This work presents a novel framework for random access in crowded scenarios of multiple-input multiple-output(MIMO) systems. A multi-antenna base station (BS) and multiple single-antenna users are considered in these systems. A huge portion of the system resources is dedicated as orthogonal pilots for accurate channel estimation which imposes a huge training overhead. This overhead can be highly mitigated by exploiting intrinsic angular domain sparsity of massive MIMO channels and the sporadic traffic of users, i.e., few number of users are active to sent or receive data in each coherence interval. In fact, the angles of arrivals (AoAs) coming from active users are continuous parameters and can take any arbitrary values. Besides, the AoAs corresponding to each active user are alongside each other forming a specific cluster. This work revolves around exploiting these features. Specifically, a blind clustering-based algorithm is proposed that not only recovers the transmitted data by users in grant free random access and primary pilots in random access blocks of coherent transmission, but also provides accurate channel estimation. Our approach is based on transforming the unknown variables into a higher dimensional space with matrix variables. An off-grid atomic norm minimization is then proposed to obtain the unknown matrix from only a few observed arrays at the BS. Then, a clustering-based approach is employed to identify which AoAs correspond to which active users. After identifying active users and their AoAs, an alternating-based approach is performed to obtain the channels and data or primary pilots of active users. Simulation results demonstrate the effectiveness of our approach in AoA detection as well as data recovery.
翻译:这项工作为多重投入多重产出(MIIMO)系统繁忙的情景下随机访问提供了一个新的框架。 这些系统中考虑的是多anterna基站(BS)和多个单an-antenna用户。 系统资源的一大部分是用于精确的频道估算的正方形实验,这带来了巨大的培训间接费用。 利用大型MIMO频道的内在角域宽度和用户的零星流量可以大大减轻这一间接费用, 也就是说, 很少有用户积极发送或接收每个一致性间隔期间的数据。 事实上, 来自活跃用户的到达点(AoA)角度是连续的参数, 并且可以随意使用任何任意的值。 此外, 与每个活跃用户对应的Aoono Ao AoA 的AoA AoA 与对方一起组成一个特定的集群。 这项工作围绕利用这些特点进行。 具体地说,基于盲组合的算法不仅恢复用户在以免费随机访问和初级访问方式发送的数据, 而且还提供准确的频道估算。 我们的方法是以未知的变量为基础, 将一个未知的基本变量转换成一个更高级的轨道路径, 以A- a orlovelyal oralalalalalalalalalal as as as as as as as as as as the as as as as to the the the as the as a as a as the fake a des the folveolviolviolviolviolviolviolviolveolveold the the the the des as the abild thesmolveild the as the as thesmational as thesmus as as the as as as thes thes thes des thesmold thesal abal abal abal labal lad thesal lad thes as abal ladd thes abal lad thes as a lad thes ad ad lad the a lad a lad thes.