Cooperative Interference Management with MISO Beamforming (0910.2771v2)

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.

• The paper characterizes the Pareto boundary in the MISO interference channel by parameterizing interference-temperature constraints.
• The paper develops a decentralized beamforming algorithm that allows each base station to optimize its transmission using local channel data.
• Simulations confirm that this approach enhances throughput and fairness while reducing the need for extensive centralized coordination.

Analysis of Cooperative Interference Management with MISO Beamforming

This paper by Rui Zhang and Shuguang Cui presents a novel approach to managing interference in a multi-cell system using downlink MISO beamforming. The central problem addressed is optimizing the transmission rates across multiple cells, each with a multi-antenna base station (BS), under the assumption of each mobile station (MS) having a single antenna. The framework treats inter-cell interference as noise, which is relevant for modern wireless systems such as LTE and WiMAX, where frequency reuse can cause high levels of interference.

Key Contributions

1. Characterization of Pareto Boundary: The paper introduces a method to characterize achievable rate-tuples on the Pareto boundary for the downlink MISO interference channel (IC). By leveraging concepts from cognitive radio (CR) MISO channels, where interference is managed through interference-temperature (IT) constraints, the authors delineate how BSs can maximally exploit channel capacities subject to interference constraints imposed by neighboring receivers.
2. Decentralized Beamforming Algorithm: A highlight of the approach is the development of a decentralized algorithm for downlink beamforming. This algorithm allows each BS to independently optimize its beamforming vectors while adhering to IT constraints. Thus, instead of relying on centralized processing with complete channel knowledge across the network, the BSs utilize local channel and interference information.
3. System Model and Theoretical Framework: The multi-cell system is modeled using a K-user MISO Gaussian interference channel, assuming each cell has one active user at a time. Interference is treated as Gaussian noise, introducing a non-convex achievable rate region. The authors simplify the problem by addressing the rate-optimal beamforming design for the Pareto boundary of this non-convex region.
4. New Parametrical Characterization: By parameterizing the rate region in terms of IT levels, the authors provide a reduced-dimensional framework for analyzing rate-optimality. This aids in developing a practical solution approach to the complex problem of multi-cell cooperative beamforming.

Results and Implications

The results highlight the viability of decentralized strategies for resource allocation in interference-limited settings. The key numerical performance indication is the convergence of the proposed algorithm to Pareto-optimal rate pairs, as verified through simulations for different network configurations. Practically, the proposed algorithm implies potential improvements in throughput and fairness in multi-cell networks without the need for intensive centralized coordination.

Theoretical and Practical Implications

• Theoretical Advancement: The work extends existing theoretical results on MISO interference channels by providing a framework that ensures Pareto-optimal solutions through decentralized coordination. This is significant as it bridges theory with practical implementation in evolving wireless systems.
• Practical Applications: The decentralized algorithm can significantly ease real-world deployment in cellular systems, where centralized approaches may be infeasible due to overheads and time delays in information sharing.

Future Work

The paper opens avenues for further exploration:

• Extending the model to scenarios with multiple active users per cell or multiple antennas per MS.
• Establishing sufficiency conditions for the derived necessary conditions for a set of IT constraints to guarantee Pareto-optimal rates.
• Analyzing the decentralized approach using game-theoretical frameworks to better understand the strategic interactions between BSs in dynamic settings.

Overall, the paper contributes a valuable perspective and methodology to interference management in multi-cell wireless networks, addressing both theoretical and practical aspects. It stands as a resourceful guide for researchers and practitioners in optimizing beamforming strategies in complex interference scenarios.