scholarly journals Backhaul-Aware Dimensioning and Planning of Millimeter-Wave Small Cell Networks

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1429
Author(s):  
Pablo Muñoz ◽  
Oscar Adamuz-Hinojosa ◽  
Pablo Ameigeiras ◽  
Jorge Navarro-Ortiz ◽  
Juan J. Ramos-Muñoz
Keyword(s):  
Millimeter Wave ◽  
Computational Cost ◽  
Small Cell ◽  
Small Cells ◽  
Line Of Sight ◽  
Small Cell Networks ◽  
Traffic Demand ◽  
Simulation Results ◽  
Backhaul Networks ◽  
Cell Networks

The massive deployment of Small Cells (SCs) is increasingly being adopted by mobile operators to face the exponentially growing traffic demand. Using the millimeter-wave (mmWave) band in the access and backhaul networks will be key to provide the capacity that meets such demand. However, dimensioning and planning have become complex tasks, because the capacity requirements for mmWave links can significantly vary with the SC location. In this work, we address the problem of SC planning considering the backhaul constraints, assuming that a line-of-sight (LOS) between the nodes is required to reliably support the traffic demand. Such a LOS condition reduces the set of potential site locations. Simulation results show that, under certain conditions, the proposed algorithm is effective in finding solutions and strongly efficient in computational cost when compared to exhaustive search approaches.

scholarly journals Content Placement in Cache-Enabled Sub-6 GHz and Millimeter-Wave Multi-Antenna Dense Small Cell Networks

2018 ◽  
Vol 17 (5) ◽  
pp. 2843-2856 ◽  
Author(s):  
Yongxu Zhu ◽  
Gan Zheng ◽  
Lifeng Wang ◽  
Kai-Kit Wong ◽  
Liqiang Zhao
Keyword(s):  
Millimeter Wave ◽  
Small Cell ◽  
Small Cell Networks ◽  
Content Placement ◽  
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.

Game Theory-Based Radio Resource Optimization in Heterogeneous Small Cell Networks (HSCNs)

2015 ◽  
pp. 137-183
Author(s):  
Mugen Peng ◽  
Yaohua Sun ◽  
Chengdan Sun ◽  
Manzoor Ahmed
Keyword(s):  
Game Theory ◽  
Resource Allocation ◽  
Small Cell ◽  
Base Stations ◽  
Radio Resource Allocation ◽  
Radio Resource ◽  
Small Cell Networks ◽  
The Game Theory ◽  
Simulation Results ◽  
Cell Networks

To optimize radio resource allocation, the game theory is utilized as a powerful tool because its characteristic can be adaptive to the distribution characteristics of in heterogeneous small cell networks (HSCNs). This chapter summarizes the recent achievements for the game theory based radio resource allocation in HSCNs, where macro base stations (MBSs) and dense small cell base stations (SBSs) share the same frequency spectrum and interfere with each other. Two kinds of game models are introduced to optimize the radio resource allocation, namely the non-cooperative Stackelberg and the cooperative coalition. System models, optimization problem formulation, problem solution, and simulation results for these two kinds of game models are presented. Particularly, the Stackelberg models for HSCNs are presented with the Stackelberg equilibrium and the closed-form expressions. The coalition formations for traditional HCSNs, cloud small cell networks, and heterogeneous cloud small cell networks are introduced. Simulation results are shown to demonstrate the proposed game theory based radio resource optimization strategies converged and efficient.

Joint FSO Fronthaul and Millimeter-Wave Access Link Optimization in Cloud Small Cell Networks: A Statistical-QoS Aware Approach

2019 ◽  
Vol 67 (6) ◽  
pp. 4208-4226 ◽  
Author(s):  
Md. Zoheb Hassan ◽  
Md. Jahangir Hossain ◽  
Julian Cheng ◽  
Victor C. M. Leung
Keyword(s):  
Millimeter Wave ◽  
Small Cell ◽  
Small Cell Networks ◽  
Cell Networks ◽  
Access Link

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.

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