In large-scale Internet of Things (IoT) deployments, efficiently allocating computing resources to IoT devices, while preserving the integrity and utility of their data, remains a critical challenge. This paper introduces a novel online probabilistic model designed to handle uncertainties in both demand and resource availability within IoT networks, where the computing tasks of requesting devices (RDs) are fulfilled by serving devices (SDs). The proposed model integrates stochastic elements and formulates an optimization problem that aims to minimize the number of active serving devices required for task offloading, subject to the constraints of available computing resources. To further enhance decision-making, the model incorporates the concept ofValue of Information (VoI)to ensure that the informational utility of each device’s data remains above a predefined threshold during task processing. The optimization problem is addressed using a heuristic algorithm. In scenarios where no serving device is immediately available, tasks are temporarily stored in a buffer and deferred to the next time slot, with their waiting time being tracked. This task allocation process is inspired by bin-packing algorithms, which are known for their efficiency in resource management and task scheduling. Moreover, the paper evaluates the performance of the proposed solution under worst-case conditions through feasibility analysis, thereby demonstrating its robustness. Two buffering strategies, First-In First-Out (FIFO) and Last-In First-Out (LIFO), are also examined to model task retrieval and execution behavior. Results show that adopting the FIFO strategy can reduce the average waiting time by approximately 50%. Overall, the proposed framework provides a reliable and scalable task computing service, with each serving device capable of supporting, on average, four requesting devices under typical operating conditions.

Reliable, ultra-low-latency communication in Vehicular Ad-hoc Networks (VANETs) is critical for autonomous vehicle safety, yet existing routing protocols struggle with high overhead and frequent link disruptions in dynamic environments. A key limitation is their failure to leverage the rich sensor data already available in modern vehicles for network-level coordination. This paper introduces a novel segmentation-based routing framework that directly addresses these challenges by transforming in-vehicle sensor data into a shared, high-resolution network topology. Our core contribution is a dynamic road occupancy matrix, created by dividing roadways into velocity-adaptive, single-vehicle segments, which provides all nodes with a unified and near real-time view of the traffic environment. This matrix is disseminated efficiently using a Time Division Integrated Sensing and Communication (TD-ISAC) framework inspired by 5G Sidelink. We propose a hybrid Lagrangian Relaxation and Branch-and-Bound algorithm to dynamically optimize the TD-ISAC frame, balancing the trade-off between sensing accuracy and communication throughput. To further enhance topological precision and mitigate vehicle mobility effects, the framework integrates a Kalman Filter and Transformer model for predictive position estimation and employs a reserve relay mechanism to ensure multi-hop link stability. Comprehensive simulations demonstrate that our framework significantly outperforms state-of-the-art protocols. Notably, it reduces end-to-end delay by up to 68% compared to ZRP and 41% against the TD-ISAC-based Starling Flocks, while simultaneously decreasing routing overhead. This dual improvement enables a more efficient and robust network for safety-critical applications.

Vehicular edge computing (VEC) is a critical enabler of low-latency and computation-intensive vehicular applications by offloading tasks from vehicles to edge servers. However, the dynamic nature of vehicular networks introduces significant uncertainty in task characteristics and network conditions. This paper proposes a robust task offloading scheme for VEC within the open radio access network (O-RAN) architecture. The proposed scheme integrates large-timescale computational resource allocation (CRA) with small-timescale task partitioning and radio resource allocation (RRA) using O-RAN’s hierarchical control framework. Our scheme minimizes the network-wide resources under latency constraints that are subjected to demand uncertainty. We employ the cutting-set method to address the demand uncertainty in the large-timescale CRA. We obtain a closed-form solution to the optimal task partitioning problem and provide a heuristic approach for the small-timescale RRA. Simulation results show that the small-timescale RRA succeeds to counteract the demand uncertainty at the large-timescale CRA, that is, no outage occurs when demands are in the assumed uncertainty set, whereas the non-robust scheme exhibits as high as 50% outage probability. Moreover, our slotted scheme consumes about 50% less bandwidth than the conventional non-slotted robust solution.

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.

In this paper, we present a quality of service (QoS)-aware priority-based spectrum management scheme to guarantee the minimum required bit rate of vertical sector players (VSPs) in the 5G and beyond generation, including the 6th generation (6G). VSPs are considered as spectrum leasers to optimize the overall spectrum efficiency of the network from the perspective of the mobile network operator (MNO) as the spectrum licensee and auctioneer. We exploit a modified Vickrey-Clarke-Groves (VCG) auction mechanism to allocate the spectrum to them where the QoS and the truthfulness of bidders are considered as two important parameters for prioritization of VSPs. The simulation is done with the help of deep deterministic policy gradient (DDPG) as a deep reinforcement learning (DRL)-based algorithm. Simulation results demonstrate that deploying the DDPG algorithm results in significant advantages. In particular, the efficiency of the proposed spectrum management scheme is about %85 compared to the %35 efficiency in traditional auction methods.

