In this paper, we address the complex resource allocation problem in a flexible-antenna architecture, referred to as a pinching-antenna (PA) system. This system utilizes a dielectric waveguide and dynamically activated small dielectric particles to serve the multiple single-antenna users via non-orthogonal multiple access (NOMA). To address the challenges posed by dynamic operating environments that necessitate real-time reconfiguration, we develop a low-complexity framework for PA placement and power allocation optimization, as traditional iterative methods are computationally prohibitive. We first formulate the optimization problem as a mixed-integer non-linear programming problem to maximize the sum rate performance under physical constraints. Due to the inherent complexity and the need for rapid inference, a two-stage solution is introduced: a user-geometry aware initialization followed by a gradient-based refinement, achieving near-optimal performance efficiently. We then model the power allocation challenge as a max-min fairness problem via quasi-convex programming and low complexity, bisection-based algorithms that achieve globally optimal solutions using only simple scalar evaluations. To enable real-time application, we employ a convolutional neural network (CNN)-based learning framework to capture the highly complex, non-linear mapping between instantaneous channel conditions and optimal power coefficients. This work is motivated by the need to achieve near-optimal performance with significantly lower inference complexity than conventional optimization-based methods. By leveraging the generalization capability of CNNs, the proposed approach enables fast inference and can predict near-optimal power allocations for unseen network configurations without retraining. Simulation results demonstrate that this integrated CNN-based NOMA approach for PA systems delivers enhanced performance in terms of sum rate and user fairness.

To mitigate the significant degradation of Ultra-Wideband (UWB) positioning accuracy in non-line-of-sight (NLoS) scenarios, this paper proposes an integrated framework that synergizes Channel Impulse Response (CIR) fingerprinting with Time of Arrival (ToA) ranging. Unlike conventional methods that treat fingerprinting and ranging independently, we introduce a novel uncertainty-aware fusion strategy. Specifically, an attention-enhanced Convolutional Neural Network (CNN) is first employed to extract environmental features from CIR data. Crucially, instead of direct coordinate regression, we utilize a Copula-based conformal prediction(CP) method to quantify the positioning uncertainty and construct a reliable spatial search constraint. This constraint then guides a Particle Swarm Optimization (PSO) algorithm to robustly identify the optimal position within the predicted region when ToA ranging errors are modeled by a Gaussian Mixture Model (GMM). This mechanism effectively filters out large outliers by leveraging the complementary strengths of feature matching and geometric ranging. Experimental results on public datasets demonstrate that the proposed method achieves a mean error of 0.0911 meters, outperforming six state-of-the-art algorithms by at least 8.35% while satisfying high computational efficiency requirements.

This paper explores the vulnerability of downlink cell-free massive MIMO systems to passive and active eaves-dropping, focusing on a 5G New Radio framework. To enhance the security of downlink transmissions over the Physical Downlink Shared Channel (PDSCH) against eavesdropping threats, we propose two novel methods based on cooperative artificial noise (AN). The first approach, called cooperative artificial noise (CAN), involves all access points (APs) broadcasting AN in the null space of the users' channel matrix to confuse potential eavesdroppers. The second approach, named partial artificial noise (PAN), divides the APs into two groups: one group cooperatively transmits AN, while the other group serves the legitimate users. Additionally, we implement three different precoding schemes for legitimate users: maximum ratio transmission, zero-forcing, and minimum mean square error. We conduct link-level simulations of wiretap channels under various frequency-selective fading scenarios and noise conditions, using tapped delay line channel models as defined by the 3GPP TR 38.901 standard. The system's security performance is evaluated by analyzing the block error rate of legitimate users and the block success rate of eavesdroppers. Despite the limitation of having only one antenna per access point, our findings demonstrate that AN can be strategically designed through the cooperation of APs. By designing appropriate groups of APs specifically for generating AN, our second approach, PAN, significantly reduces the block successive rate of eavesdroppers, lowering it from 0.2 without AN to 0.1 with CAN and further down to 0.025 with PAN.

While Wi-Fi Received Signal Strength Indicator (RSSI) fingerprinting has emerged as a prominent solution for indoor positioning, its accuracy remains hindered by labor-intensive data collection and environmental variability. To overcome these challenges, we propose a novel LCVAE-CNN methodology that integrates a Location-Conditioned Variational Autoencoder (LCVAE) and a multi-task Convolutional Neural Network (CNN) to enhance data quality and positioning performance. The LCVAE employs a dual-encoder architecture to augment RSSI fingerprints by jointly modeling signal features and spatial dependencies, introducing three key innovations: (1) dual-stream encoding that decouples RSSI and location processing for more effective feature learning, (2) a geospatial loss function that enforces topological consistency in the generated data, and (3) conditional data augmentation that preserves physical constraints of indoor spaces. The multi-task CNN then leverages shared feature extraction to jointly optimize classification and regression tasks, enabling efficient and accurate positioning. Extensive evaluations on the UJIIndoorLoc and Tampere datasets demonstrate the superiority of the LCVAE-CNN that achieves 98.80% floor classification accuracy with a Mean Positioning Error (MPE) of 6.79 meters on UJIIndoorLoc, whereas 97.22% accuracy with a MPE of 5.44 meters on the Tampere dataset. Compared to five state-of-the-art methods, it improves floor accuracy by at least 1.9% and reduces MPE by over 19%, while maintaining comparable computational overhead, thereby achieving superior accuracy-efficiency tradeoffs.

We investigate the problem of maximizing anti-jamming sum throughput in an RIS-assisted Internet of Things (IoT) network. The network’s operation is divided into two stages: 1) IoT terminals first harvest energy from the wireless energy station (WES) and 2) they then transmit their information to the information receiver (IR) using a frequency division multiple access (FDMA) protocol. We consider three different design scenarios: 1) ideal hardware; 2) phase shift error (PSE); and 3) a combination of both PSE and transceiver hardware impairments (THIs). To address the nonconvexities of these designs, we employ novel techniques, such as the Lagrangian dual method, Karush–Kuhn–Tucker (KKT) conditions, quadratic transformation (QT), element-wise block coordinate descent (EBCD), complex circle manifold (CCM), and 1-D search to obtain the optimal solutions. Numerical results are provided to illustrate that the proposed approaches outperform existing benchmarks.

