocation (DQN-AR) in NFV-enabled network for function placement and dynamic routing which considers the available network resources as DQN states. Moreover, the service’s characteristics, including the service life time and number of the arrival requests, are modeled by the Uniform and Exponential distribution, respectively. In addition, we evaluate the computational complexity of the proposed method. Numerical results carried out for different ranges of parameters reveal the effectiveness of our framework. In specific, the obtained results show that the average number of admitted requests of the network increases by 7 up to 14% and the network utilization cost decreases by 5 and 20%.

In this paper, we propose a Zero-Touch, deep reinforcement learning (DRL)-based Proactive Failure Recovery framework called ZT-PFR for stateful network function virtualization (NFV)-enabled networks. To this end, we formulate a resource-efficient optimization problem minimizing the network cost function including resource cost and wrong decision penalty. As a solution, we propose state-of-the-art DRL-based methods such as soft-actor-critic (SAC) and proximal-policy-optimization (PPO). In addition, to train and test our DRL agents, we propose a novel impending-failure model. Moreover, to keep network status information at an acceptable freshness level for appropriate decision-making, we apply the concept of age of information to strike a balance between the event and scheduling based monitoring. Several key systems and DRL algorithm design insights for ZT-PFR are drawn from our analysis and simulation results. For example, we use a hybrid neural network, consisting long short-term memory layers in the DRL agents structure, to capture impending-failures time dependency.

This paper proposes a novel approach to improve the performance of a heterogeneous network (HetNet) supported by dual connectivity (DC) by adopting multiple unmanned aerial vehicles (UAVs) as passive relays that carry reconfigurable intelligent surfaces (RISs). More specifically, RISs are deployed under the UAVs termed as UAVs-RISs that operate over the micro-wave ($\mu \text{W}$) channel in the sky to sustain a strong line-of-sight (LoS) connection with the ground users. The macro-cell operates over the$\mu \text{W}$channel based on orthogonal multiple access (OMA), while small base stations (SBSs) operate over the millimeter-wave (mmW) channel based on non-orthogonal multiple access (NOMA). We study the problem of total transmit power minimization by jointly optimizing the trajectory/velocity of each UAV, RISs’ phase shifts, subcarrier allocations, and active beamformers at each BS. The underlying problem is highly non-convex and the global optimal solution is intractable. To handle it, we decompose the original problem into two subproblems, i.e., a subproblem which deals with the UAVs’ trajectories/velocities, RISs’ phase shifts, and subcarrier allocations for$\mu \text{W}$; and a subproblem for active beamforming design and subcarrier allocation for mmW. In particular, we solve the first subproblem via the dueling deep Q-Network (DQN) learning approach by developing a distributed algorithm which leads to a better policy evaluation. Then, we solve the active beamforming design and subcarrier allocation for the mmW via the successive convex approximation (SCA) method. Simulation results exhibit the effectiveness of the proposed resource allocation scheme compared to other baseline schemes. In particular, it is revealed that by deploying UAVs-RISs, the transmit power can be reduced by 6 dBm while maintaining similar guaranteed QoS.

In this work, we propose a globally optimal joint successive interference cancellation (SIC) ordering and power allocation (JSPA) algorithm for the sum-rate maximization problem in downlink multi-cell non-orthogonal multiple access (NOMA) systems. The proposed algorithm is based on the exploration of base stations (BSs) power consumption, and closed-form of optimal powers obtained for each cell. Although the optimal JSPA algorithm scales well with larger number of users, it is still exponential in the number of cells. For any suboptimal decoding order, we propose a low-complexity near-optimal joint rate and power allocation (JRPA) strategy in which the complete rate region of users is exploited. Furthermore, we design a near-optimal semi-centralized JSPA framework for a two-tier heterogeneous network such that it scales well with larger number of small-BSs and users. Numerical results show that JRPA highly outperforms the case that the users are enforced to achieve their channel capacity by imposing the well-known SIC necessary condition on power allocation. Moreover, the proposed semi-centralized JSPA framework significantly outperforms the fully distributed framework, where all the BSs operate in their maximum power budget. Therefore, the centralized JRPA and semi-centralized JSPA algorithms with near-optimal performances are good choices for larger number of cells and users.

