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Architectural enhancement to enable MTC
The access network of LTE and its advancements, known as Evolved Universal Terrestrial Radio Access Network (E-UTRAN) consists of evolved Node Bs (eNBs) which provide user plane and control plane protocol terminations toward the User Equipment (UE). The E-UTRAN architecture is flat since there is no centralized controller in radio access network. The eNBs may be interconnected with each other by means of the X2 interface and to the core network by means of the S1 interface as illustrated in Fig. 2.3. The current LTE cellular network is designed to support only H2H services for UEs. With the introduction of M2M communications into LTE-A cellular networks, architectural enhancement needs to be addressed to accommodate future M2M services without degrading the performance of existing traditional H2H services. Fig. 2.3 illustrates three different methods which are feasible to enable M2M communications: direct access, multi-hop access and peer-to-peer access.
D2D and M2M aggregation
In a similar way to D2D communications, MTC focuses on exchanges between M2M devices or between M2M devices and the infrastructure. Nevertheless, M2M communications is technology-independent and application-oriented since there is no requirements in terms of distances for example between devices as in D2D communications. On the other hand, D2D communications aims to establish proximity connectivity services which is technology-dependent [10].
Many researchers in the literature have focused separately on the two emerging paradigms in the next generation cellular technologies, M2M and D2D communications. However, only few have addressed the combination of M2M and D2D communications. Indeed, M2M communication is usually handled by a random medium access technique. However, random access can potentially lead to increased collisions for a dense system such as massive MTC. Therefore, networkassisted D2D communication can be considered a prominent technology to accommodate M2M communications. Although few authors have exploited the benefits of D2D to accommodate M2M communications in cellular networks, the motivation between M2M and D2D aggregation has been different. For instance, motivated by the reduced power consumption and the hop gain enabled through D2D communications, authors in [78] have considered a multi-hop D2D communication for end-to-end M2M connectivity, where cellular and D2D links have been assigned orthogonal radio resources to avoid the co-channel interference. In [75], authors have developed a relay-based technique in a M2M use case and have proved that relay-aided D2D communications could be an elegant solution to provide reliable transmission as well as improve the overall network throughput.
Major challenges in enabling MTC
Different from H2H communications, M2M communications has different requirements due to their specific features. Designing efficient radio resource allocation schemes convenient to the two major scenarios of M2M communications, namely the peer-to-peer model and the client-server model, is still an open problem. Recent research works are addressing the key question of: “how existing cellular networks should be improved to enable the massive scale of MTC?”. However, only few have considered the H2H/M2M coexistence scenario. In essence, at least the following major issues have to be addressed to provide efficient radio resource allocation algorithms under a H2H/M2M coexistence scenario. The first issue to tackle is the scalability due to the limited licensed spectrum.
Current cellular networks have been optimally designed to support humanoriented services and consider only a small number of H2H users. Due to the high penetration of M2M devices in order of the billions, the competition on the limited radio resources will significantly increase. Hence, our first contribution in chapter 3 is devoted to design a spectrally efficient radio resource sharing scheme under a H2H/M2M coexistence scenario.
The second issue that must be addressed is the guarantee of the desired Quality of Service (QoS) of H2H links while maximizing the efficiency of the shared spectrum usage. Indeed, the massive scale of M2M communications introduced in cellular networks can play a detrimental role in degrading the performance of existing H2H services. Hence, our proposed radio resource sharing algorithm in the second contribution in chapter 4 is not only spectrally efficient but also power efficient with the aim to reduce the negative impact on H2H services and maximize the M2M spectrum usage which is crucially important.
