Improved SINR Approximation for Downlink SDMA-based Networks with Outdated Channel State Information

Understanding the performance of multi-user multiple-input multiple-output (MU-MIMO) systems under imperfect channel state information at the transmitter (CSIT) remains a critical challenge in next-generation wireless networks. In this context, accurate statistical modeling of the signal-to-interference-plus-noise ratio (SINR) is essential for enabling tractable performance analysis of multi-user systems. This paper presents an improved statistical approximation of the SINR for downlink (DL) MU-MIMO systems with imperfect CSIT. The proposed model retains the analytical simplicity of existing approaches (e.g., Gamma-based approximations) while overcoming their limitations, particularly the underestimation of SINR variance. We evaluate the proposed approximation in the context of Rate-Splitting Multiple Access (RSMA)-enabled MIMO DL systems with outdated CSIT. The results demonstrate excellent accuracy across a wide range of system configurations, including varying numbers of users, antennas, and degrees of CSIT staleness.

💡 Research Summary

The paper addresses a fundamental problem in next‑generation wireless networks: how to accurately model the signal‑to‑interference‑plus‑noise ratio (SINR) in downlink multi‑user multiple‑input multiple‑output (MU‑MIMO) systems when the transmitter possesses only outdated channel state information (CSIT). Accurate statistical characterization of SINR is essential for tractable performance analysis, especially for advanced multiple‑access schemes such as Rate‑Splitting Multiple Access (RSMA) that are designed to mitigate the detrimental effects of imperfect CSIT.

System Model and Channel Aging
A broadcast channel with (N_t) transmit antennas serving (K) single‑antenna users ((N_t>K)) is considered. Each user’s message is split into a common part and a private part, following a one‑layer RSMA architecture. The transmitted signal consists of a common stream and (K) private streams, linearly precoded. Zero‑forcing (ZF) precoding is employed for the private streams, while the common stream uses a random beamformer independent of the channel estimates. The channel evolves according to a first‑order Gauss‑Markov model:
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