Federated Learning (FL), as an effective decentral-ized approach, has attracted considerable attention in privacy-preserving applications for wireless edge networks. In practice, edge devices are typically limited by energy, memory, and computation capabilities. In addition, the communications be-tween the central server and edge devices are with constrained resources, e.g., power or bandwidth. In this paper, we propose a joint sparsification and optimization scheme to reduce the energy consumption in local training and data transmission. On the one hand, we introduce sparsification, leading to a large number of zero weights in sparse neural networks, to alleviate devices' computational burden and mitigate the data volume to be uploaded. To handle the non-smoothness incurred by sparsification, we develop an enhanced stochastic gradient descent algorithm to improve the learning performance. On the other hand, we optimize power, bandwidth, and learning parameters to avoid communication congestion and enable an energy-efficient transmission between the central server and edge devices. By collaboratively deploying the above two components, the numerical results show that the overall energy consumption in FL can be significantly reduced, compared to benchmark FL with fully-connected neural networks.

We propose a new formulation for the joint phase retrieval and dictionary learning problem with a reduced number of regularization parameters to be tuned. A parallel algorithm based on the block successive convex approximation framework is developed for the proposed formulation. The performance of the algorithm is evaluated when applied to sparse channel estimation in a multi-antenna random access network. Simulation results on synthetic data show the efficiency of the proposed technique compared to the state-of-the-art method.

In this paper, we study the sparse phase retrieval problem, that is, to estimate a sparse signal from a small number of noisy magnitude-only measurements. We propose an iterative soft-thresholding with exact line search algorithm (STELA). It is a parallel coordinate descent algorithm, which has several attractive features: i) fast convergence, as the approximate problem solved at each iteration exploits the original problem structure, ii) low complexity, as all variable updates have a closed-form expression, iii) easy implementation, as no hyperparameters are involved, and iv) guaranteed convergence to a stationary point for general measurements. These advantages are also demonstrated by numerical tests.

Most of the existing caching solutions for wireless networks rest on the ideal assumption that the file popularity distribution is perfectly known. In this paper, we consider optimal random caching designs for perfect, imperfect, and unknown file popularity distributions in a large-scale multi-tier wireless network. First, in the case of perfect file popularity distribution, we formulate the successful transmission probability (STP) optimization problem, which is nonconvex. We propose an efficient parallel iterative algorithm to obtain a stationary point based on parallel successive convex approximation (SCA). Then, in the case of imperfect file popularity distribution, we formulate the worst-case STP maximization problem. To solve this challenging robust optimization problem, we transform it into an equivalent complementary geometric programming (CGP) and propose an efficient iterative algorithm to obtain a stationary point based on the SCA. To the best of our knowledge, this is the first work explicitly considering the estimation error of file popularity distribution in the optimization of caching design. Next, in the case of unknown file popularity distribution, we formulate the stochastic STP (i.e., the STP in the stochastic form) maximization problem. This is a challenging nonconvex stochastic optimization problem, and we propose an efficient iterative algorithm to obtain a stationary point based on stochastic parallel SCA. As far as we know, this is the first work considering stochastic optimization in a large-scale wireless network. Finally, by numerical results, we show that the proposed solutions achieve notable gains over existing schemes in all three cases and reveal the values of the robust caching optimization and stochastic caching optimization in the cases of imperfect file popularity distribution and unknown file popularity distribution, respectively.

Towards energy savings in large-scale nonorthogonal multiple access (NOMA) networks, we investigate power and load optimization for multi-cell and multi-carrier NOMA systems in this paper. To capture the coupling relation of mutual interference among cells, firstly, we extend a load-coupling model from orthogonal multiple access (OMA) to NOMA networks. Next, with this analytical tool, we formulate the considered optimization problem in NOMA-based load-coupled systems, where optimizing load, power, and determining decoding order are the key aspects in the optimization. Theoretically, we prove that the minimum network energy consumption can be achieved by using all the time-frequency resources in each cell to deliver users' demand. To achieve the optimal load and enable efficient power optimization, we develop a power-adjustment algorithm. Numerical results demonstrate promising energy-saving gains of NOMA over OMA in large-scale cellular networks, in particular for the high-demand and resource-limited scenarios.

In this paper, we propose a convergent parallel best-response algorithm with the exact line search for the nondifferentiable nonconvex sparsity-regularized rank minimization problem. On the one hand, it exhibits a faster convergence than subgradient algorithms and block coordinate descent algorithms. On the other hand, its convergence to a stationary point is guaranteed, while ADMM algorithms only converge for convex problems. Furthermore, the exact line search procedure in the proposed algorithm is performed efficiently in closed-form to avoid the meticulous choice of stepsizes, which is however a common bottleneck in subgradient algorithms and successive convex approximation algorithms. Finally, the proposed algorithm is numerically tested.

