An Integrated Affinity Propagation and Machine Learning Approach for Interference Management in Drone Base Stations

Wang, Li-Chun; Chao, Yung-Sheng; Cheng, Shao-Hung; Han, Zhu

doi:10.1109/tccn.2019.2946864

Cited by 29 publications

(21 citation statements)

References 31 publications

(55 reference statements)

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“…Because the UL approach does not require any labeled data, it is unnecessary to collect real data or perform simulation, which easier to implement the temporary dispatched DSC networks. Our previous work [10], [11] proposed an UL approach to reduce interference for the temporary and dynamic drone base stations (i.e., an instant network topology). 3) In the RL approach, each action is matched to a corresponding reward, and the system learns the optimal actions that lead to the greatest accumulation of rewards.…”

Section: A Motivationmentioning

confidence: 99%

Proactive Power Control and Position Deployment for Drone Small Cells: Joint Supervised and Unsupervised Learning

Cheng¹,

Shih

Chang

2021

IEEE Access

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Since unmanned aerial vehicles (UAV) are easily deployed, highly mobile, and hover capability, they are utilized for many commercial applications. In particular, small cells mounted on UAVs, also known as drone small cells (DSC), may provide temporary relief or ancillary programs for the wireless network. In this paper, we design a prediction-based proactive drone management (P 2 DM) framework to reduce network interference and improve energy efficiency in the multiple DSCs scenario. The P 2 DM framework can be divided into offline and online phases. In the offline phase, supervised learning is used to build a highly accurate mobility prediction model according to the historical data. The prediction model is launched in the online phase to predict the user position only using a small sample set. The system proactively determines whether a DSC should be awake or asleep at the next timeslot due to the predicted user positions. Since DSC has more longer awake time in the deep sleeping mode, it is previously awoken to avoid data propagation delay. To further overcome the difficulty of obtaining the key performance indicator data (i.e., labeled data) in the online phase, an unsupervised learning technique is employed for DSC repositioning and power control to improve energy efficiency. Our simulation results show that the P 2 DM framework can demonstrate the advantage in terms of execution time and energy efficiency compared to the existing method based on genetic algorithm (i.e., a heuristic algorithm).INDEX TERMS Data driven, supervised learning, unsupervised learning, mobility prediction, and Drone small cells.

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Section: A Motivationmentioning

confidence: 99%

Proactive Power Control and Position Deployment for Drone Small Cells: Joint Supervised and Unsupervised Learning

Cheng¹,

Shih

Chang

2021

IEEE Access

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“…The evaluation results show that the proposed algorithm can effectively improve the throughput compared with the existing schemes. Wang et al 27 developed a learning-based multiple drone management framework to mitigate the interference of drone small cell (DSC), which maximized the DSCs' energy efficiency while guaranteeing the required data rates for ground users.…”

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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“…In our previous work [9], [10], a data-driven resource management (DDRM) framework was proposed to implement interference reduction and improve energy efficience in ultra-dense small cells. In [11], we further proposed an interference-aware power control framework with affinity propagation clustering (APC) to reduce the interference for the dynamic UAV networks, where an unsupervised APC learning technique is used to learn the hidden mutual interference structure. On the other hand, the RL algorithm that can effectively optimize the resource allocation strategy without a prior environment model.…”

Section: Introductionmentioning

confidence: 99%

Controlling Interference Structure and Transmit Power of Aerial Small Cells by Hybrid Affinity Propagation Clustering and Reinforcement Learning

Cheng¹,

Jialing²,

Wang³

2021

IEEE Open J. Veh. Technol.

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This article presents a learning-based interference management mechanism for multiple unmanned aerial vehicles mounted small cells (ASCs), called HAPPIER, standing for hybrid affinity propagation clustering (APC) and reinforcement learning (RL) power control. The proposed HAPPIER interference management mechanism consists of two main algorithms: APC and RL. First, from the macroscopic viewpoint, the APC explores the interference structure of multiple ASCs and then changes the most serious interfering ASCs into sleeping mode. As such, we can shift the complicated interference structure into the one with fewer interfering sources and thus speed up the learning process of interference management. Secondly, from the microscopic viewpoint, based on the interference structure suggested by HAPPIER, the RL is applied to adjust the transmission power of active ASCs to optimize the total throughput further. HAPPIER can achieve the optimal trade-off between system throughput and complexity. From our numerical results, subject to the same complexity constraint, our proposed HAPPIER outperforms all the existing approaches and can achieve 93% of the system throughput of the exhaustive searching algorithm.

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An Integrated Affinity Propagation and Machine Learning Approach for Interference Management in Drone Base Stations

Cited by 29 publications

References 31 publications

Proactive Power Control and Position Deployment for Drone Small Cells: Joint Supervised and Unsupervised Learning

Proactive Power Control and Position Deployment for Drone Small Cells: Joint Supervised and Unsupervised Learning

Adaptive modem and interference suppression based on deep learning

Controlling Interference Structure and Transmit Power of Aerial Small Cells by Hybrid Affinity Propagation Clustering and Reinforcement Learning

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