Sequential Parametric Convex Approximation Method Research Articles

Performance analysis of uplink optical wireless communications in the presence of a simultaneously transmitting and reflecting reconfigurable intelligent surface

AbstractRecently, reconfigurable intelligent surface (RIS) has gained research and development interests in modifying wireless channel characteristics in order to improve the performance of wireless communications, especially when the quality of the line‐of‐sight channel is not that good. In this work, for the first time in the literature, we have used simultaneously transmitting and reflecting RIS (STAR‐RIS) in a non‐orthogonal multiple‐access visible light communication system to improve the performance of the system. Achievable rates of the users are derived for two data recovery schemes, single‐user detection (SUD) and successive interference cancellation (SIC). Then, the sum‐rate optimisation problem is formulated for two operating modes of STAR‐RIS, namely energy‐splitting and mode‐switching cases. Moreover, a sequential parametric convex approximation method is used to solve the sum‐rate optimisation problems. The authors have also compared energy‐splitting and mode‐switching cases and showed that these two modes have the same performance. Finally, numerical results for SUD and SIC schemes and two benchmarking schemes, time‐sharing and max‐min fairness, are presented, and spectral‐ and energy‐efficiency, number of STAR‐RIS elements, the position of users, and access point are discussed.

Deep Learning–Based Energy Beamforming With Transmit Power Control in Wireless Powered Communication Networks

In this paper, we propose deep learning–based energy beamforming in a multi-antennae wireless powered communication network (WPCN). We consider a WPCN where a hybrid access point (HAP) equipped with multiple antennae broadcasts an energy-bearing signal to wireless devices using energy beamforming. We investigate the joint optimization of the time allocation for wireless energy transfer (WET) and wireless information transfer (WIT) with the design for energy beams while minimizing the transmit power at the HAP for efficient use of its available resources. However, this is a non-convex problem, and it is numerically intractable to solve it. In the literature, the traditional approach to solving this problem is based on an iterative algorithm that incurs high computational and time complexity, which is not feasible for real-time applications. We study and analyze a deep neural network (DNN)-based scheme and propose a faster and more efficient approach for the fair approximation of a near-optimal solution to this problem. To train the proposed DNN, we acquire training data samples from a sequential parametric convex approximation (SPCA)-based iterative algorithm. Instead of acquiring data samples and training the DNN, which is highly complex, we use offline training for the DNN to provide a faster solution to the real-time resource allocation optimization problem. Through the simulation results, we show the proposed DNN scheme provides a fair approximation of the traditional SPCA method with low computational and time complexity.

Robust Beamforming Designs in Secure MIMO SWIPT IoT Networks With a Nonlinear Channel Model

In this article, we study a robust beamforming design for multiuser multiple-input–multiple-output secrecy networks with simultaneous wireless information and power transfer (SWIPT). In this system, an access point, multiple Internet-of-Things (IoT) devices under the nonlinear energy harvesting (EH) model with a help of one cooperative jammer (CJ). We employ artificial noise (AN) generation to facilitate efficient wireless energy transfer and secure transmission. To achieve EH fairness, we aim to maximize the minimum harvested energy among users subject to secrecy rate constraint and total transmit power constraint in the presence of channel estimation errors. By incorporating a norm-bounded channel uncertainty model, the original robust problem is transformed into a two-layer optimization problem, where the inner layer problem is reformulated as semidefinite programming (SDP) and the outer layer problem is solved by a one-dimensional (1-D) line search algorithm. In addition, in order to reduce computational complexity, we propose an algorithm based on sequential parametric convex approximation (SPCA). Finally, simulation results show that the proposed SPCA method achieves the same performance as the two-layer algorithm with much lower complexity.

