We introduce a comprehensive framework for the detection and demodulation of covert electromagnetic signals using solid-state spin sensors. Our approach, named RAPID, is a two-stage hybrid strategy that leverages nitrogen-vacancy (NV) centers to operate below the classical noise floor employing a robust adaptive policy via imitation and distillation. We first formulate the joint detection and estimation task as a unified stochastic optimal control problem, optimizing a composite Bayesian risk objective under realistic physical constraints. The RAPID algorithm solves this by first computing a robust, non-adaptive baseline protocol grounded in the quantum Fisher information matrix (QFIM), and then using this baseline to warm-start an online, adaptive policy learned via deep reinforcement learning (Soft Actor-Critic). This method dynamically optimizes control pulses, interrogation times, and measurement bases to maximize information gain while actively suppressing non-Markovian noise and decoherence. Numerical simulations demonstrate that the protocol achieves a significant sensitivity gain over static methods, maintains high estimation precision in correlated noise environments, and, when applied to sensor arrays, enables coherent quantum beamforming that achieves Heisenberg-like scaling in precision. This work establishes a theoretically rigorous and practically viable pathway for deploying quantum sensors in security-critical applications such as electronic warfare and covert surveillance.

--Accurate phase demodulation is essential for vital sign detection using millimeter wave radar. The time-varying DC offsets and phase imbalance in complex scenarios can seriously interfere with the performance of demodulation. This letter proposes a novel DC offset calibration algorithm as well as a Hilbert and differential cross-m ultiply (HADCM) demodulation algorithm to solve the time-varying imbalance terms. It works by estimating the time-varying DC offsets from neighboring peaks and valleys, and uses the differential form as well as the Hilbert transform of the I/Q channel signals to obtain the vital sign signal. Simulations and experiments have verified the effectiveness of the novel algorithm under low signal-to-noise ratio. Compared with the existing demodulation algorithms, the proposed algorithm can not only recover the original signal in complex environments more accurately, but also reduce the interference of noise on the signal.

A chaotic modulation scheme is an efficient wideband communication method. It utilizes the deterministic chaos to generate pseudo-random carriers. Chaotic bifurcation parameter modulation is one of the well-known and widely-used techniques. This paper presents the machine learning based demodulation approach for the bifurcation parameter keying. It presents the structure of a convolutional neural network as well as performance metrics values for signals generated with the chaotic logistic map. The paper provides an assessment of the overall accuracy for binary signals. It reports the accuracy value of 0.88 for the bifurcation parameter deviation of 1.34% in the presence of additive white Gaussian noise at the normalized signal-to-noise ratio value of 20 dB for balanced dataset.

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 the multiple frequency shift keying weak signals transmitted with JT65A communication protocol using deep convolutional neural network. We presented the demodulation performance in form of symbol and bit error rates. We focused on the interference immunity of the protocol over an additive white Gaussian noise with average signal-to-noise ratios in the range from -30 dB to 0 dB, which was obtained for the first time. We proved that the interference immunity is about 1.5 dB less than the theoretical limit of non-coherent demodulation of orthogonal MFSK signals.

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 modulation scheme with supervised machine learning demodulation approach using simple dense end-to-end artificial neural network. We focus on the interference immunity over an additive white Gaussian noise with average signal-to-noise ratios from -20 dB to 0 dB.

A number of recent scientific and engineering problems require signals to be decomposed into a product of a slowly varying positive envelope and a quickly varying carrier whose instantaneous frequency also varies slowly over time. Although signal processing provides algorithms for so-called amplitude-and frequencydemodulation (AFD), there are well known problems with all of the existing methods. Motivated by the fact that AFD is ill-posed, we approach the problem using probabilistic inference. The new approach, called probabilistic amplitude and frequency demodulation (PAFD), models instantaneous frequency using an auto-regressive generalization of the von Mises distribution, and the envelopes using Gaussian auto-regressive dynamics with a positivity constraint. A novel form of expectation propagation is used for inference. We demonstrate that although PAFD is computationally demanding, it outperforms previous approaches on synthetic and real signals in clean, noisy and missing data settings.