Table of contents :
1 Thesis presentation
1.1 Introduction
1.2 Research challenges and contributions
1.3 Thesis organization
2 An overview of M2M communications
2.1 Foreword
2.2 M2M communications
2.3 MTC applications
2.3.1 Intelligent transportation systems
2.3.1.1 Logistics
2.3.1.2 Driving assistance
2.3.1.3 Fleet management
2.3.1.4 Smart parking
2.3.2 Healthcare
2.3.2.1 Tracking and monitoring
2.3.2.2 Identification and authentication
2.3.2.3 Data collection
2.3.3 Smart environment
2.3.3.1 Smart homes, offices and cities
2.3.3.2 Industrial plants
2.3.4 Public safety
2.4 Characteristics of MTC
2.5 SC-FDMA in LTE networks
2.6 Architectural enhancement to enable MTC
2.6.1 Direct access
2.6.2 Multi-hop access
2.6.3 Peer-to-peer access
2.7 D2D communications
2.7.1 D2D categories
2.7.2 D2D use cases
2.7.3 D2D and M2M aggregation
2.8 Major challenges in enabling MTC
2.9 Conclusions
3 Graph-Based radio resource sharing scheme for M2M communications
3.1 Foreword
3.2 Context and motivation
3.3 Related works
3.3.1 Random access procedure
3.3.2 Multi-radio access technologies in LTE-U
3.3.3 OFDM-LTE systems
3.4 System model and H2H/M2M coexistence problem formulation
3.4.1 System description
3.4.2 Resource sharing optimization problem
3.4.3 Complexity analysis
3.4.4 Interference-aware bipartite graph modeling
3.5 Fixed Radio Resource Sharing Algorithm
3.5.1 Fixed Centralized Radio Resource Sharing Algorithm
3.5.1.1 Edge weight assignment
3.5.1.2 MWM Solving
3.5.2 Fixed semi-Distributed Radio Resource Sharing Algorithm
3.5.2.1 Edge weight assignment
3.5.2.2 MWM Solving
3.5.3 Complexity analysis
3.6 Performance evaluation
3.6.1 Average network sum-rate
3.6.2 Average H2H sum-rate
3.6.3 Impact of the inter-cluster interference
3.6.4 Potential H2H users selected for resource sharing
3.7 Conclusions
4 Adaptive Graph-Based radio resource sharing scheme for M2M communications
4.1 Foreword
4.2 Context and motivation
4.3 Related works
4.3.1 Discontinuous reception
4.3.2 Energy-efficient resource allocation for MTC
4.3.3 Group-based MTC
4.4 Overview of PID and Fuzzy logic controllers
4.4.1 PID controller
4.4.2 Fuzzy Logic
4.5 System description
4.6 Fixed Radio Resource Sharing Algorithm
4.6.1 Edge weight assignment
4.6.2 MWM Solving
4.7 Adaptive Radio Resource Sharing Algorithm using PID controller
4.8 Adaptive Radio Resource Sharing Algorithm using Fuzzy Logic .
4.9 Performance evaluation
4.9.1 Network sum-throughput
4.9.2 H2H throughput and percentage of MTDs whose QoS is not met
4.9.3 H2H fairness
4.9.4 PDF of MTDs whose QoS is not met
4.10 Conclusions
5 Efficient transmission strategy selection algorithm forM2M communications
5.1 Foreword
5.2 Context and motivation
5.3 Related works
5.3.1 Non-cooperative game
5.3.2 Cooperative game
5.3.3 Discussion
5.4 System model and problem formulation
5.4.1 System model
5.4.2 Problem formulation
5.5 MTC transmission strategy selection algorithm: a hybrid-game .
5.6 Non-cooperative power control game
5.6.1 Payoff function
5.6.2 Mixed Nash Equilibrium
5.7 Cooperative power control game
5.7.1 Cost of information exchange
5.7.2 Payoff function
5.7.3 Advanced power control techniques in the strategy space .
5.7.3.1 Strategy space set using PID controller
5.7.3.2 Strategy space set using fuzzy logic
5.8 Performance evaluation
5.8.1 H2H throughput
5.8.2 H2H fairness
5.8.3 M2M power consumption
5.8.4 MTDs whose QoS is not met
5.9 Conclusions
6 Conclusions and Future works
6.1 Conclusions
6.2 Future works
List of Figures
List of Tables
Bibliography
