Optimal RF Beamforming for MIMO: Cognitive Networks

System Model And Problem Statement

In this chapter, the MIMO cognitive network model proposed in (Zhang, 2008) is considered. This model is depicted in Figure 1 while the MIMO secondary user is equipped with RF beamforming capability. According to this model, it is assumed that all the primary users and the secondary user share the same bandwidth for transmission. The MIMO secondary user consists of a N_T element transmit and a N_R-element receive array antenna. In this framework the secondary physical channel, including radio propagation environment and transmit and receive antennas, is modeled by a N_R × N_T matrix Hs. There exists N_t < N_T transmit RF chains and N_r < N_R receive RF chains. The transmit RF beamforming module, which is mathematically represented by a N_T × N_t matrix W_T, is made out of a Linear Network (LN) including power splitters/combiners and phase-shifters and variable gain amplifiers¹. The receive RF beamforming module, herein represented by a N_r × N_R matrix W_R, is practically made out of an after Low Noise Amplifier (LNA) mounted, co-called post-LNA, LN with the same components as transmit LN. We assume that the MIMO channels from the secondary transmitter to the secondary and primary receivers are perfectly known at the secondary receiver and transmitter. Thus, the secondary transmitter is able to tune its RF beamforming network based on the channel information to maximize the secondary transmit throughput and combat any interference at the primary receivers. Moreover, with the channel knowledge at the secondary receiver we will be able to optimize the applied receive RF beamforming network.