Frequency Selective Hybrid Precoding for Limited Feedback Millimeter Wave Systems

TL;DR

This paper develops frequency selective hybrid precoding techniques for wideband millimeter wave systems with limited feedback, balancing hardware complexity and performance, and introduces efficient codebooks and a low-complexity greedy algorithm.

Contribution

It derives the optimal hybrid precoding for a given RF codebook, designs efficient codebooks for wideband systems, and proposes a low-complexity greedy precoding algorithm.

Findings

Hybrid codebooks achieve near-optimal performance.

Proposed algorithms significantly reduce complexity.

Simulation confirms effectiveness over unconstrained solutions.

Abstract

Hybrid analog/digital precoding offers a compromise between hardware complexity and system performance in millimeter wave (mmWave) systems. This type of precoding allows mmWave systems to leverage large antenna array gains that are necessary for sufficient link margin, while permitting low cost and power consumption hardware. Most prior work has focused on hybrid precoding for narrowband mmWave systems, with perfect or estimated channel knowledge at the transmitter. MmWave systems, however, will likely operate on wideband channels with frequency selectivity. Therefore, this paper considers wideband mmWave systems with a limited feedback channel between the transmitter and receiver. First, the optimal hybrid precoding design for a given RF codebook is derived. This provides a benchmark for any other heuristic algorithm and gives useful insights into codebook designs. Second, efficient hybrid analog/digital codebooks are developed for spatial multiplexing in wideband mmWave systems. Finally, a low-complexity yet near-optimal greedy frequency selective hybrid precoding algorithm is proposed based on Gram-Schmidt orthogonalization. Simulation results show that the developed hybrid codebooks and precoder designs achieve very good performance compared with the unconstrained solutions while requiring much less complexity.