In this paper we propose a Timing Recovery Loop for PSK and QAM modulations based on swarm Reinforcement Learning, suitable for FPGA implementation. We apply the Q-RTS algorithm, a hardware-oriented multi-agent version of Q-Learning, to a symbol synchronizer. One agent is in charge to synchronize the In-phase component and a second agent is applied to the Quadrature component. If compared to a loop based on a single-agent Q-Learning, we obtain improved synchronization capabilities in terms of recovery time and immunity to sub-optimal sampling. The Q-RTS timing recovery is up to 3 times faster than its Q-Learning counterpart. The implementation results show a low power consumption and a high throughput allowing the proposed synchronizer to be used in high-speed telecommunications systems.

Cardarilli, G.c., Di Nunzio, L., Fazzolari, R., Giardino, D., Re, M., Ricci, A., et al. (2022). An FPGA-based multi-agent reinforcement learning timing synchronizer. COMPUTERS & ELECTRICAL ENGINEERING, 99 [10.1016/j.compeleceng.2022.107749].

An FPGA-based multi-agent reinforcement learning timing synchronizer

Cardarilli G. C.;Di Nunzio L.;Fazzolari R.;Re M.;Spano S.
2022-01-01

Abstract

In this paper we propose a Timing Recovery Loop for PSK and QAM modulations based on swarm Reinforcement Learning, suitable for FPGA implementation. We apply the Q-RTS algorithm, a hardware-oriented multi-agent version of Q-Learning, to a symbol synchronizer. One agent is in charge to synchronize the In-phase component and a second agent is applied to the Quadrature component. If compared to a loop based on a single-agent Q-Learning, we obtain improved synchronization capabilities in terms of recovery time and immunity to sub-optimal sampling. The Q-RTS timing recovery is up to 3 times faster than its Q-Learning counterpart. The implementation results show a low power consumption and a high throughput allowing the proposed synchronizer to be used in high-speed telecommunications systems.
2022
Pubblicato
Rilevanza internazionale
Articolo
Sì, ma tipo non specificato
Settore ING-INF/01 - ELETTRONICA
English
Reinforcement learning; Machine learning; Multi-agent; Timing recovery; FPGA; Symbol synchronization
Cardarilli, G.c., Di Nunzio, L., Fazzolari, R., Giardino, D., Re, M., Ricci, A., et al. (2022). An FPGA-based multi-agent reinforcement learning timing synchronizer. COMPUTERS & ELECTRICAL ENGINEERING, 99 [10.1016/j.compeleceng.2022.107749].
Cardarilli, Gc; Di Nunzio, L; Fazzolari, R; Giardino, D; Re, M; Ricci, A; Spano, S
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/292815
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