Low complexity adaptive antenna selection for cognitive radio MIMO broadcast channels

Abstract

A multi-antenna cognitive radio network, with a single pair of primary users and a secondary broadcast channel, is considered. Under perfect channel state information (CSI), the rate-optimal strategy for the primary link is waterfilling, resulting in possibly unused dimensions. The secondary base station, supplied with perfect and global CSI, employs block diagonalization for interference-free message precoding and opportunistic interference alignment to avoid disturbing primary communication. In the absence of sufficient resources to serve all secondary users, a low complexity adaptive antenna selection strategy is proposed. This scheme constructs, in each timeslot, the set of active antennas by opportunistically reusing information and resources from the preceding selection round. Numerical results indicate a complexity reduction at a small rateloss penalty, in comparison with existing selection methods.

Energy efficiency optimisation of large‐scale multiple‐input–multiple‐output system with transmit antenna selection

“…As described in [7, 8], the complexity of finding the optimal subset of antennas to maximise the capacity or minimise the bit error rate (BER) grows exponentially with the number of available antennas. Thus, lower‐complexity capacity‐maximising antenna selection methods were pursued to achieve the near‐optimal performance with reduced complexity [9–11]. In addition, previous literatures on antenna selection mainly focused on the case where the number of available antennas was fixed.…”