Secrecy Throughput Maximization for Full-Duplex Wireless Powered IoT Networks under Fairness Constraints

In this paper, we study the secrecy throughput of a full-duplex wireless powered communication network (WPCN) for internet of things (IoT). The WPCN consists of a full-duplex multi-antenna base station (BS) and a number of sensor nodes. The BS transmits energy all the time, and each node harvests energy prior to its transmission time slot. The nodes sequentially transmit their confidential information to the BS, and the other nodes are considered as potential eavesdroppers. We first formulate the sum secrecy throughput optimization problem of all the nodes. The optimization variables are the duration of the time slots and the BS beamforming vectors in different time slots. The problem is shown to be non-convex. To tackle the problem, we propose a suboptimal two stage approach, referred to as sum secrecy throughput maximization (SSTM). In the first stage, the BS focuses its beamforming to blind the potential eavesdroppers (other nodes) during information transmission time slots. Then, the optimal beamforming vector in the initial non-information transmission time slot and the optimal time slots are derived. We then consider fairness among the nodes and propose max-min fair (MMF) and proportional fair (PLF) algorithms. The MMF algorithm maximizes the minimum secrecy throughput of the nodes, while the PLF tries to achieve a good trade-off between the sum secrecy throughput and fairness among the nodes. Through numerical simulations, we first demonstrate the superior performance of the SSTM to uniform time slotting and beamforming in different settings. Then, we show the effectiveness of the proposed fair algorithms

A Stackelberg-game approach for disaster-recovery communications utilizing cooperative D2D

In this paper, we investigate disaster-recovery com- munications utilizing two-cell cooperative D2D communications. Specifically, one cell is in a healthy area while the other is in a disaster area. A user equipment (UE) in the healthy area aims to assist a UE in the disaster area to recover wireless information transfer (WIT) via an energy harvesting (EH) relay. In the healthy area, the cellular BS shares the spectrum with the UE, however, both of them may belong to different service providers. Thus, the UE pays an amount of price as incentive to the BS as part of two processes: energy trading and interference pricing. We formulate these two processes as two Stackelberg games, where their equilibrium is derived as closed- form solutions. The results help provide a sustainable framework for disaster recovery when the involving parties juggle between energy trading, interference compromise and payment incentives in establishing communications during the recovery process

Resource Scheduling for Intelligent Reflecting Surface-assisted Full-duplex Wireless Powered Communication Networks with Phase Errors

Intelligent reflecting surface (IRS) is envisioned as a promising technique to improve the performance of full-duplex wireless powered communication networks (FD-WPCNs). This paper investigates the joint phase beamforming design and resource management for IRS-assisted FD-WPCNs, where multiple wireless devices (WDs) can harvest downlink radio-frequency energy and transmit uplink information to the hybrid access point (HAP) over the same band with the aid of IRS. We first formulate a total transmission time minimization problem subject to the minimum transmit rate and energy causality constraints of WDs. In particular, the random phase error of IRS is integrated into our optimization model. Furthermore, we develop an alternating optimization method to obtain the optimal solution of formulated non-convex problem by iteratively solving two subproblems. For the phase beamforming optimization subproblem, we first convert the random phase errors to a deterministic expression, and then utilize the successive convex approximation method to solve the phase beamforming optimization problem. For the transmit power and time-slot allocation subproblem, the optimal transmit power of WDs is derived in closed-form expressions, and the approximation method and variable substitution technique are adopted to obtain the optimal time-slot allocation and transmit power of HAP. Finally, numerical results are provided to evaluate the performance of our proposed method, and reveal the benefits introduced by the IRS technique as compared to benchmark methods

Towards 6G-Enabled Internet of Things with IRS-Empowered Backscatter-Assisted WPCNs

Wireless powered communication networks (WPCNs) are expected to play a key role in the forthcoming 6G systems. However, they have not yet found their way to large-scale practical implementations due to their inherent shortcomings such as the low efficiency of energy transfer and information transmission. In this thesis, we aim to study the integration of WPCNs with other novel technologies of backscatter communication and intelligent reflecting surface (IRS) to enhance the performance and improve the efficiency of these networks so as to prepare them for being seamlessly fitted into the 6G ecosystem. We first study the incorporation of backscatter communication into conventional WPCNs and investigate the performance of backscatter-assisted WPCNs (BS-WPCNs). We then study the inclusion of IRS into the WPCN environment, where an IRS is used for improving the performance of energy transfer and information transmission in WPCNs. After that, the simultaneous integration of backscatter communication and IRS technologies into WPCNs is investigated, where the analyses show the significant performance gains that can be achieved by this integration

Jointly Active and Passive Beamforming Designs for IRS-Empowered WPCN

This paper studies an intelligent reflecting surface (IRS)-empowered wireless powered communication network (WPCN) in Internet of Things (IoT) networks. In particular, a power station (PS) with multiple antennas uses energy beamforming to enable wireless charging to multiple IoT devices, in the downlink wireless energy transfer (WET) phase; then, during the uplink wireless information transfer (WIT) phase, these IoT devices utilise the harvested energy to concurrently transmit their individual information signal to a multi-antenna access point (AP), which equips with multi-user decomposition (MUD) techniques to reconstruct the IoT devices’ signal. An IRS is deployed to improve the energy collection and information transmission capabilities in the WET and WIT phases, respectively. To examine the performance of the system under study, We maximize the sum throughput with the aim of jointly designing the optimal solutions for the active PS energy beamforming, AP receive beamforming, passive IRS beamforming, and time scheduling. Due to the multiple coupled variables, the resulting formulation is non-convex, and a two-level scheme to solve the problem is proposed. At the outer level, a one-dimensional (1-D) search method is applied to find the optimal time scheduling, while at the inner level, an iterative block coordinate descent (BCD) algorithm is proposed to design the optimal receive beamforming, energy beamforming, and IRS phase shifts. In particular, the receive beamforming part is designed by considering the equivalence between sum rate maximisation and sum mean square error (MSE) minimisation, thereby deriving a closed-form solution. Furthermore, we alternately optimize the energy beamforming and IRS phase shifts using Lagrange dual transformation (LDT), quadratic transformation (QT), and alternating direction method of multipliers (ADMM) methods. Finally, numerical results are presented to showcase the performance of the proposed solution and highlight its advant..