Massive device connectivity in Internet of Thing (IoT) networks with sporadic traffic poses significant communication challenges. To overcome this challenge, the serving base station is required to detect the active devices and estimate the corresponding channel state information during each coherence block. The corresponding joint activity detection and channel estimation problem can be formulated as a group sparse estimation problem, also known under the name “Group Lasso”. This letter presents a fast and efficient distributed algorithm to solve such Group Lasso problems, which alternates between solving small-scaled problems in parallel and dealing with a linear equation for consensus. Numerical results demonstrate the speedup of this algorithm compared with the state-of-the-art methods in terms of convergence speed and computation time.

中文翻译：

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大规模连接的分布式优化

具有零星流量的物联网 (IoT) 网络中的大规模设备连接带来了重大的通信挑战。为了克服这一挑战，服务基站需要在每个相干块期间检测活动设备并估计相应的信道状态信息。相应的联合活动检测和信道估计问题可以表述为群稀疏估计问题，也称为“群套索”。这封信提出了一种快速有效的分布式算法来解决此类 Group Lasso 问题，该算法在并行解决小规模问题和处理线性方程以达成共识之间交替。数值结果表明，该算法在收敛速度和计算时间方面与最先进的方法相比具有加速性能。