Robust Downlink Transmit Optimization under Quantized Channel Feedback via the Strong Duality for QCQP

TL;DR

This paper develops a robust downlink beamforming optimization method that accounts for quantized channel feedback errors, using strong duality for QCQP to transform and solve the problem efficiently.

Contribution

It introduces a novel robust optimization framework leveraging strong duality for QCQP to handle quantized feedback errors in downlink beamforming.

Findings

The proposed method effectively minimizes transmit power under uncertainty.

Simulation results validate the efficiency of the restricted LMI relaxation.

The approach outperforms traditional methods in robustness and power efficiency.

Abstract

Consider a robust multiple-input single-output downlink beamforming optimization problem in a frequency division duplexing system. The base station (BS) sends training signals to the users, and every user estimates the channel coefficients, quantizes the gain and the direction of the estimated channel and sends them back to the BS. Suppose that the channel state information at the transmitter is imperfectly known mainly due to the channel direction quantization errors, channel estimation errors and outdated channel effects. The actual channel is modeled as in an uncertainty set composed of two inequality homogeneous and one equality inhomogeneous quadratic constraints, in order to account for the aforementioned errors and effects. Then the transmit power minimization problem is formulated subject to robust signal-to-noise-plus-interference ratio constraints. Each robust constraint is transformed equivalently into a quadratic matrix inequality (QMI) constraint with respect to the beamforming vectors. The transformation is accomplished by an equivalent phase rotation process and the strong duality result for a quadratically constrained quadratic program. The minimization problem is accordingly turned into a QMI problem, and the problem is solved by a restricted linear matrix inequality relaxation with additional valid convex constraints. Simulation results are presented to demonstrate the performance of the proposed method, and show the efficiency of the restricted relaxation.