This article focuses on maximizing the anti-jamming sum throughput in a time division multiple access (TDMA)-based reconfigurable intelligent surfaces (RIS)-assisted wireless powered Internet of Things (WP-IoT) network. In this setup, multiple IoT devices harvest energy from wireless energy stations (WES) and then utilize the collected energy to upload their own data to an information receiver (IR). The network also includes a jammer that sends jamming signals to the IR, and a RIS is deployed to mitigate this jamming effect and enhance the sum throughput. This study addresses both an upper bound design and a robust design with fractional nonlinear energy harvesting model. The primary optimization goal is to maximize the anti-jamming sum throughput, with the constraints of RIS phase shifts and time scheduling. For both designs, closed-form expressions for time scheduling are derived using the Lagrangian duality and Karush-Kuhn-Tucker (KKT) conditions. The quadratic transformation (QT) technique is used to handle fractional functions within the optimization. Furthermore, the phase shifts are optimized iteratively using the element-wise block coordinate descent (EBCD) and Riemannian manifold optimization (RMO) algorithms. Simulation results are presented to validate the effectiveness of the proposed approaches.

Recently, the multiple-input–multiple-output (MIMO) nonorthogonal multiple-access (NOMA) transmission, denoted as MIMO- NOMA, has been widely applied for Internet of Things (IoT) systems due to its spectral efficiency. As a promising precoding method, the generalized singular value decomposition (GSVD)-based precoding scheme has been studied in MIMO- NOMA. In this study, we apply the GSVD-based precoding scheme to a downlink MIMO- NOMA communication system with an amplify-and-forward (AF) relay and two IoT users. A closed-form expression of the probability density function (PDF) of two channel matrices’ generalized singular values (GSVs) is obtained in order to facilitate the performance analysis for cooperative MIMO- NOMA transmission. In particular, by this distribution characteristic result, the users’ rates and outage probabilities achieved by MIMO- NOMA are studied for the insightful performance evaluation. In addition, the asymptotic approximations for outage probabilities at the high signal-to-noise-ratio (SNR) condition are presented. In addition to characterize the performance achieved by cooperative MIMO- NOMA, resource allocation for the addressed NOMA system is also investigated in this article, where a suboptimal power allocation algorithm to maximize the sum rate is given. The solution obtained by the proposed algorithm is studied, where the optimality condition of the solution is obtained. Finally, simulation results are presented to show the superiority of the scheme and verify these analytical results.

In recent years, the multiple-input multiple-output (MIMO) non-orthogonal multiple-access (NOMA) systems have attracted a significant interest in the relevant research communities. As a potential precoding scheme, the generalized singular value decomposition (GSVD) can be adopted in MIMO-NOMA systems and has been proved to have a good trade-off for complexity and performance. In this paper, the performance of the GSVD-based MIMO-NOMA communications with Rician fading is studied. In particular, the distribution characteristics of generalized singular values (GSVs) of channel matrices are analyzed. Two novel mathematical tools, the linearization trick and the deterministic equivalent method, which are based on operator-valued free probability theory, are exploited to derive the Cauchy transform of GSVs. An iterative process is proposed to obtain the numerical values of the Cauchy transform of GSVs, which can be exploited to derive the average data rates of the communication system. In addition, the special case when the channel is modeled as Rayleigh fading, i.e., the line-of-sight propagation is trivial, is analyzed. In this case, the closed-form expressions of average rates are derived from the proposed iterative process. Simulation results are provided to validate the derived analytical results.

This paper investigates a dual-unmanned aerial vehicle (UAV)-enabled secure communication system, in which, a UAV moves around to send confidential messages to a mobile user while another cooperative UAV transmits artificial noise signals to confuse malicious eavesdroppers. Both UAVs have energy constraints and the location information of eavesdroppers is imperfect. We consider a worst-case secrecy rate maximization problem of the mobile user over all time slots. This optimization problem is solved by jointly designing the three-dimensional (3D) trajectory of UAVs and the time allocation (recharging and service or jamming) under practical constraints including maximum UAV speed, UAV collision avoidance, UAV positioning error, and UAV energy harvesting. Specifically, we adopt a more practical UAV-ground channel model with both large-scale and small-scale fading components. Due to the non-convex feasible region constructed by the complicated constraints, directly finding the optimal solution of the original problem is intractable. To address this issue, we decouple the original optimization problem into three subproblems and develop an iterative algorithm to find its suboptimal solution by using the block coordinate descent technique. To solve each subproblem, certain advanced optimization tools, such as integer relaxation, S-procedure, and successive convex approximation techniques, are utilized. Numerical simulation results are provided to corroborate the theoretical derivations and to evaluate the performance of the proposed algorithm. Additionally, the numerical results assist to draw new insights on the 3D UAV trajectory by comparing the performance with conventional two-dimensional (2D) schemes.

This paper investigates the performance of limited-fronthaul cell-free massive multiple-input multiple-output (MIMO) taking account the fronthaul quantization and imperfect channel acquisition. Three cases are studied, which we refer to as Estimate & Quantize, Quantize & Estimate, and Decentralized, according to where channel estimation is performed and exploited. Maximum-ratio combining (MRC), zero-forcing (ZF), and minimum mean-square error (MMSE) receivers are considered. The Max algorithm and the Bussgang decomposition are exploited to model optimum uniform quantization. Exploiting the optimal step size of the quantizer, analytical expressions for spectral and energy efficiencies are presented. Finally, an access point (AP) assignment algorithm is proposed to improve the performance of the decentralized scheme. Numerical results investigate the performance gap between limited fronthaul and perfect fronthaul cases, and demonstrate that exploiting relatively few quantization bits, the performance of limited-fronthaul cell-free massive MIMO closely approaches the perfect-fronthaul performance.

The uplink of a reconfigurable intelligent surfaces (RIS)-aided cell-free massive multiple-input multiple-output (MIMO) system is analyzed, where the channel state information (CSI) is estimated using uplink pilots. First, we derive analytical expressions for the achievable rate of the system with zero forcing (ZF) receiver, taking into account the effects of pilot contamination, channel estimation error and the distributed RISs. The max-min rate optimization problem is considered with per-user power constraints. To solve this non-convex problem, we propose to decouple the original optimization problem into two sub-problems, namely, phase shift design problem and power allocation problem. The power allocation problem is solved using a standard geometric programming (GP) whereas a semidefinite programming (SDP) is utilized to design the phase shifts. Moreover, the Taylor series approximation is used to convert the nonconvex constraints into a convex form. An iterative algorithm is proposed whereby at each iteration, one of the sub-problems is solved while the other design variable is fixed. The max-min user rate of the RIS-aided cell-free massive MIMO system is compared to that of conventional cell-free massive MIMO. Numerical results indicate the superiority of the proposed algorithm compared with a conventional cell-free massive MIMO system. Finally, the convergence of the proposed algorithm is investigated.