Beamforming and Power Splitting Designs for AN-Aided Secure Multi-User MIMO SWIPT Systems

In this paper, an energy harvesting scheme for a multi-user multiple-input-multiple-output secrecy channel with artificial noise (AN) transmission is investigated. Joint optimization of the transmit beamforming matrix, the AN covariance matrix, and the power splitting ratio is conducted to minimize the transmit power under the target secrecy rate, the total transmit power, and the harvested energy constraints. The original problem is shown to be non-convex, which is tackled by a two-layer decomposition approach. The inner layer problem is solved through semi-definite relaxation, and the outer problem, on the other hand, is shown to be a single-variable optimization that can be solved by 1-D line search. To reduce computational complexity, a sequential parametric convex approximation method is proposed to find a near-optimal solution. This paper is then extended to the imperfect channel state information case with norm-bounded channel errors. Furthermore, tightness of the relaxation for the proposed schemes is validated by showing that the optimal solution of the relaxed problem is rank-one. Simulation results demonstrate that the proposed SPCA method achieves the same performance as the scheme based on 1-D but with much lower complexity.

Physical Layer Security in MIMO Backscatter Wireless Systems

Backscatter wireless communication is an emerging technique widely used in low-cost and low-power wireless systems, especially in passive radio frequency identification (RFID) systems. Recently, the requirement of high data rates, data reliability, and security drives the development of RFID systems, which motivates our investigation on the physical layer security of a multiple-input multiple-output (MIMO) RFID system. In this paper, we propose a noise-injection precoding strategy to safeguard the system security with the resource-constrained nature of the backscatter system taken into consideration. We first consider a multi-antenna RFID tag case and investigate the secrecy rate maximization (SRM) problem by jointly optimizing the energy supply power and the precoding matrix of the injected artificial noise at the RFID reader. We exploit the alternating optimization method and the sequential parametric convex approximation method, respectively, to tackle the non-convex SRM problem and show an interesting fact that the two methods are actually equivalent for our SRM problem with the convergence of a Karush-Kuhn-Tucker (KKT) point. To facilitate the practical implementation for resource-constrained RFID devices, we propose a fast algorithm based on projected gradient. We also consider a single-antenna RFID tag case and develop a low-complexity algorithm which yields the global optimal solution. Simulation results show the superiority of our proposed algorithms in terms of the secrecy rate and computational complexity.

Resource Allocation in Two-Tier Wireless Backhaul Heterogeneous Networks

This paper studies two-tier heterogeneous cellular networks, with wireless backhaul communication, consisting of macrocell and small cell tiers. A joint design of transmit beamforming, power allocation, and bandwidth partitioning for both uplink and downlink transmissions is considered. By assuming the reverse time division duplexing system, we propose a strategy to partition the bandwidth for two consecutive time slots by two separate partitioning factors. Under the proposed strategy, we formulate a constrained optimization problem with the objective of maximizing the sum rate of small cell uplink and downlink. For this non-convex problem, we leverage the sequential parametric convex approximation method to find the stationary point of the problem. In this method, a convex approximation of the problem is solved at each iteration. Furthermore, with appropriate transformations, we approximate the problem as second-order cone programming (SOCP) and propose a fast converging algorithm to attain the solution. We also evaluate the impact of imperfect channel state information by reformulating the optimization problem and applying the proposed algorithm to solve it. We conduct numerical simulations to show that the joint design of transmit beamforming, power allocation, and bandwidth partitioning leads to a better resource utilization and high spectral efficiency. Moreover, our results show that the proposed SOCP-based algorithm converges fast to a solution, which is shown to be closer to the global optimal solution achieved by the branch-and-bound algorithm compared with other works.

Wireless backhaul resource allocation and user‐centric clustering in ultra‐dense wireless networks

Wireless backhaul is a promising technology to lower the cost for connecting a large number of densely deployed access points in the emerging fifth generation wireless system. In this study, the authors consider the joint optimisation of resource allocation in wireless backhaul links and user-centric clustering in the access links. The objective is to maximise the weighted sum rate of all users under the backhaul resource constraints. To solve this intractable problem, they first adopt the l 1 -norm approximation approach to convert the non-convex backhaul constraints into the solvable forms. They then reformulate the objective function as a set of second-order cone constraints based on the sequential parametric convex approximation method. An iterative algorithm is proposed to solve the transformed problem based on its special property. Simulation results show that the proposed algorithm outperforms other existing schemes under different network settings.