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 propose a novel cooperative resource sharing technique in multi-tier edge slicing networks which is robust to imperfect channel state information (CSI) caused by user equipments’ (UEs) mobility. Due to the mobility of UEs, the dynamic requirements of their tasks, and the limited resources of the network, we propose a smart joint dynamic pricing and resources sharing (SJDPRS) scheme that can incentivize the infrastructure provider (InP) and mobile network operators (MNOs). Aiming to maximize the profits of UEs, MNOs and the InP under the task fulfillment constraints, we formulate an optimization problem by deploying the multi-objective optimization method where in addition to the resource allocation variables, the price values are also the optimization variables. To solve the problem, we adopt a new deep reinforcement learning (DRL) method based on a carefully designed reward function. The simulation results indicate that the proposed resource sharing scenario can increase total profits for the UEs, MNOs, and InP in comparison to non-cooperative case, while also providing almost complete fairness among the players. In particular, as compared to the baselines and benchmarks, the profits for each network component (MNO, InP, and UEs), under fairness considerations, are enhanced by 75%, 79%, and 76%, respectively.

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%.

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 delay-sensitive industrial Internet of Things (IIoT) applications, the age of information (AoI) is employed to characterize the freshness of information. Meanwhile, the emerging network function virtualization provides flexibility and agility for service providers to deliver a given network service using a sequence of virtual network functions (VNFs). However, suitable VNF placement and scheduling in these schemes is NP-hard and finding a globally optimal solution by traditional approaches is complex. Recently, deep reinforcement learning (DRL) has appeared as a viable way to solve such problems. In this paper, we first utilize single agent low-complex compound action actor-critic RL to cover both discrete and continuous actions and jointly minimize VNF cost and AoI in terms of network resources under end-to-end Quality of Service constraints. To surmount the single-agent capacity limitation for learning, we then extend our solution to a multi-agent DRL scheme in which agents collaborate with each other. Simulation results demonstrate that single-agent schemes significantly outperform the greedy algorithm in terms of average network cost and AoI. Moreover, multi-agent solution decreases the average cost by dividing the tasks between the agents. However, it needs more iterations to be learned due to the requirement on the agents' collaboration.

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.

Network slicing is a crucial part of the 5G networks that communication service providers (CSPs) seek to deploy. By exploiting three main enabling technologies, namely, software-defined networking (SDN), network function virtualization (NFV), and network slicing, communication services can be served to the end-users in an efficient, scalable, and flexible manner. To adopt these technologies, what is highly important is how to allocate the resources and admit the customers of the CSPs based on the predefined criteria and available resources. In this regard, we propose a novel joint resource and admission management algorithm for slice-enabled networks. In the proposed algorithm, our target is to minimize the network cost of the CSP subject to the slice requests received from the tenants corresponding to the virtual machines and virtual links constraints. Our performance evaluation of the proposed method shows its efficiency in managing CSP’s resources.

In this paper, we find trajectory planning and power allocation for a vehicular network in which an unmanned-aerial- vehicle (UAV) is considered as a relay to extend coverage for two disconnected far vehicles. We show that in a two-user network with an amplify-and-forward (AF) relay, non-orthogonal- multiple-access (NOMA) always has better or equal sum-rate performance in comparison to orthogonal-multiple-access (OMA) at high signal-to-noise-ratio (SNR) regime. However, for the cases where i) base station (BS)-to-relay link is weak, or ii) two users have similar links, or iii) BS-to-relay link is similar to relay-to-weak user link, applying NOMA has negligible sum-rate gain. Hence, due to the complexity of successive-interference- cancellation (SIC) decoding in NOMA, we propose a dynamic NOMA/OMA scheme in which the OMA mode is selected for transmission when applying NOMA has only negligible gain. Further, we formulate an optimization problem that maximizes the sum-rate of the two vehicles. This problem is non-convex, and hence we propose an iterative algorithm based on alternating- optimization (AO) method which solves trajectory and power allocation sub-problems by successive-convex-approximation (SCA) and difference-of-convex (DC) methods, respectively. Finally, the above-mentioned performance is confirmed by simulations.

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 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.

