Fuzzy-logic based Q-Learning interference management algorithms in two-tier networks

2017 ◽  
Vol 484 ◽  
pp. 358-366 ◽  
Author(s):  
Qiang Xu ◽  
Zezhong Xu ◽  
Li Li ◽  
Yan Zheng
Keyword(s):  
Fuzzy Logic ◽  
Interference Management ◽  
Q Learning ◽  
Learning Interference ◽  
Management Algorithms

scholarly journals A Reinforcement Learning Approach for Interference Management in Heterogeneous Wireless Networks

2021 ◽  
Vol 15 (12) ◽  
pp. 65
Author(s):  
Akindele Segun Afolabi ◽  
Shehu Ahmed ◽  
Olubunmi Adewale Akinola
Keyword(s):  
Reinforcement Learning ◽  
Power Level ◽  
Heterogeneous Wireless Networks ◽  
Interference Management ◽  
Base Station ◽  
User Equipment ◽  
Base Stations ◽  
Multi Agent Systems ◽  
Q Learning ◽  
Macro Cell

<span lang="EN-US">Due to the increased demand for scarce wireless bandwidth, it has become insufficient to serve the network user equipment using macrocell base stations only. Network densification through the addition of low power nodes (picocell) to conventional high power nodes addresses the bandwidth dearth issue, but unfortunately introduces unwanted interference into the network which causes a reduction in throughput. This paper developed a reinforcement learning model that assisted in coordinating interference in a heterogeneous network comprising macro-cell and pico-cell base stations. The learning mechanism was derived based on Q-learning, which consisted of agent, state, action, and reward. The base station was modeled as the agent, while the state represented the condition of the user equipment in terms of Signal to Interference Plus Noise Ratio. The action was represented by the transmission power level and the reward was given in terms of throughput. Simulation results showed that the proposed Q-learning scheme improved the performances of average user equipment throughput in the network. In particular, </span><span lang="EN-US">multi-agent systems with a normal learning rate increased the throughput of associated user equipment by a whooping 212.5% compared to a macrocell-only scheme.</span>

scholarly journals V2V Routing in VANET Based on Fuzzy Logic and Reinforcement Learning

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Wanli Zhang ◽  
Xiaoying Yang ◽  
Qixiang Song ◽  
Liang Zhao
Keyword(s):  
Fuzzy Logic ◽  
Real Time ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Routing Algorithm ◽  
Cluster Head ◽  
Convergence Speed ◽  
Q Learning ◽  
On The Road ◽  
The Impact

To ensure the transmission quality of real-time communications on the road, the research of routing protocol is crucial to improve effectiveness of data transmission in Vehicular Ad Hoc Networks (VANETs). The existing work Q-Learning based routing algorithm, QLAODV, is studied and its problems, including slow convergence speed and low accuracy, are found. Hence, we propose a new routing algorithm FLHQRP by considering the characteristics of real-time communication in VANETs in the paper. The virtual grid is introduced to divide the vehicle network into clusters. The node’s centrality and mobility, and bandwidth efficiency are processed by the Fuzzy Logic system to select the most suitable cluster head (CH) with the stable communication links in the cluster. A new heuristic function is also proposed in FLHQRP algorithm. It takes cluster as the environment state of heuristic Q-learning, by considering the delay to guide the forwarding process of the CH. This can speed up the learning convergence, and reduce the impact of node density on the convergence speed and accuracy of Q-learning. The problem of QLAODV is solved in the proposed algorithm since the experimental results show that FLHQRP has many advantages on delivery rate, end-to-end delay, and average hops in different network scenarios.

Service Robot Navigation Based on Q-Learning and Fuzzy Logic

2011 ◽  
Vol 1 (2) ◽  
pp. 1-9 ◽  
Author(s):  
Neda Daei ◽  
Hossein M. Shirazi ◽  
Reza Askari ◽  
Mehdi Ghanavati
Keyword(s):  
Fuzzy Logic ◽  
Robot Navigation ◽  
Service Robot ◽  
Q Learning

scholarly journals FQ-AGO: Fuzzy Logic Q-Learning Based Asymmetric Link Aware and Geographic Opportunistic Routing Scheme for MANETs

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 576
Author(s):  
Ali Alshehri ◽  
Abdel-Hameed A. Badawy ◽  
Hong Huang
Keyword(s):  
Fuzzy Logic ◽  
Routing Protocols ◽  
Opportunistic Routing ◽  
Link Quality ◽  
Q Learning ◽  
Routing Scheme ◽  
End To End ◽  
Geographic Opportunistic Routing ◽  
Asymmetric Link ◽  
Network Metrics

The proliferation of mobile and IoT devices, coupled with the advances in the wireless communication capabilities of these devices, have urged the need for novel communication paradigms for such heterogeneous hybrid networks. Researchers have proposed opportunistic routing as a means to leverage the potentials offered by such heterogeneous networks. While several proposals for multiple opportunistic routing protocols exist, only a few have explored fuzzy logic to evaluate wireless links status in the network to construct stable and faster paths towards the destinations. We propose FQ-AGO, a novel Fuzzy Logic Q-learning Based Asymmetric Link Aware and Geographic Opportunistic Routing scheme that leverages the presence of long-range transmission links to assign forwarding candidates towards a given destination. The proposed routing scheme utilizes fuzzy logic to evaluate whether a wireless link is useful or not by capturing multiple network metrics, the available bandwidth, link quality, node transmission power, and distance progress. Based on the fuzzy logic evaluation, the proposed routing scheme employs a Q-learning algorithm to select the best candidate set toward the destination. We implemented FQ-AGO on the ns-3 simulator and compared the performance of the proposed routing scheme with three other relevant protocols: AODV, DSDV, and GOR. For precise analysis, we considered various network metrics to compare the performance of the routing protocols. The simulation result validates our analysis and demonstrates remarkable performance improvements in terms of total network throughput, packet delivery ration, and end-to-end delay. FQ-AGO achieves up to 15%, 50%, and 45% higher throughput compared to DSDV, AODV, and GOR, respectively. Meanwhile, FQ-AGO reduces by 50% the end-to-end latency and the average number of hop-count.

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