We consider the random access problem in wireless networks such as cellular networks and massive machine type communication networks, where a large number of devices/users are connected to a base station. In order to establish communication with the base station, as a part of the random access procedure, active users select and transmit codes (pilot sequences) randomly from a codebook. Since the active users are typically a small fraction of the total users, sparse signal recovery algorithms are employed to detect the transmitted codes. In this paper, we use multiple measurement vector model for the received signal and develop a parallel greedy search algorithm and it’s stopping criteria to detect the active users. We show through simulations that the proposed scheme, compared with the existing greedy algorithms, gives better detection performance at lower false alarms levels. We study how the code length and the code word size need to be chosen in order to detect a given number of active users. We also address the scenario when there are different timing offsets for signals received from different users.

我们考虑无线网络中的随机接入问题，例如蜂窝网络和大规模机器类型通信网络，其中大量设备/用户连接到基站。为了与基站建立通信，作为随机接入过程的一部分，活动用户从码本中随机选择并发送代码（导频序列）。由于活动用户通常只占总用户的一小部分，因此采用稀疏信号恢复算法来检测传输的代码。在本文中，我们对接收信号使用多测量向量模型，并开发并行贪婪搜索算法及其停止标准来检测活跃用户。我们通过模拟表明，与现有的贪心算法相比，所提出的方案 以较低的误报水平提供更好的检测性能。我们研究如何选择代码长度和代码字大小以检测给定数量的活跃​​用户。我们还解决了从不同用户接收到的信号存在不同定时偏移的情况。