The Tactile Internet (TI) is one of the next generation wireless network services with end to end (E2E) delay as low as 1 ms. Since this ultra low E2E delay cannot be met in the current 4G network architecture, it is necessary to investigate this service in the next generation wireless network by considering new technologies such as networks function virtualization (NFV). On the other hand, given the importance of E2E delay in the TI service, it is crucial to consider the delay of all parts of the network, including the radio access part and the NFV core part. In this paper, for the first time, we investigate the joint radio resource allocation (R-RA) and NFV resource allocation (NFV-RA) in a heterogeneous network where queuing delays, transmission delays, and delays resulting from virtual network function (VNF) execution are jointly considered. For this setup, we formulate a new resource allocation (RA) problem to minimize the total cost function subject to guaranteeing E2E delay of each connection. Since the proposed optimization problem is highly non-convex, we exploit alternative search method (ASM), successive convex approximation (SCA), and heuristic algorithms to solve it. Besides, for the NFV-RA, we propose an online heuristic algorithm, and analyze its performance for the TI service. Simulation results reveal that the proposed scheme can significantly reduce the network costs compared to the case where the two problems are optimized separately. Moreover, we compare the online algorithm with its offline counterpart as well as a baseline approach and it is shown that the online algorithm outperforms both of them.

In this paper, we find trajectory planning and power allocation for a vehicular network in which an unmanned-aerial-vehicle (UAV) is considered as a relay to extend coverage for two disconnected far vehicles. We show that in a two-user network with an amplify-and-forward (AF) relay, non-orthogonal-multiple-access (NOMA) always has better or equal sum-rate in comparison to orthogonal-multiple-access (OMA) at high signal-to-noise-ratio (SNR) regime. However, for the cases where i) base station (BS)-to-relay link is weak, or ii) two users have similar links, or iii) BS-to-relay link is similar to relay-to-weak user link, applying NOMA has negligible sum-rate gain. Hence, due to the complexity of successive-interference-cancellation (SIC) decoding in NOMA, we propose a dynamic NOMA/OMA scheme in which OMA mode is selected for transmission when applying NOMA has only negligible gain. Also, we show that OMA always has better min-rate than NOMA at high SNR regime. Further, we formulate two optimization problems which maximize the sum-rate and min-rate of the two vehicles. These problems are non-convex, and hence we propose an iterative algorithm based on alternating-optimization (AO) method which solves trajectory and power allocation sub-problems by successive-convex-approximation (SCA) and difference-of-convex (DC) methods, respectively. Finally, the above-mentioned performance is confirmed by simulations.

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.

Traditional cellular networks are unable to support the delay sensitive applications (e.g. vehicular networks, augmented reality). To cope with these challenges, mobile Edge Computing (MEC) has emerged as a new paradigm with computing capabilities in close proximity to the edge of wireless cellular network. In this paper, we study resource allocation for a multi-user multi-task (MUMT) MEC system based on orthogonal frequency-division multiple access (OFDMA). Each computation task is independent with different priorities. In this regard, we propose a priority based task scheduling policy and jointly optimize the computation and communication resource allocation, so as to maximize profit of mobile network operator (MNO) while satisfying the users quality of service (QoS), power consumption at user and base station (BS), and service rate allocation. Building on the proposed model, we develop an innovative framework to improve the MEC performance, by jointly optimizing the service rate, transmit power and subcarrier allocation under satisfying maximum power and service rate, and delay constraints. Our proposed algorithms are finally verified by numerical results which show that the proposed approach outperforms other benchmark schemes. For example, in the Priority queuing schemes, the performance can be improved compared to No-priority queuing.

Summary The highly dynamic nature of the current network traffics makes the network managers to exploit the flexibility of the state‐of‐the‐art paradigm called SDN. In this way, there has been an increasing interest in hybrid networks of SDN‐MPLS. In this paper, a new traffic engineering architecture for SDN‐MPLS network is proposed. To this end, OpenFlow‐enabled switches are applied over the edge of the network to improve flow‐level management flexibility while MPLS routers are considered as the core of the network to make the scheme applicable for existing MPLS networks. The proposed scheme re‐assigns flows to the Label‐Switched Paths (LSPs) to highly utilize the network resources. In the cases that the flow‐level re‐routing is insufficient, the proposed scheme re‐computes and re‐creates the undergoing LSPs. To this end, we mathematically formulate two optimization problems, ie, i) flow re‐routing and ii) LSP re‐creation, and propose a heuristic algorithm to improve the performance of the scheme. Our experimental results show the efficiency of the proposed hybrid SDN‐MPLS architecture in traffic engineering superiors traditionally deployed MPLS networks.

