Multi-Agent Systems Negotiation Approach for Handoff in Mobile Cognitive Radio Networks

Trigui, Emna; Esseghir, Moez; Merghem-Boulahia, Leïla

doi:10.1109/ntms.2012.6208687

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…The literature has stressed the utility of ML algorithms to overcome the complexities of spectrum handover. Trigui et al [69] developed a method of negotiating multi-agent systems that enables SUs to migrate opportunistically to the most adequate spectrum band provided its characteristics, achieving around 97% of spectrum utilization. An investigation on the Hidden Markov Model (HMM) application for spectrum handover and simulated data showed that in detecting transmission opportunities, this technique can give SUs greater accuracy [70].…”

Section: Spectrum Handovermentioning

confidence: 99%

Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview

Santana

Cristo

Branco

2021

Sensors

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Unmanned Aerial Vehicles (UAVs) demand technologies so they can not only fly autonomously, but also communicate with base stations, flight controllers, computers, devices, or even other UAVs. Still, UAVs usually operate within unlicensed spectrum bands, competing against the increasing number of mobile devices and other wireless networks. Combining UAVs with Cognitive Radio (CR) may increase their general communication performance, thus allowing them to execute missions where the conventional UAVs face limitations. CR provides a smart wireless communication which, instead of using a transmission frequency defined in the hardware, uses software transmission. CR smartly uses free transmission channels and/or chooses them according to application’s requirements. Moreover, CR is considered a key enabler for deploying technologies that require high connectivity, such as Smart Cities, 5G, Internet of Things (IoT), and the Internet of Flying Things (IoFT). This paper presents an overview on the field of CR for UAV communications and its state-of-the-art, testbed alternatives for real data experiments, as well as specifications to build a simple and low-cost testbed, and indicates key opportunities and future challenges in the field.

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Section: Spectrum Handovermentioning

confidence: 99%

Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview

Santana

Cristo

Branco

2021

Sensors

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“…Nonetheless, non-desired HOs may still occur. Subsequently, the authors of [8,9] proposed a multi-agent negotiation scheme to allow PUs and SUs to share available resources and reduce the number of HOs. Both scheme negotiations with PU depend on minimal prices among the available ones.…”

Section: Figure 1 Sho Mechanismmentioning

confidence: 99%

An intelligent spectrum handoff scheme based on multiple attribute decision making for LTE-A network

Alhammadi¹,

Roslee²,

Alias³

et al. 2019

IJECE

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Cognitive radio networks (CRNs) play an important role in wireless communications which have the ability to significantly utilize the spectrum that not in used and reduce the current spectrum scarcity. CR allows unlicensed users (secondary users) to occupy the licensed spectrums without causing interference with licensed users (primary users). This can be achieved smoothly through four main CR procedures: spectrum sensing, spectrum decision, spectrum sharing, and spectrum mobility. In this paper, we propose an intelligent spectrum handoff (SH) scheme based on multiple attributes decision making. The handoff decision depends on three considered parameters: received power, traffic load and arrival rate of the primary users. The simulation results show the proposed scheme outperformed the conventional scheme by reducing the probability of SH which leads to improve system performance.

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“…In [89], Emna Trigui et al introduced a novel approach to address the spectrum handoff within the CR domain. Their approach allows CR terminals to always switch to the spectrum band that offers the best conditions by using multi-agent system negotiation.…”

Section: Markov Modelmentioning

confidence: 99%

Recent advances on artificial intelligence and learning techniques in cognitive radio networks

Abbas

Nasser

Ahmad

2015

J Wireless Com Network

123

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Cognitive radios are expected to play a major role towards meeting the exploding traffic demand over wireless systems. A cognitive radio node senses the environment, analyzes the outdoor parameters, and then makes decisions for dynamic time-frequency-space resource allocation and management to improve the utilization of the radio spectrum. For efficient real-time process, the cognitive radio is usually combined with artificial intelligence and machine-learning techniques so that an adaptive and intelligent allocation is achieved. This paper firstly presents the cognitive radio networks, resources, objectives, constraints, and challenges. Then, it introduces artificial intelligence and machine-learning techniques and emphasizes the role of learning in cognitive radios. Then, a survey on the state-of-the-art of machine-learning techniques in cognitive radios is presented. The literature survey is organized based on different artificial intelligence techniques such as fuzzy logic, genetic algorithms, neural networks, game theory, reinforcement learning, support vector machine, case-based reasoning, entropy, Bayesian, Markov model, multi-agent systems, and artificial bee colony algorithm. This paper also discusses the cognitive radio implementation and the learning challenges foreseen in cognitive radio applications. IntroductionAccording to Cisco Visual Networking Index, the global IP traffic will reach 168 exabytes per month by 2019 [1], and the number of devices will be three times the global population. In addition, the resources in terms of power and bandwidth are scarce. Therefore, novel solutions are needed to minimize energy consumption and optimize resource allocation. Cognitive radio (CR) was introduced by Joseph Mitola III and Gerald Q. Maguire in 1999 for a flexible spectrum access [2]. Basically, they defined cognitive radio as the integration of model-based reasoning with software radio technologies [3]. In 2005, Simon Haykin had given a review of the cognitive radio concept and had treated it as brain-empowered wireless communications [4]. Cognitive radio is a radio or system that senses the environment, analyzes its transmission parameters, *Correspondence: nfa23@aub.edu.lb Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon and then makes decisions for dynamic time-frequencyspace resource allocation and management to improve the utilization of the radio electromagnetic spectrum.Generally, radio resource management aims at optimizing the utilization of various radio resources such that the performance of the radio system is improved. For instance, the authors in [5] proposed an optimal resource (power and bandwidth) allocation in cognitive radio networks (CRNs), specifically in the scenario of spectrum underlay, while taking into consideration the limitations of interference temperature limits. The optimization formulations provide optimal solutions for resources allocation at, sometimes, the detriment of global convergence, computation time, and...

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Multi-Agent Systems Negotiation Approach for Handoff in Mobile Cognitive Radio Networks

Cited by 8 publications

References 10 publications

Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview

Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview

An intelligent spectrum handoff scheme based on multiple attribute decision making for LTE-A network

Recent advances on artificial intelligence and learning techniques in cognitive radio networks

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