Receive Combining vs. Multi-Stream Multiplexing in Downlink Systems with Multi-Antenna Users

TL;DR

This paper analyzes whether dividing data streams among few users or many users with multiple receive antennas yields better performance in downlink multi-antenna systems, concluding that many users with one stream each is generally optimal.

Contribution

The paper provides an analytic comparison of data stream allocation strategies, demonstrating that multi-user diversity and receive combining outperform multi-stream per user approaches under realistic conditions.

Findings

Selecting many users with one stream each enhances resilience to spatial correlation.

Receive combining offers larger benefits from multi-user diversity.

The results simplify system design by favoring user selection strategies.

Abstract

In downlink multi-antenna systems with many users, the multiplexing gain is strictly limited by the number of transmit antennas $N$ and the use of these antennas. Assuming that the total number of receive antennas at the multi-antenna users is much larger than $N$, the maximal multiplexing gain can be achieved with many different transmission/reception strategies. For example, the excess number of receive antennas can be utilized to schedule users with effective channels that are near-orthogonal, for multi-stream multiplexing to users with well-conditioned channels, and/or to enable interference-aware receive combining. In this paper, we try to answer the question if the $N$ data streams should be divided among few users (many streams per user) or many users (few streams per user, enabling receive combining). Analytic results are derived to show how user selection, spatial correlation, heterogeneous user conditions, and imperfect channel acquisition (quantization or estimation errors) affect the performance when sending the maximal number of streams or one stream per scheduled user---the two extremes in data stream allocation. While contradicting observations on this topic have been reported in prior works, we show that selecting many users and allocating one stream per user (i.e., exploiting receive combining) is the best candidate under realistic conditions. This is explained by the provably stronger resilience towards spatial correlation and the larger benefit from multi-user diversity. This fundamental result has positive implications for the design of downlink systems as it reduces the hardware requirements at the user devices and simplifies the throughput optimization.