Diffusion-based molecular communication (DMC) is one of the most promising approaches for realizing nano-scale communications for healthcare applications. The DMC systems in in-vivo environments may encounter biological entities that release molecules identical to the molecules used for signaling as part of their functionality. Such entities in the environment act as external noise sources from the DMC system's perspective. In this paper, the release of molecules by external bio-inspired noise sources is particularly modeled as a compound Poisson process. The impact of compound Poisson noise sources (CPNSs) on the performance of a point-to-point DMC system is investigated. To this end, the noise from the CPNS observed at the receiver is characterized. Considering a simple on-off keying modulation and formulating symbol-by-symbol maximum likelihood (ML) detector, the performance of the DMC system in the presence of the CPNS is analyzed. For the special case of CPNS in a high-rate regime, the noise received from the CPNS is approximated as a Poisson process whose rate is normally distributed. In this case, it is proved that a simple single-threshold detector is an optimal ML detector. Our results reveal that in general, adopting the conventional simple homogeneous Poisson noise model may lead to overly optimistic performance predictions, if a CPNS is present.

In this paper, we propose a cross layer energy efficient resource allocation and remote radio head (RRH) selection algorithm for heterogeneous traffic in power domain-non-orthogonal multiple access (PD-NOMA) based heterogeneous cloud radio access networks (H-CRANs). The main aim is to maximize the EE of the elastic users subject to the average delay constraint of the streaming users and the constraints, RRH selection, subcarrier, transmit power, and successive interference cancellation. The considered optimization problem is non-convex, NP-hard, and intractable. To solve this problem, we transform the fractional objective function into a subtractive form. Then, we utilize successive convex approximation approach. Moreover, in order to increase the processing speed, we introduce a framework for accelerating the successive convex approximation for low complexity with the Lagrangian method on graphics processing unit. Furthermore, in order to show the optimality gap of the proposed successive convex approximation approach, we solve the proposed optimization problem by applying an optimal method based on the monotonic optimization. Studying different scenarios show that by using both PD-NOMA technique and H-CRAN, the system energy efficiency is improved.

In this paper, the performance of fifth generation (5G) cellular networks under two promising technologies, namely dual connectivity and coordinated multi-point transmission (CoMP) is investigated. In this regard, a joint access and fronthaul radio resource allocation for a two tier downlink heterogeneous cloud radio access network (H-CRAN) is formulated. The main aim of the proposed problem formulation is to maximize the system energy efficiency (EE) by using both millimeter wave (mmW) and micro wave (μW) links in access and fronthaul. The proposed optimization is a mixed integer nonconvex problem with a high computational complexity solution. Therefore, existing convex optimization methods can not be used directly to solve this problem. To solve this issue, an iterative algorithm based on a successive convex approximation (SCA) approach with low complexity is exploited. As the numerical results show, via dual connectivity and CoMP EE is improved by approximately 90% compared to using only μW subcarriers.

In this paper, we propose a stochastic geometry based pricing for infrastructure sharing in Internet of Things (IoT) networks. We consider a game consisting of a Network Operator (NO) as the seller and an IoT Device Owner (DO) as the buyer in which the seller owns an infrastructure that can address the communication needs of the DO. Using the proposed scheme, we show that DO and NO can reach a win-win deal in which a reasonable cost is imposed to DO in exchange of providing an acceptable coverage by the NO. In particular, we show that the DO can achieve a coverage probability of interest at a lower cost compared to the case in which the proposed pricing model is absent. The proposed idea provides a transparent pricing model between NOs and DOs and paves the road for IoT applications to become more widespread.

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, fifth-generation (5G) cellular networks under three promising technologies, namely, dual connectivity, coordinated multi-point transmission (CoMP), and power domain non orthogonal multiple access (PD-NOMA) are investigated. The main aim is to maximize the downlink energy efficiency (EE) by using both millimeter wave (mmW) and micro wave (μW) links in access and fronthaul, while employing CoMP and PD-NOMA. In this regard, joint access and fronthaul radio resource allocation for a downlink heterogeneous cloud radio access network is considered. The proposed optimization is a mixed integer non-convex problem with a high computational complexity solution, and hence, the alternate search method based on a successive convex approximation approach using fractional programming is exploited. Furthermore, the convergence of the proposed iterative resource allocation method is proved and its computational complexity is investigated. As the numerical results show, via dual connectivity through receiving signals from both mmW and μW transmitters, the system EE is improved by approximately 50%, in contrast to using only μW subcarriers (e.g., as in local thermal equilibrium). In addition, by applying both PD-NOMA and CoMP technologies on the μW subcarriers, the EE of the system increases by approximately 45%.

