A mixed quality of service based linear transceiver design for a multiuser MIMO network with linear transmit covariance constraints

We solve a mixed quality of services (QoS) requirement problem for a multiple-input-multiple-output (MIMO) network with multiple linear transmit covariance constraints. Specifically, we design linear transceivers to satisfy the data rate requirements for a set of users while the rates of the remaining users are balanced. In addition, the design will ensure a set of multiple linear transmit covariance constraints are satisfied. The coupled structure of the transmit filters makes the original problem difficult to solve in the broadcast channel (BC). Hence, we propose an iterative algorithm to solve this mixed QoS problem based on stream-wise mean square error (MSE) duality and alternating optimization framework where the optimization problem is switched between the virtual multiple access channel (MAC) and the BC by exploiting stream-wise MSE duality. The proposed iterative algorithm solves the rate balancing problem by modifying the target rates of the users. In each iteration, a quadratically constrained quadratic programming (QCQP) is solved to obtain the virtual MAC receiver filters by incorporating multiple linear transmit covariance constraints, where the downlink receiver filters are obtained by minimizing each layer MSE. The power allocation in the virtual MAC is determined by solving a geometric programming (GP) where the product of layer MSEs of each user is balanced with total transmit power constraint. Simulation results for an underlay MIMO cognitive radio network demonstrate the convergence of the proposed algorithm.