Network MIMO with Linear Zero-Forcing Beamforming: Large System Analysis, Impact of Channel Estimation and Reduced-Complexity Scheduling

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  • 15 December 2010
Abstract
We consider the downlink of a multi-cell system with multi-antenna base stations and single-antenna user terminals, arbitrary base station cooperation clusters, distance-dependent propagation pathloss, and general "fairness" requirements. We focus on the joint transmission from the base stations in a cooperation cluster based on linear zero-forcing beamforming, subject to sum or per-base station power constraints. Analytic expressions for the system spectral efficiency are found in the large-system limit where both the numbers of users and antennas per base station tend to infinity with a given ratio. In particular, for the per-base station constraint, we find new results in random matrix theory, yielding the squared Frobenius norm of submatrices of the Moore-Penrose pseudo-inverse for the structured non-i.i.d. channel matrix resulting from the cooperation cluster, user distribution, and path-loss coefficients. The analysis is extended to the case of non-ideal channel state information obtained through explicit downlink channel training, and it is instrumental in providing insight in the joint operations of downlink multiuser beamforming, inter-cell cooperation, and opportunistic "fair" scheduling. Specifically, our results illuminate the trade-off between the benefit of a larger number of cooperating antennas and the cost of estimating higher-dimensional channel vectors. Furthermore, our analysis lead to a new simplified downlink scheduling scheme that pre-selects the users according to probabilities obtained from the large-system results. The proposed scheme performs close to the optimal (finite-dimensional) opportunistic user selection while requiring significantly less channel state feedback, since only a small fraction of pre-selected users must feed back their channel state information.

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