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• W4295159924 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 $$xin {mathbb{B}}^N$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:mrow> <mml:mi>x</mml:mi> <mml:mo>∈</mml:mo> <mml:msup> <mml:mrow> <mml:mi>B</mml:mi> </mml:mrow> <mml:mi>N</mml:mi> </mml:msup> </mml:mrow> </mml:math> – with block sparsity $$K_{B}$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:msub> <mml:mi>K</mml:mi> <mml:mi>B</mml:mi> </mml:msub> </mml:math> and in-block sparsity $$K_{I}$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:msub> <mml:mi>K</mml:mi> <mml:mi>I</mml:mi> </mml:msub> </mml:math> over block size d . Our analysis shows that the proposed scheme efficiently reduces the signature length to $$mathcal {O}(max {K_{B}log N, K_{B}K_{I}log d})$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mo>max</mml:mo> <mml:mrow> <mml:mo>{</mml:mo> <mml:msub> <mml:mi>K</mml:mi> <mml:mi>B</mml:mi> </mml:msub> <mml:mo>log</mml:mo> <mml:mi>N</mml:mi> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>K</mml:mi> <mml:mi>B</mml:mi> </mml:msub> <mml:msub> <mml:mi>K</mml:mi> <mml:mi>I</mml:mi> </mml:msub> <mml:mo>log</mml:mo> <mml:mi>d</mml:mi> <mml:mo>}</mml:mo> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> and achieves less computational complexity of $$mathcal {O}(dK_{I}^{2}+frac{N}{d}log N)$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mi>d</mml:mi> <mml:msubsup> <mml:mi>K</mml:mi> <mml:mrow> <mml:mi>I</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msubsup> <mml:mo>+</mml:mo> <mml:mfrac> <mml:mi>N</mml:mi> <mml:mi>d</mml:mi> </mml:mfrac> <mml:mo>log</mml:mo> <mml:mi>N</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> compared with standard CS algorithms. Moreover, numerical results suggest strong robustness of the proposed scheme under noisy conditions." @default.
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• W4295159924 date "2022-09-11" @default.
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• W4295159924 title "Distributed ranking-based resource allocation for sporadic M2M communication" @default.
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