Abstract
Cooperative communication is widely seen as a promising key technology for improving the energy efficiency of battery-driven multiple mobile terminals (MTs). In this study, we investigate the use of machine learning (ML) in multiuser cooperative access networks. Because MT cooperation and bandwidth allocation are considered two main issues in such networks, we design an ML-aided method to solve the bandwidth issues so that the proposed method can maximize the network’s energy efficiency. Specifically, we use machine learning with artificial neural network (ANN) trained at base station (BS) (a) to decide whether MTs in the heterogeneous access network should cooperatively communicate and (b) to determine the optimal bandwidth allocation for this communication by distributing the trained ANN to all MTs. The computer simulation results show that under the described communication environment in this paper, the proposed method can provide 99.8% correct prediction for MT cooperation and output the optimal bandwidth allocation with at least 88% accuracy, which demonstrates the effectiveness of the proposed method. Besides, the simulations also show that the proposed method can provide about 14%–25% power consumption reduction, which validates the EE performance of the proposed method.
Highlights
In recent years, as mobile terminal (MT) applications are growing dramatically, the traffic loading on networks and the power consumption of each mobile terminals (MTs) become very important issues in modern wireless networks
To evaluate the accuracy of the proposed machine learning (ML)-based prediction methods, we defined a parameter ρðn′Þ, where n′ = 1, ⋯, Npred, to represent the predicted accuracy of the n′th νop. It is expressed as where ν~opðn′Þ is the predicted optimal proportion of bandwidth for cooperative communication, which is mainly dominated by the MT deployments and communication demands. ν~opðn′Þ can be calculated by ν~op n′ = M∗2 V n′ : ð6Þ
We investigated the cooperative transmission strategy of MTs in heterogeneous network and proposed an ML-aided method to determine MT communication mode and bandwidth allocation
Summary
As mobile terminal (MT) applications are growing dramatically, the traffic loading on networks and the power consumption of each MT become very important issues in modern wireless networks. For reducing traffic loading and saving the power consumption for each MT, forwarding transmission via MTs using cooperative wireless methods, which are called user cooperation, is widely considered a promising approach [1]. Lots of studies have investigated power consumption of user cooperation in cellular systems [2,3,4]. In [2], the authors studied MT cooperation-based traffic downloading for distributing content to MTs. In [3], the authors proposed a method to increase the energy efficiency (EE) of two-MT cooperative cognitive wireless networks with network coding. In [4], the tradeoff between throughput and energy consumption in cooperative cognitive radio networks was theoretically analyzed
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