The key idea of non-orthogonal multiple access (NOMA) is to achieve the channel capacity of degraded broad-cast channels by a linear superposition coding combined with successive interference cancellation (SIC). In this line, SIC decoding order among users plays an important role in downlink NOMA systems. The SIC decoding order based on the users’ channel gains within the cell normalized by noise (channel state information (CSI)-based decoding order) is known to be optimal in downlink single-antenna single-cell NOMA. However, this strategy is not optimal in single-antenna multi-cell NOMA, because of the existing inter-cell interference (ICI) which depends on the power consumption of neighboring cells. In this work, we address the problem of finding globally optimal joint SIC ordering and power allocation strategy for the sum-rate maximization problem in downlink single-antenna multi-cell NOMA systems. We propose a globally optimal solution based on the exploration of base stations power consumption and distributed power allocation. We show that this algorithm has a reduced computational complexity compared to other existing optimal solutions. Numerical results show that the optimal decoding order results in significant performance gains in terms of outage probability and users total spectral efficiency compared to the CSI-based decoding order.

In this paper, we consider a cooperative multiple-input single-output (MISO) heterogeneous communication network based on the power domain non-orthogonal multiple access (PD-NOMA). We aim to investigate a resource allocation problem regarding the uncertainty of the channel state information at the transmitter (CSIT) and the imperfect SIC case. Since there is an essential need for low-complexity algorithms with reasonably good performance for the extremely complex access architectures, we propose two novel methods based on matching game with externalities and successive convex approximation (SCA) to realize the hybrid scheme where the number of the cooperative nodes is variable. Moreover, we propose a new matching utility function to manage the interference caused by cooperative networks and PD-NOMA. We also devise two robust beamforming techniques to cope with the channel uncertainty based on the worst-case and stochastic-case scenarios. Simulation results evaluate the performance and the sensibility of the proposed methods and demonstrate that although the performance of the proposed distributed matching algorithm is slightly inferior to that of the SCA type, the complexity of the matching theory approach is substantially lower than that of the latter one.

In this work, we design a generalized joint transmission coordinated multi-point (JT-CoMP)-non-orthogonal multiple access (NOMA) model for a virtualized multi-infrastructure network. In this model, all users benefit from multiple joint transmissions of CoMP thanks to the multi-connectivity opportunity provided by wireless network virtualization (WNV) in multi-infrastructure networks. The NOMA protocol in CoMP results in an unlimited NOMA clustering (UNC) scheme, where the order of each NOMA cluster is the maximum possible value. We show that UNC results in maximum successful interference cancellation (SIC) complexity at users. In this regard, we propose a limited NOMA clustering (LNC) scheme, where the SIC is performed to only a subset of users. We formulate the problem of joint power allocation and user association for the UNC and LNC schemes. Then, one globally and one locally optimal solution are proposed for each problem based on mixed-integer monotonic optimization and sequential programming, respectively. Numerical assessments reveal that WNV and LNC improves users sum-rate and reduces users SIC complexity by up to 35% and 46% compared to the non-virtualized CoMP-NOMA system and UNC model, respectively. Therefore, the proposed algorithms are suitable candidates for the implementation on open and intelligent radio access networks.

Ultra-low latency is the most important requirement of the Tactile Internet (TI), which is one of the proposed services for the next-generation wireless network (NGWN), e.g., fifthgeneration (5G) network. In this paper, a new queuing model for the TI is proposed for the cloud radio access network (CRAN) architecture of the NGWN by applying power domain non-orthogonal multiple access (PD-NOMA) technology. In this model, we consider both the radio remote head (RRH) and baseband processing unit (BBU) queuing delays for each endto-end (E2E) connection between a pair of tactile users. In our setup, to minimize the transmit power of users subject to guaranteeing an acceptable delay of users, and fronthaul and access constraints, we formulate a resource allocation (RA) problem. Furthermore, we dynamically set the fronthaul and access links to minimize the total transmit power. Given that the proposed RA problem is highly non-convex, in order to solve it, we utilize diverse transformation techniques such as successive convex approximation (SCA) and difference of two convex functions (DC). Numerical results show that by dynamic adjustment of the access and fronthaul delays, transmit power reduces in comparison with the fixed approach per each connection. Also, energy efficiency of orthogonal frequency division multiple access (OFDMA) and PD-NOMA are compared for our setup.