We consider a cell-free massive multiple-input multiple-output (MIMO) system where the channel estimates and the received signals are quantized at the access points (APs) and forwarded to a central processing unit (CPU). Zero-forcing technique is used at the CPU to detect the signals transmitted from all users. To solve the non-convex sum rate maximization problem, a heuristic sub-optimal scheme is proposed to convert the problem into a geometric programme (GP). Exploiting a deep convolutional neural network (DCNN) allows us to determine both a mapping from the large-scale fading (LSF) coefficients and the optimal power by solving the optimization problem using the quantized channel. Depending on how the optimization problem is solved, different power control schemes are investigated; i) small-scale fading (SSF)-based power control; ii) LSF use-and-then-forget (UatF)-based power control; and iii) LSF deep learning (DL)-based power control. The SSF-based power control scheme needs to be solved for each coherence interval of the SSF, which is practically impossible in real time systems. Numerical results reveal that the proposed LSF-DL-based scheme significantly increases the performance compared to the practical and well-known LSF-UatF-based power control.

This paper investigates a secrecy energy efficiency (SEE) optimization problem for a multiple-input multiple-output non-orthogonal multiple access network. In particular, a multi-antenna transmitter intends to send two integrated service messages: a confidential message for the stronger user and a broadcast message for both stronger and weaker users. It is assumed that both users are equipped with multi-antennas. In this secure wireless network, we consider the transmit covariance matrices design of confidential and broadcast message, under broadcast energy efficiency (BEE) constraint. In addition, it is assumed that the weaker user might turn out to be a potential eavesdropper due to the broadcast nature of wireless transmission. We formulate this transmit covariance matrices design as an SEE maximization problem which is non-convex in its original form due the non-linear fractional objective function and constraints. To realize the solution for this problem, we utilize non-linear fractional programming and difference of concave (DC) functions approach which facilitate to reformulate it into a tractable form. Based on the Dinkelbach's algorithm and DC approximation method, we propose iterative algorithms to determine a solution to the original SEE maximization problem. Numerical results are provided to demonstrate the performance of the proposed transmit covariance matrices design to maximize the SEE.

Caching is a promising solution for the cloud radio access network (Cloud‐RAN) to mitigate the traffic load problem in the fronthaul links. Multiuser downlink beamforming plays an important role in efficient utilisation of spectrum and transmission power while satisfying the user's quality of service requirements. When the number of users exceeds the serving capacity of the network, certain users will have to be dropped or rescheduled. This is normally achieved by appropriate admission control mechanisms. Introducing local storage or cache at the remote radio heads where some popular contents are cached, the authors propose beamforming and admission control techniques for cache‐enabled Cloud‐RAN in the downlink. This minimises the total network cost including power and fronthaul cost while admitting as many users as possible. They formulate this multi‐objective optimisation problem as a single objective optimisation problem. The original problem, which is a mixed‐integer non‐linear programme, is first converted to the mixed‐integer second‐order cone programming form. The branch and bound algorithm is then used to determine the optimal and suboptimal solutions. A simulation study has been conducted to assess the performance of both methods.

A cell-free massive multiple-input multiple-output (MIMO) uplink is considered, where quantize-and-forward (QF) refers to the case where both the channel estimates and the received signals are quantized at the access points (APs) and forwarded to a central processing unit (CPU) whereas in combine-quantize-and-forward (CQF), the APs send the quantized version of the combined signal to the CPU. To solve the non-convex sum rate maximization problem, a heuristic sub-optimal scheme is exploited to convert the power allocation problem into a standard geometric programme (GP). We exploit the knowledge of the channel statistics to design the power elements. Employing large-scale-fading (LSF) with a deep convolutional neural network (DCNN) enables us to determine a mapping from the LSF coefficients and the optimal power through solving the sum rate maximization problem using the quantized channel. Four possible power control schemes are studied, which we refer to as i) small-scale fading (SSF)-based QF; ii) LSF-based CQF; iii) LSF use-and-then-forget (UatF)-based QF; and iv) LSF deep learning (DL)-based QF, according to where channel estimation is performed and exploited and how the optimization problem is solved. Numerical results show that for the same fronthaul rate, the throughput significantly increases thanks to the mapping obtained using DCNN.

The downlink (DL) of a non-orthogonal-multiple-access (NOMA)-based cell-free massive multiple-input multiple-output (MIMO) system is analyzed, where the channel state information (CSI) is estimated using pilots. It is assumed that the users are grouped into multiple clusters. The same pilot sequences are assigned to the users within the same clusters whereas the pilots allocated to all clusters are mutually orthogonal. First, a user's bandwidth efficiency (BE) is derived based on his/her channel statistics under the assumption of employing successive interference cancellation (SIC) at the users' end with no DL training. Next, the classic max-min optimization framework is invoked for maximizing the minimum BE of a user under per-access point (AP) power constraints. The max-min user BE of NOMA-based cell-free massive MIMO is compared to that of its orthogonal multiple-access (OMA) counter part, where all users employ orthogonal pilots. Finally, our numerical results are presented and an operating mode switching scheme is proposed based on the average per-user BE of the system, where the mode set is given by Mode = { OMA, NOMA }. Our numerical results confirm that the switching point between the NOMA and OMA modes depends both on the length of the channel's coherence time and on the total number of users.

In this paper, we consider an energy-constrained unmanned aerial vehicle (UAV)-enabled mobile relay assisted secure communication system in the presence of a legitimate source-destination pair and multiple eavesdroppers with imperfect locations. The energy-constrained UAV employs the power splitting (PS) scheme to simultaneously receive information and harvest energy from the source, and then exploits the time switching (TS) protocol to perform information relaying. Furthermore, we consider a full-duplex destination node which can simultaneously receive confidential signals from the UAV and cooperatively transmit artificial noise (AN) signals to confuse malicious eavesdroppers. To further enhance the reliability and security of this system, we formulate a worst case secrecy rate maximization problem, which jointly optimizes the position of the UAV, the AN transmit power, as well as the PS and TS ratios. The formulated problem is non-convex and generally intractable. In order to circumvent the non-convexity, we decouple the original optimization problem into three subproblems; this facilitates the design of a suboptimal iterative algorithm. In each iteration, we propose a multi-dimensional search and numerical method to handle the subproblem. Numerical simulation results are provided to demonstrate the effectiveness and superior performance of the proposed joint design versus the conventional schemes in the literature.

