论文标题
分布式优化用于大规模连通性
Distributed Optimization for Massive Connectivity
论文作者
论文摘要
具有零星流量的物联网（IoT）网络中的大量设备连接带来了巨大的沟通挑战。为了克服这一挑战，需要使用服务基站来检测活动设备并在每个相干块期间估算相应的通道状态信息。相应的关节活动检测和通道估计问题可以作为群稀疏估计问题提出，也以“组套索”的名称而闻名。这封信提出了一种快速有效的分布式算法来解决此类套索问题，该算法在并行解决小规模的问题和处理线性方程以达成共识之间交替。数值结果证明了与最新方法相比，在收敛速度和计算时间方面，该算法的加速。
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.
