Dynamic Subarrays for Hybrid Precoding in Wideband mmWave Massive MIMO Systems
01 Jul 2016
Hybrid analog/digital precoding can potentially achieve high spectral efficiencies while requiring less cost and power consumption than fully-digital solutions. This makes it an attractive candidate architecture for millimeter wave systems, which requires deploying large antenna arrays at both the transmitter and receiver to guarantee sufficient received signal power. Most of prior work though on hybrid precoding focuses on narrow-band channels and assumes a fully-connected hybrid architecture. MmWave systems, though, are expected to employ wideband with frequency selectivity. In addition, some previous work considered a subarray structure instead of a fully connected structure to further decrease the complexity and power. They, however, assumed a predefined fixed subarray structure and did not contemplate the best subarray structure.
Focusing on the single user MIMO-OFDM, the WNCG graduate student Sungwoo Park and Ahmed Alkhateeb with WNCG professor Robert W. Heath Jr. developed a closed-form solution for the fully-connected hybrid analog/digital precoders for frequency selective mmWave systems. This solution was then extended to the partially-connected but fixed architectures in which each RF chain is connected to a specific set of the antennas. The derived solutions give useful insights into how the hybrid subarray structures should be designed. Based on that, a novel technique that dynamically constructs the hybrid subarrays based on the channel characteristics was developed. Simulation results showed that the proposed hybrid precoding solutions achieve close spectral efficiencies to that obtained with fully-digital architectures in wideband mmWave channels. The results also indicated that the developed dynamic subarray solution achieves a good gain over fixed hybrid subarray structures in various system and channel conditions.
A journal version of this work will be submitted to IEEE Transactions on Wireless Communications.
This work was supported in part by the National Science Foundation under Grant No. 1319556, and by a gift from Huawei Technologies Co. Ltd.