Coexistence And Energy Efficiency In Wireless Networks

Author : Agapi Mesodiakaki
Publisher :
ISBN 13 :
Total Pages : 165 pages
Book Rating : 4.:/5 (112 download)

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Download or read book Exploiting Cognition for Energy and Spectrum Efficient Wireless Networks written by Agapi Mesodiakaki and published by . This book was released on 2015 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main objective of this thesis is to propose and evaluate medium access layer algorithms, that will exploit different types of cognition to provide energy and spectrum efficiency enhancement. In particular, two main research directions are followed: i) the first focuses on spectrum-awareness in cognitive radio (CR) networks inspired by the pioneering work conducted by Mitola in 1999, and ii) the second on the context-aware self-adaptation of cellular heterogeneous networks (HetNets) (i.e., self-organizing networks, SONs). In both contexts, the exploitation of the available information plays a key role on providing sustainable wireless networks. However, given the different needs and features that characterize each of these networks, they are studied separately. Hence, the first part of this thesis focuses on the coexistence of secondary networks (SNs) that share the same primary user (PU) resources. A coexistence scheme is proposed as well as a novel energy-efficient contention-aware channel selection algorithm, which: i) exploits cooperative spectrum sensing to detect the free from PU activity licensed channels, ii) for each one of them, it estimates the probability of collision, and iii) selects the less contended (i.e., with the lowest probability of collision) to access first. An analytical model for the throughput and the energy efficiency of the SN under study is provided, which is validated by means of simulation. The proposed channel selection algorithm is shown to outperform its counterparts both in terms of throughput and energy efficiency. The proposed SN coexistence scheme is also shown to achieve throughput and energy efficiency gains, while maintaining fairness among the coexisting SNs. The second part of the thesis focuses on cognitive HetNets with multi-hop small cell (SC) backhaul (BH) links. The multi-hop BH is a promising solution since i) not all SCs are expected to have a direct connection to the core network and consequently they are likely to forward their traffic to the neighboring SCs to reach it and ii) the expected short length of BH links enables the use of millimeter wave (mmWave) frequencies to provide high capacity BH. In this context, this thesis studies the role of BH aiming to answer to whether or not it could constitute an energy bottleneck for the HetNet. In particular, the BH energy impact is compared to the access network (AN), i.e., the links between the users and their serving cells, under different traffic distribution scenarios and BH technologies. Moreover, the user association problem is studied aiming at the joint maximization of energy and spectrum efficiency of the network, without compromising the user equipment (UE) quality of service (QoS) requirements. To that end, analytical user association frameworks are provided, which can be used as benchmarks for the performance evaluation of different user association solutions. Given the need for low complexity solutions, we also propose efficient heuristic algorithms which exploit context-aware information (i.e., UE measurements and requirements, the HetNet architecture knowledge and the available spectrum resources of each base station (BS)) to associate the UEs in an energy and spectrum efficient way, while considering both the AN and BH energy consumption. The proposed algorithms as well as the derived optimal solutions are compared with reference approaches for different BH technologies and traffic distribution scenarios. Insights are gained into the energy and spectrum efficiency trade-off. Furthermore, our results indicate that i) mmWave BH is a promising solution to provide low consumption high capacity BH, and ii) that the proposed algorithms can achieve notable performance gains compared to the state-of-the-art, while achieving near-optimal performance.