Caching and multicasting are two promising methods to support massive content delivery in multi-tier wireless networks. In this paper, we consider a random caching and multicasting scheme with caching distributions in the two tiers as design parameters, to achieve efficient content dissemination in a two-tier large-scale cache-enabled wireless multicasting network. First, we derive tractable expressions for the successful transmission probabilities in the general region as well as the high signal-to-noise ratio (SNR) and high user density region, respectively, utilizing tools from stochastic geometry. Then, for the case of a single operator for the two tiers, we formulate the optimal joint caching design problem to maximize the successful transmission probability in the asymptotic region, which is nonconvex in general. By using the block successive approximate optimization technique, we develop an iterative algorithm, which is shown to converge to a stationary point. Next, for the case of two different operators, one for each tier, we formulate the competitive caching design game where each tier maximizes its successful transmission probability in the asymptotic region. We show that the game has a unique Nash equilibrium (NE) and adopt an iterative algorithm, which is shown to converge to the NE under a mild condition. Finally, by numerical simulations, we show that the proposed designs achieve significant gains over existing schemes.

In this paper, we investigate the energy harvesting (EH) technique and accordingly design transceivers for a K link multiple-input multiple-output interference channel. Each link consists of two full-duplex (FD) Internet of Things (IoT) nodes exchanging information simultaneously in a bi-directional communication channel. All the nodes suffer from interference, in particular strong self-interference and inter-node interference, due to operating in FD mode and simultaneous transmission at each link, respectively. Further, we divide the received signal at each node into two parts. While one part of the signal is used for information decoding, the other part is used for EH. We jointly design the transmit and receive beamforming vectors and receiver power splitting ratios by minimizing the total transmission power of the system, subject to both signal-to-interference-plus-noise ratio and EH threshold constraints. Furthermore, the case of multiple-input single-output interference channel is also included for the sake of comparison. We also revisit the above problems for the case when the available channel state information (CSI) at the transmitters is imperfect, where the errors of the CSI are assumed to be norm bounded. Simulation results show that the EH technique can harvest enough energy to support power consumption limited IoT devices by aiding in recharging their respective batteries.

Caching and multicasting are two promising methods to support massive content delivery in multi-tier wireless networks. In this paper, we consider a random caching and multicasting scheme with caching distributions in the two tiers as design parameters, to achieve efficient content dissemination in a two- tier large-scale cache-enabled wireless multicasting network. First, we derive tractable expressions for the successful transmission probabilities in the general region as well as the high SNR and high user density region, respectively, utilizing tools from stochastic geometry. Then, for the case of a single operator for the two tiers, we formulate the optimal joint caching design problem to maximize the successful transmission probability in the asymptotic region, which is nonconvex in general. By using the block successive approximate optimization technique, we develop an iterative algorithm, which is shown to coverage to a stationary point. Next, for the case of two different operators, one for each tier, we formulate the competitive caching design game where each tier maximizes its successful transmission probability in the asymptotic region. We show that the game has a unique Nash equilibrium (NE) and develop an iterative algorithm, which is shown to converge to the NE under a mild condition. Finally, by numerical simulations, we show that the proposed designs achieve significant gains over existing schemes.

In this paper, we propose a novel iterative algorithm based on successive convex approximation for the nonconvex energy efficiency optimization problem in massive MIMO networks. The stationary points of the original problem are found by solving a sequence of successively refined approximate problems, and the proposed algorithm has the following advantages: 1) fast convergence as the structure of the original energy efficiency function is preserved as much as possible in the approximate problem, and 2) easy implementation as each approximate problem is natural for parallel computation and all variable updates have a closed-form expression. The proposed algorithm is guaranteed to converge and its advantages are also illustrated numerically.

In cognitive radio (CR) systems, the primary users (PU) are protected by temperature interference constraints imposed on secondary users (SU). However, such limitations may be easily violated by SUs if perfect SU-to-PU channel state information (CSI) is not available at the secondary transmitters. In this paper, we propose a novel and distributed design of MIMO CR networks that is robust against imperfect SU-to-PU CSI. Specifically, we formulate the system design as a noncooperative game and robust global interference constraints are enforced via pricing; the prices are thus additional variables to be optimized. Building on the advanced and new theory of finite-dimensional variational inequalities (VI) in the complex domain, we analyze the proposed NE problem and devise alternative distributed algorithms along with their convergence properties.

Cognitive Radio (CR) systems are built on the coexistence of primary users (PUs) and secondary users (SUs), the latter being allowed to share spectral resources with the PUs but under strict interference limitations. However, such limitations may easily be violated by SUs if perfect SU-to-PU channel state information (CSI) is not available at the secondary transmitters, which always happens in practice. In this paper, we propose a distributed design of MIMO CR networks under global interference temperature constraints that is robust (in the worst-case sense) against SU-to-PU channel uncertainties. More specifically, we consider two alternative formulations that are complementary to each other in terms of signaling and system performance, namely: a game-theoretical design and a social-oriented optimization. To study and solve the proposed formulations we hinge on the new theory of finite-dimensional variational inequalities (VI) in the complex domain and a novel parallel decomposition technique for nonconvex sum-utility problems with coupling constraints, respectively. A major contribution of this paper is to devise a new class of distributed best-response algorithms with provable convergence. The algorithms differ in computational complexity, convergence speed, communication overhead, and achievable performance; they are thus applicable to a variety of CR scenarios, either cooperative or non-cooperative, which allow the SUs to explore the trade-off between signaling and performance.