Exploring Long-Term Commensalism: Throughput Maximization for Symbiotic Radio Networks

Abstract

Symbiotic radio (SR), combining the advantages of cognitive radio and ambient backscatter communication (AmBC), stands as a promising solution for spectrum-and-energy-efficient wireless communications. In an SR network, backscatter devices (BDs) share the spectrum resources with the primary transmitter (PT) by utilizing the incident radio frequency (RF) signal from PT for uplink non-orthogonal multiple access (NOMA) transmission. The primary receiver (PR) decodes the signals of PT and BDs via the successive interference cancellation (SIC) technique. Our goal is to establish a long-term commensalistic relationship between PT and BDs. We address the problem of maximizing the long-term average sum rate of BDs while ensuring a minimum average rate for the PT by optimizing the power reflection coefficients of the BDs. We explicitly consider practical constraints such as the required power difference among signals for SIC decoding and the unknown future channel state information (CSI). We prove the NP-hardness of the offline version of the problem and subsequently employ the Lyapunov optimization technique to convert the original problem into a series of sub-problems in each individual time slot that can be solved in an online manner without relying on future CSI. We then utilize the successive convex optimization (SCO) technique to solve the non-convex sub-problems. Extensive simulations validate that our proposed Lyapunov-SCO algorithm achieves superior performance in terms of the average sum rate of BDs while ensuring PT’s required average rate. In addition, we provide discussions on extending the proposed solution to SR networks with multiple PT-PR pairs, high-mobility BDs, and enhancing fairness among BDs.