Performance and coordination in multi-antenna cognitive radio networks

In this thesis, we initially conducted an analytical performance analysis of two of the most popular cognitive radio (CR) schemes, namely the interweaved and the underlay cognitive radio network (CRN) approaches. It was numerically shown that the behavior of each of the examined CRN approaches is highly dependent on basic system parameters. Furthermore, we studied the problem of rate-optimal receive BF and user selection, considering the uplink of a multi-user, unprioritized CRN. As the assumption of a channel state information (CSI) setting, whereby the involved channels would be merely instantaneously (resp. statistically) known is, to a great extent, optimistic (resp. pessimistic), we considered a mixed (combined) CSI scenario. Then, the problem of rate-optimal transmit BF for a MISO underlay CRN, assuming the existence of mixed CSI, was thereafter formulated. Concentrating on downlink communication, the goal of the system's design was the maximization of the secondary system's achievable ergodic capacity, subject to an average rate constraint imposed on primary communication. Continuing the investigation of the latter precoding problem with mixed, distributed channel knowledge, we developed a coordination scheme, according to which, the transmitters coordinate on the basis of statistical (covariance) information of the global channel. The proposed precoding strategy was shown to outperform conventional approaches taken from the literature. Finally, within a prioritized CRN framework, we proposed a pilot assignment algorithm, based on channels' second order statistics and we examined the designed algorithm's potential in the context of large-scale antenna systems.