Performance comparison of data-sharing and compression strategies for cloud radio access networks

This paper provides a system-level performance comparison of two fundamentally different transmission strategies for the downlink of a cloud radio access network. The two strategies, namely the data-sharing strategy and the compression-based strategy, differ in the way the limited backhaul is utilized. While the data-sharing strategy uses the backhaul to carry raw user data, the compression strategy uses the backhaul to carry compressed beamformed signals. Although these strategies have been individually studied in the literature, a fair comparison of the two schemes under practical network settings is challenging because of the complexity in jointly optimizing user scheduling, beamforming, and power control for system-level performance evaluation, along with the need to optimize cooperation clusters for the data-sharing strategy and quantization noise levels for the compression strategy. This paper presents an optimization framework for both the data-sharing and compression strategies, while taking into account losses due to practical modulation in terms of gap to capacity and practical quantization in terms of gap to rate-distortion limit. The main conclusion of this paper is that the compression-based strategy, even with a simple fixed-rate uniform quantizer, outperforms the data-sharing strategy under medium to high capacity backhauls. However, the data-sharing strategy outperforms the compression strategy under low capacity backhauls primarily because of the large quantization loss at low backhaul capacity with compression.