In this paper, we propose a novel resource allocation scheme for non‐orthogonal multiple access (NOMA) based cooperative cellular networks where in each cell, two pairs of transmitter–receiver want to communicate via the base station (BS) which serves as a decode‐and‐forward (DF) relay. In the first hop, the transmitters simultaneously transmit their information and the BS applies joint decoding method. The BS transmits the received information towards the receivers based on NOMA technique. The receivers apply the successive interference cancellation (SIC) where the SIC ordering of receivers is determined based on the direct channel gain. We formulate the resource allocation problem as an optimization problem which is non‐convex. To tackle the non‐convexity, we adopt the successive convex approximation (SCA) with difference of two concave functions (D.C.) as the approximation technique. We also study the computational complexity of our proposed scheme. Finally, we study the performance of our scheme by simulations and compare it with existing transmission schemes like the orthogonal frequency multiple access (OFDMA) and the case where both of the first hop and the second hop adopt PD‐NOMA as well as the case where a special sort of subcarrier pairing is performed between the first and second hops.

In this paper, we propose a novel framework for 5G and beyond (5G+) heterogeneous wireless networks consisting of macro aerial base stations (MABSs), small aerial base stations (SABSs), and ground base stations (GBSs) with two types of access technologies: power domain non-orthogonal multiple access (PD-NOMA) and orthogonal frequency-division multiple access (OFDMA). We aim to maximize the total network profit under some practical network constraints, e.g., NOMA and OFDMA limitations, transmit power (TP) maximum limits, and isolation of the virtualized wireless network. We formulate the resource allocation problem encompassing joint TP allocation, ABS altitude determination, user association, and sub-carrier allocation parameters. Our optimization problem is mixed integer non-linear programming (MINLP) with high computational complexity. To propose a practical approach with reduced computational complexity, we use an alternate method where the main optimization is broken down into three sub-problems with lower computational complexity. We do this by adopting successive convex approximation (SCA), geometric programming (GP), and mesh adaptive direct search (MADS) to solve each of the resulting problems, and find power allocation, altitudes of ABSs, and assignment parameters, respectively. Simulation results reveal that our proposed scenario can improve the overall network profit by up to 47 percent compared to the case where the TPs and ABS altitudes are fixed. Besides, finding the ABS altitude with fixed TPs can improve the network profit by 20 percent compared to the power allocation case with fixed ABS altitudes. Our proposed heterogeneous approach improves the network profit by up to 18, 16, 15, and 10 percent in suburban, urban, dense urban, and high-rise urban environments, respectively, compared to the cases with homogeneous ABSs.

Recently, wireless edge caching has emerged as a promising technology for future wireless networks to cope with exponentially increasing demands for high data rate and low latency multimedia services by proactively storing contents at the network edge. Here, we aim to design efficient cache placement and delivery strategies for an orthogonal frequency division multiple access (OFDMA)-based cache-enabled heterogeneous cellular network (C-HetNet) which operates in two separated phases: caching phase (CP) and delivery phase (DP). Since guaranteeing fairness among mobile users (MUs) is not well investigated in cache-assisted wireless networks, we first propose two delay-based fairness schemes called proportional fairness (PF) and min-max fairness (MMF). The PF scheme deals with minimizing the total weighted latency of MUs while MMF aims at minimizing the maximum latency among them. In the CP, we propose a novel proactive fairness and transmission-aware cache placement strategy (CPS) corresponding to each target fairness scheme by exploiting the flexible wireless access and backhaul transmission opportunities. Specifically, we jointly perform the allocation of physical resources as storage and radio, and user association to improve the flexibility of the CPSs. Moreover, in the DP of each fairness scheme, an efficient delivery policy is proposed based on the arrival requests of MUs, CSI, and caching status. Numerical assessments demonstrate that our proposed CPSs outperform the total latency of MUs up to 27 percent compared to the conventional baseline popular CPSs.

