Long Short-Term Memory for Radio Frequency Spectral Prediction and its Real-Time FPGA Implementation
Siddhartha, Yee Hui Lee, Duncan J. M. Moss, Julian Faraone, Perry Blackmore, Daniel Salmond, David Boland, Philip H. W. Leong · 2018
Reactive communication waveforms hosted in current generation tactical radios often fail to achieve good performance and resilience in highly dynamic and complex environments. Arguably, novel waveforms that can proactively adapt to anticipated channel conditions may better meet the challenges of the tactical environment. This motivates the ability to accurately predict spectral behaviour in real-time. A Long Short- Term Memory (LSTM) network is a type of recurrent neural network which has been extremely successful in dealing with time-dependent signal processing problems such as speech recognition and machine translation. In this paper, we apply it to the task of spectral prediction and present a module generator for a latency-optimised Field-Programmable Gate Array (FPGA) implementation. We show that our implementation obtains superior results to other time series prediction techniques including a naïve predictor, moving average and ARIMA for the problem of radio frequency spectral prediction. For a single LSTM layer plus a fully- connected output layer with 32 inputs and 32 outputs, we demonstrate that a prediction latency of 4.3 μ s on a Xilinx XC7K410T Kintex-7 FPGA is achievable.