Q-learning-based dynamic joint control of interference and transmission opportunities for cognitive radio

Jang, Sung-Jeen; Yoo, Sang‐Jo

doi:10.1186/s13638-018-1155-9

Cited by 4 publications

(2 citation statements)

References 41 publications

(53 reference statements)

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“…As mentioned before most recent studies are based on downlink interference cancellation, in the following we use some of these works with the most similarity to our work to prove our simulation results. which by increasing the iteration, the learning rate decreases and should converged [18], but it doesn't control the fluctuates. Also after 500 iterations, it can be shown that the capacity of MUE and FUEs are the same.…”

Section: VI Strong Interference Modementioning

confidence: 99%

Uplink Interference Mitigation in HetNets with Q-Learning Algorithm

Abbasi‐Moghadam

Etminan

Kalantarinejad

2023

Preprint

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Interference cancellation is one of the important issues in Heterogeneous networks (HetNets), due to the density of the network. In this paper, we investigate the problem of interference cancellation and resource allocation with the Q-learning approach. We consider the Inter-interference and Intra-interference between the femtocell and macro cells in the uplink scenario. With the aim of maximizing the QoS of the macro user equipment (MUE) and minimizing the interference of the MUE, a new reward function was proposed. The simulation results show the improvement of the proposed algorithm in two strong interference and increasing distance scenarios.

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Section: VI Strong Interference Modementioning

confidence: 99%

Uplink Interference Mitigation in HetNets with Q-Learning Algorithm

Abbasi‐Moghadam

Etminan

Kalantarinejad

2023

Preprint

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“…RL is an effective dynamic varying environment with integrated model-free schemes. The network model is constructed based on the unavailability of the spectrum and network conditions [11].…”

Section: Introductionmentioning

confidence: 99%

Latency Reduction in Heterogeneous 5g Networks Integrated With Reinforcement Algorithm

Kodavati

Madhu

2023

Preprint

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Heterogeneous network is considered as key technologies involved in significant utilization of available unused spectrum. Through the implementation of heterogeneous network energy utilization is achieved through effective spectral efficiency with higher throughput. However, this heterogeneous network environment is subjected to the challenge of latency. To derive the complete potential of heterogeneous networks machine learning algorithms need to be adopted for a dynamic environment. In the case of a practical scenario, it is difficult to reduce the network latency due to the complex network nature. To overcome those limitations, this paper proposed a Q-learning Reinforcement learning (QleaRL) for reducing latency. The proposed QleaRL utilizes Cooperative Q - learning based on consideration of state, action, and reward. Through optimal policy, reinforcement learning is computed based on Q -values. The performance of the proposed QleaRL is evaluated for latency. Simulation of proposed QleaRL is examined in terms of numerical analysis. The performance of the proposed QleaRL exhibits superior performance than the fixed power allocation (FPA) and tabular Q learning.

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