Machine Learning enabled Wireless Communication Network System

Chen, Jiajun; Yin, Guangwen; Zhou, Yingjun; Liu, Zhuang; Li, Da peng; Zhang, Man

doi:10.1109/iwcmc55113.2022.9824835

Cited by 7 publications

(2 citation statements)

References 34 publications

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“…In the simulations, the proposed Meta-PPO algorithm implemented its learning processes for 1000 episodes and 10 iterations for parameter updates in each episode. Figure 9 shows the curves of normalized network throughput changing with the episodes when the proposed Meta-PPO algorithm was used, which was compared with PPO [31], DQN [32], Double DQN [33], and Dueling DQN [34] algorithms. Normalized throughput is a widely accepted criterion for measuring the performance of an agent's strategy [35].…”

Section: Parametersmentioning

confidence: 99%

A Novel Intelligent Anti-Jamming Algorithm Based on Deep Reinforcement Learning Assisted by Meta-Learning for Wireless Communication Systems

Chen,

Niu,

Wan

et al. 2023

Applied Sciences

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In the field of intelligent anti-jamming, deep reinforcement learning algorithms are regarded as key technical means. However, the learning process of deep reinforcement learning algorithms requires a stable learning environment to ensure its effectiveness. Moreover, the inherent limitations of deep reinforcement learning algorithms mean that they can only demonstrate excellent learning capabilities on specific tasks with constant parameters. When parameters change, they can only resample and relearn to converge. In a changing jamming environment, its stability and convergence speed may be challenged, thereby affecting its robustness and generalization capabilities. Aiming at the naive yet unique similarity characteristics of the communication anti-jamming problem, this paper designs a new Meta-PPO deep reinforcement learning algorithm that combines Proximal Policy Optimization (PPO) and MAML meta-learning ideas. The proposed algorithm engrafts the principle of meta-learning used in the Model Agnostic Meta-Learning (MAML) model onto the Proximal Policy Optimization (PPO)-based schemes, enabling the communication systems to harness its prior learned experiences acquired from previous anti-jamming tasks to facilitate and speed up its optimal decision-making process when faced with incoming jamming attacks with similar features. The proposed algorithm is verified through computer simulation analyses and the results show that the proposed novel Meta-PPO algorithm can outperform traditional DQN- and PPO-based algorithms in terms of better robustness and generalization abilities, which can be used to enhance the anti-jamming capabilities of wireless communication systems.

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Section: Parametersmentioning

confidence: 99%

A Novel Intelligent Anti-Jamming Algorithm Based on Deep Reinforcement Learning Assisted by Meta-Learning for Wireless Communication Systems

Chen,

Niu,

Wan

et al. 2023

Applied Sciences

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“…From homes and offices to public spaces, wireless networks have permeated every aspect of our daily lives. The proliferation of wireless networks has brought convenience and flexibility, allowing us to access the internet anytime, anywhere, and facilitating efficient communication and information exchange [1,2]. However, along with the rapid advancement of wireless networks, challenges and issues have also emerged.…”

Section: Introductionmentioning

confidence: 99%

Wireless Network Resource Allocation for Video Transmission Based on Non-Orthogonal Multiple Access

Li,

Xu,

Song

et al. 2024

Preprint

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In wireless networks, we study the downlink scenario of a non-orthogonal multiple access (NOMA) network based on scalable video coding techniques. The resource allocation problem based on wireless NOMA network is modeled as a mixed-integer nonlinear programming problem, which is subject to video coding characteristics, sub-channel capacity, system available power, and real-time requirements of wireless network. In order to solve the resource allocation problem of wireless NOMA network, three sub-problems are proposed, e.g., user grouping sub-problem, power allocation sub-problem, and video layer allocation sub-problem. Firstly, we propose an auction-based solution for the user grouping sub-problem. Then, for the power allocation sub-problem, we choose successive convex approximation and Lagrangian dual decomposition method. Finally, for the video layer allocation sub-problem, we propose a greedy strategy. Simulation experiments show that our proposed algorithms ensure the real-time and reliability requirements of the wireless network and also improve the quality of the video experience.

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A comprehensive systematic review on machine learning application in the 5G-RAN architecture: Issues, challenges, and future directions

Talal,

Garfan,

Qays

et al. 2025

Journal of Network and Computer Applications

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Machine Learning enabled Wireless Communication Network System

Cited by 7 publications

References 34 publications

A Novel Intelligent Anti-Jamming Algorithm Based on Deep Reinforcement Learning Assisted by Meta-Learning for Wireless Communication Systems

A Novel Intelligent Anti-Jamming Algorithm Based on Deep Reinforcement Learning Assisted by Meta-Learning for Wireless Communication Systems

Wireless Network Resource Allocation for Video Transmission Based on Non-Orthogonal Multiple Access

A comprehensive systematic review on machine learning application in the 5G-RAN architecture: Issues, challenges, and future directions

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