Physical Layer Information Research Articles

State-Aware Rate Adaptation for UAVs by Incorporating On-Board Sensors

Nowadays unmanned aerial vehicles (UAVs) are being widely applied to a wealth of civil and military applications. Robust and high-throughput wireless communication is the crux of these UAV applications. Yet, air-to-ground links suffer from time-varying channels induced by the agile mobility and dynamic environments. Rate adaptation algorithms are generally used to choose the optimal data rate based on the current channel conditions. State-of-the-art approaches leverage physical layer information for rate adaptation, and they work well under certain conditions. However, the above protocols still have limitation under constantly changing flight states and environments for air-to-ground links. To solve this problem, we propose StateRate, a state-optimized rate adaptation algorithm that fully exploits the characteristics of UAV systems using a hybrid deep learning model. The key observation is that the rate adaptation strategy needs to be adjusted according to motion-dependent channel models, which can be reflected by flight states. In this work, the rate adaptation protocol is enhanced with the help of the on-board sensors in UAVs. To make full use of the sensor data, we introduce a learning-based prediction module by leveraging the internal state to dynamically store temporal features under variable flight states. We also present an online learning algorithm by employing the pre-trained model that adapts the rate adaptation algorithm to different environments. We implement our algorithm on a commercial UAV platform and evaluate it in various environments. The results demonstrate that our system outperforms the best-known rate adaptation algorithm up to 53% in terms of throughput when the velocity is 2-6~m/s.

Intelligent Contactless Gesture Recognition Using WLAN Physical Layer Information

Contactless gesture recognition is an emerging interactive technique in ubiquitous and mobile computing. It combines the linguistics with the wireless signals to analyze, judge, and integrate human gestures by the usage of intelligent algorithms. The existing contactless gesture recognition studies can achieve gesture recognition with the machine learning technologies. But in practice, some objective factors, such as the user's position, the non-line of sight condition, can seriously affect the performance of these gesture recognition systems. In this paper, we propose an intelligent and robust contactless gesture recognition using physical layer information. Instead of the usage of machine learning, we learn the gesture characteristics based on the Fresnel zone model of wireless signals. First, we denoise the collected channel state information (CSI) in a sliding window. Then, we extract the eigenvalues of channel phase information based on Fresnel zone model to depict four basic gestures. The features of gestures are independent of the user's position and the signal amplitude. Finally, common-gesture recognition is achieved based on the decision tree classification. Moreover, we develop a hidden Markov model to achieve the complex-gesture recognition. The extensive experimental results show that our proposed method is position-independent and robust. The accuracy of basic-gesture recognition is as high as 91% on average. And, the accuracy of the complex-gesture recognition is also above 85% on average.

Network-Assisted Neural Adaptive Naked-Eye 3D Video Streaming Over Wireless Networks

High quality transmission of Virtual Reality (VR) video depends strictly on bandwidth and delay requirements. It becomes possible with the maturity and commercialization of 5G technology. However, to transmit VR video streaming over wireless communication system, we must cope with the challenge that fluctuation exists in the wireless channel conditions. For example, available channel bandwidth, packet loss rate, and interference level may vary with time due to channel fading and user's mobility. The problem is more prominent when transmitting naked-eye 3D video which generally consists of multiple viewpoints with different resolutions. In order to optimize the quality of experience (QoE) of watching naked-eye VR video over wireless networks, this paper proposes a network-assisted neural adaptive video streaming algorithm (NAVSA). Specifically, we present a modified QoE function to describe the quality of naked-eye 3D streaming quantitatively, which not only considers the quality of naked-eye 3D video itself, but also considers the phenomenon of rebuffering and video fluctuation that occurs during video transmission. Next, with the network-assisted feedback, the physical layer information, the buffer occupancy of the video client, and the size of the next video chunk are collected to train a reinforcement learning model. Based on dynamic adaptive streaming over HTTP (DASH), the model can automatically choose appropriate viewpoints and resolutions corresponding to the current condition of the wireless networks such that the network capacity can be fully explored. To verify the performance of our proposed NAVSA, we simulate 3 naked-eye 3D video application scenarios on the NS3 platform. The results show that the performance of NAVSA is about 5~8% better than some state-of-the-art algorithms in wireless networks.

Anomaly Detection for Controller Area Network in Braking Control System With Dynamic Ensemble Selection

The controller area networks (CAN) in the braking control system of metro trains are used to transmit the important control instruction and condition information, whose anomaly will endanger the security of trains running seriously. Due to the harsh work environment, there are various known and previously unknown fault types, current scheduled maintenance cannot detect early anomaly in time, and constructing an accurate and stable anomaly detector is a challenging task. In this paper, an anomaly detection approach is proposed to detect anomaly based on a dynamic ensemble selection system (DESS) without the expert knowledge, which involves two-class and one-class classifiers, and the base classifiers are trained with the network features extracted from the physical-layer information. To conduct the fusion, the support function of “distance-based” classifier is redefined as a class-conditional probability density function, and the source competence of base classifier is estimated by the entropy-based method in validate space and extended to entire decision space using the normalized Gaussian potential function. For different fault types, the competence classifiers are selected and the anomaly detection result is finally achieved by weighted majority voting. The comparative experiments are included in this paper to demonstrate the effectiveness and robustness in anomaly detection, including varying fault types.