Molecular communication is a novel approach for data transmission between miniaturized devices, especially in contexts where electrical signals are to be avoided. The communication is based on sending molecules (or other particles) at nano scale through channel instead sending electrons over a wire. Molecular communication devices have a large potential in medical applications as they offer an alternative to antenna-based transmission systems that may not be applicable due to size, temperature, or radiation constraints. The communication is achieved by transforming a digital signal into concentrations of molecules. These molecules are then detected at the other end of the communication channel and transformed back into a digital signal. Accurately modeling the transmission channel is often not possible which may be due to a lack of data or time-varying parameters of the channel (e. g., the movements of a person wearing a medical device). This makes demodulation of the signal very difficult. Many approaches for demodulation have been discussed with one particular approach having tremendous success: artificial neural networks. These networks imitate the decision process in the human brain and are capable of reliably classifying noisy input data. Training such a network relies on a large set of training data. As molecular communication as a technology is still in its early development phase, this data is not always readily available. We discuss neural network-based demodulation approaches relying on synthetic data based on theoretical channel models as well as works using actual measurements produced by a prototype test bed. In this work, we give a general overview over the field molecular communication, discuss the challenges in the demodulations process of transmitted signals, and present approaches to these challenges that are based on artificial neural networks.

Low-Power Wide-Area Networks (LPWANs) are an emerging Internet-of-Things (IoT) paradigm marked by low-power and long-distance communication. Among them, LoRa is widely deployed for its unique characteristics and open-source technology. By adopting the Chirp Spread Spectrum (CSS) modulation, LoRa enables low signal-to-noise ratio (SNR) communication. The standard LoRa demodulation method accumulates the chirp power of the whole chirp into an energy peak in the frequency domain. In this way, it can support communication even when SNR is lower than -15 dB. Beyond that, we proposed NELoRa, a neural-enhanced decoder that exploits multi-dimensional information to achieve significant SNR gain. This paper presents the dataset used to train/test NELoRa, which includes 27,329 LoRa symbols with spreading factors from 7 to 10, for further improvement of neural-enhanced LoRa demodulation. The dataset shows that NELoRa can achieve 1.84-2.35 dB SNR gain over the standard LoRa decoder. The dataset and codes can be found at https://github.com/daibiaoxuwu/NeLoRa_Dataset.

--Shallow water environments create a challenging channel for communications. In this paper, we focus on the challenges posed by the frequency-selective signal distortion called the Doppler effect. We explore the design and performance of machine learning (ML) based demodulation methods -- (1) Deep Belief Network-feed forward Neural Network (DBN-NN) and (2) Deep Belief Network-Convolutional Neural Network (DBN-CNN) in the physical layer of Shallow Water Acoustic Communication (SW AC). The proposed method comprises of a ML based feature extraction method and classification technique. First, the feature extraction converts the received signals to feature images. An analysis of the ML based proposed demodulation shows that despite the presence of instantaneous frequencies, the performance of the algorithm shows an invariance with a small 2dB error margin in terms of bit error rate (BER).

A number of recent scientific and engineering problems require signals to be decomposed into a product of a slowly varying positive envelope and a quickly varying carrier whose instantaneous frequency also varies slowly over time. Although signal processing provides algorithms for so-called amplitude- and frequency-demodulation (AFD), there are well known problems with all of the existing methods. Motivated by the fact that AFD is ill-posed, we approach the problem using probabilistic inference. The new approach, called probabilistic amplitude and frequency demodulation (PAFD), models instantaneous frequency using an auto-regressive generalization of the von Mises distribution, and the envelopes using Gaussian auto-regressive dynamics with a positivity constraint. A novel form of expectation propagation is used for inference. We demonstrate that although PAFD is computationally demanding, it outperforms previous approaches on synthetic and real signals in clean, noisy and missing data settings.