Mobile edge caching (MEC) and device-todevice (D2D) communications are two potential technologies to resolve traffic overload problems in the Internet of Things. Previous works usually investigate them separately with MEC for traffic offloading and D2D for information transmission. In this article, a joint framework consisting of MEC and cache-enabled D2D communications is proposed to minimize the energy cost of systematic traffic transmission, where file popularity and user preference are the critical criteria for small base stations (SBSs) and user devices, respectively. Under this framework, we propose a novel caching strategy, where the Markov decision process is applied to model the requesting behaviors. A novel scheme based on reinforcement learning (RL) is proposed to reveal the popularity of files as well as users' preference. In particular, a Q-learning algorithm and a deep Q-network algorithm are, respectively, applied to user devices and the SBS due to different complexities of status. To save the energy cost of systematic traffic transmission, users acquire partial traffic through D2D communications based on the cached contents and user distribution. Taking the memory limits, D2D available files, and status changing into consideration, the proposed RL algorithm enables user devices and the SBS to prefetch the optimal files while learning, which can reduce the energy cost significantly. Simulation results demonstrate the superior energy saving performance of the proposed RL-based algorithm over other existing methods under various conditions.

Energy efficiency (EE) and spectral efficiency (SE) are two of the key performance metrics in future wireless networks, covering both design and operational requirements. For previous conventional resource allocation techniques, these two performance metrics have been considered in isolation, resulting in severe performance degradation in either of these metrics. Motivated by this problem, in this paper, we propose a novel beamforming design that jointly considers the trade-off between the two performance metrics in a multiple-input single-output non-orthogonal multiple access system. In particular, we formulate a joint SE-EE based design as a multi-objective optimization (MOO) problem to achieve a good trade-off between the two performance metrics. However, this MOO problem is not mathematically tractable and, thus, it is difficult to determine a feasible solution due to the conflicting objectives, where both need to be simultaneously optimized. To overcome this issue, we exploit a priori articulation scheme combined with the weighted sum approach. Using this, we reformulate the original MOO problem as a conventional single objective optimization (SOO) problem. In doing so, we develop an iterative algorithm to solve this non-convex SOO problem using the sequential convex approximation technique. Simulation results are provided to demonstrate the advantages and effectiveness of the proposed approach over the available beamforming designs.

In this paper, we investigate energy harvesting capabilities of non-orthogonal multiple access (NOMA) scheme integrated with the conventional time division multiple access (TDMA) scheme, which is referred to as hybrid TDMA-NOMA system. In a such hybrid scheme, users are divided into a number of groups, with the total time allocated for transmission is shared between these groups through multiple time slots. In particular, a time slot is assigned to serve each group, whereas the users in the corresponding group are served based on power-domain NOMA technique. Furthermore, simultaneous wireless power and information transfer technique is utilized to simultaneously harvest energy and decode information at each user. Therefore, each user splits the received signal into two parts, namely, energy harvesting part and information decoding part. In particular, we jointly determine the power allocation and power splitting ratios for all users to minimize the transmit power under minimum rate and minimum energy harvesting requirements at each user. Furthermore, this joint design is a non-convex problem in nature. Hence, we employ successive interference cancellation to overcome these non- convexity issues and determine the design parameters (i.e., the power allocations and the power splitting ratios). In simulation results, we demonstrate the performance of the proposed hybrid TDMA-NOMA design and show that it outperforms the conventional TDMA scheme in terms of transmit power consumption.

Mobile edge caching (MEC) and device to device (D2D) communications are two potential technologies to resolve traffic overload in heterogeneous networks. Prior works usually investigate them separately with MEC for traffic offloading and D2D for information transmission. In this paper, a composite framework consists of MEC and cache-enabled D2D communications is proposed to minimize the energy cost of systematic traffic transmission, where file popularity and user preference are the critical criteria for small base stations (SBSs) and users respectively. Under this framework, we propose a novel caching strategy where Markov decision process (MDP) is applied to model the requesting behaviors of users. A new algorithm based on reinforcement learning (RL) is proposed to reveal the popularity of files as well as users' preference. In particular, Q-learning (QL) algorithm and deep Q- network (DQN) algorithm are respectively applied to users and SBS due to different complexity of status. To save the energy cost of systematic traffic transmission, users acquire partial traffic through D2D communications based on the cached contents. Taking the memory limits, D2D available files and status changing into consideration, the proposed RL algorithm enables users' devices and SBS to prefetch the optimal files while learning, and hence reducing the energy cost significantly. Simulation results demonstrate the superior energy saving performance of the proposed RL-based algorithm over other existing methods under various conditions.

In this paper, we study a joint energy harvesting (EH) and task offloading (TO) design for an energy- constrained fog computing network, which consists of a mobile terminal and two fog nodes. The energy- constrained terminal employs time switching (TS) protocol to harvest energy from the signals sent by the circuit-powered fog node, and then exploits the harvested energy to perform local computing and offload computing. Our aim is to minimize the product of energy consumption and time delay with the constraints of TS ratio and EH requirements. To determine the optimal solution of the original non-convex problem, we decouple it into three subproblems based on the time delay assumption and then solve these subproblems through our proposed constraints activation algorithm. Furthermore, we also derive the closed form expressions for the optimal TO and TS ratios. Simulation results are presented to illustrate the effectiveness and superior performance of the proposed joint design against other conventional schemes in the literature.

In this paper, non-orthogonal-multiple-access (NOMA)-based cell-free massive multiple-input multiple-output (MIMO) is investigated, where the users are grouped into multiple clusters. Exploiting conjugate beamforming, the bandwidth efficiency (BE) of the system is derived while the assumption that the users performing realistic successive interference cancellation (SIC) based on only the knowledge of channel statistics. The max-min fairness problem of maximizing the lowest user BE is investigated and an iterative bisection method is developed to determine the optimal solution to the max-min BE problem. Numerical results are presented for validating the proposed design's performance, and a mode switching scheme is conceived for selecting a specific Mode = {OMA, NOMA} that maximizes the system's BE.

We investigate the energy efficiency performance of cell-free Massive multiple-input multiple-output (MIMO), where the access points (APs) are connected to a central processing unit (CPU) via limited-capacity links. Thanks to the distributed maximum ratio combining (MRC) weighting at the APs, we propose that only the quantized version of the weighted signals are sent back to the CPU. Considering the effects of channel estimation errors and using the Bussgang theorem to model the quantization errors, an energy efficiency maximization problem is formulated with per-user power and backhaul capacity constraints as well as with throughput requirement constraints. To handle this non-convex optimization problem, we decompose the original problem into two sub-problems and exploit a successive convex approximation (SCA) to solve original energy efficiency maximization problem. Numerical results confirm the superiority of the proposed optimization scheme.

