Distributed ranking-based resource allocation for sporadic M2M communication

Abstract

Abstract 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 $$x\in {\mathbb{B}}^N$$ x ∈ B N – with block sparsity $$K_{B}$$ K B and in-block sparsity $$K_{I}$$ K I over block size d. Our analysis shows that the proposed scheme efficiently reduces the signature length to $$\mathcal {O}(\max \{K_{B}\log N, K_{B}K_{I}\log d\})$$ O ( max { K B log N , K B K I log d } ) and achieves less computational complexity of $$\mathcal {O}(dK_{I}^{2}+\frac{N}{d}\log N)$$ O ( d K I 2 + N d log N ) compared with standard CS algorithms. Moreover, numerical results suggest strong robustness of the proposed scheme under noisy conditions.

Keywords

• Compressed sensing
• Block sparsity
• M2M
• Distributed resource allocation
• Sketching