Multi-Cell LTE-U/Wi-Fi Coexistence Evaluation Using a Reinforcement Learning Framework

Nowadays, broadband applications that use the licensed spectrum of the cellular network are growing fast. For this reason, Long-Term Evolution-Unlicensed (LTE-U) technology is expected to offload its traffic to the unlicensed spectrum. However, LTE-U transmissions have to coexist with the existing WiFi networks. Most existing coexistence schemes consider coordinated LTE-U and WiFi networks where there is a central coordinator that communicates traffic demand of the co-located networks. However, such a method of WiFi traffic estimation raises the complexity, traffic overhead, and reaction time of the coexistence schemes. In this article, we propose Experience Replay (ER) and Reward selective Experience Replay (RER) based Q-learning techniques as a solution for the coexistence of uncoordinated LTE-U and WiFi networks. In the proposed schemes, the LTE-U deploys a WiFi saturation sensing model to estimate the traffic demand of co-located WiFi networks. We also made a performance comparison between the proposed schemes and other rule-based and Q-learning based coexistence schemes implemented in non-coordinated LTE-U and WiFi networks. The simulation results show that the RER Q-learning scheme converges faster than the ER Q-learning scheme. The RER Q-learning scheme also gives 19.1% and 5.2% enhancement in aggregated throughput and 16.4% and 10.9% enhancement in fairness performance as compared to the rule-based and Q-learning coexistence schemes, respectively.

Section: Coexistence In Coordinated Lte and Wifi Networkmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

Section: Assumptionsmentioning

confidence: 99%

See 1 more Smart Citation

Coexistence Scheme for Uncoordinated LTE and WiFi Networks Using Experience Replay Based Q-Learning

Girmay

Maglogiannis

Naudts

et al. 2021

“…Consequently, the network becomes sustainable and self-adaptive. Neto et al in [25] proposed Q-Learning to LTE-U to adjust the duty cycle parameters to reduce coexistence interference and improve the system data rate. A saturated network scenario is considered to evaluate the proposed scheme in the ns-3 simulator.…”

Section: A Brief Review Of Articles Of This Special Issuementioning

confidence: 99%

Internet of Multimedia Things (IoMT): Opportunities, Challenges and Solutions

Zikria

Afzal

Kim

2020

With the immersive growth of the Internet of Things (IoT) and real-time adaptability, quality of life for people is improving. IoT applications are diverse in nature and one crucial aspect of it is multimedia sensors and devices. These IoT multimedia devices form the Internet of Multimedia Things (IoMT). It generates a massive volume of data with different characteristics and requirements than the IoT. The real-time deployment scenarios vary from smart traffic monitoring to smart hospitals. Hence, Timely delivery of IoMT data and decision making is critical as it directly involves the safety of human beings. In this paper, we present a brief overview of IoMT and future research directions. Afterward, we provide an overview of the accepted articles in our special issue on the IoMT: Opportunities, Challenges, and Solutions.

“…With the development of Wireless Sensor Network (WSN) [ 34 ] and 5th-Generation (5G) communication technologies [ 35 ], the IoT connects millions of devices, including vehicles, smartphones, home appliance, and other electronics, enabling these objects exchange data [ 36 ]. The traditional Internet has been extensively up-scaled via IoT [ 37 , 38 , 39 ]. As shown in Figure 4 .…”

Section: Related Workmentioning

confidence: 99%

An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control

Yong

et al. 2020

With smart city infrastructures growing, the Internet of Things (IoT) has been widely used in the intelligent transportation systems (ITS). The traditional adaptive traffic signal control method based on reinforcement learning (RL) has expanded from one intersection to multiple intersections. In this paper, we propose a multi-agent auto communication (MAAC) algorithm, which is an innovative adaptive global traffic light control method based on multi-agent reinforcement learning (MARL) and an auto communication protocol in edge computing architecture. The MAAC algorithm combines multi-agent auto communication protocol with MARL, allowing an agent to communicate the learned strategies with others for achieving global optimization in traffic signal control. In addition, we present a practicable edge computing architecture for industrial deployment on IoT, considering the limitations of the capabilities of network transmission bandwidth. We demonstrate that our algorithm outperforms other methods over 17% in experiments in a real traffic simulation environment.