Channel Assignment for D2D communication : A Regret Matching Based Approach

Lhazmir, Safae; Kobbane, Abdellatif; Ben‐Othman, Jalel

doi:10.1109/iwcmc.2018.8450520

Cited by 9 publications

(3 citation statements)

References 27 publications

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“…Distributed channel assignment among U‐D2D pairs is formulated as a non‐cooperative game and regret‐matching learning algorithm to find the optimal RB allocation [20]. Q‐learning‐based distributed channel and synchronous power allocation algorithm is proposed for a self‐organised femtocell network in [21].…”

Section: Related Workmentioning

confidence: 99%

Weighted cooperative reinforcement learning‐based energy‐efficient autonomous resource selection strategy for underlay D2D communication

Sharma

Singh

2019

IET Communications

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Section: Related Workmentioning

confidence: 99%

Weighted cooperative reinforcement learning‐based energy‐efficient autonomous resource selection strategy for underlay D2D communication

Sharma

Singh

2019

IET Communications

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“…Finally, the last few years have seen the emergence of machine learning based approaches for resource allocation and interference mitigation in D2D enabled networks (e.g., Reference [ 14 ] and the references therein). However, in the literature [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ], we observe that light-weight, time critical on-line mechanisms for adapting resource allocation and enhancing ESE are not available. Furthermore, such works use typically centralized approaches that do not exploit the OSA resources available in this type of scenario; when the approach is partly distributed, as in Reference [ 23 ], Q-learning is exploited just for the system throughput.…”

Section: Related Workmentioning

confidence: 94%

Q-Learning Based Joint Energy-Spectral Efficiency Optimization in Multi-Hop Device-to-Device Communication

Khan

Reggiani

Alam

et al. 2020

Sensors

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In scenarios, like critical public safety communication networks, On-Scene Available (OSA) user equipment (UE) may be only partially connected with the network infrastructure, e.g., due to physical damages or on-purpose deactivation by the authorities. In this work, we consider multi-hop Device-to-Device (D2D) communication in a hybrid infrastructure where OSA UEs connect to each other in a seamless manner in order to disseminate critical information to a deployed command center. The challenge that we address is to simultaneously keep the OSA UEs alive as long as possible and send the critical information to a final destination (e.g., a command center) as rapidly as possible, while considering the heterogeneous characteristics of the OSA UEs. We propose a dynamic adaptation approach based on machine learning to improve a joint energy-spectral efficiency (ESE). We apply a Q-learning scheme in a hybrid fashion (partially distributed and centralized) in learner agents (distributed OSA UEs) and scheduler agents (remote radio heads or RRHs), for which the next hop selection and RRH selection algorithms are proposed. Our simulation results show that the proposed dynamic adaptation approach outperforms the baseline system by approximately 67% in terms of joint energy-spectral efficiency, wherein the energy efficiency of the OSA UEs benefit from a gain of approximately 30%. Finally, the results show also that our proposed framework with C-RAN reduces latency by approximately 50% w.r.t. the baseline.

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“…It used by many research papers which show that Regret matching learning achieves better performance in term of system throughput more than the random allocation, and converges rapidly to a correlated equilibrium of the system. In addition of that, it allows us to consider the neighbors' actions and to adapt to the environment [16,[21][22][23].…”

Section: No-regret Learning Based On Matching Gamementioning

confidence: 99%

No-Regret Matching Game Algorithm for NOMA Based UAV-Assisted NB-IoT Systems

Metwaly¹,

El-Haleem²,

Elghandour³

2021

ISI

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NB-IoT is the standardized technology for machine type communication (MTC) in Long Term Evolution (LTE). NB-IoT can achieve IoT requirements nevertheless, it suffers a low rate and capacity. On the other hand, Unmanned aerial vehicles (UAV) and Non-Orthogonal Multiple Access (NOMA) are promising technology used to enhance the throughput, capacity, and coverage of wireless communication networks. In this paper, we propose a heterogeneous network scenario where a UAV small Base Station (UBS) is used to assist the LTE Macro Base Station (MBS) with the help of the Non-Orthogonal Multiple Access technique to solve the NB-IoT throughput and capacity issues. Matching game based no-regret learning algorithm is proposed to optimize the NB-IoT device association and using NOMA pairing at each base station to provide the maximum system total rate and capacity. Simulation results show that our proposed scheme increases the total rate of the system by 60% and the system capacity by at least 80%, compared to NOMA without UAV and the total rate and capacity of the system by 200% and 85% respectively, with OMA scheme.

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Channel Assignment for D2D communication : A Regret Matching Based Approach

Cited by 9 publications

References 27 publications

Weighted cooperative reinforcement learning‐based energy‐efficient autonomous resource selection strategy for underlay D2D communication

Weighted cooperative reinforcement learning‐based energy‐efficient autonomous resource selection strategy for underlay D2D communication

Q-Learning Based Joint Energy-Spectral Efficiency Optimization in Multi-Hop Device-to-Device Communication

No-Regret Matching Game Algorithm for NOMA Based UAV-Assisted NB-IoT Systems

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