Low Complexity Channel Estimation for Millimeter Wave Systems with Hybrid A/D Antenna Processing

TL;DR

This paper introduces low complexity compressed sensing algorithms for channel estimation in mmWave systems with hybrid A/D processing, leveraging temporal correlation to improve efficiency and accuracy in large antenna array scenarios.

Contribution

It proposes two novel compressed sensing algorithms that exploit temporal correlation for efficient channel estimation in hybrid mmWave systems, reducing computational complexity.

Findings

Algorithms outperform traditional methods in accuracy

Exploiting temporal correlation reduces estimation complexity

Performance insights on system parameters and estimation accuracy

Abstract

The availability of large bandwidth at millimeter wave (mmWave) frequencies is one of the major factors that rendered very high frequencies a promising candidate enabler for fifth generation (5G) mobile communication networks. To confront with the intrinsic characteristics of signal propagation at frequencies of tens of GHz and being able to achieve data rates of the order of gigabits per second, mmWave systems are expected to employ large antenna arrays that implement highly directional beamforming. In this paper, we consider mmWave wireless systems comprising of nodes equipped with large antenna arrays and being capable of performing hybrid analog and digital (A/D) processing. Intending at realizing channel-aware transmit and receive beamforming, we focus on designing low complexity compressed sensing channel estimation. In particular, by adopting a temporally correlated mmWave channel model, we present two compressed sensing algorithms that exploit the temporal correlation to reduce the complexity of sparse channel estimation, one being greedy and the other one being iterative. Our representative performance evaluation results offer useful insights on the interplay among some system and operation parameters, and the accuracy of channel estimation.