This paper presents a novel strategy to decentralize the soft detection procedure in an uplink cell-free massive multiple-input-multiple-output network. We propose efficient approaches to compute the a posteriori probability-per-bit, exactly or approximately when having a sequential fronthaul. More precisely, each access point (AP) in the network computes partial sufficient statistics locally, fuses it with received partial statistics from another AP, and then forwards the result to the next AP. Once the sufficient statistics reach the central processing unit, it performs the soft demodulation by computing the log-likelihood ratio (LLR) per bit, and then a channel decoding algorithm (e.g., a Turbo decoder) is utilized to decode the bits. We derive the distributed computation of LLR analytically.

翻译：本文提出了在无上链接的无细胞大规模多投入-多输出网络中下放软检测程序的新战略。 我们提出了在连续前厅时精确或大致计算后位概率的高效方法。 更确切地说, 网络中每个接入点在当地计算部分足够的统计数据, 与从另一个AP得到的部分统计数据相结合, 然后将结果传送到下一个AP。 一旦足够统计数据到达中央处理单位, 它通过计算日志- 类似比率( LLLR ) / 位进行软下降, 然后使用频道解码算法( 例如, Turbo decoder) 来解码这些位数。 我们用分布式的LLR 分析计算结果 。