QoS Aware Uplink Scheduling for M2M Communication in LTE/LTE-A Network: A Game Theoretic Approach

Singh, Upendra N.; Dua, Amit; Kumar, Neeraj; Guizani, Mohsen

doi:10.1109/tvt.2021.3132535

Cited by 9 publications

(3 citation statements)

References 67 publications

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“…The method uses motion of single antenna to synthesize a geographically separated antenna array to support navigation. A game theory based QoS aware uplink scheduling (GTQoS) is presented in [11], which uses a Quality-of-Service aware uplink packet scheduling scheme and uses time domain and frequency domain scheduling. The performance measure of LTE in multi floor building in [12], which analyze performance of methods in various metrics.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Adaptive LTE Selection Model for Improved QoS Performance in Wireless Networks

2024

nano-ntp

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Presence of LTE (Long Term Evolution) devices in modern networks supports higher QoS achievements. The QoS of any modern network is about achieving higher throughput performance. In this case, number of approaches available, but providing seamless transmission in urban network where there is poor network connectivity is a challenging task. Number of approaches is presented in literature, which involve in handover when there is poor connectivity which works according to the signal strength and other factors. The methods suffer to achieve higher performance in QoS in wireless networks. To handle this issue, an Energy Efficient Adaptive LTE Selection Model (EEA-LTE-SM) is presented in this paper. The proposed method monitors the data rate currently available and identifies the list of LTE available around the corner. At each time stamp, the method computes the value of Data Rate Support (DRS) available. Based on the value of DRS, the method performs LTE selection to maintain the QoS of WSN. For each LTE identified, the method computes Future Data Support (FDS) according to the data rate available, energy, mobility speed. According to the FDS value, the method discovers the list of LTE around the node and selects a optimal LTE to perform further transmission. The proposed method improves the QoS performance of wireless networks.

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Section: Related Workmentioning

confidence: 99%

Energy Efficient Adaptive LTE Selection Model for Improved QoS Performance in Wireless Networks

2024

nano-ntp

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“…Recently, game theory has gained substantial attention as an effective tool to address these challenges [22], [23]. Game theory provides a framework for modeling strategic interactions among self-interested players, facilitating the design of distributed algorithms capable of achieving optimal system performance solutions [24], [25].…”

Section: Introductionmentioning

confidence: 99%

Distributed User Association and Computation Offloading in UAV-Assisted Mobile Edge Computing Systems

Wang,

You

2024

IEEE Access

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Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) systems have emerged as promising solutions for enhancing the computational capabilities and reducing latency in next-generation wireless networks. However, the finite energy capacity of UAVs presents a significant challenge. In this study, we formulate a joint user association, computing resource allocation, and task offloading problem, called User Association and Computation Offloading (UACO), to minimize the energy consumption of both mobile devices and UAVs by considering the computational resource limitations of UAVs and the minimum user offloading data rate requirements. The UACO problem is a mixed-integer linear programming (MILP) problem that makes it NP-hard. We transform UACO into an Offload Exact Constrained Potential Game (OFECPG) based on game theory, which facilitates distributed execution. We propose the Best Response dynamics based on Local Decomposition (BR-LD) and Better Response dynamics based on the Local Switch operator (BR-LS) to enhance computational efficiency. We prove the existence of a pure strategy Nash Equilibrium (NE) and the convergence of the proposed algorithms. Extensive simulations demonstrated the effectiveness of the OFECPG, BR-LD, and BR-LS, showing significant improvements in system energy consumption compared to the baseline schemes. Our approach offers valuable insights into the design of efficient association and offloading schemes for multi-UAV MEC networks.INDEX TERMS Constrained potential game, Nash Equilibrium, unmanned aerial vehicle (UAV), Multiaccess edge computing, mixed-integer linear programming (MILP), computation offloading, User Association

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“…It pertains to data exchange involving single or multiple entities, independent of human intervention, as defined by the 3GPP standards [2]. The M2M deployment requires a suitable data transfer network to create connectivity among M2M devices, which had previously relied on wired data transfer networks and radio networks [3]. An abundant proliferation of connected devices within the communication network results in a substantial surge in mobile traffic.…”

Section: Introductionmentioning

confidence: 99%

An Energy-Aware Tailored Resource Management for Cellular-Based Zero-Touch Deterministic Industrial M2M Networks

Prathiba,

Raja,

Saiabirami

et al. 2024

IEEE Access

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Zero-Touch Deterministic Industrial Machine-to-Machine (ZT-DI-M2M) serves as a customized communication solution designed to meet the specific needs of industrial settings. Though 5G is the promising solution for ZT-DI-M2M, optimized scheduling in 5G remains challenging to design over aspects such as network overload and congestion control, especially in the uplink transmission of ZT-DI-M2M. Effective allocation of resources for Machine-Type Communication (MTC) devices stands out as a pivotal hurdle within the domain of 5G networks. This challenge directly influences the longevity of batteryoperated devices and the Quality of Service (QoS) experienced by applications. This paper aims to develop a Group-Based and Energy Aware (GBEA) resource allocation algorithm for M2M communication in the 5G networks. The GBEA algorithm solves the resource allocation problem by initially clustering the active nodes concerning their delays. Consequently, inter and intra-cluster resource distribution occurs through the Gaussian Mixture Model Expectation Maximization (GMM-EM) algorithm. The GBEA algorithm optimizes the resource allocation of M2M devices by factoring delay, energy, proximity, and fairness into the allocation process. The simulation outcomes unveil the superiority of the GBEA scheduling algorithm over established state-of-the-art resource allocation methods. It showcases remarkable enhancements in terms of throughput, delay sensitivity, and energy efficiency, boasting nearly 1.5-fold, 1.75-fold, and 6.5-fold respective improvements.

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QoS Aware Uplink Scheduling for M2M Communication in LTE/LTE-A Network: A Game Theoretic Approach

Cited by 9 publications

References 67 publications

Energy Efficient Adaptive LTE Selection Model for Improved QoS Performance in Wireless Networks

Energy Efficient Adaptive LTE Selection Model for Improved QoS Performance in Wireless Networks

Distributed User Association and Computation Offloading in UAV-Assisted Mobile Edge Computing Systems

An Energy-Aware Tailored Resource Management for Cellular-Based Zero-Touch Deterministic Industrial M2M Networks

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