Joint Power Control and Channel Allocation for Interference Mitigation Based on Reinforcement Learning

Zhao, Guofeng; Li, Yong; Xu, Chuan; Han, Zhenzhen; Yu, Shui

doi:10.1109/access.2019.2937438

Cited by 46 publications

(31 citation statements)

References 43 publications

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“…In [15], Zhao et al proposed the joint transmit power control and channel allocation optimization to reduce interference and improve the throughput. First, they analyzed the correlation between transmit power and channel and formulated the interference optimization as a mixed integer nonlinear programming (MINLP) problem.…”

Section: Related Workmentioning

confidence: 99%

Joint Optimization Method of Channel Assignment and Transmission Power for Concurrently Communicating Multiple Access-Points in Wireless Local-Area Network

Briantoro

Funabiki

Rahman

et al. 2021

IJNC

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Currently, IEEE 802.11n wireless local-area network (WLAN) is popular for the Internet access due to mobility, flexibility, and scalability. Multiple access-points (APs) are often allocated in WLAN to cover the wide area, which may cause interferences and reduce the performances. Previously, we have studied the transmission power optimization method for two concurrently communicating APs to reduce interferences. It selects either the maximum or minimum power for each AP such that signal-to-noise ratio (SNR) is highest. However, it was found that the channel assignment is also important when multiple APs are closely allocated in dense WLAN. In this paper, we propose a joint optimization method of channel assignment and transmission power for concurrently communicating multiple APs in WLAN. First, the same channel is assigned to the nearby APs where the CSMA/CA protocol works well, and the most distant channels are to the other APs. Second, the transmission power is optimized by selecting the highest measured SNR. To reduce the SNR measurement load, 1) the maximum power is assigned to every AP, 2) the initial RSS from the associated host is measured, 3) the minimum power is assigned to one AP in descending order of the initial RSS, and the SNR is measured, and 4) the power combination for the highest SNR is selected. For evaluations, we conduct extensive experiments under various network topologies using up to four Raspberry Pi APs. The results show that the proposal always selects the best channel and transmission power for each AP that offers the highest throughput performance.

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Section: Related Workmentioning

confidence: 99%

Joint Optimization Method of Channel Assignment and Transmission Power for Concurrently Communicating Multiple Access-Points in Wireless Local-Area Network

Briantoro

Funabiki

Rahman

et al. 2021

IJNC

View full text Add to dashboard Cite

show abstract

“…, ∀i, j ∈ K, (16) where g i,m (t) represents the channel gain between UAV i and UE m, N 0 is the noise power spectral density. Then, the rate of UE m served by UAV i can be obtained as…”

Section: A System Modelmentioning

confidence: 99%

“…The authors in [15] utilized an RL method to investigate the resource management scheme in the Internet of Vehicles communication networks. In [16], the RL approach was proposed to obtain the joint power control and channel allocation strategy in dense wireless local area networks. Moreover, by combining the deep neural networks with RL, deep reinforce-ment learning (DRL) [17] method has been recently attracted increasing interests in wireless communication domains.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Deep Reinforcement Learning for Trajectory Design and Power Allocation in Multi-UAV Networks

2020

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Unmanned aerial vehicle (UAV) is regarded as an effective technology in future wireless networks. However, due to the non-convexity feature of joint trajectory design and power allocation (JTDPA) issue, it is challenging to attain the optimal joint policy in multi-UAV networks. In this paper, a multi-agent deep reinforcement learning-based approach is presented to achieve the maximum long-term network utility while satisfying the user equipments' quality of service requirements. Moreover, considering that the utility of each UAV is determined based on the network environment and other UAVs' actions, the JTDPA problem is modeled as a stochastic game. Due to the high computational complexity caused by the continuous action space and large state space, a multi-agent deep deterministic policy gradient method is proposed to obtain the optimal policy for the JTDPA issue. Numerical results indicate that our method can obtain the higher network utility and system capacity than other optimization methods in multi-UAV networks with lower computational complexity. INDEX TERMS UAV networks, trajectory design, power allocation, multi-agent deep reinforcement learning.

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“…Despite inaccuracy in received signal autocorrelation matrix due to finite snapshots number, a promising performance was still acquired. Zhao et al 26 proposed a joint power control and channel allocation based on reinforcement learning algorithm combining with statistical channel state information to reduce interference adaptively. The evaluation results show that the proposed algorithm can effectively improve the throughput compared with the existing schemes.…”

Section: Introductionmentioning

confidence: 99%

Adaptive modem and interference suppression based on deep learning

Wei

Wang

Luo

2021

Trans Emerging Tel Tech

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With the increasingly fierce competition of electromagnetic spectrum, developing intelligent communication systems that can reconfigure its waveform can effectively improve the communication system's ability to adapt to the complex electromagnetic environment. In this paper, an adaptive modem based on a deep autoencoder network (DAN) is designed, and the demodulation performance that is close to, consistent with, or better than traditional MPSK or QAM is achieved. This DAN can be trained by a unified loss function and a unified optimization algorithm to implement a variety of modems. For high-order modems, the constellation diagrams generated by the DAN are radically different from the traditional modulation and very difficult to distinguish linearly, which is beneficial to improve the anti-interception ability of the communication systems. Additionally, based on the trained DAN, a convolutional neural network (CNN) with a single convolutional layer is incorporated to suppress a variety of interferences. By using transfer learning, the new deep learning (DL) model with the CNN converges fast with a few epochs of training. Training and test results verify that the new DL model can improve the anti-interference ability of the communication systems by learning and suppressing the interferences in both frequency and power domains.

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Joint Power Control and Channel Allocation for Interference Mitigation Based on Reinforcement Learning

Cited by 46 publications

References 43 publications

Joint Optimization Method of Channel Assignment and Transmission Power for Concurrently Communicating Multiple Access-Points in Wireless Local-Area Network

Joint Optimization Method of Channel Assignment and Transmission Power for Concurrently Communicating Multiple Access-Points in Wireless Local-Area Network

Multi-Agent Deep Reinforcement Learning for Trajectory Design and Power Allocation in Multi-UAV Networks

Adaptive modem and interference suppression based on deep learning

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