Adaptive OFDM Beamformer with Constrained Weights for Cognitive Radio

Cognitive Radio (CR) is an intelligent radio system, which is aware of the communication environment and consequently adapts to it. Orthogonal Frequency Division Multiplexing (OFDM) is a proper and efficient modulation scheme for CR. Adaptive beamforming technique improves CR reliable communication by strengthening the desired signals and rejecting interferences. In this paper, two post IFFT/FFT OFDM adaptive beamformers with iterative weights computing are proposed. The first one directly heritages the spectrum masking concept and deactivates the weights for the OFDM CR subcarriers, which hold the same frequency bands with the Licensed Users (LU). Another beamformer adds extra constraints to those weights instead of masking them, directs the main beam towards the CR users and nulls towards the LU and interferences, and consequently shares the same spectrum bands with LU. Simulation results show that both two OFDM adaptive beamformers guarantee that the CR system not to disturb the LU. Furthermore, it is shown that beamforming with constrained weights provides a lower BER, higher bit rate, more efficient spectrum usage, and suffers no side lobe interference from the LU.