This paper studies a novel dual-mode scheduling framework that jointly performs power allocation, beamforming, and sparse code multiple access (SCMA) based scheduling over microwave and millimeter wave (mmW) bands. We propose a robust secure transmission scheme assuming imperfect channel state information for the eavesdropper links. The proposed scheduling framework allows users to schedule simultaneously on each dual-mode BS, based on SCMA, to maximize the joint access secrecy and backhaul rates under transmit power constraints. It is shown that the proposed scheduling framework can find an effective scheduling solution over both microwave and mmW in polynomial time. Simulation results show that the dual connectivity, and joint solutions have 22.5%, and 20% performance gain compared to only microwave, and the disjoint solution, respectively.

The authors consider a content‐centric two‐tier heterogeneous cellular network where the macro base stations (MBSs) and small base stations (SBSs) are spread based on two independent Poisson point processes. The set of contents are stored in the MBSs while a subset of the contents which are the most popular are cached in the SBSs. We assume that there is no direct link between the MBSs and the users, and users send their content requests to their nearest SBS. If the SBS has not the requested content, the nearest MBS to the serving SBS serves the user through that SBS. Furthermore, the authors incorporated the automatic repeat request (ARQ)‐based transmission with a fixed number of the ARQ rounds, and analyse the delay experienced by users. The authors obtained closed‐form expressions for the average outage probability and the average delay in case of successful ARQ transmission. It is shown that the content distribution based on popularity decreases the average outage probability and average delay. Also, it is shown that the transmission from MBS to the user through SBS improves the performance in terms of average outage probability and average delay. Finally, using simulations the authors study the proposed schemes for different network parameters.

In this paper, we consider the downlink of a multiple-input-single-output (MISO) orthogonal frequency division multiple access (OFDMA) based heterogeneous cellular network (HetNet) with multiple legitimate users and eavesdroppers. We assume that the channel state information (CSI) values from eavesdroppers to all base stations (BSs) and the CSI values between small base stations (SBSs) and macro users (MUEs) are uncertain. To overcome these uncertainties and eavesdroppers overhearing, we devise a secure robust communications by proposing a robust transmit power and spectrum allocation and antenna selection algorithm. In this regard, we propose an optimization problem at which the main aim is to maximize the sum secrecy rate subject to transmit power limitation, interference power restriction from SBSs to MUEs, and sub-carrier allocation constraints. Since the optimization problem is non-convex and intractable, we propose an iterative algorithms at which the main problem is decoupled to four subproblems: 1) power allocation, 2) sub-carrier allocation, 3) eavesdropper selection, and 4) antenna selection. These subproblems are iteratively solved until convergence. Simulation results verify the efficiency of the proposed approach.

In this paper, the tradeoff among the energy efficiency, fairness, harvested energy, and system sum rate is studied. In this regard, various fairness methods, namely, max-min fairness, proportional fairness, and minimum delay potential fairness in power-domain non-orthogonal multiple access-based heterogeneous cellular networks are investigated. In order to perform successive interference cancellation (SIC), we use two ordering approaches and compare their performance. To this end, we propose joint subcarrier and power allocation algorithms to achieve fair energy efficient resource allocation for each fairness method and SIC ordering. Since the proposed optimization problems are non-convex and intractable, the existing methods to solve the convex problems could not be directly used. To overcome this difficulty, an iterative algorithm based on successive convex approximation is used. Moreover, to show the optimality gap of the proposed solution method, an optimal approach based on the monotonic optimization is applied in which we first transform each of the proposed optimization problems into a monotonic optimization problem of canonical form, and then, we obtain the optimal solution of each problem, which coincides with the optimal solution of the original non-convex problem. We finally study the performance of the proposed schemes using simulations for different values of the system parameters.

In this paper, a new approach for multiple access (MA) in fifth generation (5G) of cellular networks called power domain sparse code multiple access (PSMA) is proposed. In PSMA, we adopt both the power domain and the code domain to transmit multiple users' signals over a subcarrier simultaneously. In such a model, the same sparse code multiple access (SCMA) codebook can be used by multiple users where, for these users, power domain non-orthogonal multiple access (PD-NOMA) technique is used to send signals non-orthogonally. Although different SCMA codebooks are orthogonal and produce no interference over each other, the same codebook used by multiple users produces interference over these users. We investigate the signal model as well as the receiver and transmitter of the PSMA method. To evaluate the performance of PSMA, we consider a single cell with multiple users. In this case, our design objective is to maximize the system sum rate of the network subject to some system level and QoS constraints such as transmit power constraints. We formulate the proposed resource allocation problem as an optimization problem and solve it by successive convex approximation (SCA) techniques. Finally, the effectiveness of the proposed approach is investigated using numerical results.

