The thesis is divided into seven chapters:
∙ Chapter 1 provides an introduction to energy efficiency in wireless communi- cations, opportunities, and possible applications in different layers of wireless network; followed by the novel contributions to the addressed research ques- tions.
∙ Chapter-2 provides the summary of the broad literature review, including a brief theoretical introduction on the mathematical tools that are of relevant to the work of this thesis.
∙ Chapter-3 presents the part of the research work, where we use rate adapta- tion to increase energy efficiency in device level. This work addresses research question-1.
∙ In Chapter-4, we propose a three state model for the transceivers of a base station to increase energy efficiency of the BS. This work addresses the issue mentioned in research question-2.
∙ Chapter 5 presents our work, where we propose an alogorithm to apply Markov decision process on the three state transceivers of an Aerial base station. This work addresses the issue raised in research question-3.
∙ In Chapter 6, we propose an energy efficient handover algorithm for LTE-A heterogeneous networks. This work addresses research question-4.
∙ Chapter 7 provides a brief conclusion on the outcome of the work done in the prior chapters along with some potential future work; hence, concluding the overall novelty and potential of this thesis.
Chapter 2
Background and Related Work
The ever-expanding wireless communication infrastructure is withdrawing higher en- ergy than ever, raising the need for finding more efficient systems. The design chal- lenges of the network architectures and protocols for energy efficient wireless com- munications have motivated a significant amount of innovations and research in this area. In order to address this issue some new network architectures, advanced physi- cal layer techniques and radio and network resource management schemes have been proposed in literature. Several international research projects, such as the Green Touch initiative directed by Bell Labs, which are dedicated to energy-efficient wire- less communications, are being carried out to revoke the problem before it converts to a blown up issue. Nevertheless there exit some challenges which still need to be sorted out.
In this chapter, we provide broad and detail literature review on energy efficiency at different levels of wireless networks, followed by some background information on handover procedure as well as theoritical background of Markov decision process and reinforcement learning.
2.1
Energy Efficient Metric and Energy Consump-
tion Models for Wireless Devices
One of the essential factors of the energy efficient network design is the accurate metrics for energy efficiency (EE). This is because the optimization of protocol layers depends on the EE Metrics. Existing literature have utilized several different EE metrics. The most common metric systems are definitely the bits per joule method, which is defined as the system throughput for unit-energy consumption. In [76], this metric system capacity is being analysed at the network level; its capacity increased with the number of nodes across the network. Their research let to the realization that the suitability of the large scale and the energy limited sensors as well as the ad-hoc networks are suitable mainly for the data applications that are delay-tolerant. It is noteworthy that the authors in [76] only consider the transmit power associated with data transmission rate for its energy consumption models. But the fact is that the operation costs also include more factors, than just the transmit power. Hence, when other parts of the system power consumption are taken into account then these energy-efficient schemes will not be appropriate anymore. In [8] an energy consump- tion model, which considered both of the transmission energy and the circuit energy consumption has been proposed and analysed. They considered the cases where a given bit error probability, the signal-to-noise-ratio (SNR) per bit requirement rises with M for M-ary quadrature amplitude modulation (MQAM) and falls with M for M-ary frequency-shift keying (MFSK). It is believed that MFSK have greater effi- ciency than MQAM [77], but when the power consumption of the circuit in [8] was taken into account; this was no longer true. The authors had shown that only when transmit power dominates the total power consumption, as for long-range applica- tions, then MFSK is more energy-efficient than MQAM. On the other hand, when the circuit power dominates the total power consumption, as for short-range applica- tions, MQAM is more energy-efficient. In [8] up to 80% of energy has been saved by optimizing the transmission time and the modulation parameters, compared to a non- optimized strategy for uncoded systems. A similar work has been extended to fading
channels in [78]. It is noticeable, that the different methods for modeling the energy consumption have significant impact on the bits-per-Joule metric. Thus, a proper set up for the energy consumption model is vital. Researchers in [7], investigated energy consumption models of macrocellular and microcellular base stations. The energy consumption at the base station with no traffic load, was dubbed the ’static energy part’, while in the scenario with traffic loads was dubbed ’dynamic energy part’, the overall energy consumption for the base station is the sum of both of these energy parts. Although, for a macrocell base station, the energy consumption is dominated by the static part and does not significantly depend on the transmission parameters of each user. Definition of throughput also affects the accuracy of the bits-per-Joule metric. Since not all transmitted data are real information bits, hence transmitted data should not be included into the throughout. For example, the header required in different protocols, signalling information, destroyed packets, and duplicate packets all present overhead bits. In [79], energy consumption of training sequences for chan- nel estimation in fading channels is considered; where the optimal power allocation for pilot and data symbol in terms of EE can reduce transmit power consumption by 84.5% compared with optimal power allocation scheme for maximizing the capacity.