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

Towards Energy Efficient Relay Placement and Load Balancing in Future Wireless Networks

This paper presents an energy efficient relay deployment algorithm that determines the optimal location and number of relays for future wireless networks, including Long Term Evolution (LTE)-Advanced heterogeneous networks. We formulate an energy minimization problem for macro-relay heterogeneous networks as a Mixed Integer Linear Programming (MILP) problem. The proposed algorithm not only optimally connects users to either relays or eNodeBs (eNBs), but also allows eNBs to switch into inactive mode. This is possible by enabling relay-to-relay communication which forms the basis for relays to act as donors for neighboring relays instead of eNBs. Moreover, it relaxes traffic load of some eNBs in order to allow them to enter the inactive mode. We characterize the optimal as well as provide an approximate solution, which, however, performs very closely to the optimum. Our performance evaluation shows that an optimal relay deployment with relays acting as donors can significantly improve system energy efficiency

A Study on Energy-Efficient Wireless Sensor Network based on Machine Learning Techniques

Wireless sensor networks (WSNs) are advantageous when there is no existing infrastructure (such as in military applications, emergency relief efforts, etc.) and it is necessary to develop a network at a low cost. A predetermined routing protocol or intrusion detection system is not available to Wireless Sensor Networks because they are dynamic by nature and need separating the network's nodes to do this. Because nodes in the majority of WSN applications are mobile and rely on battery capacity and the availability of restricted resources, energy consumption is an important research area for carrying out a variety of activities in WSNs. Self-learning algorithms that function without scripting or human involvement can be effectively used to report this problem depending on the applications need. This study investigated different ML-based WSN systems and exploring the ML techniques for energy efficiency along with some open issues

5G Technology in Smart Healthcare and Smart City Development Integration with Deep Learning Architectures

As more and more medical devices, including as mobile phones, sensors, and remote monitoring equipment, require Internet access, wireless networks have gained considerable traction in the healthcare sector. High-performance technologies, such as the forthcoming fifth generation/sixth generation (5G/6G), are needed for data transit to and from medical equipment in order to give patients with state-of-the-art medical treatments. Furthermore, much better optimization techniques must be used when creating its primary components. Intelligent system design affects how all medical equipment operates, which presents a challenging issue in medical applications. Using information from many sources, electronic health records are built and stored there. These data are compiled in several formats and techniques. There are various big data strategies that could be utilised to reconcile the conflicting data. Artificial intelligence, machine learning and deep learning methods can be used to forecast diseases or other problems using the knowledge gathered from big data analytics. With the advent of 5G, augmented reality, virtual reality and spatial computing are all enhanced, which has a profound effect on healthcare informatics by allowing for real-time remote monitoring. With the advent of 5G technologies, healthcare services can be provided over vast distances via a vast network of interconnected devices and high-performance computation. Disease detection and treatment using dynamic data can be accomplished with the help of deep learning techniques such as Deep Convolutional Neural Networks (DCNN). Deep convolutional neural networks that incorporate images of sick regions are frequently employed for classification tasks

Optimal Spectrum Utilization and Flow Controlling In Heterogeneous Network with Reconfigurable Devices

Fairness provisioning in heterogeneous networks is a prime issue for high-rate data flow, wherein the inter-connectivity property among different communication devices provides higher throughput. In Hetnet, optimal resource utilization is required for efficient resource usage. Proper resource allocation in such a network led to higher data flow performance for real-time applications. In view of optimal resource allocation, a resource utilization approach for a reconfigurable cognitive device with spectrum sensing capability is proposed in this paper.  The allocation of the data flow rate at device level is proposed for optimization of network fairness in a heterogeneous network.  A dynamic approach of rate-inference optimization is proposed to provide fairness in dynamic data traffic conditions. The simulation results validate the improvement in offered quality in comparison to multi-attribute optimization