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

TL;DR

This paper analyzes the spectral efficiency of multi-cell MIMO systems with linear zero-forcing beamforming in large-system limits, considering channel estimation errors and proposing a low-feedback scheduling scheme.

Contribution

It provides new large-system analytical expressions for spectral efficiency, including random matrix theory results for structured channel matrices, and introduces a simplified, near-optimal user scheduling method.

Findings

Large-system spectral efficiency expressions derived.

New random matrix theory results for structured channels.

Proposed scheduling scheme reduces feedback while maintaining performance.

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. Base stations in the same cooperation cluster employ joint transmission with linear zero-forcing beamforming, subject to sum or per-base station power constraints. Inter-cluster interference is treated as noise at the user terminals. 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 power 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 at the Transmitters (CSIT) obtained through explicit downlink channel training and uplink feedback. 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 leads to a new simplified downlink scheduling scheme that pre-selects the users according to probabilities obtained from the large-system results, depending on the desired fairness criterion. 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.