Compressed Sensing Based Multi-User Millimeter Wave Systems: How Many Measurements Are Needed?

TL;DR

This paper investigates the number of compressed sensing measurements required for efficient channel estimation in multi-user millimeter wave systems, demonstrating significant reductions in training overhead while maintaining performance.

Contribution

It introduces a compressed sensing-based channel estimation method for multi-user mmWave systems and analyzes the optimal number of measurements needed for near-perfect channel knowledge.

Findings

Requires an order of magnitude less training overhead than traditional methods.

Optimal number of measurements balances channel estimate quality and training overhead.

Achieves near-perfect channel knowledge with fewer measurements.

Abstract

Millimeter wave (mmWave) systems will likely employ directional beamforming with large antenna arrays at both the transmitters and receivers. Acquiring channel knowledge to design these beamformers, however, is challenging due to the large antenna arrays and small signal-to-noise ratio before beamforming. In this paper, we propose and evaluate a downlink system operation for multi-user mmWave systems based on compressed sensing channel estimation and conjugate analog beamforming. Adopting the achievable sum-rate as a performance metric, we show how many compressed sensing measurements are needed to approach the perfect channel knowledge performance. The results illustrate that the proposed algorithm requires an order of magnitude less training overhead compared with traditional lower-frequency solutions, while employing mmWave-suitable hardware. They also show that the number of measurements need to be optimized to handle the trade-off between the channel estimate quality and the training overhead.