Very-low-SNR cognitive receiver based on wavelet preprocessed signal patterns and neural network

Abstract

A pattern-based cognitive communication system (PBCCS) that optimizes non-periodic RF waveforms for security applications is proposed. PBCCS is a cross-layer approach that merges the channel encoding and modulation. The transmitter encodes sequences of bits into continuous signal patterns by selecting the proper symbol glossaries. The cognitive receiver preprocesses the received signal by extracting a limited set of wavelet features. The extracted features are fed into an artificial neural network (ANN) to recover the digital data carried by the distorted symbol. The PBCCS system offers a flexible management for robustness against a high noise level and increases the spectral efficiency. In this study, the spectral efficiency and robustness of a PBCCS scheme for an additive white Gaussian noise (AWGN) channel is investigated. The results show that at an SNR of −5 dB, a 3-bit glossary achieves a bit error rate (BER) of 10 −5 . Also, the link spectral efficiency (LSE) of the proposed system is 2.61 bps/Hz.

A comparative analysis of 1-level multiplier-free discrete wavelet transform implementations on FPGAs

“…Eq. (2) shows that output y is the sum of the multiplication of the filter coefficients and the input. DAA eliminates the need for hardware multipliers by performing the arithmetic operations in a bit serial computation [13].…”

“…Discrete wavelet transform (DWT) [1][2][3], a linear signal processing technique that transforms a signal from the time domain to the wavelet domain [4], employs different algorithms for decomposing a signal into an orthonormal time series with different frequency bands. The signal analysis can be performed using either the pyramid algorithm (PA) [4] or recursive pyramid transform (RPT) [5].…”

“…The conventional convolution-based DWT requires massive computations and consumes much area and power, which could be overcome by using the lifting-based scheme for the DWT that was introduced by Sweldens [9]. Although, the lifting scheme is used to compute the output of low-and high-pass filters using fewer components, it may not be well suited for our application due to the nature of the pattern-based cognitive communication system, where the low frequencies components are more important than those of the higher ones [2]. Another advantage of using convolution-based DWT over lifting approach is that it does not require temporary registers to store the intermediate results and with the appropriate design strategy, it could have better area and power efficiency [10,11].…”

“…However, RPT decomposes the signal x[n] into two parts using high-and low-pass filters, which can be implemented using filter banks [6]. 1-D DWT has been implemented for signal denoising, feature extraction, and pattern recognition and compression in [2,3,7,8]. The conventional convolution-based DWT requires massive computations and consumes much area and power, which could be overcome by using the lifting-based scheme for the DWT that was introduced by Sweldens [9].…”