Adaptive Compression and Joint Detection for Fronthaul Uplinks in Cloud Radio Access Networks

PROJECT TITLE :

Adaptive Compression and Joint Detection for Fronthaul Uplinks in Cloud Radio Access Networks

ABSTRACT:

Cloud radio access network (C-RAN) has recently attracted much attention as a promising design for future mobile networks to sustain the exponential growth of data rate. In C-RAN, one data processing center or baseband unit (BBU) communicates with users via distributed remote radio heads (RRHs), that are connected to the BBU via high capacity, low latency fronthaul links. In this paper, we have a tendency to study the compression on fronthaul uplinks and propose a joint decompression algorithm at the BBU. The central premise behind the proposed algorithm is to take advantage of the correlation between RRHs. Our contribution is threefold. First, we have a tendency to propose a joint decompression and detection (JDD) algorithm which jointly performs decompressing and detecting. The JDD algorithm takes into thought each the fading and compression effect during a single decoding step. Second, block error rate (BLER) of the proposed algorithm is analyzed in closed-form by using pair-wise error likelihood analysis. Third, based mostly on the analyzed BLER, we propose adaptive compression schemes subject to quality of service (QoS) constraints to reduce the fronthaul transmission rate whereas satisfying the pre-defined target QoS. As a dual drawback, we tend to conjointly propose a scheme to minimize the signal distortion subject to fronthaul rate constraint. Numerical results demonstrate that the proposed adaptive compression schemes can achieve a compression ratio of three hundred% in experimental setups.

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