scholarly journals Q-Learning Based Fair and Efficient Coexistence of LTE in Unlicensed Band

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2875 ◽  
Author(s):  
Rojeena Bajracharya ◽  
Rakesh Shrestha ◽  
Sung Won Kim
Keyword(s):  
Dynamic Environment ◽  
Multimedia Communications ◽  
Duty Cycling ◽  
Q Learning ◽  
Iot Applications ◽  
Term Evolution ◽  
Learning Techniques ◽  
Dynamic Channel ◽  
Selection Of

The increased demand for spectrum resources for multimedia communications and a limited licensed spectrum have led to widespread concern regarding the operation of long term evolution (LTE) in the unlicensed (LTE-U) band for internet of things (IoT) systems. Because Wi-Fi and LTE are diverse with dissimilar physical and link layer configurations, several solutions to achieve an efficient and fair coexistence have been proposed. Most of the proposed solutions facilitate a fair coexistence through a discontinuous transmission using a duty cycling or contention mechanism and an efficient coexistence through a clean channel selection. However, they are constrained only by fairness or efficient coexistence but not both. Herein, we propose joint adaptive duty cycling (ADC) and dynamic channel switch (DCS) mechanisms. The ADC mechanism supports a fair channel access opportunity by muting certain numbers of subframes for Wi-Fi users whereas the DCS mechanism offers more access opportunities for LTE-U and Wi-Fi users by preventing LTE-U users from occupying a crowded channel for a longer time. To support these mechanisms in a dynamic environment, LTE-U for IoT applications is enhanced using Q-learning techniques for an automatic selection of the appropriate combination of muting period and channel. Simulation results show the fair and efficient coexistence achieved from using the proposed mechanism.

scholarly journals Implementation of modified Q learning technique in EMCAP control architecture

2017 ◽  
Vol 7 (1.5) ◽  
pp. 269
Author(s):  
D. Ganesha ◽  
Vijayakumar Maragal Venkatamuni
Keyword(s):  
Learning Algorithm ◽  
Control Strategies ◽  
Cognitive Architecture ◽  
Dynamic Environment ◽  
Learning System ◽  
Selection Strategy ◽  
Q Learning ◽  
Learning Techniques ◽  
Markov Decision ◽  
Environment Experiment

This research introduces a self learning modified (Q-Learning) techniques in a EMCAP (Enhanced Mind Cognitive Architecture of pupils). Q-learning is a modelless reinforcement learning (RL) methodology technique. In Specific, Q-learning can be applied to establish an optimal action-selection strategy for any respective Markov decision process. In this research introduces the modified Q-learning in a EMCAP (Enhanced Mind Cognitive Architecture of pupils). EMCAP architecture [1] enables and presents various agent control strategies for static and dynamic environment.  Experiment are conducted to evaluate the performace for each agent individually. For result comparison among different agent, the same statistics were collected. This work considered varied kind of agents in different level of architecture for experiment analysis. The Fungus world testbed has been considered for experiment which is has been implemented using SwI-Prolog 5.4.6. The fixed obstructs tend to be more versatile, to make a location that is specific to Fungus world testbed environment. The various parameters are introduced in an environment to test a agent’s performance.his modified q learning algorithm can be more suitable in EMCAP architecture.  The experiments are conducted the modified Q-Learning system gets more rewards compare to existing Q-learning.

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

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 6977
Author(s):  
Merkebu Girmay ◽  
Vasilis Maglogiannis ◽  
Dries Naudts ◽  
Adnan Shahid ◽  
Ingrid Moerman
Keyword(s):  
Performance Comparison ◽  
Wifi Networks ◽  
Rule Based ◽  
Traffic Demand ◽  
Q Learning ◽  
Learning Scheme ◽  
Learning Techniques ◽  
Experience Replay ◽  
Sensing Model

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.

scholarly journals Towards a QoE-Driven Resource Control in LTE and LTE-A Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Gerardo Gómez ◽  
Javier Lorca ◽  
Raquel García ◽  
Quiliano Pérez
Keyword(s):  
Evaluation Process ◽  
Resource Control ◽  
Data Services ◽  
Mobile Operator ◽  
Term Evolution ◽  
Lte Advanced ◽  
Potential Use ◽  
Selection Of

