Allocation Schemes for Relay Communications: A Multiband Multichannel Approach Using Game Theory

Mughal, Bushra; Fadlullah, Zubair Md.; Fouda, Mostafa M.; Ikki, Salama

doi:10.1109/lsens.2021.3137152

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…4c signifies the highly dynamic relay conditions since the SNR of the links between the RNs (or relay-destination nodes) is treated as a function of the link power, frequency bands dynamics, and the changing distance under the effects of mobility. Based on our earlier work in [7], we can treat this optimization problem as a computationally hard (NP) problem that cannot be solved in polynomial time for a relay network with a large number of RNs, frequency bands, and channels. Furthermore, complete information across the entire relay topology is required by a central oracle to solve this problem even for a relatively small search space.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

“…In contrast, in a distributed approach, the RNs need to make their own individual decisions in accordance with their local channel environment. The centralized approach requires complete information and coordination from/with RNs to provide a one-shot (static) optimal solution, which are more likely invalid by the time the central oracle decides them [6], [7]. Such an approach is impractical and not suitable for a time-sensitive and dynamically-changing network.…”

Section: Introductionmentioning

confidence: 99%

“…Our previous work considered distributed algorithms [6], [7] to solve this problem under the assumption of stable channel conditions and static radios. In this paper, we consider a dynamic relay network scenario with unstable channel environments and mobile radios.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing Packet Forwarding Performance in Multiband Relay Networks via Customized Reinforcement Learning

Mughal

Fadlullah

Fouda

et al. 2022

IEEE Open J. Commun. Soc.

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In next-generation wireless networks, relay-based packet forwarding, emerged as an appealing technique to extend network coverage while maintaining the required service quality. The incorporation of multiple frequency bands, ranging from MHz/GHz to THz frequencies, and their opportunistic and/or simultaneous exploitation by relay nodes can significantly improve system capacity, however at the risk of increased packet latency. Since a relay node can use different bands to send and receive packets, there is a pressing need to design an efficient channel allocation algorithm without a central oracle. While existing greedy heuristics and game-theoretic techniques, which were developed for multi-band channel assignment to relay nodes, achieve minimum packet latency, their performance drops significantly when network dynamism (i.e., user mobility, non-quasi-static channel conditions) is introduced. Since this problem involves multiple relay nodes, we model it as a Markov Decision Process (MDP) involving various stages, which essentially means that achieving an optimal and stable solution is a computationally hard problem. Since solving the MDP, traditionally, consumes a great deal of time and is intractable for relay nodes, we explore how to approximate the optimal solution in a distributed manner by reformulating a reinforcement learning-based, smart channel adaptation problem in the considered multi-band relay network. By customizing a Q-Learning algorithm that adopts an epsilon-greedy policy, we can solve this re-formulated reinforcement learning problem. Extensive computer-based simulation results demonstrate that the proposed reinforcement learning algorithm outperforms the existing methods in terms of transmission time, buffer overflow, and effective throughput. We also provide the convergence analysis of the proposed model by systematically finding and setting the appropriate parameters.

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Section: System Model and Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing Packet Forwarding Performance in Multiband Relay Networks via Customized Reinforcement Learning

Mughal

Fadlullah

Fouda

et al. 2022

IEEE Open J. Commun. Soc.

Self Cite

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“…Additionally, HB systems should attain satisfying arrangements as soon as possible in order to maintain a high quality of service (QoS) in various application environments. Implementing efficient HBS is not straightforward because of the rapidly adapting environments where their performance relies on a plethora of parameters [3]. As a result, there is a strong direction in the wireless world to use machine learning (ML) instead of classic HBS protocols based on predefined rules or heuristics, where learning from the past is a powerful direction.…”

Section: Introductionmentioning

confidence: 99%

Cost-Aware Bandits for Efficient Channel Selection in Hybrid Band Networks

et al. 2022

Self Cite

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Recently, hybrid band communications have received much attention to fulfil the exponentially growing user demands in next-generation communication networks. Still, determining the best band to communicate over is a challenging issue, especially in the dynamic channel conditions in multi-band wireless systems. In this paper, we manipulate a practical online-learning-based solution for the best band/channel selection in hybrid radio frequency and visible light communication (RF/VLC) wireless systems. The best band selection difficulty is formulated as a multi-armed bandit (MAB) with cost subsidy, in which the learner (transmitter) endeavors not only to increase his total reward (throughput) but also reduce his cost (energy consumption). Consequently, we propose two hybrid band selection (HBS) algorithms, named cost subsidy upper confidence bound (CSUCB-HBS) and cost subsidy Thompson sampling (CSTS-HBS), to efficiently handle this problem and obtain the best band with high throughput and low energy consumption. Extensive simulations confirm that CSTS-/CSUCB-HBS outperform the naive TS/UCB and heuristic HBS approaches regarding energy consumption, energy efficiency, throughput, and convergence speed.

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Joint Provisioning of QoS and Privacy with Federated Learning

Fadlullah

Fouda

2022

Towards a Wireless Connected World: Achievements and New Technologies

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Allocation Schemes for Relay Communications: A Multiband Multichannel Approach Using Game Theory

Cited by 8 publications

References 14 publications

Optimizing Packet Forwarding Performance in Multiband Relay Networks via Customized Reinforcement Learning

Optimizing Packet Forwarding Performance in Multiband Relay Networks via Customized Reinforcement Learning

Cost-Aware Bandits for Efficient Channel Selection in Hybrid Band Networks

Joint Provisioning of QoS and Privacy with Federated Learning

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