A Reinforcement Learning-Based QAM/PSK Symbol Synchronizer

Matta, Marco; Cardarilli, G.C.; Nunzio, Luca Di; Fazzolari, Rocco; Giardino, Daniele; Nannarelli, Alberto; Re, M.; Spanò, Sergio

doi:10.1109/access.2019.2938390

Cited by 20 publications

(8 citation statements)

References 35 publications

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“…The DQN algorithm was adopted to realize cooperative spectrum sensing in cognitive radio networks in [40]. In [41], a Reinforcement Learning-based symbol synchronizer is proposed and a proved valid timing recovery is proven. However, none of these work considered using the history of actions executed by the agent as an aid for the next step.…”

Section: Related Workmentioning

confidence: 99%

Improved Q-Learning Algorithm Based on Approximate State Matching in Agricultural Plant Protection Environment

Sun

Wang

Zhang

2021

Entropy

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An Unmanned Aerial Vehicle (UAV) can greatly reduce manpower in the agricultural plant protection such as watering, sowing, and pesticide spraying. It is essential to develop a Decision-making Support System (DSS) for UAVs to help them choose the correct action in states according to the policy. In an unknown environment, the method of formulating rules for UAVs to help them choose actions is not applicable, and it is a feasible solution to obtain the optimal policy through reinforcement learning. However, experiments show that the existing reinforcement learning algorithms cannot get the optimal policy for a UAV in the agricultural plant protection environment. In this work we propose an improved Q-learning algorithm based on similar state matching, and we prove theoretically that there has a greater probability for UAV choosing the optimal action according to the policy learned by the algorithm we proposed than the classic Q-learning algorithm in the agricultural plant protection environment. This proposed algorithm is implemented and tested on datasets that are evenly distributed based on real UAV parameters and real farm information. The performance evaluation of the algorithm is discussed in detail. Experimental results show that the algorithm we proposed can efficiently learn the optimal policy for UAVs in the agricultural plant protection environment.

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Section: Related Workmentioning

confidence: 99%

Improved Q-Learning Algorithm Based on Approximate State Matching in Agricultural Plant Protection Environment

Sun

Wang

Zhang

2021

Entropy

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“…The training of the agent is obtained through the maximization of a reward signal that represents a figure of merit depicting the effectiveness of the action taken by the agent. RL is an expanding sector that is found in a wide range of applications such as finance 1 , robotics 2 – 4 , natural language processing 5 , and communications 6 .…”

Section: Introductionmentioning

confidence: 99%

Resilient multi-agent RL: introducing DQ-RTS for distributed environments with data loss

Canese,

Cardarilli,

Di Nunzio

et al. 2024

Sci Rep

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This paper proposes DQ-RTS, a novel decentralized Multi-Agent Reinforcement Learning algorithm designed to address challenges posed by non-ideal communication and a varying number of agents in distributed environments. DQ-RTS incorporates an optimized communication protocol to mitigate data loss between agents. A comparative analysis between DQ-RTS and its decentralized counterpart Q-RTS, or Q-learning for Real-Time Swarms, demonstrates the superior convergence speed of DQ-RTS, achieving a remarkable speed-up factor ranging from 1.6 to 2.7 in scenarios with non-ideal communication. Moreover, DQ-RTS exhibits robustness by maintaining performance even when the agent population fluctuates, making it well-suited for applications requiring adaptable agent numbers over time. Additionally, extensive experiments conducted on various benchmark tasks validate the scalability and effectiveness of DQ-RTS, further establishing its potential as a practical solution for resilient Multi-Agent Reinforcement Learning in dynamic distributed environments.

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“…Machine learning (ML) algorithms were proposed last year in various applications like energy [ 17 , 18 , 19 ], health [ 20 , 21 ], communication [ 17 , 22 ], agriculture [ 23 , 24 ] and so forth. It was achieved due to the following reasons: (1) growing computational abilities of microprocessors and circuits, and (2) access to big data offered by the internet.…”

Section: Introductionmentioning

confidence: 99%

Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO

Sun

Wei

et al. 2021

Sensors

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Indoor navigation has attracted commercial developers and researchers in the last few decades. The development of localization tools, methods and frameworks enables current communication services and applications to be optimized by incorporating location data. For clinical applications such as workflow analysis, Bluetooth Low Energy (BLE) beacons have been employed to map the positions of individuals in indoor environments. To map locations, certain existing methods use the received signal strength indicator (RSSI). Devices need to be configured to allow for dynamic interference patterns when using the RSSI sensors to monitor indoor positions. In this paper, our objective is to explore an alternative method for monitoring a moving user’s indoor position using BLE sensors in complex indoor building environments. We developed a Convolutional Neural Network (CNN) based positioning model based on the 2D image composed of the received number of signals indicator from both x and y-axes. In this way, like a pixel, we interact with each 10 × 10 matrix holding the spatial information of coordinates and suggest the possible shift of a sensor, adding a sensor and removing a sensor. To develop CNN we adopted a neuro-evolution approach to optimize and create several layers in the network dynamically, through enhanced Particle Swarm Optimization (PSO). For the optimization of CNN, the global best solution obtained by PSO is directly given to the weights of each layer of CNN. In addition, we employed dynamic inertia weights in the PSO, instead of a constant inertia weight, to maintain the CNN layers’ length corresponding to the RSSI signals from BLE sensors. Experiments were conducted in a building environment where thirteen beacon devices had been installed in different locations to record coordinates. For evaluation comparison, we further adopted machine learning and deep learning algorithms for predicting a user’s location in an indoor environment. The experimental results indicate that the proposed optimized CNN-based method shows high accuracy (97.92% with 2.8% error) for tracking a moving user’s locations in a complex building without complex calibration as compared to other recent methods.

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A Reinforcement Learning-Based QAM/PSK Symbol Synchronizer

Cited by 20 publications

References 35 publications

Improved Q-Learning Algorithm Based on Approximate State Matching in Agricultural Plant Protection Environment

Improved Q-Learning Algorithm Based on Approximate State Matching in Agricultural Plant Protection Environment

Resilient multi-agent RL: introducing DQ-RTS for distributed environments with data loss

Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO

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