Q2A-NOMA: A Q-Learning-based QoS-Aware NOMA System Design for Diverse Data Rate Requirements

2022 ◽  
pp. 1-1
Author(s):  
Muhammad Waseem Akhtar ◽  
Syed Ali Hassan ◽  
Aamir Mahmood ◽  
Haejoon Jung ◽  
Hassaan Khaliq Qureshi ◽  
...  
Keyword(s):  
System Design ◽  
Data Rate ◽  
Q Learning ◽  
Diverse Data

scholarly journals Reinforcement Learning Aided UAV Base Station Location Optimization for Rate Maximization

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2953
Author(s):  
Sudheesh Puthenveettil Gopi ◽  
Maurizio Magarini
Keyword(s):  
Reinforcement Learning ◽  
Clustering Algorithm ◽  
Base Station ◽  
Data Rate ◽  
Cellular System ◽  
Q Learning ◽  
Data Rates ◽  
In The Beginning ◽  
Exploratory Movements ◽  
Station Location

The application of unmanned aerial vehicles (UAV) as base station (BS) is gaining popularity. In this paper, we consider maximization of the overall data rate by intelligent deployment of UAV BS in the downlink of a cellular system. We investigate a reinforcement learning (RL)-aided approach to optimize the position of flying BSs mounted on board UAVs to support a macro BS (MBS). We propose an algorithm to avoid collision between multiple UAVs undergoing exploratory movements and to restrict UAV BSs movement within a predefined area. Q-learning technique is used to optimize UAV BS position, where the reward is equal to sum of user equipment (UE) data rates. We consider a framework where the UAV BSs carry out exploratory movements in the beginning and exploitary movements in later stages to maximize the overall data rate. Our results show that a cellular system with three UAV BSs and one MBS serving 72 UE reaches 69.2% of the best possible data rate, which is identified by brute force search. Finally, the RL algorithm is compared with a K-means algorithm to study the need of accurate UE locations. Our results show that the RL algorithm outperforms the K-means clustering algorithm when the measure of imperfection is higher. The proposed algorithm can be made use of by a practical MBS–UAV BSs–UEs system to provide protection to UAV BSs while maximizing data rate.

A LOW COST SIMULTANEOUS VOICE AND DATA COMMUNICATIONS LINK FOR LOCAL AREA NETWORKS ON A 4 WIRE SYSTEM

1994 ◽  
Vol 04 (02) ◽  
pp. 161-172
Author(s):  
DAVID A. BROWN ◽  
MAHER E. RIZKALLA
Keyword(s):  
System Design ◽  
Local Area Network ◽  
Effective Means ◽  
Technical Problem ◽  
Local Area ◽  
Data Rate ◽  
Full Duplex ◽  
Data Communications ◽  
Area Network ◽  
Worst Case

This paper provides details concerning the design of an integrated voice and data communications link for a Local Area Network that must utilize residential type 2 pair telephone wiring as its transmission medium. The communications link can provide a worst case total data rate of 80 K bps (full duplex), 80 K analog samples of audio signals total (full duplex), as well as power and ground for remote terminals. In a system design that utilizes the communications link discussed here, the above total values of data and audio information flow can be divided by the number of terminals anticipated in the system in any ratio desired. The intent of the design of the communications link is to provide what is believed to be the most cost effective means of implementing an intelligent network of consumer based products in a residential or small business type of environment. In this document, the original technical problem is discussed along with details concerning the development of the solution presented here. The system design example that is presented is only provided as an aid to understanding how the communications link can be applied and is not the focus of the central concept. In that example, the system is comprised of 8 remote terminals and one central control terminal — all of which are voice and data devices. Therefore, the available continuous data rate per terminal is 10 K bps with 5 Khz full duplex audio.

A multicode approach for high data rate UWB system design

2009 ◽  
Vol 57 (2) ◽  
pp. 553-561 ◽  
Author(s):  
Mohamed Kamoun ◽  
Laurent Mazet ◽  
Marc De Courville ◽  
Pierre Duhamel
Keyword(s):  
System Design ◽  
Data Rate ◽  
High Data Rate ◽  
High Data

Image Representation of Time Series for Reinforcement Learning Trading Agent

2020 ◽  
Author(s):  
Guinther K. da Costa ◽  
Leandro Dos S. Coelho ◽  
Roberto Z. Freire1
Keyword(s):  
Time Series ◽  
Reinforcement Learning ◽  
Model Accuracy ◽  
Q Learning ◽  
Learning Agents ◽  
Wide Range ◽  
Trading Agent ◽  
And Performance ◽  
Diverse Data ◽  
Representation Of Time

The availability of diverse data has increased the demand for expertise in algorithmic trading strategies. Reinforcement learning has shown interesting applicability in a wide range of tasks, especially in some challenging problems as trading, where slow model convergence, inference speed, and reduced model accuracy appear as barriers in this type of application. In this paper, we propose the transformation of time series into images considering a transfer learning based on a semi-supervised model with deep Q learning agents, where labels were generated by an evolutionary algorithm to improve both training speed and performance measures.

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