A learning based cognitive radio receiver

He, Fangming; Xu, Xingzhong; Zhou, Lei; Man, Hong

doi:10.1109/milcom.2011.6127675

Cited by 7 publications

(14 citation statements)

References 19 publications

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“…These results are compared with the performance of the conventional coherent demodulator such as optimum detector and other machine learning based demodulators. ML based demodulators include one-dimensional convolutional neural network (1-D CNN) [15], artificial neural network (ANN) based demodulator named as neural network demodulator (NND) [11], and multiple layper perceptron (MLP) based classifier named as MaxMLP [10]. All these ML based demodulator take oversampled received signal as input to process and detect the respective binary bits.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Nakayama and Imai, [9] proposed an amplitude shift keying demodulator based on a neural network to combine the wideband noise rejection, pulse waveform shaping, and decoding into a single neural network. Multi-layer perceptron (MLP) based demodulator was proposed in [10], [11]. Moreover, He et al [10] used multiple MLPs to construct a demodulator named MaxMLP classifier, which automatically detected different modulated signals (BPSK, QPSK, and GMSK) without using complex signal processing algorithms.…”

Section: A Related Workmentioning

confidence: 99%

“…Multi-layer perceptron (MLP) based demodulator was proposed in [10], [11]. Moreover, He et al [10] used multiple MLPs to construct a demodulator named MaxMLP classifier, which automatically detected different modulated signals (BPSK, QPSK, and GMSK) without using complex signal processing algorithms. Önder et.al.…”

Section: A Related Workmentioning

confidence: 99%

“…Backpropagation, also referred to as backward error propagation, is the method for computing the gradient of the loss function with respect to the weights. As the output is binary, the binary crossentropy function is used as loss function in the DNN detector as shown in (10).…”

Section: Architecture Of the Proposed Dnn Detectormentioning

confidence: 99%

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DeepDeMod: BPSK Demodulation Using Deep Learning Over Software-Defined Radio

et al. 2022

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In wireless communication, signal demodulation under non-ideal conditions is one of the important research topic. In this paper, a novel non-coherent binary phase shift keying demodulator based on deep neural network, namely DeepDeMod, is proposed. The proposed scheme makes use of neural network to decode the symbols from the received sampled signal. The proposed scheme is developed to demodulate signal under fading channel with additive white Gaussian noise along with hardware imperfections, such as phase and frequency offset. The time varying nature of hardware imperfections and channel poses a additional challenge in signal demodulation. In order to address this issue, additionally we propose transfer learning based DeepDeMod scheme. Pilot symbols along with data is transmitted in a packet which is used to learn the time varying parameters from the pilot reception followed by data demodulation. Results show that compared with the conventional demodulators and other machine learning based demodulators, our proposed DeepDeMod provides significantly better performance in term of bit error rate. We also implement the proposed DeepDeMod on software defined radio and present the experimental results.INDEX TERMS Binary phase shift keying (BPSK), deep neural network (DNN), demodulation, softwaredefined radio (SDR), transfer learning, universal software radio peripheral (USRP).

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Section: Simulation Resultsmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Section: Architecture Of the Proposed Dnn Detectormentioning

confidence: 99%

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DeepDeMod: BPSK Demodulation Using Deep Learning Over Software-Defined Radio

et al. 2022

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“…Recently, some scholars have attempted to introduce machine learning into demodulation technology. For the neural network demodulators mentioned in [21][22][23], the demodulation principle is to analyse the modulated data in every symbol period by neural network. The modulated data is divided into symbols according to the number of samplings.…”

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of a BPSK 1D-CNN Demodulator

Yan

Shen

et al. 2018

Applied Sciences

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In this paper, we propose a field programmable gate array (FPGA) implementation of a one-dimensional convolution neural network (1D-CNN) demodulator for binary phase shift keying (BPSK). The 1D-CNN demodulator includes two 1D-CNNs and a decision module. Discrete time series of BPSK signals are imported into the well-trained 1D-CNNs. The 1D-CNNs detect the phase shifts' moment and type, including phase shift from 0 to π and that from π to 0. The decision module combines results of the two 1D-CNNs and outputs the demodulated data. In order to improve the efficiency of resource utilization and operation speed of the FPGA circuit, a time-delay network for convolutional calculation and a structure for piecewise approximation for the activation function were designed. To enhance the performance of the 1D-CNN demodulator, universal and diversity training data considering five impact factors were generated specially. Experimental results under different channel conditions show that the proposed demodulator has good adaptability to frequency offset and short latency. The demodulation loss of the proposed demodulator can almost be kept within 2 dB.

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