Robust and Efficient Distributed Compression for Cloud Radio Access Networks (1206.3602v1)

Abstract: This work studies distributed compression for the uplink of a cloud radio access network where multiple multi-antenna base stations (BSs) are connected to a central unit, also referred to as cloud decoder, via capacity-constrained backhaul links. Since the signals received at different BSs are correlated, distributed source coding strategies are potentially beneficial, and can be implemented via sequential source coding with side information. For the problem of compression with side information, available compression strategies based on the criteria of maximizing the achievable rate or minimizing the mean square error are reviewed first. It is observed that, in either case, each BS requires information about a specific covariance matrix in order to realize the advantage of distributed source coding. Since this covariance matrix depends on the channel realizations corresponding to other BSs, a robust compression method is proposed for a practical scenario in which the information about the covariance available at each BS is imperfect. The problem is formulated using a deterministic worst-case approach, and an algorithm is proposed that achieves a stationary point for the problem. Then, BS selection is addressed with the aim of reducing the number of active BSs, thus enhancing the energy efficiency of the network. An optimization problem is formulated in which compression and BS selection are performed jointly by introducing a sparsity-inducing term into the objective function. An iterative algorithm is proposed that is shown to converge to a locally optimal point. From numerical results, it is observed that the proposed robust compression scheme compensates for a large fraction of the performance loss induced by the imperfect statistical information. Moreover, the proposed BS selection algorithm is seen to perform close to the more complex exhaustive search solution.

• The paper proposes robust distributed compression strategies for Cloud RAN uplink to handle capacity-constrained backhaul and imperfect covariance information at multiple base stations.
• A novel robust compression method based on a deterministic worst-case formulation is introduced, along with an iterative algorithm shown to converge and mitigate performance loss from statistical inaccuracies.
• A joint approach combining compression with base station selection using a sparsity-inducing term is developed to enhance network energy efficiency by minimizing the number of active base stations.

Robust and Efficient Distributed Compression for Cloud Radio Access Networks

In this paper, the authors present an examination of compression strategies and base station (BS) selection algorithms for the uplink in cloud radio access networks characterized by capacity-constrained backhaul links. The central focus is on distributed compression techniques that leverage the correlation in signals received at multiple BSs, thereby enhancing the achievable rate and minimizing mean-square error (MSE) in a network featuring multi-antenna BSs.

To address the requirement for robust compression in scenarios where covariance information across BSs is imperfect, the authors propose a novel compression strategy based on a deterministic worst-case formulation. This robust method significantly mitigates the performance loss traditionally associated with statistical imperfections. An iterative algorithm is developed, demonstrating convergence to a stationary point, thereby optimizing this robust model.

Additionally, the paper explores BS selection methods aiming to enhance the network's energy efficiency by minimizing the number of active BSs. A joint approach combining compression with BS selection is introduced. This is achieved through an optimization framework incorporating a sparsity-inducing term, efficiently reducing active BSs while maintaining high network performance levels.

The numerical analysis presented in the paper confirms the efficacy of the robust compression scheme, indicating substantial compensation for statistical inaccuracies. Moreover, the proposed BS selection algorithm closely approximates the performance of exhaustive search techniques without the associated computational complexity.

The findings have significant implications for next-generation mobile networks, particularly in enhancing scalability and reducing energy consumption. The proposed methodologies contribute to a more efficient deployment of cloud radio access networks, optimizing both the data compression and resource allocation processes. The theoretical insights and practical solutions outlined may influence future developments in AI-driven network optimization, particularly in the development of more sophisticated algorithms for resource allocation and data compression in complex network environments.