A Secure Full-Duplex Wireless Circulator Enabled by Non-Reciprocal Beyond-Diagonal RIS

The new radio (NR) system is recently proposed to deploy alongside the long term evolution (LTE) system to improve the wireless communication capacity. The Vandermonde-subspace frequency division multiplexing (VFDM) is an efficient technique for such a two tier network, whereby the NR transmitter can reuse the frequency spectrum of LTE system meanwhile guaranteeing interference free to LTE. In this paper, we first investigate the robust rate-maximization problem in the VFDM system. In the condition of imperfect channel state information (CSI), the precoder is optimized to satisfy both non-interference to LTE system and rate-maximization in the NR system. To tackle the challenge in solving such a non-convex problem, an iterative algorithm is proposed based on the block coordinate descent (BCD) and penalty dual decomposition (PDD) methods. Finally, simulation results demonstrate the superiority of the proposed algorithm in terms of robustness and achievable rate over the conventional SVD-based VFDM technique.

Deep Feedforward Neural Networks' (DFNNs) weights estimation relies on the solution of a very large nonconvex optimization problem that may have many local (no global) minimizers, saddle points and large plateaus. As a consequence, optimization algorithms can be attracted toward local minimizers which can lead to bad solutions or can slow down the optimization process. Furthermore, the time needed to find good solutions to the training problem depends on both the number of samples and the number of variables. In this work, we show how Block Coordinate Descent (BCD) methods can be applied to improve performance of state-of-the-art algorithms by avoiding bad stationary points and flat regions. We first describe a batch BCD method ables to effectively tackle the network's depth and then we further extend the algorithm proposing a \textit{minibatch} BCD framework able to scale with respect to both the number of variables and the number of samples by embedding a BCD approach into a minibatch framework. By extensive numerical results on standard datasets for several architecture networks, we show how the application of BCD methods to the training phase of DFNNs permits to outperform standard batch and minibatch algorithms leading to an improvement on both the training phase and the generalization performance of the networks.

This paper optimizes the energy consumption of the downlink of a multiple-antenna base station (BS) transmitting to several single-antenna users. The BS utilizes simultaneous wireless information and power transfer (SWIPT) while receivers apply power-splitting (PS) with a nonlinear energy harvesting model leading to PS-SWIPT. We use an intelligent reflecting surface (IRS) and propose a joint design to optimize the active data and the BS's energy beamformers, IRS's passive beamformers, and the receivers' PS ratios under perfect and imperfect CSI availability. In particular, the total BS transmit power is minimized while guaranteeing a minimum rate and harvested energy for each receiver. We apply the block coordinate descent (BCD) method to optimize active and passive beamformers iteratively. We enforce the rank-one constraint and solve the corresponding optimization problem via successive convex approximation (SCA) for accurate semidefinite relaxations. Furthermore, we propose a worst-case robust design for the imperfect CSI case and reformulate this problem with infinitely many constraints. With the BCD method, the problem is iteratively solved via semidefinite programming (SDP) and a second sub-problem with a linear objective and quadratic matrix inequalities, which is also solved via SCA. Numerical results show significant improvements (e.g., 30% decrease in transmit power) than those of no-IRS and IRS with random phase shifts.

Block coordinate descent (BCD) methods approach optimization problems by performing gradient steps along alternating subgroups of coordinates. This is in contrast to full gradient descent, where a gradient step updates all coordinates simultaneously. BCD has been demonstrated to accelerate the gradient method in many practical large-scale applications. Despite its success no convergence analysis for inverse problems is known so far. In this paper, we investigate the BCD method for solving linear inverse problems. As the main theoretical result, we show that for operators having a particular tensor product form, the BCD method combined with an appropriate stopping criterion yields a convergent regularization method. To illustrate the theory, we perform numerical experiments comparing the BCD and the full gradient descent method for a system of integral equations. We also present numerical tests for a nonlinear inverse problem not covered by our theory, namely one-step inversion in multispectral X-ray tomography.

The cell-free MIMO concept relying on hybrid precoding constitutes an innovative technique capable of dramatically increasing the network capacity of millimeter-wave (mmWave) communication systems. It dispenses with the cell boundary of conventional multi-cell MIMO systems, while drastically reducing the power consumption by limiting the number of radio frequency (RF) chains at the access points (APs). In this paper, we aim for maximizing the weighted sum rate (WSR) of mmWave cell-free MIMO systems by conceiving a low-complexity hybrid precoding algorithm. We formulate the WSR optimization problem subject to the transmit power constraint for each AP and the constant-modulus constraint for the phase shifters of the analog precoders. A block coordinate descent (BCD) algorithm is proposed for iteratively solving the problem. In each iteration, the classic Lagrangian multiplier method and the penalty dual decomposition (PDD) method are combined for obtaining near-optimal hybrid analog/digital precoding matrices. Furthermore, we extend our proposed algorithm for deriving closed-form expressions for the precoders of fully digital cell-free MIMO systems. Moreover, we present the convergency analysis and complexity analysis of our proposed method. Finally, our simulation results demonstrate the superiority of the algorithms proposed for both fully digital and hybrid precoding matrices.

