scholarly journals Downlink Beamforming for Energy-Efficient Heterogeneous Networks With Massive MIMO and Small Cells

2018 ◽  
Vol 17 (5) ◽  
pp. 3386-3400 ◽  
Author(s):  
Long D. Nguyen ◽  
Hoang Duong Tuan ◽  
Trung Q. Duong ◽  
Octavia A. Dobre ◽  
H. Vincent Poor
Keyword(s):  
Heterogeneous Networks ◽  
Energy Efficient ◽  
Massive Mimo ◽  
Small Cells ◽  
Downlink Beamforming

scholarly journals Utility-Based Wireless Routing Algorithm for Massive MIMO Heterogeneous Networks

2020 ◽  
Vol 10 (20) ◽  
pp. 7261
Author(s):  
Wei Zhao ◽  
Wen-Hsing Kuo
Keyword(s):  
Heterogeneous Networks ◽  
Massive Mimo ◽  
Routing Algorithm ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Base Stations ◽  
Small Cells ◽  
Multihop Wireless ◽  
Wireless Backhaul ◽  
Input Multiple Output

With the development of 5G communication, massive multiple input multiple output (MIMO) technology is getting more and more attention. Massive MIMO uses a large amount of simultaneous transmitting and receiving antennas to reduce power consumption and raise the level of transmission quality. Meanwhile, the diversification of user equipment (UE) in the 5G environment also makes heterogeneous networks (HetNets) more prevalent. HetNets allow UE of different network standards to access small cells, while the base stations of small cells access a macro base station (BS) to form a multihop wireless heterogeneous backhaul network. However, how to effectively combine these two technologies by efficiently allocating the antennas of each BS during the route construction process of heterogeneous wireless backhaul networks is still an important issue that is yet to be solved. In this paper, we propose an algorithm called preallocated sequential routing (PSR). Based on the links’ channel conditions and the available antennas and location of BSs, it builds a wireless heterogeneous network backhaul topology and adjusts each link’s transmitting and receiving antennas to maximize total utility. Simulation results showed that the proposed algorithm significantly improved the overall utility and the utility of the outer area of heterogeneous networks.

scholarly journals Multi-Objective Optimization of Energy Saving and Throughput in Heterogeneous Networks Using Deep Reinforcement Learning

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7925
Author(s):  
Kyungho Ryu ◽  
Wooseong Kim
Keyword(s):  
Reinforcement Learning ◽  
Heterogeneous Networks ◽  
Energy Efficient ◽  
Service Providers ◽  
Wireless Networking ◽  
Throughput Maximization ◽  
Small Cells ◽  
Mixed Integer ◽  
Mobile Service ◽  
Multi Objective

Wireless networking using GHz or THz spectra has encouraged mobile service providers to deploy small cells to improve link quality and cell capacity using mmWave backhaul links. As green networking for less CO2 emission is mandatory to confront global climate change, we need energy efficient network management for such denser small-cell heterogeneous networks (HetNets) that already suffer from observable power consumption. We establish a dual-objective optimization model that minimizes energy consumption by switching off unused small cells while maximizing user throughput, which is a mixed integer linear problem (MILP). Recently, the deep reinforcement learning (DRL) algorithm has been applied to many NP-hard problems of the wireless networking field, such as radio resource allocation, association and power saving, which can induce a near-optimal solution with fast inference time as an online solution. In this paper, we investigate the feasibility of the DRL algorithm for a dual-objective problem, energy efficient routing and throughput maximization, which has not been explored before. We propose a proximal policy (PPO)-based multi-objective algorithm using the actor-critic model that is realized as an optimistic linear support framework in which the PPO algorithm searches for feasible solutions iteratively. Experimental results show that our algorithm can achieve throughput and energy savings comparable to the CPLEX.

Energy-Efficient Mobility-Aware Caching Algorithms for Clustered Small Cells in Ultra-Dense Networks

2019 ◽  
Vol 68 (7) ◽  
pp. 6833-6846 ◽  
Author(s):  
Iman Keshavarzian ◽  
Zolfa Zeinalpour-Yazdi ◽  
Aliakbar Tadaion
Keyword(s):  
Energy Efficient ◽  
Small Cells ◽  
Dense Networks
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