Emerging communication networks are envisioned to support massive wireless connectivity of heterogeneous devices with sporadic traffic and diverse requirements in terms of latency, reliability, and bandwidth. Providing multiple access to an increasing number of uncoordinated users and sharing the limited resources become essential in this context. In this work, we revisit the random access (RA) problem and exploit the continuous angular group sparsity feature of wireless channels to propose a novel RA strategy that provides low latency, high reliability, and massive access with limited bandwidth resources in an all-in-one package. To this end, we first design a reconstruction-free goal-oriented optimization problem, which only preserves the angular information required to identify the active devices. To solve this, we propose an alternating direction method of multipliers (ADMM) and derive closed-form expressions for each ADMM step. Then, we design a clustering algorithm that assigns the users in specific groups from which we can identify active stationary devices by their angles. For mobile devices, we propose an alternating minimization algorithm to recover their data and their channel gains simultaneously, which allows us to identify active mobile users. Simulation results show significant performance gains in terms of active user detection and false alarm probabilities as compared to state-of-the-art RA schemes, even with limited number of preambles. Moreover, unlike prior work, the performance of the proposed blind goal-oriented massive access does not depend on the number of devices.
翻译:根据设想,新兴通信网络将支持零星交通和延迟、可靠性和带宽方面各种要求的多种不同装置的大规模无线连接。向越来越多的不协调用户提供多重接入,并分享有限资源,在这方面变得至关重要。在这项工作中,我们重新审视随机访问(RA)问题,利用无线频道连续的角形群体宽度特征,提出新的RA战略,在全方位组合中提供低延迟、高可靠性和宽频资源有限的大规模接入。为此,我们首先设计一个无重建目标优化问题,仅保留识别活动装置所需的三角信息。为了解决这个问题,我们提议采用乘数交替方向方法(ADMMM),并为每个ADMM步骤推出封闭式表达方式。然后,我们设计一个组合算法,将用户指派到特定群体,从他们的角度确定积极的固定装置。对于移动装置,我们建议一种交替的最小化算法,以同时恢复其数据和频道收益,从而使我们能够识别积极的移动用户。模拟结果显示,即使是在主动用户检测和大规模访问前目标定位方面所取得的重大业绩收益,比亚前的大规模目标比亚更低的先验前目标。