A survey on machine-learning techniques in cognitive radios

M Bkassiny, Y Li, SK Jayaweera - … Communications Surveys & …, 2012‏ - ieeexplore.ieee.org
In this survey paper, we characterize the learning problem in cognitive radios (CRs) and
state the importance of artificial intelligence in achieving real cognitive communications …

A survey on radio resource allocation in cognitive radio sensor networks

A Ahmad, S Ahmad, MH Rehmani… - … Surveys & Tutorials, 2015‏ - ieeexplore.ieee.org
Wireless sensor networks (WSNs) use the unlicensed industrial, scientific, and medical
(ISM) band for transmissions. However, with the increasing usage and demand of these …

Reinforcement learning algorithms with function approximation: Recent advances and applications

X Xu, L Zuo, Z Huang - Information sciences, 2014‏ - Elsevier
In recent years, the research on reinforcement learning (RL) has focused on function
approximation in learning prediction and control of Markov decision processes (MDPs). The …

Reinforcement learning-based routing protocols for vehicular ad hoc networks: A comparative survey

RA Nazib, S Moh - IEEE Access, 2021‏ - ieeexplore.ieee.org
Vehicular-ad hoc networks (VANETs) hold great importance because of their potentials in
road safety improvement, traffic monitoring, and in-vehicle infotainment services. Due to high …

Intelligent wireless communications enabled by cognitive radio and machine learning

X Zhou, M Sun, GY Li, BHF Juang - China Communications, 2018‏ - ieeexplore.ieee.org
The ability to intelligently utilize resources to meet the need of growing diversity in services
and user behavior marks the future of wireless communication systems. Intelligent wireless …

Learning and reasoning in cognitive radio networks

L Gavrilovska, V Atanasovski… - … Surveys & Tutorials, 2013‏ - ieeexplore.ieee.org
Cognitive radio networks challenge the traditional wireless networking paradigm by
introducing concepts firmly stemmed into the Artificial Intelligence (AI) field, ie, learning and …

Learning transfer-based adaptive energy minimization in embedded systems

RA Shafik, S Yang, A Das… - … on Computer-Aided …, 2015‏ - ieeexplore.ieee.org
Embedded systems execute applications with varying performance requirements. These
applications exercise the hardware differently depending on the computation task …

Federated learning for 6G: Paradigms, taxonomy, recent advances and insights

MB Driss, E Sabir, H Elbiaze, W Saad - arxiv preprint arxiv:2312.04688, 2023‏ - arxiv.org
Artificial Intelligence (AI) is expected to play an instrumental role in the next generation of
wireless systems, such as sixth-generation (6G) mobile network. However, massive data …

Distributed heuristically accelerated Q-learning for robust cognitive spectrum management in LTE cellular systems

N Morozs, T Clarke, D Grace - IEEE Transactions on Mobile …, 2015‏ - ieeexplore.ieee.org
In this paper, we propose an algorithm for dynamic spectrum access (DSA) in LTE cellular
systems-distributed ICIC accelerated Q-learning (DIAQ). It combines distributed …

[ספר][B] Cognitive radio communication and networking: Principles and practice

RC Qiu, Z Hu, H Li, MC Wicks - 2012‏ - books.google.com
The author presents a unified treatment of this highly interdisciplinary topic to help define the
notion of cognitive radio. The book begins with addressing issues such as the fundamental …