Multi-Cell MIMO User Rate Balancing with Partial CSIT

Abstract

In this paper, we consider the problem of user rate balancing in the downlink of multi-cell multi-user (MU) Multiple-Input-Multiple-Output (MIMO) systems with partial Channel State Information at the Transmitter (CSIT). With MIMO leading to multiple streams per user, user rate balancing involves both aspects of balancing and sum rate optimization. We linearize the problem by introducing a rate minorizer and by formulating the balancing operation as constraints leading to a Lagrangian, allowing to transform rate balancing into weighted sum minimization with Perron Frobenius theory. We provide original analytical expressions for the Lagrange multipliers for the multiple power constraints which can also handle the case in which some power constraints are satisfied with inequality, as can arise in a multi-cell scenario. We introduce two partial CSIT formulations. One is based on the ergodic rate Mean Squared Error (EMSE) relation, the other involves an original rate minorizer in terms of the received interference plus noise covariance matrix, in the partial CSIT case applied to the Expected Signal and Interference Power (ESIP) rate. The simulation results exhibit the improved performance of the proposed techniques over naive partial CSIT beamforming based on perfect CSIT algorithms, and in particular illustrate the close to optimal performance of the ESIP approach.

Multi-Cell Multi-User MIMO Imperfect CSI Transceiver Design with Power Method Generalized Eigenvectors

“…VII. SIMULATION RESULTS We have evaluated numerically the performance of the proposed algorithms by following [10]. At convergence, the same results are reproduced by replacing generalized eigenvalue computations by power method iterations and inverse-free variations proposed here.…”

“…For details about the (prior) separable channel correlation model and it's impact on the posterior channel model, please see [10]. It leads to e.g.…”

“…The rest of this EMSE based approach is elaborated in [10]. The main issue that interests us here is that the Lagrangian of the expected rate balancing leads to a Weighted Sum EMSE minimization for the Tx filters with solution…”

“…The balancing of the rates in (8) or equivalently the weighted interference plus noise powers in (23) now finds the user powers as the Perron-Frobenius eigenvector of the matrix (see [10])…”

“…We consider a multi-cell multiuser MIMO system with imperfect CSIT, which combines both channel estimates and channel (error) covariance information. In particular, we introduce a novel [10] extension of [11] to imperfect CSIT, maximizing an expected rate lower bound in terms of expected MSE. Furthermore, we introduce a second algorithm by exploiting a better approximation of the expected rate as the Expected Signal and Interference Power (ESIP) rate.…”