Abstract

Abstract This article studies the transmission of a single cell-edge user's signal using statistical channel state information at cooperative base stations (BSs) with a general jointly correlated multiple-input multiple-output (MIMO) channel model. We first present an optimal scheme to maximize the ergodic sum capacity with per-BS power constraints, revealing that the transmitted signals of all BSs are mutually independent and the optimum transmit directions for each BS align with the eigenvectors of the BS's own transmit correlation matrix of the channel. Then, we employ matrix permanents to derive a closed-form tight upper bound for the ergodic sum capacity. Based on these results, we develop a low-complexity power allocation solution using convex optimization techniques and a simple iterative water-filling algorithm (IWFA) for power allocation. Finally, we derive a necessary and sufficient condition for which a beamforming approach achieves capacity for all BSs. Simulation results demonstrate that the upper bound of ergodic sum capacity is tight and the proposed cooperative transmission scheme increases the downlink system sum capacity considerably.

Highlights

  • Multi-antenna systems, widely known as multiple-input multiple-output (MIMO), have shown considerable gain in spectral efficiency and attracted much attention in recent years, e.g., [1]

  • [28] derived a closed-form upper bound for the ergodic capacity of the jointly-correlated MIMO channel

  • We aim to investigate coordinated downlink transmission with cooperative base stations (BSs) assuming that the mobile user has perfect channel state information (CSI) but the BSs know only statistical CSI

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Summary

Introduction

Multi-antenna systems, widely known as multiple-input multiple-output (MIMO), have shown considerable gain in spectral efficiency and attracted much attention in recent years, e.g., [1]. We employ matrix permanents to derive a closed-form tight upper bound for the ergodic sum capacity of the jointly correlated MIMO channel. Based on this bound, we propose an iterative power allocation algorithm using convex optimization techniques, which converges within only a few iterations. We establish the beamforming optimality conditions for all the BSs. Our study for BS cooperation in the jointly-correlated MIMO downlink channel generalizes the result in [33]. The (k,l)th element of Ωi, i.e., ωk(il), corresponds to the average power of the (k,l)th element of Hi , i.e., h(kil), which captures the average coupling between the kth receive eigenmode and the lth transmit eigenmode of BSi

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