A Pricing-Based Spectrum Leasing Framework with Adaptive Distributed Learning for Cognitive Radio Networks

Handouf, Sara; Sabir, Essaïd; Sadik, Mohammed

doi:10.1007/978-981-287-990-5_4

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…A distributed spectrum allocation framework based on multi-agent deep reinforcement learning was also proposed in [ 22 ]. Additionally, the authors in [ 23 ] proposed learning schemes that enable cognitive users to jointly learn their optimal payoffs and strategies for both continuous and discrete actions. The authors in [ 24 ] proposed an actor–critic reinforcement learning scheme for downlink transmission based on radio resource scheduling policy for long term evolution—advanced (LTE-A), to accomplish resource scheduling efficiently by maintaining user fairness and high QoS capabilities.…”

Section: Related Workmentioning

confidence: 99%

D2D Mobile Relaying Meets NOMA—Part II: A Reinforcement Learning Perspective

Driouech

Sabir

Ghogho

et al. 2021

Sensors

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Structureless communications such as Device-to-Device (D2D) relaying are undeniably of paramount importance to improving the performance of today’s mobile networks. Such a communication paradigm requires a certain level of intelligence at the device level, thereby allowing it to interact with the environment and make proper decisions. However, decentralizing decision-making may induce paradoxical outcomes, resulting in a drop in performance, which sustains the design of self-organizing yet efficient systems. We propose that each device decides either to directly connect to the eNodeB or get access via another device through a D2D link. In the first part of this article, we describe a biform game framework to analyze the proposed self-organized system’s performance, under pure and mixed strategies. We use two reinforcement learning (RL) algorithms, enabling devices to self-organize and learn their pure/mixed equilibrium strategies in a fully distributed fashion. Decentralized RL algorithms are shown to play an important role in allowing devices to be self-organized and reach satisfactory performance with incomplete information or even under uncertainties. We point out through a simulation the importance of D2D relaying and assess how our learning schemes perform under slow/fast channel fading.

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Section: Related Workmentioning

confidence: 99%

D2D Mobile Relaying Meets NOMA—Part II: A Reinforcement Learning Perspective

Driouech

Sabir

Ghogho

et al. 2021

Sensors

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“…Learning is a fundamental component of intelligence and cognition; it is defined as the ability of synthesizing the acquired knowledge through practice, training, or experience in order to improve the future behavior of the learning agent. In this section we introduce a learning scheme [20], [21] aiming to understand the behavior of users during the interactions and to reach the eventual convergence toward Nash Equilibrium. The goal is that UAVs service providers learn their own payoffs and determine the NE joint Availability-pricing strategy.…”

Section: A Insights On Real-world Implementation: Fully Distributed mentioning

confidence: 99%

Strategic Availability and Cost-Effective UAV-Based Flying Access Networks: S-Modular Game Analysis

Handouf

Sabir

2019

Mobile Information Systems

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Nowadays, ubiquitous network access has become a reality thanks to Unmanned Aerial Vehicles (UAVs) that have gained extreme popularity due to their flexible deployment and higher chance of Line-of-Sight (LoS) links to ground users. Telecommunication service providers deploy UAVs to provide flying network access in remote rural areas, disaster-affected areas or massive-attended events (sport venues, festivals, etc.) where full set-up to provide temporary wireless coverage would be very expensive. Of course, a UAV is battery-powered which means limited energy budget for both mobility aspect and communication aspect. An efficient solution is to allow UAVs swhiching their radio modules to sleep mode in order to extend battery lifetime. This results in temporary unavailability of communication feature. Within such a situation, the ultimate deal for a UAV operator is to provide a cost effective service with acceptable availability. This would allow to meet some target Quality of Service (QoS) while having a good market share granting satisfactory benefits. In this article, we exhibit a new framework with many interesting insights on how to jointly define the availability and the access cost in UAVempowered flying access networks to opportunistically cover a target geographical area. Yet, we construct a duopoly model to capture the adversarial behavior of service providers in terms of their pricing policies and their respective availability probabilities. Optimal periodic beaconing (small messages advertising existence of a UAV) is a vital issue that needs to be addressed, given the UAVs limited battery capacity and their recharging constraints. A full analysis of the game outcome, both in terms of equilibrium pricing and equilibrium availability, is derived. We show that the availability-pricing game exhibits some nice features as it is sub-modular with respect to the availability policy, whereas it is super-modular with respect to the service fee. Furthermore, we implement a learning scheme using best-response dynamics that allows operators to learn their joint pricing-availability strategies in a fast, accurate and distributed fashion. Extensive simulations show convergence of the proposed scheme to the joint pricing-availability equilibrium and offer promising insights on how the game parameters should be chosen to efficiently control the duopoly game.

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