Energy Harvesting-Enabled Symbiotic Radio Networks with Channel-Aware TDMA Scheduling

Abstract

In this paper, we propose a novel energy harvesting (EH)-enabled time-division multiple access symbiotic radio (TDMA-SR) system to support massive ambient Internet of Things (IoT) connections. To address the challenge of heterogeneous link conditions in large-scale deployments, we introduce a channelaware frame structure consisting of three consecutive phases for each backscatter device (BD). In the first phase, all BDs harvest energy from the incident cellular signals, where BDs are ordered in descending order of their forward link gains, ensuring that devices with weaker channels accumulate energy over longer durations spanning both the common harvesting phase and the transmission slots of all preceding devices. In the second phase, each BD sequentially occupies its assigned time slot to perform backscatter communication, after which each BD enters the third phase, transitioning to a deep-sleep state for the remainder of the frame to reduce power consumption to a negligible level. We formulate a joint optimization problem over the time slot durations, the receive beamforming vector at the multi-antenna cooperative receiver (CoR), and the BS transmit power, with the objective of minimizing the BS transmit power while satisfying minimum rate requirements for both the cellular and IoT transmissions as well as energy causality constraints. An alternating optimization (AO) algorithm leveraging successive convex approximation (SCA) and semidefinite programming (SDP) is developed to solve the resulting non-convex problem. Simulation results validate the effectiveness of the proposed channel-aware scheme and demonstrate its superiority over the benchmark scheme, confirming its potential as a sustainable solution for ambient IoT networks.