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 a cooperative diffusion-based molecular communication (MC) network consisting of single source, single decode-and-forward relay, and single destination. The source and the relay nodes use different types of molecules for information transmission. Considering both the relay and the direct links at the destination node, we have two different received signals that can be used by the destination node to retrieve the data transmitted by the source node. Therefore, diversity arises at the destination node. For detection, we adopt the energy detector applying the diversity combining technique. Since the optimum relay position in relay-aided networks plays a crucial role to enhance their performance, we study the relay location optimization problem. We first derive a closed-form expression for the bit error probability of the proposed detection scheme in which the received energy from the source and the relay nodes are linearly combined at the destination node. Then, in order to find the optimum relay position as well as the optimal destination decision threshold for a given number of molecules released by the source and the relay nodes, we formulate a joint optimization problem whose objective is to minimize the bit error probability of the network. To solve this optimization problem, we propose an iterative algorithm based on the block coordinate descent algorithm and study its convergence behavior. Numerical results show that the error performance can be improved by optimizing the position of the relay node. Our analyses help us in designing a reliable cooperative diffusion-based MC network.

We propose a robust resource allocation framework to improve the physical layer security in the presence of an active eavesdropper. In the considered system, we assume that both legitimate receiver and eavesdropper are full-duplex (FD) while most works in the literature concentrate on passive eavesdroppers and half-duplex (HD) legitimate receivers. In this paper, the adversary intends to optimize its transmit and jamming signal parameters so as to minimize the secrecy data rate of the legitimate transmission. In the literature, assuming that the receiver operates in HD mode, secrecy data rate maximization problems subject to the power transmission constraint have been considered in which cooperating nodes act as jammers to confound the eavesdropper. This paper investigates an alternative solution in which we take advantage of FD capability of the receiver to send jamming signals against the eavesdroppers. The proposed self-protection scheme eliminates the need for external helpers. Moreover, we consider the channel state information uncertainty on the links between the active eavesdropper and other legitimate nodes of the network. Optimal power allocation is then obtained based on the worst-case secrecy data rate maximization, under a legitimate transmitter power constraint in the presence of the active eavesdropper. Numerical results confirm the advantage of the proposed secrecy design and in certain conditions, demonstrate substantial performance gain over the conventional approaches.

Nowadays, with the advent of new services, the need for high transmission data rates is inevitable. Thus, to solve this problem, the simultaneous use of multiple antennas at the transmitter and the receiver is mandatory. On the other hand, due to limited resources, their allocation is essential. To allocate resources in satellite systems, multiple‐input–multiple‐output, orthogonal frequency division multiple access, and adaptive coding and modulation techniques can be simultaneously applied. In this study, the resource allocation for non‐delay‐sensitive services is evaluated. The proposed optimisation problem is to maximise the sum data rate under the upward transmission power constraint. The authors solve the considered optimisation problem via the dual decomposition methods for both continuous and discrete cases. Finally, through simulation, the proposed algorithms are investigated and confirmed.

In this study, the authors consider the downlink radio resource allocation for heterogeneous traffic in generalised frequency division multiplexing (GFDM)‐non‐orthogonal multiple access (NOMA) based heterogeneous cellular networks. In this scheme, multiple number of users can be allocated on each subcarrier. Two types of traffic are considered, elastic and streaming. The problem of maximising the weighted sum‐rate of elastic users is addressed subject to streaming users minimum rate in addition to subcarrier and transmit power constraints. This problem is a non‐convex NP‐hard optimisation problem. To solve this problem, the authors divide it into two subproblems, subcarrier allocation and power allocation then an iterative algorithm is proposed. Subcarrier allocation is updated by solving an integer linear program, where a successive convex approximation approach is adopted to transform the power allocation subproblem to a sequence of convex subproblems, using one of the three methods, successive convex approximation for low ComplExity, arithmetic‐geometric mean approximation (AGMA) and difference of two concave functions to find the power allocation optimal solutions. Numerical experiments show that the proposed algorithms can improve the system performance. Furthermore, they show that AGMA can achieve a sum‐rate near to the global optimal solution, at the expense of more computational time.

