Game Theoretic Approach for Multipriority Data Transmission in 5G Vehicular Networks

Sun, Gang; Sheng, Li; Luo, Long; Yu, Hongfang

doi:10.1109/tits.2022.3198046

Cited by 80 publications

(16 citation statements)

References 33 publications

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“…The ASP selection problem is analogous to a resource-constrained task assignment problem, where the aim is to allocate incoming tasks to available resources, satisfying resource constraints and maximizing overall utility. This problem is frequently encountered in wireless networks, where resources are scarce and their efficient utilization is crucial to achieving the desired performance, including task scheduling and resource allocation in wireless networks [23][24][25].…”

Section: B Aigc Service Provider Selectionmentioning

confidence: 99%

“…where η a is the learning rate of the actor. By iteratively performing (24), D2SAC learns an optimal policy parameters that maximizes the sub-goal (21).…”

Section: Reverse Diffusion Chainmentioning

confidence: 99%

“…where H (π θ (s l )) is the entropy of the action probability distribution π θ (s l ), and the temperature coefficient α controls the strength of the entropy term. Following the derivation process similar to ( 21)-( 24), the update rule in (24) should add the gradient term of the entropy term ∇ θe H (π θe (s l )), as follow,…”

Section: Reverse Diffusion Chainmentioning

confidence: 99%

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Generative AI-aided Optimization for AI-Generated Content (AIGC) Services in Edge Networks

Du¹,

Li²,

Niyato³

et al. 2023

Preprint

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As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) offers a promising solution to this challenge. However, the training and deployment of large AI models necessitate significant resources. To address this issue, we introduce an AIGCas-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks, ensuring ubiquitous access to AIGC services for Metaverse users. Nonetheless, a key aspect of providing personalized user experiences requires the careful selection of AIGC service providers (ASPs) capable of effectively executing user tasks. This selection process is complicated by environmental uncertainty and variability, a challenge not yet addressed well in existing literature. Therefore, we first propose a diffusion model-based AI-generated optimal decision (AGOD) algorithm, which can generate the optimal ASP selection decisions. We then apply AGOD to deep reinforcement learning (DRL), resulting in the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, which achieves efficient and effective ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it a promising approach for the future research on AIGC-driven services in Metaverse. The implementation of our proposed method is available at: https://github.com/Lizonghang/AGOD.

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Section: B Aigc Service Provider Selectionmentioning

confidence: 99%

“…where η a is the learning rate of the actor. By iteratively performing (24), D2SAC learns an optimal policy parameters that maximizes the sub-goal (21).…”

Section: Reverse Diffusion Chainmentioning

confidence: 99%

Section: Reverse Diffusion Chainmentioning

confidence: 99%

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Generative AI-aided Optimization for AI-Generated Content (AIGC) Services in Edge Networks

Du¹,

Li²,

Niyato³

et al. 2023

Preprint

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“…Authors in Reference 11 propose a joint mode selection and power control scheme for V2V communication in 5G networks to improve resource utilization and QoS. They use a game‐theoretic approach considering multipriority packet requirements and stringent latency constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

A machine learning access network selection in a heterogeneous wireless environment

Bendaoud,

Abdennebi

2023

Concurrency and Computation

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In the context of Heterogeneous Wireless Networks (HWN), achieving the best connectivity is crucial for users. With the availability of multiple Radio Access Technologies (RATs), users are constantly looking to be “Always Best Connected” (ABC) by connecting to the best possible RAT. This challenge is also important in Next‐Generation Networks (NGN), where researchers are proposing models and algorithms to make the ABC paradigm a reality. One such challenge is the network selection problem, which involves the automatic and transparent selection of the best RAT at a given time. Many papers in this field have used Multi‐Attribute Decision‐Making (MADM) methods, which have been implemented with various objectives such as increasing quality of service (QoS), reducing energy consumption and costs etc. This paper proposes a modified machine learning algorithm, specifically an adapted K‐means algorithm, to address this challenge. The results show that this proposal outperforms legacy MADM methods in terms of latency, jitter, packet loss, and data rate. So, the paper presents a new approach to network selection in HWN and NGN, and the results suggest that it could be a promising solution to this important challenge.

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“…In their research, R.H. Yang and J.X. Jin (Yang and Jin, 2021;Kakouche et al, 2022;Sun et al, 2022) employed a cost-effective energy storage device integrated into the UPQC for a protective strategy. This scheme involved utilizing both a series-side converter and a parallelside converter to mitigate power oscillations.…”

Section: Introductionmentioning

confidence: 99%

Unified power quality conditioner-based solar EV charging station using the GBDT–JS technique

Prasada Rao,

Pandian,

Reddy

et al. 2024

Front. Energy Res.

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This manuscript proposes a novel hybrid artificial intelligence (AI) approach for a unified power quality conditioner (UPQC) designed specifically for electric vehicle charging stations (EVCSs). The aim is to integrate multiple vehicle-to-grid (V2G) functionalities, thereby mitigating the challenges associated with electric vehicle (EV) grid integration and the incorporation of distributed energy resources (DERs). The hybrid technique presented in this manuscript combines the gradient boosting decision tree (GBDT) algorithm and the jellyfish search (JS) algorithm, referred to as the GBDT–JS technique. This innovative approach involves utilizing the charging station to offer EV charging services and facilitating the discharge of EVs to the power grid. Integration of the UPQC with DERs, such as photovoltaic (PV), is implemented to decrease the power rating of converters and fulfill power demand requirements. The initial converter within the UPQC is employed to manage the direct current (DC) voltage, while the second converter oversees the power charging or discharging processes of EVs. Additionally, it mitigates the impact of battery voltage fluctuations. The UPQC with vehicle-to-grid functionality minimizes the load pressure on the grid, preventing over-current issues. The presented approach regulates the UPQC converters to mitigate power quality issues such as harmonic currents and voltage sags. Subsequently, the effectiveness of this technique is demonstrated using the MATLAB/Simulink operating platform. The evaluation of GBDT–JS performance involves a comparative analysis with existing techniques. This assessment reveals that the proposed method effectively alleviates power quality issues, specifically reducing total harmonic distortion (THD), and delivers optimal outcomes.

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Game Theoretic Approach for Multipriority Data Transmission in 5G Vehicular Networks

Cited by 80 publications

References 33 publications

Generative AI-aided Optimization for AI-Generated Content (AIGC) Services in Edge Networks

Generative AI-aided Optimization for AI-Generated Content (AIGC) Services in Edge Networks

A machine learning access network selection in a heterogeneous wireless environment

Unified power quality conditioner-based solar EV charging station using the GBDT–JS technique

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