Green Networking in Cellular HetNets: A Unified Radio Resource Management Framework with Base Station ON/OFF Switching

In this paper, the problem of energy efficiency in cellular heterogeneous networks (HetNets) is investigated using radio resource and power management combined with the base station (BS) ON/OFF switching. The objective is to minimize the total power consumption of the network while satisfying the quality of service (QoS) requirements of each connected user. We consider the case of co-existing macrocell BS, small cell BSs, and private femtocell access points (FAPs). Three different network scenarios are investigated, depending on the status of the FAPs, i.e., HetNets without FAPs, HetNets with closed FAPs, and HetNets with semi-closed FAPs. A unified framework is proposed to simultaneously allocate spectrum resources to users in an energy efficient manner and switch off redundant small cell BSs. The high complexity dual decomposition technique is employed to achieve optimal solutions for the problem. A low complexity iterative algorithm is also proposed and its performances are compared to those of the optimal technique. The particularly interesting case of semi-closed FAPs, in which the FAPs accept to serve external users, achieves the highest energy efficiency due to increased degrees of freedom. In this paper, a cooperation scheme between FAPs and mobile operator is also investigated. The incentives for FAPs, e.g., renewable energy sharing and roaming prices, enabling cooperation are discussed to be considered as a useful guideline for inter-operator agreements.Comment: 15 pages, 9 Figures, IEEE Transactions on Vehicular Technology 201

Base Station Power Optimization for Green Networks Using Reinforcement Learning

The next generation mobile networks have to provide high data rates, extremely low latency, and support high connection density. To meet these requirements, the number of base stations will have to increase and this increase will lead to an energy consumption issue. Therefore “green” approaches to the network operation will gain importance. Reducing the energy consumption of base stations is essential for going green and also it helps service providers to reduce operational expenses. However, achieving energy savings without degrading the quality of service is a huge challenge. In order to address this issue, we propose a machine learning based intelligent solution that also incorporates a network simulator. We develop a reinforcement-based learning model by using deep deterministic policy gradient algorithm. Our model update frequently the policy of network switches in a way that, packet be forwarded to base stations with an optimized power level. The policies taken by the network controller are evaluated with a network simulator to ensure the energy consumption reduction and quality of service balance. The reinforcement learning model allows us to constantly learn and adapt to the changing situations in the dynamic network environment, hence having a more robust and realistic intelligent network management policy set. Our results demonstrate that energy efficiency can be enhanced by 32% and 67% in dense and sparse scenarios, respectively

Non-uniform deployment of power beacons in wireless powered communication networks

© 2002-2012 IEEE. In wireless powered communication networks (WPCNs), base station (BS) and power beacons (PBs) can offer supplement power for uplink transmission of user equipments (UEs). However, the aggregate power consumption of massively deployed PBs may exceed that of a BS. We propose a non-uniform deployment scheme for PBs in WPCNs, where a cell is divided into inner and outer areas, such that BS and PBs can cooperate to power UEs. To be more specific, a BS located in the center of a cell provides downlink power supply for the inner area UEs and uplink information decoding for all the UEs in the cell; while the PBs power UEs in the outer area. With multiple antennas, maximum ratio transmission and maximum ratio combining are adopted for downlink energy beamforming and uplink information reception. Considering a finite area of the network, we derive the distribution of the distance from a non-center-located UE to its nearest PB in the outer area. An optimization problem is formulated to minimize total average power consumption while satisfying BS average transmission power constraint and coverage probability threshold. Moreover, coverage probability is derived for performance evaluation. The numerical results show that the power consumption of the proposed scheme is reduced significantly compared to PB-only WPCNs

Joint Deployment and Mobility Management of Energy Harvesting Small Cells in Heterogeneous Networks

Small heterogeneous cells have been introduced to improve the system capacity and provide the ubiquitous service requirements. In order to make flexible deployment and management of massive small cells, the utilization of self-powered small cell base stations with energy harvesting (EH-SCBSs) is becoming a promising solution due to low-cost expenditure. However, the deployment of static EH-SCBSs entails several intractable challenges in terms of the randomness of renewable energy arrival and dynamics of traffic load with spatio-temporal fluctuation. To tackle these challenges, we develop a tractable framework of the location deployment and mobility management of EH-SCBSs with various traffic load distributions an environmental energy models. In this paper, the joint optimization problem for location deployment and mobile management is investigated for maximizing the total system utility of both users and network operators. Since the formulated problem is a NP-hard problem, we propose a low-complex algorithm that decouples the joint optimization into the location updating approach and the association matching approach. A suboptimal solution for the optimization problem can be guaranteed using the iteration of two stage approaches. Performance evaluation shows that the proposed schemes can efficiently solve the target problems while striking a better overall system utility, compared with other traditional deployment and management strategies

Performance evaluation of handover triggering condition estimation using mobility models in heterogeneous mobile networks

Heterogeneous networks (HetNets) refer to the communication network, consisting of different types of nodes connected through communication networks deploying diverse radio access technologies like LTE, Wi-Fi, Zigbee, and Z-wave, and using different communication protocols and operating frequencies. Vertical handover, is the process of switching a mobile device from one network type to another, such as from a cellular network to a Wi-Fi network, and is critical for ensuring a seamless user experience and optimal network performance, within the handover process handover triggering estimation is one of the crucial step affecting the overall performance. A mathematical analysis is presented for the handover triggering estimation. The performance evaluation shows significant improvement in the probability of successful handover using the proposed handover triggering condition based on speed, distance, and different mobility models. The handover triggering condition is optimised based on the speed of the mobile node, handover completion time, and the coverage range of the current and the target networks of the HetNet node, with due consideration of the mobility model