The ergodic resource allocation (ERA) problem for uplink transmission in underlay cognitive radio networks (CRNs) is investigated. The objective is to maximize the ergodic sum-rate of secondary users (SUs) considering the unavailability of perfect channel state information (CSI), and subject to transmit power limitations of SUs, and the interference threshold constraint to guarantee the quality of service of primary users. Since with average-based formulation of ERA, the interference threshold constraint and transmit power limitations of SUs do not hold instantaneously, one can replace the average-based constraints in ERA with their outage-based counterparts. For the uncertainty on the CSI values, we utilize the robust optimization theory where the uncertain parameters are modeled as a sum of the estimated value and error which is assumed to be bounded. We then map the considered ERA problems to their robust counterparts. Generally, the robust approaches degrade the performance (e.g., sum rate of SU), as they conservatively consider the error to be in the maximum extent and try to preserve the constrains under any condition of error (worst-case scenario). We aim to moderate this effect by using appropriate models for uncertain parameters, relaxing the worst-case scenario, and stochastically preserving the constraints. Moreover, robust problems are in general non-convex and suffer from high computational complexity due to the existence of uncertain system parameters. Therefore, we use effective suboptimal approaches to solve them with a reasonable complexity. This includes methods based on chance constraint approach as well as an iterative scheme. The proposed solutions provide a trade-off between robustness, performance, and complexity. Simulation results reveal that by using the proposed schemes, stable sum-rate of SUs in the presence of CSI uncertainties can be achieved while the instantaneous power and interference constraints are met with a desired probability.

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 propose a robust resource allocation framework to provide physical layer security for a multiple input single output (MISO) communication system. In the considered system, we assume that the both legitimate receiver and eavesdropper are in full-duplex (FD) mode and compare the corresponding performance to conventional cooperative jamming frameworks where a half-duplex (HD) receiver is at hand. In the present paper, the adversary intends to optimize its transmit and jamming signal parameters so as to minimize the MISO secrecy rate between the legitimate transmitter and receivers. The proposed self-protection scheme eliminates the need for external helpers and provides system robustness. Moreover, we investigate robustness against channel state information uncertainty. Optimal power allocation is obtained based on worst-case secrecy rate maximization, under legitimate transmitter power constraint in the presence of an active eavesdropper. Numerical results are then provided to confirm the advantages of using FD receivers.

This paper considers radio resource allocation (RRA) in the downlink of an orthogonal frequency division multiple access (OFDM)-based spectrum-sharing network. The objective of RRA is to maximize the average achievable throughput subject to the primary service interference threshold and the secondary service transmit power constraint. RRA is usually implemented based on a time window T over which system parameters are averaged and checked against resource constraints. We use short-term ( T=1 time slot) and long-term ( T ≫ 1 slots) averaging as approximations to instantaneous and average constraints, respectively. RRA is also investigated for this system with long-term interference threshold and short-term interference threshold constraints. RRA optimization is a nonconvex optimization problem in which the duality principle is adopted to obtain approximate solutions. The duality gap indicates the degree of approximation in the thus-obtained solution. We prove that the duality gap corresponding to each resource allocation asymptotically decays at least with an exponential rate of T. We further show that OFDMA is, asymptotically, the optimal subcarrier assignment. We also propose a practically implementable online power and subcarrier allocation with on-the-fly channel state information measurement. An extensive simulation study has been conducted to verify the theoretically predicted duality gap behavior and to investigate the impact of different system parameters on the secondary service performance. The developed algorithms are also validated to be robust in practical settings and converge fast to theoretical bounds, and thus practically implementable.

In this paper, the dynamic power allocation problem over the multiple-access channel (MAC) against overhearing of eavesdroppers is investigated with the objective to maximize the users' secrecy rate under transmitted power constraints. Lyapunov drift approach is applied to derive the sub-optimal solution for this inherent non-convex optimization problem. Convergence condition and performance of the developed algorithm are investigated. Simulation results indicate that it outperforms the difference- of-two-convex-functions (DC) programming with much less computation time, and its achieved secrecy rate is close to the global optimum solution.

A message passing (MP) algorithm for resource allocation (RA) in an Orthogonal Frequency Division Multiple Access (OFDMA) based spectrum sharing system is developed in this paper. We derive optimal power and subcarrier allocations. We further shed light on advantages of the MP algorithm particularly against the dual solution and highlight its suitability in reducing the computational burdens for practical configurations. It is observed that MP algorithm converges only after two iterations compared to 400 required iterations in the dual case. This considerably reduces the corresponding computational time, which in turn results in much lower consumed processing energy. The saved processing energy and time can then be exploited for enhancing the scheduled data rate especially when the number of users is high.

In this paper, the effect of channel distribution information (CDI) imperfectness on the performance of wireless networks is investigated. In the literature, ergodic resource allocation problems are solved assuming that perfect CDI is available which might not be a practical assumption. Therefore, we adopt the nonparametric density estimation methods for estimating the channel gain distribution. The estimation is first carried out through the well-known kernel density estimation (KDE) method. Since KDE is too sensitive to contaminated data, we adopt the robust kernel density estimation (RKDE) method. The analysis is performed over an ergodic resource allocation problem framework in the uplink of an orthogonal frequency division multiple access based network. In the proposed problem, the objective is to maximize the average total rate subject to total power constraint for each user. Simulation results indicate that for large enough number of nominal data and a reasonable number of outlier data, RKDE can provide a sum rate very close to the one obtained based on the actual CDI.