In this paper, energy efficiency (EE) is maximized for the reconfigurable intelligent surface (RIS) aided millimeter-Wave (mmWave) networks with non-orthogonal multiple access (NOMA) and multiple mobile devices. To this end, we first propose the EE optimization, under the constraints of maximum power, minimal rate of devices and constant modulus of beamforming (BF) vectors. Then, the joint resource allocation scheme of power allocation (PA) and BF is designed. Specifically, given PA, an effective iterative algorithm based on the majorizationminimization, concave-convex procedure and block coordinate descent (BCD) is presented to obtain closed-form solutions of suboptimal passive BF (PBF) and analog BF (ABF) for each iteration. Then, given PBF and ABF, an effective iterative algorithm based on the successive convex approximation, BCD and Dinkelbach methods is derived to achieve suboptimal closedform PA for each iteration. By incorporating these two algorithms into the BCD method, a joint optimization algorithm for EE maximization is presented. As a result, joint resource allocation of PA, PBF and ABF is attained. Besides, the convergence and complexity of the algorithms are analyzed. For comparison, the benchmark scheme based on the multidimensional search method and artificial bee colony algorithm is also presented. Simulation results show that the proposed joint scheme is effective and higher EE can be obtained with lower complexity.

Prolonging battery lifetime, enhancing computation capability and improving spectral efficiency have been the key design challenges in Internet of Things (IoT) era. This paper provides a novel solution to jointly optimize the allocation of the communication, computing and energy resources in IoT, with the aid of some advanced wireless communication technologies including Wireless Energy Transfer (WET), Mobile-Edge Computing (MEC) and Full-Duplex (FD). Specifically, the Hybrid Access-Point (HAP) (integrated with a MEC server) operates in FD mode to simultaneously broadcast energy and receive computation tasks to/from the mobile devices in the same band. Each mobile relies on the harvested energy to accomplish computation tasks by locally executing or (partial) offloading to the HAP. We concentrate on max-min energy efficiency optimization problem (MMEP) with the joint the optimization of the transmission power at the HAP, computation energy consumption and offloaded bits at each mobile device, time slots for energy transfer and computation offloading. We study the cases with perfect and imperfect self-interference cancellation at the HAP. To solve the non-convex MMEP, we apply the fractional programming theory and Block Coordinate Descent (BCD) method to design the algorithms with low complexity. Numerical results demonstrate that the proposed solutions outperform the baseline scheme in terms of the worst-case mobile EE. Moreover, the proposed algorithms can converge to the optimal solution through a few iterations.

Unmanned aerial vehicles (UAVs) are one of the effective means to provide emergency communication services in post-disaster areas. In this article, we consider data dissemination in post-disaster areas, where all Internet of Things (IoT) nodes may not have data needs all the time. The energy consumption in data dissemination is one of the key metrics to pay attention to since the charging facilities for UAVs may be limited due to the destruction of existing infrastructure. In addition, UAVs have limited endurance or lifetime, so unnecessarily flying over IoT nodes that may not have data is time consuming. Therefore, given the energy budget and data requirements of the IoT nodes, we formulated a data dissemination problem using multiple UAVs in a post-disaster area while optimizing their trajectory, mission completion time, and energy consumption. After time discretization, the formulated problem is a mixed-integer nonconvex problem and thus difficult to solve in general. For this reason, we jointly use the bisection search technique and the block coordinate descent (BCD) method to solve the entire problem while aiming to optimize the trajectory and mission completion time of the considered UAV as well as the overall energy consumption. In each iteration of the BCD method, we solve the user association, trajectory optimization, and power optimization subproblems one after the other in an alternating fashion. To solve each subproblem, we employ the geometric programming (GP)-based optimization technique that transforms the variables and constraints. Regarding the initial trajectory of the UAV, we utilized dynamic programming techniques based on unsaturated data requirements of IoT nodes. We performed extensive simulations in many realistic environments to verify the effectiveness and efficiency of the proposed data dissemination scheme in post-disaster scenarios.

Intelligent reflective surface (IRS) is envisioned as a promising transmission technique for next-generation communication systems owing to its ability to reconfigure the wireless propagation environments. In this paper, we aim to maximize the energy-efficiency (EE) for an IRS-assisted downlink multiple-input single-output system. To solve the multi-variable non-convex optimization problem, a joint optimization of the transmit beamforming vector, power, and phase shift matrix at the IRS is performed with the help of the block coordinate descent (BCD) method. Presented simulation results demonstrate that the convergence of the BCD method is remarkably fast and the single iteration based solution has almost the same EE performance (less than 2.5%) as the iterative one. Meanwhile, the proposed scheme can achieve a 70.7% gain in EE when compared with the IRS-free counterpart.