IEEE 802.11ah is a promising wireless standard proposed to support the emerging machine-to- machine (M2M) communications. To solve the bottleneck problem of contention-based protocol, 802.11ah limits the number of channel access from the high number of connected M2M nodes by partitioning them into multiple groups with respective restricted access window (RAW). In this paper, we model the 802.11ah as an evolutionary game to analyse the network throughput and its dynamic decision-making process. In particular, the evolution dynamic of backoff misbehaviour by selfish nodes and its impact is studied through simulation of strategy revision process of the player after comparing the projected payoff of each strategy. The obtained results show that selfish strategy can cause network performance degradation by more than 10 percent although being the Evolutionary Stable Strategy (ESS) of the game. Moreover, it is observed that the selfish player has a massive advantage over the honest player in terms of throughput by more than three times. With the help of our game model, the population evolution is simulated, and the result demonstrates the increase of selfish player proportion over each strategy revision opportunity which poses a severe concern for resource availability of the honest player.

In this paper, we propose two beamforming designs for a multiple-input single-output non-orthogonal multiple access system considering the energy efficiency (EE) fairness between users. In particular, two quantitative fairness-based designs are developed to maintain fairness between the users in terms of achieved EE: max-min energy efficiency (MMEE) and proportional fairness (PF) designs. While the MMEE-based design aims to maximize the minimum EE of the users in the system, the PF-based design aims to seek a good balance between the global energy efficiency of the system and the EE fairness between the users. Detailed simulation results indicate that our proposed designs offer many-fold EE improvements over the existing energy-efficient beamforming designs.

In this paper, we propose a beamforming design that jointly considers two conflicting performance metrics, namely the sum rate and fairness, for a multiple-input single-output non-orthogonal multiple access system. Unlike the conventional rate-aware beamforming designs, the proposed approach has the flexibility to assign different weights to the objectives (i.e., sum rate and fairness) according to the network requirements and the channel conditions. In particular, the proposed design is first formulated as a multi-objective optimization problem, and subsequently mapped to a single objective optimization (SOO) problem by exploiting the weighted sum approach combined with a prior articulation method. As the resulting SOO problem is non-convex, we use the sequential convex approximation technique, which introduces multiple slack variables, to solve the overall problem. Simulation results are provided to demonstrate the performance and the effectiveness of the proposed approach along with detailed comparisons with conventional rate-aware-based beamforming designs.

When considering the future generation wireless networks, non-orthogonal multiple access (NOMA) represents a viable multiple access technique for improving the spectral efficiency. The basic performance of the NOMA is often enhanced using downlink beamforming and power allocation techniques. Although downlink beamforming has been previously studied with different performance criteria, such as sum-rate and max-min rate, it has not been studied in the multiuser, multiple-input single-output (MISO) case, particularly with the energy efficiency criteria. In this paper, we investigate the design of an energy efficient beamforming technique for downlink transmission in the context of a multiuser MISO-NOMA system. In particular, this beamforming design is formulated as a global energy efficiency (GEE) maximization problem with minimum user rate requirements and transmit power constraints. By using the sequential convex approximation technique and the Dinkelbach's algorithm to handle the non-convex nature of the GEE-Max problem, we propose two novel algorithms for solving the downlink beamforming problem for the MISO-NOMA system. Our evaluation of the proposed algorithms shows that they offer similar optimal designs and are effective in offering substantial energy efficiencies compared with the designs based on conventional methods.

Non-orthogonal multiple access (NOMA) has been envisioned as a promising multiple access technique for 5G and beyond wireless networks due to its significant enhancement of spectral efficiency. In this paper, we investigate a robust energy efficiency design for multi-user multiple-input single-output (MISO) NOMA systems, where the imperfect channel state information is available at the base station (BS). A clustering algorithm is applied to group the users into different clusters, and then, the NOMA technique is employed to share the available resources fairly among the users in each cluster. To remove the interference between clusters, two different types of zero-forcing (ZF) designs, namely, hybrid-ZF and full-ZF, are employed at the BS. The full-ZF scheme completely removes the interference leakage at the cost of more number of antennas, and the hybrid-ZF scheme partially mitigates the interference leakage. To solve the problem, Dinkelbach’s algorithm is employed to convert the non-linear fractional programming problem into a simple subtractive form. Finally, simulation results reveal that hybrid-ZF outperforms the full-ZF scheme with a few clusters, while full-ZF shows a better performance with higher number of clusters. Numerical results confirm that our proposed robust scheme outperforms the non-robust scheme in terms of the rate-satisfaction ratio at each user.

Cell-free massive multiple-input-multiple-output (MIMO) is considered, where distributed access points (APs) multiply the received signal by the conjugate of the estimated channel, and send back a quantized version of this weighted signal to a central processing unit (CPU). For the first time, we present a performance comparison between the case of perfect fronthaul links, the case when the quantized version of the estimated channel and the quantized signal are available at the CPU, and the case when only the quantized weighted signal is available at the CPU. The Bussgang decomposition is used to model the effect of quantization. The max-min problem is studied, where the minimum rate is maximized with the power and fronthaul capacity constraints. To deal with the non-convex problem, the original problem is decomposed into two sub-problems (referred to as receiver filter design and power allocation). Geometric programming (GP) is exploited to solve the power allocation problem whereas a generalized eigenvalue problem is solved to design the receiver filter. An iterative scheme is developed and the optimality of the proposed algorithm is proved through uplink-downlink duality. A user assignment algorithm is proposed which significantly improves the performance. The numerical results demonstrate the superiority of the proposed schemes.

A cell-free massive multiple-input multiple-output system is considered using a max-min approach to maximize the minimum user rate with per-user power constraints. First, an approximated uplink user rate is derived based on channel statistics. Then, the original max-min signal-to-interference-plus-noise ratio problem is formulated for the optimization of receiver filter coefficients at a central processing unit and user power allocation. To solve this max-min non-convex problem, we decouple the original problem into two sub-problems, namely, receiver filter coefficient design and power allocation. The receiver filter coefficient design is formulated as a generalized Eigenvalue problem, whereas the geometric programming (GP) is used to solve the user power allocation problem. Based on these two sub-problems, an iterative algorithm is proposed, in which both problems are alternately solved while one of the design variables is fixed. This iterative algorithm obtains a globally optimum solution, whose optimality is proved through establishing an uplink-downlink duality. Moreover, we present a novel sub-optimal scheme which provides a GP formulation to efficiently and globally maximize the minimum uplink user rate. The numerical results demonstrate that the proposed scheme substantially outperforms the existing schemes in the literature.

