Robust adaptive beamforming using worst-case optimization with constrained constant modulus criterion

We develop an adaptive beamforming algorithm based on the worst-case criterion and the constrained constant modulus approach. Similarly to the existing worst-case optimization-based beamformer with the minimum variance design, the problem can be reformulated as a second-order cone (SOC) program and solved with interior point methods. We also show some relations which are useful for the choice of the parameter ϵ used in the worst-case constraint, where a misadjustment leads to a performance degradation. Simulations show that the proposed algorithm, termed Worst-Case Optimization-based Constrained Constant Modulus (WC-CCM), performs better than the existing robust beamforming algorithms.