The Viterbi algorithm is widely adopted in digital communication systems because of its capability of realizing maximum-likelihood signal sequence detection. However, implementation of the Viterbi algorithm requires instantaneous channel state information (CSI) to be available at the receiver. This is difficult to satisfy in some emerging communication systems such as molecular communications, underwater optical communications, etc, where the underlying channel models are highly complex or completely unknown. ViterbiNet, developed in the prior literature, is a promising framework to cope with this challenge, where deep learning (DL) techniques are combined with the Viterbi Algorithm to enable near-optimal signal detection without CSI. This paper offers a non-trivial variation of ViterbiNet based on generative adversarial networks (GAN). Specifically, a novel architecture using GAN is designed to directly learn the channel transition probability (CTP) from receiver observations, which is the only part of the Viterbi algorithm that is channel-dependent. With the learned CTP, the classical Viterbi algorithm can be implemented without modifications. To make the proposed architecture applicable to time-varying channels, we further develop two methods to fine-tune the learned CTP online. In the first method, pilots within each frame are exploited to update the CTP learning network; In the second method, a decision-directed approach is devised to generate training data in real-time, which is utilized to re-train the learning network. By combining these two approaches, the receiver is able to track the dynamic channel conditions without being trained from scratch. Numerical simulations demonstrate the superiority of the proposed design compared to existing methods.

In many emerging communications systems such as molecular communications and cross-medium communications, the channel models are extremely difficult to be analytically formulated, which makes it impossible for the receivers to perform signal detection using traditional approaches. To address this issue, we propose a deep-learning based method for signal sequence detection. In particular, a novel neural network (NN) architecture, called Cooperative and Time-varying Bidirectional Recurrent Neural Network (CTBRNN), is developed, which learns from the training data and estimates the transmitted signal sequence without knowing the underlying channel model. Furthermore, we develop a chemical communication experimental platform to collect real data, which is used to train the NN and evaluate the performance of the developed detector. Experimental results demonstrate that, the proposed detection method outperforms the existing NN-based and NN-free candidate solutions in terms of the detection accuracy.

Due to the openness of wireless medium and the public structure of pilot signals, wireless sensing procedure is vulnerable to eavesdropping, which causes privacy concerns. In this paper, a novel physical layer obfuscation solution termed as multi-antenna signal masking is proposed to realize privacy-preserving sensing. The privacy protection is realized via controlling the phase difference between the sensing signals of different antennas. Considering the fact that the channel state information (CSI) variation caused by changes of the physical environment typically slowly varies with time, the phase difference is designed as a slowly-varying function with temporal-correlation such that the real variation pattern in CSI is masked and the eavesdropper is thus unable to perform sensing based on the measured CSI. Furthermore, we also devise a round-trip transmission method to avoid secret information exchange between legitimate users, hence realizing privacy-protection without additional overhead. Simulation results demonstrate the superiority of the proposed method in terms of sensing accuracy and privacy-protection capability compared with existing works.

Contactless vital signs monitoring can be realized by wireless sensing techniques, where the variations of channel state information (CSI) are utilized to acquire information about a remote object. Due to the openness of wireless medium and the public structure of the pilot signals, any passive observer (eavesdropper) can sniff the signal transmission and perform channel estimation, based on which the vital signs information can be extracted. To realize privacy protection, in this letter, a pilot masking technique is proposed, where the pilot signal with crafted spectral characteristics is designed for wireless sensing such that the artificial variation patterns are introduced into the equivalent CSI obtained by the eavesdropper. Consequently, the real CSI variations are masked, which leads the eavesdropper to make an incorrect estimation. Further, a round-trip signaling method is developed to avoid secret information exchange between legitimate users. Simulation results demonstrate the superiority of the proposed method in terms of sensing accuracy and privacy-protection capability compared with existing works.

In this paper, an uplink signal detection problem is considered for Internet-of-Things (IoT) networks. Owing to the imperfections of IoT devices including I/Q imbalance and amplifier non-linearity, exact end-to-end channel models and accurate channel state information (CSI) are typically unavailable at the receiver, which obstructs the application of traditional model-based signal detection algorithms. A consensus has been reached recently that Deep learning (DL) is a promising tool to cope with this problem. However, for the IoT scenarios under consideration, devices typically transmit data using short packets with few pilot symbols, the amount of which is insufficient for each device to individually train a detector. In order to combat the data scarcity barrier and enable few-shot learning, a novel training paradigm is proposed where pilot symbols from different devices are aggregated in an intelligent manner to train a universal signal detector. Specifically, this paper devises a knowledge-driven signal detector architecture following the modular design methodology typically used in classical communication system receivers. Under this framework, three neural networks (NNs), a signal classifier, a channel feature extractor, and a signal feature extractor are created to form decision statistics and produce estimates of the transmitted symbols. Furthermore, borrowing ideas from domain adaptation, a novel component referred to as a link discriminator is integrated into the architecture to improve its generalizability. The proposed signal detector exploits pilot symbols from various IoT devices to train a universal detector that can be applied to different channel conditions without retraining, including those not seen in the training phase. Simulation results verify the superiority of the proposed knowledge-driven detector compared with existing solutions in the sense that it enjoys higher detection accuracy and can be well trained with less data.

This paper considers a signal detection problem for uplink transmission in Internet-of-Things (IoT) networks. Due to the non-idealities of IoT devices such as amplifier’s non-linearity, the exact end-to-end channel model as well as the accurate channel state information (CSI) is not available at the receiver (i.e., base station), which precludes the possibility of using classical model-based signal detection methods. Deep learning (DL) techniques have been recognized recently as an effective tool to deal with this challenge. However, for IoT scenarios, devices typically transmit data using short packets with few pilot symbols. The amount of training data is insufficient to train a detector for each device individually. In order to combat the data scarcity barrier and enable few-shot learning, this paper proposes to aggregate pilot symbols from different devices in an intelligent manner to train a universal signal detector which is applicable to all possible channel conditions. To be specific, a knowledge-driven signal detector architecture is devised following the modular design methodology for classical communication system receivers. Under this framework, three neural networks (NN), termed as channel feature extractor, signal feature extractor, and signal classifier, respectively, are employed to form decision statistics and make estimates on the transmitted symbols. Furthermore, borrowing the ideas in domain adaptation, a novel component termed as link discriminator is integrated into the architecture to improve the generalization capability of the detector. Simulation results demonstrate that the proposed knowledge-driven detector outperforms the existing solutions in the sense that it enjoys higher detection accuracy and can be well trained with less training data.