Q-Learning Assisted Energy-Aware Traffic Offloading and Cell Switching in Heterogeneous Networks

Abstract

Cell switching has been identified as a major approach to significantly reduce the energy consumption of Heterogeneous Networks (HetNets). The main idea behind cell switching is to turn off idle or lightly loaded Base Stations (BSs) and to offload their traffic to neighbouring active cell(s). However, the impact of the offloaded traffic on the power consumption of the neighbouring cell(s) has not been studied sufficiently in the literature, thereby leading to the development of sub-optimal cell switching mechanisms. In this work, we first considered a Control/Data Separated Architecture (CDSA) with a macro cell serving as the Control Base Station (CBS) and multiple small cells as Data Base Stations (DBS). Then, a Q-learning assisted cell switching algorithm is developed in order to determine the small cells to switch off by considering the increase in power consumption of the macro cell due to offloaded traffic from the sleeping cells. The capacity of the macro cell is also taken into consideration to ensure that the Quality of Service (QoS) requirements of users are maintained. Simulation results show that the proposed cell switching algorithm can achieve up to 50% reduction in the total energy consumption of the considered HetNet scenario.

The Role of Artificial Intelligence Driven 5G Networks in COVID-19 Outbreak: Opportunities, Challenges, and Future Outlook

“…This amounts to energy wastage and financial loss on the part of the network operators. Hence, there is a need to develop intelligent traffic prediction and load adaptive cell switching techniques (Feng et al, 2017;Abubakar et al, 2019;Asad et al, 2019), such that the traffic demand on the network can be continually monitored to identify underutilized BSs and automatically switch them off. This will result in significant energy savings and reduction in electricity bills, particularly during the pandemic.…”

“…In addition, software-defined networking paradigms and caching improve backhaul usage, as discussed in Jaber et al (2016a). Dynamic BS switching can be adopted to ensure energy efficiency due to changing traffic loads as traffic shifts from city centers to residential areas (Abubakar et al, 2019). The advanced features that make 5G networks autonomous and dynamically adapt to traffic changes are fully explored in the cognitive networking discussion presented in the next subsection.…”

Mobility Management-Based Autonomous Energy-Aware Framework Using Machine Learning Approach in Dense Mobile Networks

“…Several studies based on travellers mobility despite randomness show sufficient user movement predictions and optimisation. Cellular towers that exploit various methods of predicting user mobility patterns where ML-based algorithms are popular predictors shown in [13][14][15][16][17]. However, with the 5G limitations [12], ML predictors proved to be smart chosen alternative in order to study traffic flow and user behaviour.…”

“…ES gains by switching On-Off operation would improve the situation to only a limited degree for a given throughput until they are further enriched with the proactive and autonomous approaches by intelligent switching methods. In this direction of research, some recent works show promising results in terms of potential ES [1,[12][13][14][15][16][17]25,26]. However, they fall short for 5G demands, to the best of our knowledge, due to the following five limitations:…”

Load-Aware Cell Switching in Ultra-Dense Networks: An Artificial Neural Network Approach

“…It takes advantage of the spatio-temporal variations in user traffic demands to match power consumption with traffic demand per time thereby avoiding energy wastage during period of low or no traffic load. Various BS switching schemes have been proposed in literature employing analytical [2], heuristic [3], and machine learning [4], [5] techniques.…”