No abstract

In this paper, we consider a wireless powered cooperative network, where two energy-constrained buffer-aided relays harvest energy from the source to assist the information delivery to the destination. To improve the spectral efficiency, a wireless powered successive relaying (WPSR) protocol is proposed to allow both relays to receive the information from the source and forward the buffered data to the destination, taking into consideration of the inter-relay-interference (IRI). We first reveal the impact of data buffers and energy storages at the relays by the achievable throughput comparison of the WPSR scheduling schemes with and without considering data buffers and energy storages. Our analysis validates the great potential of the buffer-and-forward data transmission and the harvest-store-use energy management strategies. Then, the network throughput is maximized via adaptive time and power allocation subject to the stability constraints of data buffers and energy buffers, and an adaptive wireless powered buffer-aided successive relaying (WPBSR) scheduling scheme is proposed. The proposed scheme approaches the optimal throughput of the wireless powered successive relaying network with bounded delay and finite length of data and energy buffers. Numerical results validate the analysis and noticeable spectral efficiency gain.

In this paper, we investigate an intelligent reflecting surface (IRS)-assisted wireless powered Internet of Things (WP-IoT) network that operates in multiple resource blocks (RBs). Particularly, the IRS helps in both downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT), in a way that it improves energy reflection in WET from a power station (PS) to various IoT devices and boosts information delivery in WIT from the IoT devices to an access point (AP). Those IoT devices are capable of utilizing the collected energy, and adopting the time-division multiple access (TDMA) or nonorthogonal multiple access (NOMA) scheme in the uplink WIT. Aiming to maximize the average throughput as the overall performance indicator of the considered network, we jointly optimize the transmit power allocation of the PS, the time scheduling, and the IRS phase shifts. These coupled variables lead to the non-convexity of this optimization problem, which cannot be solved directly. To address this problem, we first design the optimal PS's transmit power allocation for each RB. For the TDMA-based scheme, we design the closed-form IRS beam pattern of the uplink WIT. Then, the closed-form downlink and uplink time allocations are derived by the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions. In addition, the quadratic transformation (QT)-based Alternating Direction Method of Multipliers (ADMM) approach is proposed to iteratively derive the sub-optimal IRS beam pattern of the downlink WET in an alternated fashion. For the NOMA-based scheme, we propose to apply an alternating optimization (AO) algorithm to iteratively optimize the IRS phase shifts, where the uplink IRS beam pattern is iteratively designed by the Riemannian Manifold Optimization (RMO) approach, and the QT-based ADMM method is adopted to alternately derive the sub-optimal downlink IRS phase shifts. Finally, numerical results demonstrate the improved performance of the proposed solution approaches compared to the benchmark schemes, also highlight advantages of the application of IRS in multiple RB scenarios.