title: Energy efficiency in next generation cellular networks
Creators: name: Abubakar, Attai Ibrahim
Publisher: name: University of Glasgow
Year: 2022

description: There is an exponential growth in the energy consumption of cellular networks due to the surge in data traffic, explosion of handheld and Internet-of-Things (IoT) devices, development of data-hungry mobile applications, increasing support for new and emerging use cases, the introduction of ultra-dense networks, and aerial base stations (BSs). This will create the challenge of increased energy consumption for next-generation cellular networks that is bound to escalate, if not properly managed. This thesis seeks to address this challenge and make future cellular networks more energy- and cost-efficient, and environmentally sustainable. To achieve this, analytical methods, conventional approaches, and machine learning solutions are utilized to develop novel optimization frameworks that can minimize the energy consumption in heterogeneous cellular networks (HetNets) while satisfying quality of service (QoS) constraints. First, energy optimization in ultra-dense heterogeneous networks (UDHNs) through cell switching and traffic offloading is studied. Though dynamic cell switching is a common technique for reducing energy consumption in UDHNs, most current methods are computationally demanding, making them unsuitable for practical applications in UDHNs with a large number of BSs. As a result, scalable and computationally efficient cell switching and traffic offloading frameworks using Q-learning, a reinforcement learning algorithm, and artificial neural networks (ANN), a supervised learning algorithm, is initially developed. However, these solutions are effective only in small- to medium-sized networks. Subsequently, a lightweight cell switching scheme called Threshold-based Hybrid cEll SwItching Scheme (THESIS), which combines the benefits of multi-level clustering (MLC) and exhaustive search (ES) algorithms is proposed. In addition, the two components of the THESIS algorithm, k-means and ES, are used for benchmarking. The performance evalaution reveals that THESIS algorithm is able to find a good trade-off between optimal energy saving performance and computational complexity. Hence, it is suitable for cell switching purposes in real networks with large dimension. Second, the cell switching solution is extended to include spectrum leasing. Spectrum leasing involves leasing out unused spectrum for a fee (in this case, those originally occupied by switched off BSs). A solution to enable mobile network operators (MNOs) gain additional revenue from leasing dormant spectrum, in addition to reducing energy consumption (electricity bills) via cell switching, is proposed. In this direction, a network scenario comprising primary network (PN) operators, who hold the spectrum license, and secondary network (SN) operators, who need to lease the spectrum is considered. Moreover, both non-delaytolerant (NDT), and delay-tolerant (DT) spectrum demand scenarios are also considered. A cell switching and spectrum leasing framework based on the simulated annealing (SA) algorithm is developed to maximize the revenue of the PN while satisfying the QoS constraints. The simulation results reveal that the DT spectrum demand is more beneficial to both PN and SN operators as it results in 19% increase in the revenue generated by the PN, while leading to a 21% surge in the amount of spectrum that can be accessed by the SN. Third, energy consumption has been identified as one of the major factors limiting the adoption of unmanned ariel vehicles (UAVs) in cellular networks (e.g., for providing additional offloading capacity during cell switching, and spectrum leasing operations), hence, the quest for green UAV-based cellular communications. To this end, a comprehensive survey on energy optimization techniques in UAVbased cellular networks is conducted, which revealed that it is energy-inefficient to continuously make UAV-BSs hover or fly to provide wireless coverage. Thus, an alternative deployment scheme where UAV-BSs land on designated locations, known as landing stations (LSs), is considered, and the appropriate separation distances (∆) between LSs and the optimal hovering position (OHP) are evaluated. Mathematical frameworks using stochastic geometry are developed to model the relationship between power consumption, coverage probability, throughput, and ∆. Numerical results reveal about 95% reduction in energy consumption, which results in more than 20 times increase in the service time of UAV-BS when the LSs are exploited compared to OHP. However, this energy reduction is obtained at the expense of some degradation in coverage probability and throughput, which can be compensated for by increasing the transmit power of the UAV-BS as ∆ increases. This leads to a slight increase in the energy consumption of UAV at LS which is significantly lesser than that of the UAV at OHP. In summary, this thesis presents scalable and computationally efficient energy and revenue optimization frameworks for terrestrial and aerial cellular networks that can be applied to large-scale networks, which are typical in next generation of cellular networks. The proposed solutions would lead to a reduction in operating cost, increased profitability, and the achievement of net-zero emission target.