Spectral Efficient and Energy Aware Clustering in Cellular Networks

The current and envisaged increase of cellular traffic poses new challenges to Mobile Network Operators (MNO), who must densify their Radio Access Networks (RAN) while maintaining low Capital Expenditure and Operational Expenditure to ensure long-term sustainability. In this context, this paper analyses optimal clustering solutions based on Device-to-Device (D2D) communications to mitigate partially or completely the need for MNOs to carry out extremely dense RAN deployments. Specifically, a low complexity algorithm that enables the creation of spectral efficient clusters among users from different cells, denoted as enhanced Clustering Optimization for Resources' Efficiency (eCORE) is presented. Due to the imbalance between uplink and downlink traffic, a complementary algorithm, known as Clustering algorithm for Load Balancing (CaLB), is also proposed to create non-spectral efficient clusters when they result in a capacity increase. Finally, in order to alleviate the energy overconsumption suffered by cluster heads, the Clustering Energy Efficient algorithm (CEEa) is also designed to manage the trade-off between the capacity enhancement and the early battery drain of some users. Results show that the proposed algorithms increase the network capacity and outperform existing solutions, while, at the same time, CEEa is able to handle the cluster heads energy overconsumption

A blockchain-based trust management system for 5G network slicing enabled C-RAN

The mobility nature of the wireless networks and the time-sensitive tasks make it necessary for the system to transfer the messages with a minimum delay. Cloud Radio Access Network (C-RAN) reduces the latency problem. However, due to the trustlessness of 5G networks resulting from the heterogeneity nature of devices. In this article, for the edge devices, there is a need to maintain a trust level in the C-RAN node by checking the rates of devices that are allowed to share data among other devices. The SDN controller is built into a macro-cell that plays the role of a cluster head. The blockchain-based automatically authenticates the edge devices by assigning a unique identification that is shared by the cluster head with all C-RAN nodes connected to it. Simulation results demonstrate that, compared with the benchmark, the proposed approach significantly improves the processing time of blocks, the detection accuracy of malicious nodes, and transaction transmission delay

Terminal cooperation in next generation wireless networks: aerial and regional access networks

Throughout the years, progress of humankind has depended on the power of communication and over the decades, the ways of communication has witnessed mammoth changes. Specifically wireless communication in the last decade has completely revolutionized the way we communicate with each other. Smartphones have become an ubiquitous part of our life. With most operators throughout the world deploying fourth generation wireless communication systems, peculiar use cases and scenarios are being envisioned such as public safety networks, aerial networks, etc. to be addressed by the next generation wireless systems. Moreover, as urban areas are becoming saturated commercial network operators are looking for business cases to move towards the untapped regional areas. However, to deploy networks in regional areas economically, novel technologies and architectures need to be developed and investigated. In this thesis, we study the novel concept of terminal cooperation in the context of next generation wireless communication systems especially looking into aerial and regional access networks. In the first part of the thesis, we investigate the physical radio channel for device-to-device (D2D) communication which would help in enabling terminal cooperation in wireless networks. Specifically, we propose propagation model for D2D in rural areas using 922 MHz and 2466 MHz, a channel model for vehicular communications using 5.8 GHz and a propagation model for D2D using millimetre wave frequencies. In the second part of the thesis, we evaluate the coverage performance of aerial access networks using different technologies and develop algorithms to enhance the coverage using terminal cooperation in regional access networks. Specifically, we evaluate the performance of two different technologies, LTE and WiFi, in aerial access networks. We propose game-theoretic algorithms to enable terminal cooperation to enhance coverage in regional access networks and perform system level simulation to evaluate the proposed algorithms. In the last part of this thesis, we analyse and develop techniques to enhance energy efficiency in aerial access networks using terminal cooperation. Specifically, we propose a clustering algorithm called EECAN which improves the energy efficiency of the terrestrial nodes accessing the aerial base-station, a clustering algorithm based on Matern Hardcore Point Process which allows us to optimize cluster head spacing analytically and we further enhance this algorithm by including impairments introduced by the wireless channel. Throughout this thesis, we verify and validate our analytic results, algorithms and techniques with Monte-Carlo simulations of the considered scenarios. Most of the work presented in this thesis was published in-part or as a whole in conferences, journals, book-chapters, project reports or otherwise undergoing a review process. These publications and reports are highlighted in the course of the thesis. Lastly, we invite the reader to enjoy exploring this thesis and we hope that it will add more understanding to this promising new technology of terminal cooperation in aerial and regional access networks

Spectral efficient and energy aware clustering in cellular networks

The current and envisaged increase of cellular traffic poses new challenges to mobile network operators (MNO), who must densify their radio access networks (RAN) while maintaining low capital expenditure and operational expenditure to ensure long-term sustainability. In this context, this paper analyzes optimal clustering solutions based on device-to-device communications to mitigate partially or completely the need for MNOs to carry out extremely dense RAN deployments. Specifically, a low-complexity algorithm that enables the creation of spectral efficient clusters among users from different cells, denoted as enhanced clustering optimization for resources' efficiency is presented. Due to the imbalance between uplink and downlink traffic, a complementary algorithm, known as clustering algorithm for load balancing, is also proposed to create nonspectral efficient clusters when they result in a capacity increase. Finally, in order to alleviate the energy overconsumption suffered by cluster heads, the clustering energy efficient algorithm (CEEa) is also designed to manage the tradeoff between the capacity enhancement and the early battery drain of some users. Results show that the proposed algorithms increase the network capacity and outperform existing solutions, while, at the same time, CEEa is able to handle the cluster heads energy overconsumption

Spectral Efficient and Energy Aware Clustering in Cellular Networks

The current and envisaged increase of cellular traffic poses new challenges to mobile network operators (MNO), who must densify their radio access networks (RAN) while maintaining low capital expenditure and operational expenditure to ensure long-Term sustainability. In this context, this paper analyzes optimal clustering solutions based on device-To-device communications to mitigate partially or completely the need for MNOs to carry out extremely dense RAN deployments. Specifically, a low-complexity algorithm that enables the creation of spectral efficient clusters among users from different cells, denoted as enhanced clustering optimization for resources' efficiency is presented. Due to the imbalance between uplink and downlink traffic, a complementary algorithm, known as clustering algorithm for load balancing, is also proposed to create nonspectral efficient clusters when they result in a capacity increase. Finally, in order to alleviate the energy overconsumption suffered by cluster heads, the clustering energy efficient algorithm (CEEa) is also designed to manage the tradeoff between the capacity enhancement and the early battery drain of some users. Results show that the proposed algorithms increase the network capacity and outperform existing solutions, while, at the same time, CEEa is able to handle the cluster heads energy overconsumption. © 1967-2012 IEEE