Structured Random Compressed Channel Sensing for Millimeter-Wave Large-Scale Antenna Systems

In millimeter-wave (mmWave) communications, large-scale antenna system (LSAS) is considered an essential technology to realize beamforming gain to compensate for huge propagation loss. However, channel estimation for LSASs poses a formidable challenge, especially when hybrid analog-digital structures are adopted for ensuring reasonable complexity and cost. To address this challenge, compressed channel sensing (CCS) is leveraged to measure mmWave channels via a random sensing codebook. By exploiting the sparse nature in mmWave channels, only a small number of measurements is required for channel recovery. However, signaling or storing the configuration of a full random sensing codebook leads to a huge burden. Moreover, because the sensing beam of a full random sensing codebook always spreads its power over the channel, the CCS has a stringent requirement on the signal-to-noise ratio (SNR) for robust channel recovery. To overcome these issues, we propose a structured random sensing codebook inspired by the random convolutional measurement process. Owing to its structured nature, signaling or storing overhead from the codebook configuration is significantly reduced. Additionally, the structured random sensing codebook can concentrate its power in a local angle coverage for a sectorized cell, thus improving the robustness in low-SNR regimes. Simulation results demonstrate that the recovery performance of the proposed structured random sensing codebook design is comparable to that of the full random design. Regarding the robustness in low-SNR regimes, the recovery performance is substantially improved by the structured random sensing codebook with power concentration for a local angle coverage.