Robust MMSE Transceiver Designs for Downlink MIMO Systems with Multicell Cooperation

The robust-generalized iterative approach (Robust-GIA), robust-fast iterative approach (Robust-FIA), and robust-decoder covariance optimization approach (Robust-DCOA) are proposed for designing MMSE transceivers of downlink multicell multiuser MIMO systems with per-cell and per-antenna power constraints and possibly imperfect channel state information. The Robust-DCOA is the most restrictive but is always optimum, the Robust-GIA is the most general, and the Robust-FIA is the most efficient. When the Robust-DCOA is applicable and the decoder covariance matrices are full rank, the three proposed approaches are equivalent and all provide the optimum solution. Numerical results show that the proposed robust approaches outperform their non-robust counterparts in various single-cell and multicell examples with different system configurations, channel correlations, power constraints, and cooperation scenarios. Moreover, performances of the robust approaches are insensitive to estimation errors of channel statistics (correlations and path loss). With cell-cooperation, cell edge interference problems can be remedied without reducing the number of data streams by using the proposed robust approaches. 1. Introduction Joint transceiver designs with criteria such as minimum mean square error (MMSE), maximum sum capacity, and minimum bit error rate (BER), and so forth, for multiple-input-multiple-output (MIMO) systems, with both uplink and downlink configurations, have been studied intensively in recent literature (e.g., see [1, 2]). Discussed in this paper is the robust MMSE transceiver design with respect to channel estimation errors for downlink multicell multiuser MIMO systems. Assuming perfect channel state information (CSI), joint MMSE transceiver design has been studied by many researchers. A closed form design subject to the total power constraint for a single-user MIMO system is derived in [3]. Unfortunately though, this closed form design cannot be extended either to the multiuser case or to the per-antenna power constraint. For multiuser uplink MIMO problems subject to per-user power constraint, numerical solutions are provided mainly by the transmit covariance optimization approach (TCOA) [4, 5] and iterative approaches such as in [4]. We have developed a generalized iterative approach (GIA) for the uplink to deal with arbitrary linear power constraints (including the more practical per-antenna power constraint) [6]. Recently, we have also extended the TCOA to deal with arbitrary linear power constraints and have shown that the GIA and the TCOA are