One of the major capacity boosters for 5G networks is the deployment of ultra-dense heterogeneous networks (UDHNs). However, this deployment results in tremendousincrease in the energy consumption of the network due to the large number of base statio
ns (BSs) involved. In addition to enhanced capacity, 5G networks must also be energy efficient for it to be economically viable and environmentally friendly. Dynamic cell switching is a very common way of reducing the total energy consumption of the network but most of the proposed methods are computationally demanding which makes them unsuitable for application in ultra-dense network deployment with massive number of BSs. To tackle this problem, we propose a lightweight cell switching scheme also known as Threshold-based Hybrid cEllswItching Scheme (THESIS) for energy optimization in UDHNs. The developed approach combines the benefits of clustering and exhaustive search (ES) algorithm to produce a solution whose optimality is close to that of the ES (which is guaranteed tobe optimal), but is computationally more efficient than ES and as such can be applied for cell switching in real networks even when their dimension is large. The performance evaluation shows that the THESIS produces a significant reduction in the energy consumption of the UDHN and is able to reduce the complexity of finding a near-optimal solution from exponential to polynomial complexity.
One of the ways of achieving improved capacity in mobile cellular networks is via network densification. Even though densification increases the capacity of the network, it also leads to increased energy consumption which can be curbed by dynamically
switching off some base stations (BSs) during periods of low traffic. However, dynamic cell switching has the challenge of spectrum under-utilizationas the spectrum originally occupied by the BSs that are turned off remains dormant. This dormant spectrum can be leased by the primary network (PN) operators, who hold the license, to the secondary network (SN) operators who cannot afford to purchase the spectrum license. Thus enabling the PN to gain additional revenue from spectrum leasing as well as from electricity cost savings due to reduced energy consumption. Therefore, in this work, we propose a cell switching and spectrum leasing framework based on simulated annealing (SA) algorithm to maximize the revenue of the PN while respecting the quality-of-service constraints. The performance evaluation reveals that the proposed method is very close to optimal exhaustive search method with a significant reduction in the computation complexity.
Ultra-dense deployments in 5G, the next generation of cellular networks, are an alternative to provide ultra-high throughput by bringing the users closer to the base stations. On the other hand, 5G deployments must not incur a large increase in energ
y consumption in order to keep them cost-effective and most importantly to reduce the carbon footprint of cellular networks. We propose a reinforcement learning cell switching algorithm, to minimize the energy consumption in ultra-dense deployments without compromising the quality of service (QoS) experienced by the users. In this regard, the proposed algorithm can intelligently learn which small cells (SCs) to turn off at any given time based on the traffic load of the SCs and the macro cell. To validate the idea, we used the open call detail record (CDR) data set from the city of Milan, Italy, and tested our algorithm against typical operational benchmark solutions. With the obtained results, we demonstrate exactly when and how the proposed algorithm can provide energy savings, and moreover how this happens without reducing QoS of users. Most importantly, we show that our solution has a very similar performance to the exhaustive search, with the advantage of being scalable and less complex.
A novel approach is presented in this work for context-aware connectivity and processing optimization of Internet of things (IoT) networks. Different from the state-of-the-art approaches, the proposed approach simultaneously selects the best connecti
vity and processing unit (e.g., device, fog, and cloud) along with the percentage of data to be offloaded by jointly optimizing energy consumption, response-time, security, and monetary cost. The proposed scheme employs a reinforcement learning algorithm, and manages to achieve significant gains compared to deterministic solutions. In particular, the requirements of IoT devices in terms of response-time and security are taken as inputs along with the remaining battery level of the devices, and the developed algorithm returns an optimized policy. The results obtained show that only our method is able to meet the holistic multi-objective optimisation criteria, albeit, the benchmark approaches may achieve better results on a particular metric at the cost of failing to reach the other targets. Thus, the proposed approach is a device-centric and context-aware solution that accounts for the monetary and battery constraints.