In this paper, we consider the max-min signal-to- interference plus noise ratio (SINR) problem for the uplink transmission of a cell-free Massive multiple-input multiple-output (MIMO) system. Assuming that the central processing unit (CPU) and the users exploit only the knowledge of the channel statistics, we first derive a closed-form expression for uplink rate. In particular, we enhance (or maximize) user fairness by solving the max-min optimization problem for user rate, by power allocation and choice of receiver coefficients, where the minimum uplink rate of the users is maximized with available transmit power at the particular user. Based on the derived closed-form expression for the uplink rate, we formulate the original user max-min problem to design the optimal receiver coefficients and user power allocations. However, this max-min SINR problem is not jointly convex in terms of design variables and therefore we decompose this original problem into two sub- problems, namely, receiver coefficient design and user power allocation. By iteratively solving these sub-problems, we develop an iterative algorithm to obtain the optimal receiver coefficient and user power allocations. In particular, the receiver coefficients design for a fixed user power allocation is formulated as generalized eigenvalue problem whereas a geometric programming (GP) approach is utilized to solve the power allocation problem for a given set of receiver coefficients. Numerical results confirm a three-fold increase in system rate over existing schemes in the literature.

We consider a cell-free Massive multiple-input multiple-output (MIMO) system and investigate the system performance for the case when the quantized version of the estimated channel and the quantized received signal are available at the central processing unit (CPU), and the case when only the quantized version of the combined signal with maximum ratio combining (MRC) detector is available at the CPU. Next, we study the max-min optimization problem, where the minimum user uplink rate is maximized with backhaul capacity constraints. To deal with the max-min non-convex problem, we propose to decompose the original problem into two sub-problems. Based on these sub- problems, we develop an iterative scheme which solves the original max-min user uplink rate. Moreover, we present a user assignment algorithm to further improve the performance of cell-free Massive MIMO with limited backhaul links.

This paper proposes a low-complexity hybrid beamforming design for multi-antenna communication systems. The hybrid beamformer is comprised of a baseband digital beamformer and a constant modulus analog beamformer in the radio frequency (RF) part of the system. As in singular-value-decomposition (SVD)-based beamforming, hybrid beamforming design aims to generate parallel data streams in multi-antenna systems, however, due to the constant modulus constraint of the analog beamformer, the problem cannot be solved similarly. To address this problem, mathematical expressions of the parallel data streams are derived in this paper and desired and interfering signals are specified per stream. The analog beamformers are designed by maximizing the power of desired signal while minimizing the sum-power of interfering signals. Finally, digital beamformers are derived by defining the equivalent channel observed by the transmitter/receiver. Regardless of the number of the antennas or type of channel, the proposed approach can be applied to a wide range of MIMO systems with hybrid structure wherein the number of the antennas is more than the number of the RF chains. In particular, the proposed algorithm is verified for sparse channels that emulate mm-wave transmission as well as rich scattering environments. In order to validate the optimality, the results are compared with those of the state-of-the-art and it is demonstrated that the performance of the proposed method outperforms state-of-the-art techniques, regardless of type of the channel and/or system configuration.

Physical layer security (PLS) has been shown to hold promise as a new paradigm for securing wireless links. In contrast with the conventional cryptographic techniques, PLS methods exploit the random fading in wireless channels to provide link security. As the channel dynamics prevent a constant rate of secure communications between the legitimate terminals, the outage probability of the achievable secrecy rate is used as a measure of the secrecy performance. This work investigates the secrecy outage probability of a simultaneous wireless information and power transfer (SWIPT) system, which operates in the presence of multiple eavesdroppers that also have the energy harvesting capability. The loss in secrecy performance due to eavesdropper collusion, i.e., information sharing between the eavesdroppers to decode the secret message, is also analyzed. We derive closed‐form expressions for the secrecy outage probability for Nakagami‐m fading on the links and imperfect channel estimation at the receivers. Our analysis considers different combinations of the separated and the integrated SWIPT receiver architectures at the receivers. Numerical results are provided to validate our analysis.

In this paper, the optimal power allocation scheme is investigated for sum rate maximization of non- orthogonal multiple access (NOMA) with adaptive rates and α-fairness. Compared to the existing fairness NOMA models, α-fairness can only utilize a single scalar to achieve different user fairness levels. Specifically, the power allocation problem is first formulated to maximize the instantaneous sum rate with α-fairness, where user rates are adapted according to the instantaneous channel state information (CSI). Then, a simple alternate algorithm is proposed to solve the Karush-Kuhn Tucker (KKT) conditions of the formulated power allocation problem. Analytical results demonstrate that the proposed algorithm converges and yields the optimal solution. Numerical results reveal that, at the same fairness level, NOMA significantly outperforms the conventional orthogonal multiple access (MA).

Recently, non-orthogonal multiple access (NOMA) has received considerable attention as a promising candidate for 5G systems. In this paper, a robust beamforming approach is investigated for NOMA based multiple-input single-output (MISO) downlink transmission. We consider an outage probability based robust scheme by incorporating channel uncertainties, where the total transmit power is minimized while satisfying these outage constraints at each user. Although the original problem is non-convex in terms of beamforming vectors, an intractable optimization problem is reformulated with a linear matrix inequality (LMI) form by exploiting semidefinite relaxation (SDR) technique. Finally, simulation results have been provided to validate the performance of the proposed robust design, where these results confirm that the robust scheme outperforms the non-robust scheme in terms of the rate satisfaction ratio at each user.

This paper investigates the optimal power allocation scheme for sum throughput maximization of non-orthogonal multiple access (NOMA) system with α-fairness. In contrast to the existing fairness NOMA models, α-fairness can only utilize a single scalar to achieve different user fairness levels. Two different channel state information at the transmitter (CSIT) assumptions are considered, namely, statistical and perfect CSIT. For statistical CSIT, fixed target data rates are predefined, and the power allocation problem is solved for sum throughput maximization with α-fairness, through characterizing several properties of the optimal power allocation solution. For perfect CSIT, the optimal power allocation is determined to maximize the instantaneous sum rate with α-fairness, where user rates are adapted according to the instantaneous channel state information (CSI). In particular, a simple alternate optimization algorithm is proposed, which is demonstrated to yield the optimal solution. Numerical results reveal that, at the same fairness level, NOMA significantly outperforms the conventional orthogonal multiple access for both the scenarios with statistical and perfect CSIT.