We propose a novel architecture for providing quality of experience (QoE) awareness to mobile operator networks. In particular, we describe a possible architecture for QoE-driven resource control for long-term evolution (LTE) and LTE-advanced networks, including a selection of KPIs to be monitored in different network elements. We also provide a description and numerical results of the QoE evaluation process for different data services as well as potential use cases that would benefit from the rollout of the proposed framework.

scholarly journals Modeling an Inverted Pendulum via Differential Equations and Reinforcement Learning Techniques

2020 ◽  
Author(s):  
Siddharth Sharma
Keyword(s):  
Reinforcement Learning ◽  
Differential Equations ◽  
Control Theory ◽  
Inverted Pendulum ◽  
Control Mechanism ◽  
Dynamic Environment ◽  
Mathematical Technique ◽  
Q Learning ◽  
Research Systems ◽  
Learning Techniques

The prevalence of differential equations as a mathematical technique has refined the fields of control theory and constrained optimization due to the newfound ability to accurately model chaotic, unbalanced systems. However, in recent research, systems are increasingly more nonlinear and difficult to model using Differential Equations only. Thus, a newer technique is to use policy iteration and Reinforcement Learning, techniques that center around an action and reward sequence for a controller. Reinforcement Learning (RL) can be applied to control theory problems since a system can robustly apply RL in a dynamic environment such as the cartpole system (an inverted pendulum). This solution successfully avoids use of PID or other dynamics optimization systems, in favor of a more robust, reward-based control mechanism. This paper applies RL and Q-Learning to the classic cartpole problem, while also discussing the mathematical background and differential equations which are used to model the aforementioned system.

scholarly journals Parameters and determinants of responses to selection in antibody libraries

2021 ◽  
Vol 17 (3) ◽  
pp. e1008751
Author(s):  
Steven Schulz ◽  
Sébastien Boyer ◽  
Matteo Smerlak ◽  
Simona Cocco ◽  
Rémi Monasson ◽  
...  
Keyword(s):  
Affinity Maturation ◽  
Response To Selection ◽  
Evolutionary Potential ◽  
Term Evolution ◽  
A Genome ◽  
Antibody Libraries ◽  
Selection For ◽  
The Impact ◽  
Selection Of

The sequences of antibodies from a given repertoire are highly diverse at few sites located on the surface of a genome-encoded larger scaffold. The scaffold is often considered to play a lesser role than highly diverse, non-genome-encoded sites in controlling binding affinity and specificity. To gauge the impact of the scaffold, we carried out quantitative phage display experiments where we compare the response to selection for binding to four different targets of three different antibody libraries based on distinct scaffolds but harboring the same diversity at randomized sites. We first show that the response to selection of an antibody library may be captured by two measurable parameters. Second, we provide evidence that one of these parameters is determined by the degree of affinity maturation of the scaffold, affinity maturation being the process by which antibodies accumulate somatic mutations to evolve towards higher affinities during the natural immune response. In all cases, we find that libraries of antibodies built around maturated scaffolds have a lower response to selection to other arbitrary targets than libraries built around germline-based scaffolds. We thus propose that germline-encoded scaffolds have a higher selective potential than maturated ones as a consequence of a selection for this potential over the long-term evolution of germline antibody genes. Our results are a first step towards quantifying the evolutionary potential of biomolecules.

Link-Level Simulator for Wireless local Area Network

2018 ◽  
Vol 6 (7) ◽  
pp. 314
Author(s):  
Chaithra. H. U ◽  
Vani H.R
Keyword(s):  
Wireless Local Area Network ◽  
Local Area Network ◽  
Local Area Networks ◽  
Local Area ◽  
Long Term Evolution ◽  
Wireless Local Area Networks ◽  
Area Network ◽  
Network Simulator ◽  
Term Evolution

Now a days in Wireless Local Area Networks (WLANs) used in different fields because its well-suited simulator and higher flexibility. The concept of WLAN  with  advanced 5th Generation technologies, related to a Internet-of-Thing (IOT). In this project, representing the Network Simulator (NS-2) used linked-level simulators for Wireless Local Area Networks and still utilized IEEE 802.11g/n/ac with advanced IEEE 802.11ah/af technology. Realization of the whole Wireless Local Area Networking linked-level simulators inspired by the recognized Vienna Long Term Evolution- simulators. As a outcome, this is achieved to link together that simulator to detailed performances of Wireless Local Area Networking with Long Term Evolution, operated in the similar RF bands. From the advanced 5th Generation support cellular networking, such explore is main because different coexistences scenario can arise linking wireless communicating system to the ISM and UHF bands.

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