Collaborative Nonlinear Transceiver Optimization in Multi-Tier MIMO Cognitive Radio Networks with Deterministically Imperfect CSI

The problem of nonlinear transceiver optimization in a multi-tier Multiple-Input Multiple-Output (MIMO) network in Cognitive Radio Network (CR-Net) configuration is studied. The employed transmission schemes are based on the Matrix Decision Feedback Equalizer (DFE) and Tomlinson-Harashima Precoder (THP). It is assumed that the Channel State Information (CSI) is not known perfectly. % The former type of uncertainty is modeled using Stochastic Error (SE) model while the latter one is modeled using Norm Bounded Error (NBE) model. The performance measure used for optimizing the network is based on the sum Mean Square Error (MSE) of symbol estimation in the system. The design problem is constrained by the transmit power of the Secondary Users (SU's) as well as the maximum allowed interfering power to the Primary Users (PU's). The design problem is not jointly convex in its design variables and has infinitely many constraints. To overcome this, a suboptimal iterative procedure is proposed. Based on the chosen model for uncertainty, the two resultant problems are Semidefinite Programs (SDP). These two problems are solved numerically. Finally simulations results are provided to assess the performance of the system.