Utility function design for strategic radio resource management games: An overview, taxonomy, and research challenges

Yang, Chungang; Li, Jiandong; Ejaz, Waleed; Anpalagan, Alagan; Guizani, Mohsen

doi:10.1002/ett.3128

Cited by 11 publications

(6 citation statements)

References 101 publications

(209 reference statements)

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“…These utility functions should be very well aligned with the final outcomes of each game as well as the specific scenario requisites to be satisfied. A taxonomy and research challenges of utility functions for strategic radio resource management games is available in [82]. Also, [83] proposes an algorithm to pair strategic agents in a generic two-sided market without the conventional allocation of utilities among users.…”

Section: Multi-access Edge Computing Foundational Aspectsmentioning

confidence: 99%

Game Theory for Multi-Access Edge Computing: Survey, Use Cases, and Future Trends

Moura

Hutchison

2019

IEEE Commun. Surv. Tutorials

182

108

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Game Theory (GT) has been used with significant success to formulate, and either design or optimize, the operation of many representative communications and networking scenarios. The games in these scenarios involve, as usual, diverse players with conflicting goals. This paper primarily surveys the literature that has applied theoretical games to wireless networks, emphasizing use cases of upcoming Multi-Access Edge Computing (MEC). MEC is relatively new and offers cloud services at the network periphery, aiming to reduce service latency backhaul load, and enhance relevant operational aspects such as Quality of Experience or security. Our presentation of GT is focused on the major challenges imposed by MEC services over the wireless resources. The survey is divided into classical and evolutionary games. Then, our discussion proceeds to more specific aspects which have a considerable impact on the game's usefulness, namely: rational vs. evolving strategies, cooperation among players, available game information, the way the game is played (single turn, repeated), the game's model evaluation, and how the model results can be applied for both optimizing resource-constrained resources and balancing diverse trade-offs in real edge networking scenarios. Finally, we reflect on lessons learned, highlighting future trends and research directions for applying theoretical model games in upcoming MEC services, considering both network design issues and usage scenarios.

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Section: Multi-access Edge Computing Foundational Aspectsmentioning

confidence: 99%

Game Theory for Multi-Access Edge Computing: Survey, Use Cases, and Future Trends

Moura

Hutchison

2019

IEEE Commun. Surv. Tutorials

182

108

View full text Add to dashboard Cite

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“… U i is the utility function of player i . Utility function expresses perceived preference in a numerical manner which depicts satisfaction level of a player 77 . The commonly used concept in game theoretic problem is NE.…”

Section: Slot Allocation Process Using Evolutionary Game Theorymentioning

confidence: 99%

Game theoretic application for energy efficient mobility handling in wireless sensor network

Maheshwari

Sharma

Verma

2020

Trans Emerging Tel Tech

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Wireless sensor network (WSN) are used to sense, process, and send important information relating to surrounding environment. In these network, one of the important issue is timely delivery of packets to destination and thus avoiding packet loss when sensor nodes are mobile. Most of the current research does not allow handling of packets of mobile sensor nodes till next Time‐division multiple access (TDMA) schedule. In this work, we investigate the problem of allocating reserved slots to mobile nodes which come in vicinity of new cluster head in currenTDMAschedule only. We model allocation of reserved slots to mobile nodes as cooperative game. We then propose cooperative coordination scheme to allow cooperation among mobile sensor nodes for accessing of reserved slots with the objective of avoiding unnecessary delay and packet loss in the network. We consider solution for both the cases when reserved slot is limited to 1 and when reserved slots are more than 1. We then propose evolutionary game theory‐based slot allocation scheme for calculation of probability by solving nash equilibrium point. This probability is used by mobile nodes to send data packets to new cluster head. Our scheme avoids end to end delay of packets and network congestion due to buffer overflows. We then present extensive simulation results comparing our scheme with existing techniques. The results show higher performance in terms of average residual energy, network lifetime and packet delivery ratio etc.

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“…α ≥ 0 and α = 1, proportional fairness function ln(r t i ), or other suitable QoS (quality of service) utilities (see [23] and the reference therein). For analytical clarity, we consider in this paper a specific linear utility function to highlight the features of the LyDROO framework.…”

Section: B Problem Formulationmentioning

confidence: 99%

Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks

Bi¹,

Huang²,

Wang³

et al. 2020

Preprint

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Opportunistic computation offloading is an effective method to improve the computation performance of mobile-edge computing (MEC) networks under dynamic edge environment. In this paper, we consider a multi-user MEC network with time-varying wireless channels and stochastic user task data arrivals in sequential time frames. In particular, we aim to design an online computation offloading algorithm to maximize the network data processing capability subject to the long-term data queue stability and average power constraints. The online algorithm is practical in the sense that the decisions for each time frame are made without the assumption of knowing future channel conditions and data arrivals. We formulate the problem as a multi-stage stochastic mixed integer non-linear programming (MINLP) problem that jointly determines the binary offloading (each user computes the task either locally or at the edge server) and system resource allocation decisions in sequential time frames. To address the coupling in the decisions of different time frames, we propose a novel framework, named LyDROO, that combines the advantages of Lyapunov optimization and deep reinforcement learning (DRL). Specifically, LyDROO first applies Lyapunov optimization to decouple the multi-stage stochastic MINLP into deterministic per-frame MINLP subproblems. By doing so, it guarantees to satisfy all the long-term constraints by solving the per-frame subproblems that are much smaller in size. Then, LyDROO integrates model-based optimization and model-free DRL to solve the per-frame MINLP problems with very low computational complexity. Simulation results show that under various network setups, the proposed LyDROO achieves optimal computation performance while stabilizing all queues in the system. Besides, it induces very low execution latency that is particularly suitable for real-time implementation in fast fading environments.

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Utility function design for strategic radio resource management games: An overview, taxonomy, and research challenges

Cited by 11 publications

References 101 publications

Game Theory for Multi-Access Edge Computing: Survey, Use Cases, and Future Trends

Game Theory for Multi-Access Edge Computing: Survey, Use Cases, and Future Trends

Game theoretic application for energy efficient mobility handling in wireless sensor network

Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks

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