Recent advancement in Frequency Domain Duplexing (FDD) enables wireless systems to use different frequency bands for uplink and downlink communication without explicit channel feedback information. The current state-of-the-art approaches either estimate the underlying variables in the uplink channel or use an artificial neural network architecture to estimate the downlink channel from the uplink channel. However, such techniques fail to perform accurately in multipath-rich environments and environments unseen during training. This paper presents HORCRUX, a physics-based machine learning system that can be generalized and scaled to any environment while predicting downlink channels with high accuracy and applies to single-antenna and MIMO systems. Our approach uses multiple neural networks, trained on the standard wireless channel model, firstly to divide the uplink channel into smaller sub-channels and secondly to generate coarse estimates for the variables for each of the underlying sub-channels. Finally, we use an efficient and fast optimization framework to get fine-tuned variable estimates to predict the downlink channel. We implement our system using software-defined radios. Our evaluations show that HORCRUX performs ~8 dB better than state of the art in downlink channel prediction accuracy in diverse wireless environments. 1

As the demand for high-speed and reliable wireless networks continues to increase, multi-user multiple-input multiple-output (MU-MIMO) technology has become a popular choice for wireless communication systems. However, this technology also brings new security challenges, one of which is the vulnerability during the channel sounding process. In this paper, we propose an active eavesdropping attack targeting MU-MIMO systems. The attack consists of two phases. First, the attacker sends a forged pilot packet to the victims. After that, the access point transmits streams intended for victims to the attacker, who operates in full-duplex mode and relays the streams to the victims. Compared to existing eavesdropping attacks targeting MU-MIMO systems, our proposed attack requires less prior knowledge and coordination from attackers and maximizes eavesdropping opportunities. We evaluate the proposed attack in various settings and prove its effectiveness with multiple victims and partial channel knowledge. Additionally, we explore the use of physical-layer features to detect our proposed attack.

The field of ship autonomous navigation has always garnered significant interest due to its future development potential for intelligent ships and unmanned ships. While there has been extensive research on autonomous navigation in open waters, less focus has been given to coastal waters due to the complexity of the environment and traffic flow. In order to resolve this problem, the dynamic adaptive decision-making method for ship autonomous navigation in coastal waters is presented. A digital twin environment model tailored to the characteristics of coastal waters has been developed, which can dynamically replicate the current ship navigation environment by incorporating multi-source heterogeneous information from ship equipment. The autonomous navigation decision-making method is obtained by integrating an Improved Velocity Obstacle (IVO) for ship collision avoidance and a Line of Sight (LOS) algorithm for ship trajectory tracking. Moreover, a time-rolling algorithm is employed to facilitate specific navigation decision-making in time-varying environments and to account for the uncertainty of target ship motion over time. This comprehensive algorithm has been tested and validated in two different scenarios. The results demonstrate that the proposed navigation decision-making method is reasonable and effective for the ship navigating in the coastal water, particularly in multi-ship encounter situations of target ships suddenly altering course or changing speed.

Full-duplex devices can compromise the integrity of wireless channel measurements through signal relaying and several attacks have been proposed based on this vulnerability. Existing source authentication methods relying on previously-collected signatures face significant challenges in detecting these attacks because a relay attacker can gradually inject the channels so that the manipulated channels will fall within the tolerance range of the authentication methods and are mistaken as new signatures. In this paper, we propose RelayShield, a system for detecting malicious relays and recovering the legitimate transmitter-receiver channels from the manipulated channels. RelayShield requires only one channel measurement at the receiver. It analyzes signal path information resolved from input channels to detect relays and recover channels. RelayShield achieves over 95% detection accuracy with channels collected in two typical indoor environments. The recovered channels can support a wide range of applications, including secret generation protocols and sensing systems.

Thanks to the highly-dense lighting infrastructure in public areas, visible light emerges as a promising means to indoor localization and navigation. State-of-the-art techniques generally require customized hardware (sensing boards), and mainly work with one single light source (e.g., customized LEDs). This greatly limits their application scope. In this paper, we propose NaviLight, a generic indoor localization and navigation framework based on existing lighting infrastructure with any unmodified light sources (e.g., LED, fluorescent, and incandescent lights). NaviLight simply adopts commercial off-the-shelf mobile phones as receivers, and light intensity values as location signatures. Unlike existing WiFi systems, a single light intensity value is not discriminative enough over space though the light intensity field does vary, which makes our design more challenging. We thus propose a LightPrint as a location signature using a vector of multiple light intensity values obtained during user's walks. Such LightPrints are created by leveraging any user movement (of varying distance and direction) in order to minimize user efforts. A set of techniques are proposed to achieve quick LightPrint matching, which includes a coarse-grained classification and a fine-grained matching over dynamic time warping. We have implemented NaviLight to provide real-time service on Android phones in three typical indoor environments, covering a total area size over 1000m2. Our experiments show that NaviLight can achieve sub-meter localization accuracy to meet practical engineering requirements.

Exploiting cognition to the cache-enabled device-to- device (D2D) communication underlaying the multi- channel cellular network is the main focus of this paper. D2D pairs perform direct communications via sensing the available cellular channels, bypassing the base station (BS). Dynamic service is considered and the network performance is evaluated with the stochastic geometry. Node locations are first modeled as mutually independent Poisson Point Processes, and the service queueing process is formulated. Then the corresponding tier association and cognitive access protocol are developed. The delay and the length for the queue at the BS and D2D transmitter are further elaborated, with modeling the traffic dynamics of request arrivals and departures as the discrete-time multiserver queue with priorities. Moreover, impacts of the physical layer and content-centric features on the system performance are jointly investigated to provide a valuable insight.

The ubiquitous QR codes and some similar barcodes are becoming a convenient and popular approach to impromptu communication between mobile devices and their surrounding cyber-physical world. However, such codes suffer from two common drawbacks: poor viewing experience and inability to be identified through itself. In this work, we propose ART-code-- Adaptive Robust doT matrix barcode, which aims to preserve ART and CODE features in one visual pattern. It works on any surface (paper or electronic displays) and is able to convert any image or any form of human-readable contents (e.g., a picture, a logo, a slogan) into an ARTcode. It looks like an image which retains human-readable and aesthetically pleasant contents, and in the meanwhile, it acts as a QR code which conveys data bits over the visual channel. The core enablers in ARTcode are (1) the design of the colored dot matrix for data embedding with little distortion from the original image and (2) a comprehensive error correction scheme which enhances decoding robustness against noises and interferences from the original image in ARTcode. We implement ARTcode with the receiver on Android phones and the sender from a PC or a phone (it can be printed in paper). We conduct extensive user survey and experiments for evaluation. It validates the effectiveness and wide applicability of ARTcode: It works well with all of 197 images randomly downloaded, covering representative categories of the gray-scale images, logos, colored ones with low/medium/strong contrasts. The image quality is quite acceptable in a subjective user-perception survey with 50 participants and data communication accuracy achieves as high as 99% in almost all the cases (> 96% raw accuracy in ARTcode without error detection and other schemes).