Space-time ML Receiver and its reciprocal transmitter design for interference networks

This paper examines a type of linear beamforming for communications in interference networks. First, a receiver beamformer design is derived from a Maximum Likelihood (ML) framework with Automatic Gain Control (AGC) constraints. The studied beamformer keeps the complexity of the receiver low and it is able either to remove full coherent arrivals or to enhance them when required when different AGC constraints are used. Second, the reciprocal transmit beamformer which is of interest for the interference networks is obtained. This dual beamformer is derivated via revisting the idea of directivity for wireless multiuser communication scenarios. Furthermore, the duality in interference networks is reviewed. Numerical simulations demonstrate the effectiveness of the studied schemes.