New fast multi‐user beam training scheme based on compressed sensing theory for millimetre‐wave communication

Abstract

Compensating for the considerable propagation loss, millimetre-wave communication systems adopt large antenna arrays and beamforming technologies to obtain high antenna gains. The beamforming training is rather time-consuming especially in multi-user cases. To reduce the training overhead, a new fast multi-user training scheme based on Compressed Sensing theory is proposed. This scheme includes a multi-user training algorithm and a corresponding semi-fixed semi-random codebook design strategy. The base station (BS) simultaneously broadcasts the training pilots to all user equipments (UEs). Then all UEs independently conduct the proposed training algorithm from channel responses. Finally, all UEs send back the training results to the BS. Compared to the exhaustive search algorithm's training overhead ξ = K r K t , the cost of the proposed scheme is only O ( ξ ) = log ⁡ ( K r K t ) , where K r and K t denote the number of beam patterns in the receiver and the transmitter. Simulation results show that the proposed scheme achieves excellent performance in high SNR (signal-to-noise ratio) cases with only 3 − 5 % performance difference from the theoretical bound. And in contrast with other Compressed Sensing schemes with random codebooks, the proposed scheme reduces the sensitivity to transmission SNR and improves approximately 3 dB performance in low SNR cases.