The number of wireless devices (e.g., cellular phones, IoT, laptops) connected to Wireless Local Area Networks (WLAN) grows each year exponentially. The orchestration of the connected devices becomes infeasible, especially when the number of resources available at the single access point (e.g., Base Station, Wireless Access Points) is limited. On the other hand, the number of antennas at each device grows too. We leverage the large number of antennas to suggest a massive multiple-user multiple-input-multiple-output (MU-MIMO) scheme using sparse coding based on Group Testing (GT) principles, which reduces overhead and complexity. We show that it is possible to jointly identify and decode up to $K$ messages simultaneously out of $N\cdot C$ messages (where $N$ is the number of users and $C$ is the number of messages per user) without any scheduling overhead or prior knowledge of the identity of the transmitting devices. Our scheme is order-optimal in the number of users and messages, utilizing minimal knowledge of channel state and an efficient (in both run-time and space) decoding algorithm requiring $O(K\log NC)$ antennas. We derive sufficient conditions for vanishing error probability and bound the minimal number of antennas necessary for our scheme.
翻译:连接到无线局域网的无线装置(例如移动电话、IoT、膝上型计算机)的数量每年以指数指数增长。连接装置的管弦化变得不可行,特别是当单一接入点(例如基地站、无线接入点)可用资源数量有限时。另一方面,每个装置的天线数量也在增长。我们利用大量天线,利用基于集团测试原则的稀少编码,提出大规模多用户多输入多输出(MU-MIMO)计划,降低间接费用和复杂性。我们表明,有可能同时从$N\cdot C信息中找出和解码高达$K的信息(其中美元是用户数量,美元是每个用户的信息数量),而没有安排管理或事先知道传输装置的身份。我们的计划在用户数量和信息数量上保持最优化,利用最低限度的频道状态知识和足够高效的天线(我们运行和进入必要的天线),要求最低的频率和最小的天线(我们运行时和最小的天线),要求有最低的轨道和最小的天线(我们运行和最小的天平的天线),以便获得必要的天线。