This paper investigates group secret key generation problems for different types of wireless networks, by exploiting physical layer characteristics of wireless channels. A new group key generation strategy with low complexity is proposed, which combines the well-established point-to-point pairwise key generation technique, the multisegment scheme, and the one-time pad. In particular, this group key generation process is studied for three types of communication networks: 1) the three-node network; 2) the multinode ring network; and 3) the multinode mesh network. Three group key generation algorithms are developed for these communication networks, respectively. The analysis shows that the first two algorithms yield optimal group key rates, whereas the third algorithm achieves the optimal multiplexing gain. Next, for the first two types of networks, we address the time allocation problem in the channel estimation step to maximize the group key rates. This non-convex max-min time allocation problem is first reformulated into a series of geometric programming, and then, a single-condensation-method-based iterative algorithm is proposed. Numerical results are also provided to validate the performance of the proposed key generation algorithms and the time allocation algorithm.

In the present study, the authors investigate robust secrecy rate optimisation problems for a multiple‐input‐single‐output secrecy channel with multiple device‐to‐device (D2D) communications. The D2D communication nodes access this secrecy channel by sharing the same spectrum, and help to improve the secrecy communications by confusing the eavesdroppers. In return, the legitimate transmitter ensures that the D2D communication nodes achieve their required rates. In addition, it is assumed that the legitimate transmitter has imperfect channel state information of different nodes. For this secrecy network, the authors solve two robust secrecy rate optimisation problems: (a) robust power minimisation problem, subject to the probability based secrecy rate and the D2D transmission rate constraints; (b) robust secrecy rate maximisation problem with the transmit power, the probabilistic based secrecy rate and the D2D transmission rate constraints. Owing to the non‐convexity of robust beamforming design based on two statistical channel uncertainty models, the authors present two conservative approximation approaches based on ‘Bernstein‐type’ inequality and ‘S‐Procedure’ to solve these robust optimisation problems. Simulation results are provided to validate the performance of these two conservative approximation methods, where it is shown that ‘Bernstein‐type’ inequality based approach outperforms the ‘S‐Procedure’ approach in terms of achievable secrecy rates.

The authors propose a coordinated multi‐cell beamforming technique for signal‐to‐interference plus noise ratio (SINR) balancing under multiple base station power constraints. Instead of balancing SINR of all users in all cells to the same level, the authors’ propose a new approach to balance SINR of users in various cells to different maximum possible values. This has the ability to allow users in cells with relatively more transmit power or better channel condition to achieve a higher balanced SINR than that achieved by users in the worst‐case cells. This multi‐level SINR balancing problem is solved using SINR constraints based SINR balancing criterion and subgradient method. The simulation results support the optimality of the results through comparison with semi‐definite programming‐based optimisation.

Emerging cloud computing infrastructure replaces traditional outsourcing techniques and provides flexible services to clients at different locations via Internet. This leads to the requirement for data classification to be performed by potentially untrusted servers in the cloud. Within this context, classifier built by the server can be utilized by clients in order to classify their own data samples over the cloud. In this paper, we study a privacy-preserving (PP) data classification technique where the server is unable to learn any knowledge about clients’ input data samples while the server side classifier is also kept secret from the clients during the classification process. More specifically, to the best of our knowledge, we propose the first known client-server data classification protocol using support vector machine. The proposed protocol performs PP classification for both two-class and multi-class problems. The protocol exploits properties of Pailler homomorphic encryption and secure two-party computation. At the core of our protocol lies an efficient, novel protocol for securely obtaining the sign of Pailler encrypted numbers.

We solve a mixed quality of services (QoS) requirement problem for a multiple-input-multiple-output (MIMO) network with multiple linear transmit covariance constraints. Specifically, we design linear transceivers to satisfy the data rate requirements for a set of users while the rates of the remaining users are balanced. In addition, the design will ensure a set of multiple linear transmit covariance constraints are satisfied. The coupled structure of the transmit filters makes the original problem difficult to solve in the broadcast channel (BC). Hence, we propose an iterative algorithm to solve this mixed QoS problem based on stream-wise mean square error (MSE) duality and alternating optimization framework where the optimization problem is switched between the virtual multiple access channel (MAC) and the BC by exploiting stream-wise MSE duality. The proposed iterative algorithm solves the rate balancing problem by modifying the target rates of the users. In each iteration, a quadratically constrained quadratic programming (QCQP) is solved to obtain the virtual MAC receiver filters by incorporating multiple linear transmit covariance constraints, where the downlink receiver filters are obtained by minimizing each layer MSE. The power allocation in the virtual MAC is determined by solving a geometric programming (GP) where the product of layer MSEs of each user is balanced with total transmit power constraint. Simulation results for an underlay MIMO cognitive radio network demonstrate the convergence of the proposed algorithm.

The authors investigate a spectrum sharing peer‐to‐peer relay network where multiple source nodes with multiple antennas communicate with their desired destination nodes with multiple antennas through a multiple‐input and multiple‐output (MIMO) relay. The authors establish the duality between uplink and downlink peer‐to‐peer MIMO channels with any number of antennas at each node and demonstrate mean square error (MSE) of a downlink peer‐to‐peer network can be achieved in a virtual uplink network with the same total network transmission power constraint. By applying this result, the authors develop an iterative algorithm to optimise the source, relay and receiver processing matrices such that the weighted MSE of the retrieved signal at the receivers is minimised. The simulation results demonstrate satisfactory performance of the proposed algorithm.

The authors propose two algorithms to solve sum mean‐square error (MSE) minimisation and mixed quality of service (QoS) requirement problems for a multiuser multiple‐input‐multiple‐output system with multiple linear transmit covariance constraints. These original problems in the downlink are complicated because of the coupled structure of the transmitter filters. To overcome this issue, MSE duality proposed in the literature is extended at different levels for a general linear transmit covariance constraint. Exploiting the general sum‐MSE duality and subgradient method, the sum‐MSE minimisation algorithm is proposed first for multiple linear transmit covariance constraints. Secondly, a novel algorithm is proposed to solve mixed QoS requirement problem, where multiple linear transmit covariance constraints are incorporated in the design of the receiver filters in the equivalent multiple access channel. This algorithm is developed based on stream‐wise MSE duality and alternating optimisation framework. Simulation results have been provided to validate the convergence of the proposed algorithms. In addition, the proposed sum‐MSE minimisation algorithm with per‐antenna power constraints outperforms the existing algorithm in terms of achieved sum‐MSE and power consumption at each transmit antenna.

