Cooperative Interference Management with MISO Beamforming

TL;DR

This paper introduces a decentralized beamforming method for multi-cell MISO systems, optimizing interference management by leveraging the relationship with cognitive radio channels to achieve Pareto-optimal rates.

Contribution

It establishes a novel connection between MISO interference channels and cognitive radio channels, enabling decentralized Pareto boundary characterization and beamforming.

Findings

Achieves Pareto boundary rate-tuples in a decentralized manner.

Develops a new algorithm for multi-cell cooperative beamforming.

Demonstrates the effectiveness of interference-temperature constraints.

Abstract

This correspondence studies the downlink transmission in a multi-cell system, where multiple base stations (BSs) each with multiple antennas cooperatively design their respective transmit beamforming vectors to optimize the overall system performance. For simplicity, it is assumed that all mobile stations (MSs) are equipped with a single antenna each, and there is one active MS in each cell at one time. Accordingly, the system of interests can be modeled by a multiple-input single-output (MISO) interference channel (IC), termed as MISO-IC, with interference treated as noise. We propose a new method to characterize different rate-tuples for active MSs on the Pareto boundary of the achievable rate region for the MISO-IC, by exploring the relationship between the MISO-IC and the cognitive radio (CR) MISO channel. We show that each Pareto-boundary rate-tuple of the MISO-IC can be achieved in a decentralized manner when each of the BSs attains its own channel capacity subject to a certain set of interference-power constraints (also known as interference-temperature constraints in the CR system) at the other MS receivers. Furthermore, we show that this result leads to a new decentralized algorithm for implementing the multi-cell cooperative downlink beamforming.