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 nonrobust 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.
A novel generalized iterative approach (GIA) is proposed for jointly designing the MMSE MIMO processors (precoders, relay processors and decoders) for the analog Network Coding scheme. The MMSE performance can be achieved for MIMO communication links with two end nodes and multiple relay nodes subject to either the per-node power constraint or the practical per-antenna power constraint. Numerical results show that the analog Network Coding scheme is more spectral and energy efficient than the conventional Direct Transmission (DT) scheme.
Interference Alignment (IA) transceiver designs are currently of great interest to the community. They are, however, limited to certain configurations. This paper seeks to show that for general multiuser MIMO systems, a) there is a relationship between MMSE designs and IA, and b) MMSE designs should be used instead of IA ones. The former is done by proving that MMSE designs naturally have IA-like behaviors. The latter is done using several arguments. One of them is based on the MIMO X network numerical results involving the generalized iterative approach (GIA) and the previously proposed MMSE-IA. In the simulation, the hybrid IA approach is clearly outperformed by the purely MMSE based GIA. The GIA is also run for the MIMO M network.
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