Channel-Aware SINR-Maximizing Spreading Codes

Abstract

Direct-sequence spread spectrum (DSSS) is used in many wireless communication systems requiring reliable operation under interference and multipath propagation, including Internet of Things (IoT) networks, telemetry, positioning, and satellite communication systems. It plays a key role in achieving robust, interference-resistant communication. However, its performance is limited in multipath fading channels, since the codes’ statistical properties are affected and conventional code designs remain independent of the channel characteristics. Classical codes such as Walsh, Gold, and Kasami exhibit a decline in signal-to-interference-plus-noise ratio (SINR) under these conditions, resulting in higher error rates. To address this concern, in this study, we propose MaxSINR, a channel-aware code design framework that directly maximizes post-despreading SINR while ensuring fairness across user equipments. Using gradient-based optimization, MaxSINR generates codes that jointly mitigate inter-symbol and multi-user interference. Simulation results consistently demonstrate SINR and BER gains, with up to 60% transmit power savings as compared to traditional codes. These results highlight the potential of SINR-driven code design to deliver more reliable and energy-efficient DSSS communication.