An FPGA-based multi-agent Reinforcement Learning timing synchronizer

Cardarilli, Gian Carlo

doi:10.1016/j.compeleceng.2022.107749

An FPGA-based multi-agent Reinforcement Learning timing synchronizer

2022-04-01

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Cardarilli, Gian Carlo

DOI：10.1016/j.compeleceng.2022.107749

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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.

Keywords:

Reinforcement learning

Machine learning

Multi-agent

Timing recovery

FPGA

Symbol synchronization

An FPGA-based multi-agent Reinforcement Learning timing synchronizer

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