Iterative Gaussian Approximation for Random Spreading Unsourced Random Access

Abstract: Massive machine-type communications (mMTC) demand robust solutions to support extensive connectivity efficiently. Unsourced random access (URA) has emerged as a promising approach, delivering high spectral and energy efficiency. Among URA code structures, the random spreading (RS) category is a key enabler, providing strong anti-interference capabilities through spectrum spreading gain. Notably, RS-URA approaches theoretical performance limits over the Gaussian multiple access channel in scenarios with few active users. In this paper, we propose an iterative Gaussian approximation decoder designed universally for RS-URA categories. The proposed receiver iterates extrinsic and intrinsic soft information to enhance decoding performance, requiring only a few iterations to converge. Numerical results validate the decoder's effectiveness in terms of performance and robustness.