Adaptive and efficient radio resource sharing schemes for machine type communications underlying cellular networks

Abstract : The Internet-of-Things (IoT) refers to the ever-growing network of everyday objects that interconnect to each other or to other Internet-enabled systems via wireless sensors attached to them. IoT envisions a future where billions of smart devices will be connected and managed through a range of communication networks and cloud-based servers, enabling a variety of monitoring and control applications. Machine-to-Machine (M2M) communications supported by cellular networks, also known as Machine-Type-Communications (MTC) acts as a key technology for partially enabling IoT. M2M communications is a new technology paradigm that facilitates the ubiquitous connectivity between a myriad of devices without requiring human intervention. The surge in the demand for connectivity has further challenged network operators to design novel radio resource allocation algorithms at affordable costs to handle the massive scale of MTC.Different from current radio access technologies tailored to traditional Human- to-Human (H2H) communications, the goal of this thesis is to provide novel efficient and adaptive radio resource sharing schemes for MTC under a H2H/M2M coexistence scenario. We first provide a suitable multiple access scheme to address the joint spectrum scarcity, scalability and Base Station (BS) overload issues. Toward this end, we design a group-based operation where MTC corresponds to local uplink communications between Machine-Type-Devices (MTDs), which represent a specific type of devices that do not rely on the presence of a human interface, and a Machine-Type-Head (MTH). This latter plays the role of a cluster head that relays the information to the BS. We thus address the need to aggregate M2M and Device-to-Device (D2D) technology, as one of the major components of the future evolving cellular networks. Having said that, we first propose in this thesis to model the radio resource sharing problem between MTDs and H2H users as a bipartite graph and develop a novel interference-aware graph-based radio resource sharing algorithm for MTC so as to mitigate the co-channel interference and thus enhance network efficiency. Moreover, low-complexity semi-distributed solution is investigated to alleviate the communication overhead of a centralized solution that we propose as well. Then, as a second contribution, we examine how M2M devices can share the available radio resources in cellular networks with no or limited impact on existing H2H services. Consequently, we propose a joint spectrally and power efficient radio resource sharing scheme. Convinced by the strength of the bipartite graph modeling for the resource sharing problem between H2H users and M2M devices, we empower the graph-based radio resource sharing algorithm with a novel adaptive power control feature using one of two following mechanisms: the Proportional Integral Derivative (PID) controller and the fuzzy logic. Finally, in our third contribution of this thesis, we develop a power efficient and fully-distributed radio resource sharing framework for MTC underlying cellular networks. We use game theory and model the resource sharing problem as an efficient hybrid-game where M2M devices compete for radio resources and switch opportunistically, as M2M devices are selfish in nature, between non-cooperative and cooperative games. The different derived solutions are extended to existing cellular networks, and extensive simulation studies in the context of LTE are conducted. The various simulation results show that the proposed solutions can significantly increase the efficiency of the spectrum usage, mitigate the negative effect on H2H services and save the battery life of M2M devices
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Submitted on : Tuesday, March 27, 2018 - 11:20:33 AM
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Safa Hamdoun. Adaptive and efficient radio resource sharing schemes for machine type communications underlying cellular networks. Networking and Internet Architecture [cs.NI]. Université Paris-Est, 2017. English. ⟨NNT : 2017PESC1096⟩. ⟨tel-01744203⟩



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