Receiver Architecture Design and Analysis for NOMA-Based Multi-User Communication Systems

Abstract

The sixth-generation (6G) wireless network aims to deliver remarkable advancements in system throughput, energy efficiency, traffic capacity per area, spectral efficiency, and low latency. Achieving these goals requires a highly adaptable radio interface capable of efficiently managing limited frequency resources, necessitating the development of new multiple access techniques and waveforms. In large-bandwidth multi-user networks, intersymbol interference (ISI) and inter-user interference (IUI) pose significant design challenges. Time reversal (TR) has emerged as a promising waveform candidate for 6G, as it focuses signal energy in both the time and space domains within multipath environments. Meanwhile, non-orthogonal multiple access (NOMA) offers high spectral efficiency and improved connectivity by serving multiple users over the same time-frequency-code resources. This paper explores the integration of NOMA and TR to address these challenges and proposes, for the first time in the literature, a novel receiver architecture for downlink NOMA-based TR communications, which does not require precoding at the transmitter. Specifically, power-domain NOMA is employed at the transmitter, and TR filtering is applied at each receiver. We derive novel approximated expressions for the pairwise error probability (PEP), a key element in determining the union bound on the bit error rate (BER), to assess user performance. Extensive Monte Carlo simulations are carried out to validate these analytical expressions, providing critical insights into the error rate performance for each user. Additionally, we evaluate the performance gains of the proposed NOMA-based TR receiver over the orthogonal multiple access scheme, known as time-reversal multiple access (TRMA). Results show that our approach significantly outperforms TRMA in terms of BER, particularly in sparse multipath environments, with an average BER improvement of 73.5% to 98.31%. Furthermore, our findings reveal that at high signal-to-noise ratios, the diversity gain for a specific user is proportional to the product of the user’s order, determined by its channel strength, and the number of its channel taps.