Femtocell networks are expected to offer significant performance improvement with low cost. However, adaptive transmission based on maximising the sum rate of macro and femto networks requires perfect channel quality information (CQI) of the macro and femto links. This assumption requires an infinite resolution feedback link, which is not always practical since it requires an excessive amount of bandwidth. This study considers the problem of maximising average sum rate under the constraint of average maximum power transmission at macro and femto users. A suboptimal iterative algorithm is developed for finding the optimal CQI quantisers as well as the discrete power and rate at macro and femto transmitter for each quantised CQI level so as to maximise the average sum rate of the system. The author's numerical results give the number of bits required to sufficiently represent the CQI to achieve almost the maximum sum rate attained using full knowledge of the CQI.

A robust Ergodic Resource Allocation (ERA) scheme is proposed in this paper in the framework of an orthogonal frequency division multiple access (OFDMA) based underlay heterogeneous network in which the allocations are made so as to maximize the average network sum-rate while guaranteeing the macro network interference requirements with any desired high probability. In previously proposed ERA schemes, both in conventional and heterogeneous networks, the optimal solution is obtained assuming that average of constraints are satisfied. In a heterogeneous network, this is translated into the fact that instantaneous level of interference on macro users can not be guaranteed, i.e., there is an uncontrolled probability of collision which is not acceptable by macro network. In this paper, we reformulate the ERA problem by replacing the average based constraints, in our case, femto total power constraint and macro interference threshold constraint, with their probabilistic counterparts so that both constraints are satisfied instantaneously with any desired high probability. We consider both cases of continuous and quantized channel state information. The proposed problems are then solved based on three methods, namely, iterative, analytical, and hybrid approaches. The optimality of the proposed methods is also assessed and the iterative approach is shown to have a performance quite close to that of the optimal solution. Simulation results confirm the effectiveness of the proposed scheme to provide a robust instantaneous imposed interference on macro network and a robust femto total transmit power. We also investigate the convergence properties of the proposed iterative approach.

In this paper, we introduce a new cooperative paradigm for secure communication in cognitive radio networks (CRNs) where secondary users (SUs) are allowed to access the spectrum of primary users (PUs) as long as they preserve the secure communication of PUs in the presence of malicious eavesdroppers. To do so, the SU transmission is divided into two hops: at first hop, the SU transmitter sends the information to a relay set and the SU receiver acts as a friendly jammer to disturb the overhearing of eavesdroppers and at the second hop, one of the relays is selected to pass the information to the SU receiver and the SU transmitter acts as a friendly jammer for the PU. In this new setup, the time duration for each hop, the power transmissions of all nodes in CRN, and relay selection at the second hop are allocated in such a way that the secrecy rate of the SU is maximized subject to the minimum required PU's secrecy rate. From primary service perspective, this transforms the possibly disturbing secondary service activities into a beneficial network element. We investigate instantaneous and ergodic resource allocation problems for perfect and imperfect channel state information (CSI). Since these problems are non-convex, we propose a solution based on decomposition of main optimization problem into three subproblems related to the power allocation, time allocation, and relay selection. We show that the power allocation problem can be transformed into a generalized geometric programming (GGP) model via the so-called scaled algorithm and it can be solved very efficiently. Simulation results indicate that in terms of the secondary secrecy rate, the proposed setup outperforms the conventional setup in which the secrecy rate of the PU is not guaranteed.

This paper investigates the ergodic sum capacity of a spectrum-shared Multiple Access Channel (MAC). We assume that the secondary service (SS) only knows the channel distribution information (CDI) between its transmitters and the primary receivers. Availability of CDI results in collision incidences at the primary receivers because of conflicting levels of intolerable interference. We introduce the concept of collision probability constraint to manage the unexpected QoS degradation of primary service in the secondary resource allocation (RA). This RA problem is inherently difficult to solve and its objective function is not necessarily convex. Two well-known approaches, called Iterative Approach (IA) and Analytical Approach (AA), each with several cases/categories, are then used to find solutions. IA solves the problem iteratively by reconstructing convex optimization problems from the original (non-convex) one in a number of iterative loops until the collision probability constraints are satisfied. IA is shown to converge quickly to a suitable solution. Furthermore, by using a control parameter, the system designer can make a tradeoff between the speed of convergence and the ergodic sum capacity. AA, on the other hand, solves the RA problem by suggesting tractable versions of collision probability constraints. Unlike IA, AA does not require extra signaling between transmitters and the base station to tune parameters, thus facilitating the implementation of SS. Our in-depth simulations have shown the proposed approach to yield lower spectral efficiency than IA.

