This work proposes a novel scheme for distributed ranking-based and contention-free resource allocation in large-scale machine-to-machine (M2M) communication networks. We partition a network of N devices into disjoint clusters based on service type, and assign to each cluster a cluster-specific signature for active cluster members to indicate their active status. The devices in each cluster are totally ordered in some a priori-known manner, which gives rise to an active ranking of active cluster members. In order to tackle complexity issues in large-scale M2M networks with a massive number of devices, we propose a distributed resource allocation scheme using the framework of compressed sensing (CS), which mainly consists of three phases: (i) In a full-duplex acquisition phase, the devices transmit their cluster-specific signatures simultaneously and the network activation pattern is collected in a distributed manner. (ii) The base station detects the active clusters and the number of active devices per cluster using block sketching, and allocates resources to each active cluster accordingly. (iii) Each active device determines its active ranking in the cluster and accesses a specific resource according to the ranking position. By exploiting the sparsity in the activation pattern of the M2M devices, the proposed scheme is formulated as a CS support recovery problem for a particular binary block-sparse signal \(x\in {\mathbb{B}}^N\) – with block sparsity \(K_{B}\) and in-block sparsity \(K_{I}\) over block size d. Our analysis shows that the proposed scheme efficiently reduces the signature length to \(\mathcal {O}(\max \{K_{B}\log N, K_{B}K_{I}\log d\})\) and achieves less computational complexity of \(\mathcal {O}(dK_{I}^{2}+\frac{N}{d}\log N)\) compared with standard CS algorithms. Moreover, numerical results suggest strong robustness of the proposed scheme under noisy conditions.

这项工作提出了一种在大规模机器对机器(M2M) 通信网络中基于分布式排序和无竞争资源分配的新方案。我们根据服务类型将N个设备的网络划分为不相交的集群，并为每个集群分配一个集群特定的签名，用于活动集群成员以指示它们的活动状态。每个集群中的设备以某种先验已知的方式完全排序，这导致了活跃集群成员的活跃排名。为了解决具有大量设备的大规模 M2M 网络的复杂性问题，我们提出了一种使用压缩感知框架的分布式资源分配方案（CS），主要包括三个阶段：（i）在全双工采集阶段，设备同时传输其集群特定的签名，并以分布式方式收集网络激活模式。(ii) 基站使用块草图检测活动集群和每个集群的活动设备数量，并相应地为每个活动集群分配资源。(iii) 每个活跃设备确定其在集群中的活跃排名，并根据排名位置访问特定资源。通过利用 M2M 设备激活模式中的稀疏性，所提出的方案被表述为特定二进制块稀疏信号\(x\in {\mathbb{B}}^N\)的 CS 支持恢复问题- 与块稀疏性\(K_{B}\)和块内稀疏性\(K_{I}\)超过块大小d。我们的分析表明，所提出的方案有效地将签名长度减少到\(\mathcal {O}(\max \{K_{B}\log N, K_{B}K_{I}\log d\})\)和与标准 CS 算法相比， \(\mathcal {O}(dK_{I}^{2}+\frac{N}{d}\log N)\)的计算复杂度更低。此外，数值结果表明所提出的方案在噪声条件下具有很强的鲁棒性。