Opportunistic beamforming with beam selection in sparse networks

Exploiting spatial multiplexing and multiuser diversity in multi-antenna broadcast channels, orthogonal multiple opportunistic beamforming (OBF) is proven to achieve the same capacity scaling as dirty paper coding (DPC) when there exists a large number of users, and unfortunately, OBF yields degraded performance if the number of users is not large enough (here this scenario is called as sparse networks). To resolve the problem, propose a new OBF based on beam selection (OBFBS). For a system with base station (BS) equipped with M antennas and K single-antenna users, there exists M beam-user pairs communicating at the same time at most, select the optimal beam subset by maximizing system sum rate, consider that the strongest signal-to-interference-and-noise ratio (SINR) beam-user pair makes the greatest contribution to sum rate, rank M beam-user pairs in SINR descending order, add a pair to the optimal beam subset every time until the sum rate no longer increases or M pairs have been implemented. This study demonstrates that, OBFBS improves the sum rate performance significantly in sparse networks compared to conventional OBF.