Software defined demodulation of multiple frequency shift keying with dense neural network for weak signal communications

M Kozlenko, V Vialkova - 2020 IEEE 15th International …, 2020 - ieeexplore.ieee.org
In this paper we present the symbol and bit error rate performance of the weak signal digital
communications system. We investigate orthogonal multiple frequency shift keying …

Deepdemod: Bpsk demodulation using deep learning over software-defined radio

A Ahmad, S Agarwal, S Darshi, S Chakravarty - IEEE Access, 2022 - ieeexplore.ieee.org
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 …

Software demodulation of weak radio signals using convolutional neural network

M Kozlenko, I Lazarovych, V Tkachuk… - 2020 IEEE 7th …, 2020 - ieeexplore.ieee.org
In this paper we proposed the use of JT65A radio communication protocol for data exchange
in wide-area monitoring systems in electric power systems. We investigated the software …

Demodulation of faded wireless signals using deep convolutional neural networks

AS Mohammad, N Reddy, F James… - 2018 IEEE 8th Annual …, 2018 - ieeexplore.ieee.org
This paper demonstrates exceptional performance of approximately 10.0 dB learning-based
gain using the Deep Convolutional Neural Network (DCNN) for demodulation of a Rayleigh …

Four single-sideband M-QAM modulation using soft input soft output equalizer over OFDM

AM Mustafa, QN Nguyen, T Sato… - 2018 28th International …, 2018 - ieeexplore.ieee.org
The Single Sideband (SSB) modulation through Hilbert transformation has successfully
transmitted data using only half bandwidth for the same amount of contained information …

DemodNet: Learning soft demodulation from hard information using convolutional neural network

S Zheng, X Zhou, S Chen, P Qi, C Lou… - ICC 2022-IEEE …, 2022 - ieeexplore.ieee.org
Soft demodulation is a basic module of traditional communication receivers. It converts
received symbols into soft bits, that is, log likelihood ratios (LLRs). However, in the non-ideal …

Impulse noise mitigation using subcarrier coding of OFDM-MFSK scheme in powerline channel

O Kolade, L Cheng - 2019 IEEE International Conference on …, 2019 - ieeexplore.ieee.org
The powerline channel is classified as harsh due to its original design which was not
intended for communication. Permutation codes have shown to combine efficiently with M …

Deep convolutional learning-aided detector for generalized frequency division multiplexing with index modulation

M Turhan, E Öztürk, HA Çırpan - 2019 IEEE 30th Annual …, 2019 - ieeexplore.ieee.org
In this paper, a deep convolutional neural network-based symbol detection and
demodulation is proposed for generalized frequency division multiplexing with index …

M-QAM demodulation based on machine learning

RN Toledo, C Akamine, F Jerji… - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
This paper presents a new Quadrature Amplitude Modulation (M-QAM) demodulation
method using Machine Learning techniques. The new method significantly reduces the …

Intelligent demodulation method for communication signals based on multi-layer deep belief network

Q Miao, Y Zhang, X Zhang - 2019 International Conference on …, 2019 - ieeexplore.ieee.org
Aiming at the problem of signal demodulation under noise interference channel, a signal
recognition method using deep learning is proposed. The signal demodulation is completed …