Application of the sequential parametric convex approximation method to the design of robust trusses

We study an algorithm recently proposed, which is called sequential parametric approximation method, that finds the solution of a differentiable nonconvex optimization problem by solving a sequence of differentiable convex approximations from the original one. We show as well the global convergence of this method under weaker assumptions than those made in the literature. The optimization method is applied to the design of robust truss structures. The optimal structure of the model considered minimizes the total amount of material under mechanical equilibrium, displacements and stress constraints. Finally, Robust designs are found by considering load perturbations.

Robust Beamforming for Simultaneous Wireless Information and Power Transfer in MISO Interference Channels

Taking the channel uncertainty into account, we investigate the robust beamforming design for simultaneous wireless information and power transfer in multiple-input single-output interference channels, where multiple multi-antenna transmitters communicate with their targeted single-antenna receivers and simultaneously interfere with all other receivers. Under quality-of-service and energy harvesting constraints at the receivers which adopt power splitting scheme, we aim to minimize the total transmit power at the transmitters when the channel uncertainty is modeled as the norm-bounded. The robust optimization is non-convex and the global optimal solution is challenging. With the help of the extension of the S-Procedure and rank-one relaxation, the robust beamforming is transformed into a semidefinite programming . We develop an novel method to recover a rank-one feasible and locally optimal solution, which is based on difference of convex programming and sequential parametric convex approximation method. Simulations show that our proposed algorithm outperforms the well-known Gaussian randomization method and has much lower computational complexity.

On the spectral efficiency of full-duplex small cell wireless systems

We investigate the spectral efficiency of full-duplex small cell wireless systems, in which a full-duplex capable base station (BS) is designed to send/receive data to/from multiple halfduplex users on the same system resources. The major hurdle for designing such systems is due to the self-interference at the BS and co-channel interference among users. Hence, we consider a joint beamformer design to maximize the spectral efficiency subject to certain power constraints. The design problem is first formulated as a rank-constrained optimization one, and the rank relaxation method is then applied. However the relaxed problem is still nonconvex, and thus optimal solutions are hard to find. Herein, we propose two provably convergent algorithms to obtain suboptimal solutions. Based on the concept of the difference of convex functions programming, we approximate the design problem by a determinant maximization program in each iteration of the first algorithm. The second method is built upon the sequential parametric convex approximation method, which allows us to transform the relaxed problem into a semidefinite program in each iteration. Extensive numerical experiments under small cell setups illustrate that the full-duplex system with the proposed algorithms can achieve a large gain over the half-duplex one.

Joint Optimization of Electricity and Communication Cost for Meter Data Collection in Smart Grid

Smart grid is recently proposed as an enhancement for the next generation power grid. To achieve efficient status monitoring, control, and billing, a large number of smart meters are deployed and they would produce a huge amount of data. To efficiently collect them imposes a great challenge on the communication networks. In this paper, we study the efficient meter data collection problem by exploring the secondary spectrum market in cellular networks. The electricity power reserved by sending meter data via leased secondary channels would be charged at a lower price. With the objective of reducing the overall cost of both power and communication, we formulate a problem called cost minimization for meter data collection (CMM) that is to find optimal solution of channel selection and transmission scheduling. The CMM problem under a linear power pricing model is formulated as a mixed integer linear programming problem and is then solved by a branch-and-bound algorithm. Under a nonlinear power pricing model, we formulate it as a nonconvex mixed integer nonlinear programming problem and propose an optimal algorithm by integrating the sequential parametric convex approximation method into the branch-and-bound framework. Extensive simulation results show that our proposal can significantly reduce the overall cost.

A sequential parametric convex approximation method with applications to nonconvex truss topology design problems

We describe a general scheme for solving nonconvex optimization problems, where in each iteration the nonconvex feasible set is approximated by an inner convex approximation. The latter is defined using an upper bound on the nonconvex constraint functions. Under appropriate conditions, a monotone convergence to a KKT point is established. The scheme is applied to truss topology design (TTD) problems, where the nonconvex constraints are associated with bounds on displacements and stresses. It is shown that the approximate convex problem solved at each inner iteration can be cast as a conic quadratic programming problem, hence large scale TTD problems can be efficiently solved by the proposed method.