We propose a two-way relay based spatial multiplexing technique for a cognitive radio relay network (CR). The relay coefficients and the transmission powers are optimized to maximize the worst-case user signal-to interference and noise ratio (SINR), while ensuring interference leakage from the relays to the primary users (PUs) in the network is below a threshold. We solve this problem through an iterative procedure that uses semidefinite and geometric programming along with bisection search method. We evaluate the performance of the proposed scheme in terms of the mean SINR for different number of relays and transmission power at the relays.

In this paper, we consider a two-way amplify-and-forward relaying scheme, which consists of multiple transceivers and r relay nodes. Assuming that both the transceivers and the relays are equipped with single antennas, we deploy a signal-to-interference and noise-ratio (SINR) balancing technique, where the smallest of the transceivers SINRs is maximized under a total transmit power constraint. We solve this problem through an iterative procedure that uses semidefinite and geometric programming along with bisection search methods. We evaluate the performance of the proposed scheme in terms of the mean SINR for various relays and power at the relays.

We solve the rate balancing problem in the downlink for a multiuser multiple-input-multiple-output (MIMO) system with multiple linear transmit covariance constraints. In particular, we adopt a linear transceiver structure to maximize the worst-case rate of the user while satisfying multiple linear transmit covariance constraints. The original rate balancing problem in the downlink is more complicated due to the coupled structure of the transmit filters. Hence, this optimization problem is solved in an alternating manner by switching between the virtual uplink and the downlink and exploiting the stream-wise mean square error (MSE) duality. An iterative algorithm has been proposed based on stream-wise MSE duality to obtain transceiver filters. In each iteration, the virtual uplink receiver filter design is formulated into a quadratically constrained quadratic programming (QCQP) by incorporating the multiple linear constraints, where the downlink receiver filters are obtained by minimizing each layer MSE. A geometric programming (GP) is solved to obtain the power allocation in the virtual uplink where the product of layer MSEs of each user is balanced with total transmit power constraint. Simulation results have been provided to validate the performance of the proposed algorithm.

We consider an underlay cognitive radio network, in which the cognitive users (also referred to as secondary users (SUs)) are allowed to access the licensed spectrum simultaneously with the primary users (PUs). Specifically we solve a beamforming and power allocation problem in the downlink with mixed quality-of-service (QoS) requirements where a set of SUs are required to achieve a specific signal-to-interference and noise ratio (SINR) targets whilst the SINRs for the remaining SUs are balanced. This mixed QoS requirement problem is more complicated in the downlink because of the coupled structure of beamformers and power allocations. Hence, we solve an equivalent uplink problem based on the uplink-downlink duality and subgradient method. An iterative algorithm is proposed to determine the optimal beamformers and power allocation. Simulation results are provided to validate the optimality of the result and the convergence of the proposed algorithm.

We propose a capacity balancing beamforming technique for a multiple inputs and multiple outputs (MIMO) based cognitive radio (CR) network. The proposed algorithm is based on mean square error (MSE) duality and it is aimed at maximizing the worst case user capacity of multiple secondary users (SUs) by jointly designing their transceiver beamformers while ensuring the interference leakage to multiple primary users (PUs) are below a specific set of thresholds. We use an iterative approach to determine the transceiver beamformers so that the capacities achieved by all SUs are balanced and maximized. The performance of the algorithm is demonstrated through Monte Carlo simulation results.

We propose a novel signal to interference and noise (SINR) balancing technique for a downlink cognitive radio network (CRN) wherein multiple cognitive users (also referred to as secondary users (SUs)) coexist and share the licensed spectrum with the primary users (PUs) using the underlay approach. The proposed beamforming technique maximizes the worst SU SINR while ensuring that the interference leakage to PUs is below specific thresholds. Due to the additional interference constraints imposed by PUs, the principle of uplink-downlink duality used in the conventional downlink beamformer design cannot be directly applied anymore. To circumvent this problem, using an algebraic manipulation on the interference constraints, we propose a novel SINR balancing technique for CRNs based on uplink-downlink iterative design techniques. Simulation results illustrate the convergence and the optimality of the proposed beamformer design.

We consider a network of cognitive users (also referred to as secondary users (SUs)) coexisting and sharing the spectrum with primary users (PUs) in an underlay cognitive radio network (CRN). Specifically, we consider a CRN wherein the number of SUs requesting channel access exceeds the number of available frequency bands and spatial modes. In such a setting, we propose a joint fast optimal resource allocation and beamforming algorithm to accommodate maximum possible number of SUs while satisfying quality of service (QoS) requirement for each admitted SU, transmit power limitation at the secondary network basestation (SNBS) and interference constraints imposed by the PUs. Recognizing that the original user maximization problem is a nondeterministic polynomial-time hard (NP), we use a mixed-integer programming framework to formulate the joint user maximization and beamforming problem. Subsequently, an optimal algorithm based on branch and bound (BnB) method has been proposed. In addition, we propose a suboptimal algorithm based on BnB method to reduce the complexity of the proposed algorithm. Specifically, the suboptimal algorithm has been developed based on the first feasible solution it achieves in the fast optimal BnB method. Simulation results have been provided to compare the performance of the optimal and suboptimal algorithms.

Cognitive radio networks opportunistically operate in frequency bands that have been licensed to other networks. Therefore, communication between unlicensed users should ensure the interference leaked to the licensed users is kept below an acceptable level while achieving the required quality of services. In this paper, we extend SINR balancing technique to serve multiple cognitive users in the downlink while imposing constraints on interference temperature of primary users. We show that when the set interference temperatures is fixed, the proposed SINR balancing technique will always have a unique solution that is identical to semidefinite programming based optimal solution. The advantages and disadvantages of the SINR balancing technique and semidefinite programming based techniques are also discussed.

We propose a cooperative relaying strategy for wireless mesh networks where a set of relay nodes assists forwarding signals from multiple sources to multiple destinations. A semi- definite programming framework is used to ensure target SINRs at the destination are achieved with minimum possible transmit power at the relay layers. The proposed algorithm also considers various level of quantization applied to the messages passed between relays within its optimization framework. The proposed technique has been shown to outperform a non-cooperation based relaying strategy for various values of SINR targets.