Abstract
Apart from the the increasing demand of smartphones in human-to-human (H2H) communications, the introduction of machine-to-machine (M2M) devices poses significant challenges to wireless cellular networks. In order to offer the ability to connect billion of devices to propel the society into a new era of connectivity in our homes, officies and smart cities, we design novel radio resource sharing algorithms in a H2H/M2M coexistence case to accommodate M2M communications while not severely degrading existing H2H services. We propose group-based M2M communications that share the same spectrum with H2H communications through device-to-device (D2D) communication, as one of the technology components of 5G architecture. First, we formulate radio resource sharing problem as a sum-rate maximization, problem for which the optimal solution is non-deterministic polynomial-time hard (NP-hard). To overcome the computational complexity of the optimal solution, we model the resource sharing problem as a bipartite graph, then propose a novel interference-aware graph-based resource sharing scheme using a fixed M2M transmit power. To further enhance the protection of H2H services, we introduce an adaptive power control mechanism into the interference-aware graph-based resource sharing scheme. M2M transmit power is efficiently adjusted using one among the two following alternative controllers, namely, either the proportional integral derivative (PID) or the fuzzy logic. The latter is proposed within the aim to assure the desired quality-of-service (QoS) of H2H users and increase the efficiency of M2M spectrum usage. In both cases (fixed and adaptive), a centralized and a semi-distributed instantiations are given. Simulation results show that adaptive M2M radio resource sharing scheme using fuzzy logic is the one that achieves the best compromise. In fact, it guarantees H2H performance in terms of throughput and fairness while maximizing the efficiency of M2M spectrum usage. Simulation results also show that in spite of its quite good performance, semi-distributed M2M resource sharing instantiation achieves them with a decline of up to 10% in terms of H2H throughput compared to the centralized instantiation. This is achieved through a markedly lower communication overhead.
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Appendix: Kuhn-munkres algorithm to solve maximum weighted matching problem
Appendix: Kuhn-munkres algorithm to solve maximum weighted matching problem
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Step 0 For each cluster, start with an arbitrary feasible vertex labeling \(f^{l} ={f^{l}_{0}}\), determine the equality subgraph \(G_{f^{l}}\).
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Step 1 Choose an arbitrary maximum matching Ml in \(G_{f^{l}}\). If Ml is perfect for Gl, then Ml is optimal. Stop. Otherwise, there is some unmatched Ui ∈ Un,k. Set \(S= \left \{U_{i} \right \}\) and T = ∅
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Step 2 If \(J_{G_{f^{l}}}\) ≠ T, go to step 3. Otherwise, \(J_{G_{f^{l}}}=T\). Find
$$ {\alpha_{f}^{l}}= \displaystyle \min_{U_{i} \in , U_{j} \in T^{c}} \left\{ f^{l}(U_{i})+f^{l}(U_{j}) - w(e_{i,j}^{l}) \right\} $$Where Tc denotes the complement of T in Ui, and construct a new labeling \({f^{l}}^{\prime }\) by
$$ {f^{l}}^{\prime}=\left\{ \begin{array}{l} f^{l}(u_{i})-\alpha_{1}, u_{i} \in S \\ f^{l}(u_{i})+\alpha_{1}, u_{i} \in T \\ f^{l}(u_{i}), otherwise \end{array} \right. $$Note that α1 > 0 and \(J_{G_{f^{l}}}\) ≠ T. Replace fl by \({f^{l}}^{\prime }\) and \({G_{f^{l}}}\) by \({G_{f^{l}}^{\prime }}\)
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Step 3 Choose a vertex \(U_{j} \in J_{G_{f^{l}}}(S) \setminus T\). If Uj is matched in M, say with Uk ∈ Ui, replace S by S ∪ Uk and T by T ∪ Uj, and go to step 2. Otherwise, there will be an M-alternating path from Ui to Uj, and we may use this path and a larger matching \({M^{l}}^{\prime }\) ∈ \( {G_{l}}^{\prime }\). Replace Ml by \({M^{l}}^{\prime }\) and go to step 1.
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Hamdoun, S., Rachedi, A. & Ghamri-Doudane, Y. Graph-Based Radio Resource Sharing Schemes for MTC in D2D-based 5G Networks. Mobile Netw Appl 25, 1095–1113 (2020). https://doi.org/10.1007/s11036-020-01527-1
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DOI: https://doi.org/10.1007/s11036-020-01527-1