User-Centric Cross-Tier Base Station Clustering and Cooperation in Heterogeneous Networks: Rate Improvement and Energy Saving

2016 ◽  
Vol 34 (5) ◽  
pp. 1192-1206 ◽  
Author(s):  
Weili Nie ◽  
Fu-Chun Zheng ◽  
Xiaoming Wang ◽  
Wenyi Zhang ◽  
Shi Jin
Keyword(s):  
Energy Saving ◽  
Heterogeneous Networks ◽  
Base Station ◽  
Rate Improvement ◽  
User Centric

scholarly journals Energy-Saving Management Mechanism Based on Hybrid Energy Supplies for LTE Heterogeneous Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Peng Yu ◽  
Lei Feng ◽  
Wenjing Li ◽  
Xuesong Qiu
Keyword(s):  
Energy Saving ◽  
Heterogeneous Networks ◽  
Power Supply ◽  
Low Complexity ◽  
Base Station ◽  
Green Economy ◽  
User Association ◽  
Management Mechanism ◽  
Hybrid Energy ◽  
Self Organized

Aiming at the lack of integrated energy-saving (ES) methods based on hybrid energy supplies in LTE heterogeneous networks, a novel ES management mechanism considering hybrid energy supplies and self-organized network (SON) is proposed. The mechanism firstly constructs ES optimization model with hybrid energy supplies. And then a SON framework is proposed to resolve the model under practical networks. According to the framework, we divide the ES problem into four stages, which are traffic variation prediction, regional Base Station (BS) mode determination, BS-user association, and power supply. And four corresponding low-complexity algorithms are proposed to resolve them. Simulations are taken on under LTE underlay heterogeneous networks. Compared with other algorithms, results show that our mechanism can save 47.4% energy consumption of the network, while keeping coverage, interference, and service quality above acceptable levels, which takes on great green-economy significance.

Energy-saving analysis of telecommunication base station with thermosyphon heat exchanger

2013 ◽  
Vol 66 ◽  
pp. 537-544 ◽  
Author(s):  
Feng Zhou ◽  
Jie Chen ◽  
Guoyuan Ma ◽  
Zhongliang Liu
Keyword(s):  
Heat Exchanger ◽  
Energy Saving ◽  
Base Station

scholarly journals Energy Saving Schemes for Scalable Mobile Computing Networks

2021 ◽  
Author(s):  
Ali Alnoman
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
User Satisfaction ◽  
Virtual Machines ◽  
Base Station ◽  
Data Rate ◽  
Mobile Users ◽  
Task Completion ◽  
Programming Approach ◽  
Network Scalability

With the growing popularity of smart applications that contain computing-intensive tasks, the provision of radio and computing resources with high quality is becoming more and more challenging. Moreover, supporting network scalability is crucial to accommodate the massive numbers of connected devices. In this thesis, we present effective energy saving strategies that consider the utilization of network elements such as base stations and virtual machines, and implement on/off mechanisms taking into account the quality of service (QoS) required by mobile users. Moreover, we investigate the performance of a NOMA-based resource allocation scheme in the context of Internet of Things aiming to improve network scalability and reduce the energy consumption of mobile users. The system model is mainly built upon the M/M/k queueing system that has been widely used in most relevant works. First, the energy saving mechanism is formulated as a 0-1 knapsack problem where the weight and value of each small base station is determined by the utilization and proportion of computing tasks at that base station, respectively. The problem is then solved using the dynamic programming approach which showed significant energy saving performance while maintaining the cloud response time at desired levels. Afterwards, the energy saving mechanism is applied on edge computing to reduce the amount of under-utilized virtual machines in edge devices. Herein, the square-root staffing rule and the Halfin-Whitt function are used to determine the minimum number of virtual machines required to maintain the queueing probability below a threshold value. On the user level, reducing energy consumption can be achieved by maximizing data rate provision to reduce the task completion time, and hence, the transmission energy. Herein, a NOMA-based scheme is introduced, particularly, the sparse code multiple access (SCMA) technique that allows subcarriers to be shared by multiple users. Not only does SCMA help provide higher data rates but also increase the number of accommodated users. In this context, a power optimization and codebook allocation problems are formulated and solved using the water-filling and heuristic approaches, respectively. Results show that SCMA can significantly improve data rate provision and accommodate more mobile users with improved user satisfaction.

scholarly journals User Association and Power Control for Energy Efficiency Maximization in M2M-Enabled Uplink Heterogeneous Networks with NOMA

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5307 ◽  
Author(s):  
Shuang Zhang ◽  
Guixia Kang
Keyword(s):  
Energy Efficiency ◽  
Power Control ◽  
Heterogeneous Networks ◽  
Base Station ◽  
Sequential Optimization ◽  
User Association ◽  
Decode And Forward ◽  
Vast Number ◽  
Machine Type ◽  
Machine Type Communication

To support a vast number of devices with less energy consumption, we propose a new user association and power control scheme for machine to machine enabled heterogeneous networks with non-orthogonal multiple access (NOMA), where a mobile user (MU) acting as a machine-type communication gateway can decode and forward both the information of machine-type communication devices and its own data to the base station (BS) directly. MU association and power control are jointly considered in the formulated as optimization problem for energy efficiency (EE) maximization under the constraints of minimum data rate requirements of MUs. A many-to-one MU association matching algorithm is firstly proposed based on the theory of matching game. By taking swap matching operations among MUs, BSs, and sub-channels, the original problem can be solved by dealing with the EE maximization for each sub-channel. Then, two power control algorithms are proposed, where the tools of sequential optimization, fractional programming, and exhaustive search have been employed. Simulation results are provided to demonstrate the optimality properties of our algorithms under different parameter settings.

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