Relay-Enabled Backscatter Communications: Linear Mapping and Resource Allocation

TL;DR

This paper proposes a discrete-time resource allocation scheme for relay-enabled backscatter communication systems, employing linear mapping and an iterative algorithm to maximize throughput under practical integral time constraints.

Contribution

It introduces a novel linear mapping approach and formulates a mixed-integer optimization problem for resource allocation in relay-enabled backscatter communication, considering practical discrete time constraints.

Findings

Proposed algorithm effectively maximizes system throughput.

Scheme outperforms existing continuous-time resource allocation methods.

Numerical results show the impact of network parameters on throughput.

Abstract

Relay-enabled backscatter communication (BC) is an intriguing paradigm to alleviate energy shortage and improve throughput of Internet-of-Things (IoT) devices. Most of the existing works focus on the resource allocation that considered the unequal and continuous time allocation for both source-relay and relay-destination links. However, the continuous time allocation may be infeasible since in practice, the time allocation shall be carried out in integral multiple of the subframe duration unit. In this article, we study a discrete time scheme from the perspective of frame structure, where one transmission block is divided into two phases and the linear mapping is employed as a re-encoding method to determine the number of subframes for both phases and the power allocation for each subframe in a relay-enabled BC system. Based on this, we derive an accurate system-throughput expression and formulate a mixed-integral non-convex optimization problem to maximize the system throughput by jointly optimizing the power reflection coefficient (PRC) of the IoT node, the power allocation of the hybrid access point (HAP) and the linear mapping matrix, and solve it via a three-step approach. Accordingly, we propose a low complexity iterative algorithm to obtain the throughput maximization-based resource allocation solution. Numerical results analyze the performance of our proposed algorithm, verify the superiority of our proposed scheme, and evaluate the impacts of network parameters on the system throughput.