In this paper, we consider the problem of resource allocation for secure communications in decode-and-forward (DF) relay-assisted orthogonal frequency-division multiple access (OFDMA) networks. In our setting, users want to securely communicate to the base station (BS) with the help of a set of relay stations (RSs) in the presence of multiple eavesdroppers. We assume that all channel state information (CSI) of the legitimate links and only the channel distribution information (CDI) of the eavesdropper links are available. We formulate our problem as an optimization problem whose objective is to maximize the sum secrecy rate of the system subject to individual transmit power constraint for each user and RS. As a first work which considers limited feedback schemes for secure communications in cooperative OFDMA networks, we consider the limited-rate feedback case, where in addition to transmit power and subcarrier assignments, channel quantization should be performed and boundary regions of channels should be computed. We further consider the noisy feedback channel. We solve our problem using the dual Lagrange approach and propose an iterative algorithm whose convergence is analyzed. Using simulations, we evaluate the performance of the proposed scheme in numerous situations.

We develop a game theoretic framework for distributed resource allocation in the uplink of a cognitive radio network where secondary users (SUs) share the bandwidth with primary users (PUs). Each PU has a fixed data rate and applies power control to achieve its target SINR, while each SU jointly adjusts its data rate and transmit power by maximizing its utility. Since the SUs' total interference on the primary network is kept below a given threshold, there exist couplings between SUs' transmit power levels. Hence, the SUs' game belongs to the generalized Nash equilibrium (GNE) problems. We separately analyze the proposed resource allocation algorithm for SUs and the one utilized by PUs, and derive the condition under which the PUs' power control algorithm converges for SUs' fixed transmit power levels. We also derive the sufficient condition under which the SUs' joint data rate and power control algorithm converges for fixed transmit power levels of PUs. We then derive the sufficient condition for convergence of the algorithms when PUs and SUs simultaneously apply their resource allocation schemes. Simulations confirm our analysis and demonstrate that the proposed framework is energy efficient and provides PUs and SUs with their quality of service requirements.

We formulate the resource allocation problem for the uplink in code division multiple access (CDMA) networks using a game theoretic framework, propose an efficient and distributed algorithm for a joint rate and power allocation, and show that the proposed algorithm converges to the unique Nash equilibrium (NE) of the game. Our choice for the utility function enables each user to adapt its transmit power and throughput to its channel. Due to users' selfish behavior, the output of the game (its NE) may not be a desirable one. To avoid such cases, we use pricing to control each user's behavior, and analytically show that similar to the no-pricing case, our pricing-based algorithm converges to the unique NE of the game, at which, each user achieves its target signal-to-interference-plus-noise ratio (SINR). We also extend our distributed resource allocation scheme to multi-cell environments for base station assignment. Simulation results confirm that our algorithm is computationally efficient and its signalling overhead is low. In particular, we will show that in addition to its ability to attain the required QoS of users, our scheme achieves better fairness in allocating resources and can significantly reduce transmit power as compared to existing schemes.

In this paper, we propose a low complexity heuristic algorithm for radio resource allocation in orthogonal frequency division multiple access (OFDMA) systems based on subcarrier channel distribution information (CDI). We consider practical rate adaptation in which rate is adapted using a predefined set of modulation levels, which is in contrast to previous works that consider continuous rate. We formulate the problem of resource allocation in an OFDMA system with streaming traffic which requires a minimum guaranteed average rate, and elastic traffic with flexible rate requirements. The main objective is to maximize the total transmission rate of the elastic users, while average rate guarantees for streaming traffic as well as maximum transmission power constraints are satisfied. To reduce the computational complexity, we decouple the resource allocation problem into two sub-problems corresponding to two traffic types. For streaming traffic, we optimally allocate subcarrier and power and then the remaining radio resources including the unassigned subcarriers and unallocated transmission power of the base station are optimally allocated to the elastic traffic. We then develop a heuristic algorithm based on Lagrangian method to obtain an approximation of the optimal solution. Using simulations, we study the impact of number of fading regions. Simulations also provides insight on the trade-off between the number of streaming and elastic users.