Secure Transmission in Wireless Sensor Networks via Reconfigurable Intelligent Surface

In the coming sixth-generation era, unmanned aerial vehicles (UAVs) have been explored for convenient air access and strong environmental adaptability to provide communication services for ground Internet-of-Things (IoT) devices. However, the inherent broadcast nature of wireless makes UAV communications vulnerable to eavesdropping attacks. This paper explores a secure uplink communication in a heterogeneous network, where a UAV provides communication services to ground IoT nodes in the presence of an eavesdropper. We propose a transmission scheme to maximize the overall sum secrecy rate, in which the cooperative jamming technique and coalition formation game are employed, and the UAV endurance is considered. Specifically, a double-layer optimization problem is formulated, including a non-convex optimization problem in the lower layer, and a game theory problem in the upper layer. We use block coordinate descent and successive convex approximation methods to decompose the lower layer problem into UAV deployment and power allocation sub-problems, and convert them into a difference of two convex functions programming problem and a second-order cone programming problem, respectively. Moreover, we develop an algorithm to solve the upper layer problem, obtaining a stable coalition formation. Simulation results show that the proposed transmission scheme can effectively improve the overall sum secrecy rate, and has dynamic adaptability to eavesdropping attacks.

This paper considers a lens antenna array-assisted millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) system. The base station's beam selection matrix and user terminals' phase-only beamformers are jointly designed with the aim of maximizing the uplink sum rate. In order to deal with the formulated mixed-integer optimization problem, a penalty dual decomposition (PDD)-based iterative algorithm is developed via capitalizing on the weighted minimum mean square error (WMMSE), block coordinate descent (BCD), and minorization-maximization (MM) techniques. Moreover, a low-complexity sequential optimization (SO)-based algorithm is proposed at the cost of a slight sum rate performance loss. Numerical results demonstrate that the proposed methods can achieve higher sum rates than state-of-the-art methods.

Wireless data aggregation (WDA) is a pivotal enabling technique for Internet of Things (IoT). Recently, an emerging WDA technique, over-the-air computation (AirComp), has been proposed to perform fast data aggregation, with enhanced spectrum utilization and shortened transmission delay. AirComp leverages the superposition property of a wireless multiple access channel to accomplish functional computation of data, which can support the model aggregation in distributed machine learning and fusion of sensing data. In this paper, we consider a multi-cell AirComp system with spectrum sharing, in which every user in a multi-cell network shares a common part of the spectrum. The mean squared error (MSE) is used as the performance metric to quantify the computation accuracy at each access point (AP). With the aim of coordinating the MSEs among multiple APs, two different objective functions, weighted sum MSE and proportional fairness, are adopted. Accordingly, to minimize the weighted sum MSE, the block coordinate descent (BCD) method is employed. The analytical solutions are also provided. To minimize the proportional fairness, a tractable solution is developed, which is based on the successive convex approximation (SCA) technique. The effectiveness of the proposed schemes is validated by our numerical results.

Unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) system will play an essential role in future wireless networks, owing to the flexible deployment of the UAVs and the Line-of-Sight (LoS) dominant channels. In this article, we take the limited onboard energy into account and investigate the three-dimensional (3-D) trajectory of the UAV and the transmission scheduling of the ground devices (GDs) for the UAV-enabled IoT system, where multiple GDs transmit data to the UAV in a time-division multiple access fashion. Specifically, taking the angle-dependent probabilistic LoS channel into account, we derive the mathematical expression of the amount of the transmitted data of the GDs and model the energy consumption of the UAV. Next, considering the fairness among the GDs, we aim to maximize the minimum expected amount of the transmitted data of the GDs, and formulate it as an optimization problem, which is further discretized to a problem with a finite number of variables. Due to the nonconvexity of the discretized problem, we transform the problem into a tractable form, and develop an effective iterative algorithm to solve it by using the block coordinate descent and successive convex approximation methods. The convergence and the complexity of the developed algorithm are analytically evaluated. Simulation results demonstrate that our designed 3-D UAV trajectory can effectively improve the minimum expected amount of the transmitted data of the GDs.

This letter investigates an active reconfigurable intelligent surface (RIS)-aided mobile edge computing (MEC) system. Compared with passive RIS, the active RIS is equipped with active reflective amplifier, which can effectively circumvent the “double path loss” attenuation. We propose a joint computing and communication design to minimize the maximum computational latency (MCL), subject to both the phase shift constraints and the edge computing capability constraints. Specifically, the original problem is decoupled into four subproblems, and then the block coordinate descent (BCD) method and the successive convex approximation (SCA) method are applied to alternately optimize the subproblems. The simulation results show that with the same power budget, the performance gain achieved by the active RIS is much larger than that by the passive RIS.