scholarly journals IMPSO and Linear Programming-Based Energy-Efficient Cell Association Algorithm for Backhaul-Constrained Ultra-Dense Small-Cell Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Huilin Jiang ◽  
Wenxiang Zhu ◽  
Xiang Song ◽  
Guilu Wu
Keyword(s):  
Energy Efficiency ◽  
Linear Programming ◽  
Optimization Problem ◽  
Small Cell ◽  
Small Cells ◽  
Small Cell Networks ◽  
Efficiency Optimization ◽  
Cell Association ◽  
Backhaul Link ◽  
Cell Networks

This paper studies the energy efficiency optimization problem for coordinated multipoint (CoMP)-enabled and backhaul-constrained ultra-dense small-cell networks (UDNs). Energy efficiency is an eternal topic for future wireless communication networks; however, taking actual bottleneck of the backhaul link and the coordinated network architecture into consideration, it is difficult to find an effective way to improve the energy efficiency of the network. Aiming at this problem, we propose to combine cell association, subchannel allocation, backhaul resource allocation, and sleep/on of the cells together to develop an optimization algorithm for energy efficiency in UDN and then solve the formulated energy efficiency optimization problem by means of improved modified particle swarm optimization (IMPSO) and linear programming in mathematics. Simulation results indicate that nearly 13 % energy cost saving and 21 % energy efficiency improvement can be obtained by combining IMPSO with linear programming, and the backhaul link data rate can be improved by 30 % as the number of small cells increases. From the results, it can be found that by combining IMPSO with linear programming, the proposed algorithm can improve the network energy efficiency effectively at the expense of limited complexity.

Energy Efficiency Optimization in LTE-U Based Small Cell Networks

2019 ◽  
Vol 68 (2) ◽  
pp. 1963-1967 ◽  
Author(s):  
Rui Yin ◽  
Yunfeng Zhang ◽  
Fang Dong ◽  
Anding Wang ◽  
Chau Yuen
Keyword(s):  
Energy Efficiency ◽  
Small Cell ◽  
Small Cell Networks ◽  
Efficiency Optimization ◽  
Cell Networks

Distributed Dynamic Resource Allocation for OFDMA-Based Cognitive Small Cell Networks Using a Regret-Matching Game Approach

2015 ◽  
pp. 230-253 ◽  
Author(s):  
Wei-Sheng Lai ◽  
Tsung-Hui Chang ◽  
Ta-Sung Lee
Keyword(s):  
Resource Allocation ◽  
Small Cell ◽  
Small Cells ◽  
Allocation Strategy ◽  
Small Cell Networks ◽  
Distributed Resource Allocation ◽  
Cell Network ◽  
Resource Allocation Strategy ◽  
Matching Game ◽  
Cell Networks

Game theoretical approaches have been used to develop distributed resource allocation technologies for cognitive heterogeneous networks. In this chapter, we present a novel distributed resource allocation strategy for cognitive small cell networks based on orthogonal frequency-division multiple access. In particular, we consider a heterogeneous network consisting of macrocell networks overlaid with cognitive small cells that opportunistically access the available spectrum. We focus on a regret-matching game approach, aiming at maximizing the total throughput of the small cell network subject to cross-tier interference and quality of service (QoS) constraints. The regret-matching game approach exploits a regret procedure to learn the optimal resource allocation strategy from the regrets of the actions of cognitive users. Furthermore, the regret-matching game approach is extended to the joint resource allocation and user admission control problem. Numerical results are presented to demonstrate the effectiveness of the proposed regre-matching approaches.

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