In this paper, the downlink ergodic resource allocation (ERA) in a relay-assisted OFDMA-based cognitive network is considered with the objective of maximizing the average secondary service sum-rate. This is subject to the average total transmission power constraint and the collision probability constraint on each subcarrier at each hop of transmission to guarantee the primary quality of service with any arbitrarily high probability. In the proposed scheme, no interaction between secondary and primary networks is necessary as opposed to previously proposed frameworks. To reduce the signaling overhead between secondary base station and secondary users, which is considerably higher in relay-assisted networks compared to ordinary networks, we propose to use channel quantization. In channel quantization instead of channel gain values, the index of the fading region corresponding to that value is fed back. Due to the probabilistic nature of the collision probability constraint, the proposed ERA problem cannot be solved by conventional methods such as the dual decomposition method. Hence, we propose two novel sub-optimal solutions called Iterative Approach and Analytical Approach. Simulations results indicate the efficiency of the proposed solutions with the iterative approach slightly outperforming the analytical approach at the expense of higher complexity. We also compare continuous and quantized ERA. Simulation results demonstrate a trade-off between the volume of required feedback information and performance.

In this paper, we consider an underlay spectrum sharing system and propose utilizing discrete rate Trellis Coded Modulation Orthogonal Frequency Division Multiple Access (TCM-OFDMA) technique in the secondary system. Downlink radio resource allocation schemes are then proposed for such system. Simulation results indicate that using the proposed scheme, we can significantly increase the sum rate compared to conventional uncoded OFDMA schemes with only a moderate increase in system complexity. It is also shown that in cases where the secondary network is unable to achieve higher rates by increasing the total power and/or interference threshold, utilizing TCM-OFDMA acts as a smart alternative.

In this study, the authors formulate the optimal radio resource allocation for OFDM-based relay networks with heterogeneous delay requirements. The authors consider streaming traffic that requires a maximum guaranteed average delay and elastic traffic with flexible rate requirements. The main objective is to maximise the total transmission rate of elastic users, while average delay constraint for streaming traffic is satisfied. In the proposed formulation we also consider transmit power constraint for the base station as well as each relay. The authors then propose an algorithm based on dual approach to find the optimum subcarrier, relay assignment and power allocation. Using simulations, the authors evaluate the impact of streaming traffic on the total rate of elastic users for different number of relays and various power constraints.

Here our focus is on the downlink radio resource allocation in underlay spectrum sharing based on OFDMA technology. Both continuous and discrete rate strategies are investigated. We consider the practical case in which for the wireless channel between the secondary base station and secondary users only partial channel state information (CSI) is available at the secondary base station. We formulate the resource allocation problem in the secondary network as an optimization problem in which the objective is to maximize the secondary users weighted sum rate. Two main constraints at the secondary base station are the maximum total transmission power, and the primary service collision probabilities. The only available a priori information is the channel distribution information (CDI) for the channel between the secondary base station and the primary receivers. Since the optimal radio resource allocation is non convex we utilize dual optimization method to obtain suboptimal solutions. The computational complexity due to the constraints in the original radio resource allocation is then reduced by exploiting system specifications and substituting the original constraints with the equivalent constraints on the transmission power and rate. Simulations studies are conducted to investigate the impact of the different system parameters. We also compare the proposed algorithm with the conventional radio resource allocation and show that by the proposed algorithm we are able to enforce the collision probability constraint in the primary service which in return results in a slight decreasing in the sum rate of the secondary system. Furthermore, we show that the proposed schemes are able to keep the outage probability imposed by imperfect CSI below a given threshold.

In this study, the authors propose a cross-layer resource allocation scheme for push to talk (PTT) service over orthogonal frequency division multiple access (OFDMA)-based networks. In our modelling, the authors consider two traffic types: streaming traffic, which requires a maximum guaranteed average delay and elastic traffic with flexible rate requirements. PTT traffic is also considered as a streaming traffic with more flexible delay requirements comparing to conventional streaming traffic. The authors consider queue state information as well as channel state information in a cross-layer framework to maximise the total transmission rate of the elastic users, while average delay constraint for streaming users, and maximum transmission power constraint are satisfied. An algorithm is then proposed based on dual decomposition method to obtain the subcarrier assignment and power allocation for each user. Using simulations, the authors then evaluate the impact of the streaming PTT traffic and other system parameters on the total rate of elastic users.

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.