Efficient Computation of Pareto Optimal Beamforming Vectors for the MISO Interference Channel with Successive Interference Cancellation

TL;DR

This paper introduces efficient algorithms for computing Pareto-optimal beamforming vectors in a two-user MISO interference channel with successive interference cancellation, significantly reducing computational complexity while analyzing the impact of channel conditions.

Contribution

It presents a novel, low-complexity approach to find Pareto-optimal beamforming solutions by splitting the problem into subproblems based on decoding strategies and reformulating them as quasi-concave problems.

Findings

Proposed methods are several orders-of-magnitude faster than existing techniques.

The algorithms effectively characterize the rate region under various channel conditions.

Analysis shows how channel strength and correlation influence the rate region shape.

Abstract

We study the two-user multiple-input single-output (MISO) Gaussian interference channel where the transmitters have perfect channel state information and employ single-stream beamforming. The receivers are capable of performing successive interference cancellation, so when the interfering signal is strong enough, it can be decoded, treating the desired signal as noise, and subtracted from the received signal, before the desired signal is decoded. We propose efficient methods to compute the Pareto-optimal rate points and corresponding beamforming vector pairs, by maximizing the rate of one link given the rate of the other link. We do so by splitting the original problem into four subproblems corresponding to the combinations of the receivers' decoding strategies - either decode the interference or treat it as additive noise. We utilize recently proposed parameterizations of the optimal beamforming vectors to equivalently reformulate each subproblem as a quasi-concave problem, which we solve very efficiently either analytically or via scalar numerical optimization. The computational complexity of the proposed methods is several orders-of-magnitude less than the complexity of the state-of-the-art methods. We use the proposed methods to illustrate the effect of the strength and spatial correlation of the channels on the shape of the rate region.