Design and Research of New Network Address Coding

Abstract To solve a series of problems caused by address space and information security of contemporary Internet, Chinese scientists have proposed a new generation of network architecture. Since the release of IPv4 RFC791 in 1981, it has become the first widely used Internet of things protocol due to its characteristics of easy implementation and good operability. It constitutes the basic protocol of current Internet technology. However, due to the defects in address classification, address resources are largely wasted. As the scale of the Internet continues to grow, especially the number of addresses used to surge, the address shortage has limited the Internet further development. IPv6 has solved the problem of insufficient IPv4 addresses to some extent, but it is not widely for more than 20 years used because it integrates the information of physical layer and application layer, which confuses the network layer and brings many security risks. Based on the study of IPv4 and IPv6, this paper proposes a new generation network architecture, which is designed from the aspects of addressing model, address writing, address prefix writing and address type. The address structure is compatible with IPv4 and IPv6, so that the previous design results can be retained, fundamentally solve the space address and information security issues, and provide a new solution for the next generation of Internet applications and research.

Butterfly

RFID tag authentication is challenging because most commodity tags cannot run cryptographic algorithms. Prior research demonstrates that physical layer information based authentication is a promising solution, which uses special features from the physical backscatter signals from tags as their fingerprints. However, our recent studies show that existing physical-layer authentication may fail if feature collection and authentication are conducted in different locations, due to location-dependent noises, environmental factors, or reader hardware differences. This paper presents a new physical layer authentication scheme, called Butterfly, which is resilient to environment and location changes. Butterfly utilizes a pair of adjacent tags as an identifier of each object. By using the difference between the RF signals of the two tags as their fingerprint, the environmental factors can be effectively canceled. Butterfly is fully compatible with commodity RFID systems and standards. We set up a prototype Butterfly using commodity readers, tags, and RF devices. Extensive experiments show that Butterfly achieves high authentication accuracy for substantially different environments and device changes.

Intelligent adaptive coherent optical receiver based on convolutional neural network and clustering algorithm.

In a cognitive, heterogeneous, optical network, it would be important to identify physical layer information, especially the modulation formats of transmitted signals. The modulation format information is also indispensable for carrier-phase-recovery in a coherent optical receiver. Because constellation diagrams of modulation signals are susceptible to various noises, we utilize a convolutional neural network to process the amplitude data after the modulation-format-agnostic clock recovery. Furthermore, for the carrier-phase-recovered data, we use the clustering method based on a fast search and find the density peaks to classify the constellation clusters and use the k-nearest-neighbor method to label the samples. The proposed receiver system has a simple architecture to identify the modulation format based on the amplitude information and can track fast changes of the signals to improve the accuracy of the symbol decision. We have demonstrated this experimentally and have achieved remarkable BER improvement.

Energy-Efficient Cross-Layer Resource Allocation for Heterogeneous Wireless Access

In this paper, an uplink cross-layer resource allocation problem based on imperfect channel state information (CSI) is modeled as min-max fractional stochastic programming for heterogeneous wireless access. The resource allocation is subject to constraints in delay, service outage probability, system radio bandwidth, and total power consumption. The joint bandwidth and power allocations are based on CSI at the physical layer and queue state information (QSI) at the link layer. In order to determine the transmission rate of each mobile terminal according to the queue buffer occupancy, a probability upper bound of exceeding the maximum packet delay in terms of a required transmission rate is presented based on M/D/1 model. Then, the bandwidth and power allocation problem is transformed into bi-convex programming, and an optimal distributed bandwidth and power allocation algorithm is proposed. To reduce computational complexity, a suboptimal distributed bandwidth and power allocation algorithm is presented. Simulation results demonstrate that the proposed algorithms improve the energy efficiency greatly.

Research on Collaborative Communication Strategy Based on Physical Layer Security

Collaborative communication and physical layer information security are two valuable aspects in the field of modern wireless communication. Combining the characteristics of the two, the physical layer security model based on cooperative communication is studied, and the amplification and forwarding strategy and cooperation among them in this paper, the best security capacity of congestion strategy is analyzed. In the light of the fact that related researches do not involve the analysis of performance scenarios, the comparison of security capacity performance under different scenarios is carried out for amplification forwarding strategy and collaborative congestion strategy. Simulation studies show that the amplification and forwarding strategy is more stable and less susceptible to scenario changes than the cooperative congestion strategy. In addition, the performance improvement caused by the increased number of relay antennas is also equivalent to increasing the number of trunks.

Word-Fi: Accurate Handwrite System Empowered by Wireless Backscattering and Machine Learning

Word-Fi is a handwriting input system, driven by wireless backscattering technology and machine learning methods. It could effectively mitigate the surrounding noise and extract the weak signals incurred by tiny writing gestures accurately. Leveraging our customized wireless backscattering system, Word-Fi could be noise tolerant across relatively complex environments, especially when multiple persons are presented around, which significantly differs from status quo wireless sensing systems that suffer from multi-user presentation. For weak signal extraction, Word- Fi incorporates an efficient feature selection scheme for classification and improves the classifier by fully exploiting the physical layer information. After using the word suggestion module, it could recognize writing words with fairly high accuracy (above 90 percent) across different volunteers (7-10).

Securing On-Body IoT Devices By Exploiting Creeping Wave Propagation

On-body devices are an intrinsic part of the Internet-of-Things (IoT) vision to provide human-centric services. These on-body IoT devices are largely embedded devices that lack a sophisticated user interface to facilitate traditional pre-shared key-based security protocols. Motivated by this real-world security vulnerability, this paper proposes SecureTag, a system designed to add defense in depth against active attacks by integrating physical layer (PHY) information with upper-layer protocols. The underpinning of SecureTag is a signal processing technique that extracts the peculiar propagation characteristics of creeping waves to discern on-body devices. Upon overhearing a suspicious transmission, SecureTag initiates a PHY-based challenge-response protocol to mitigate attacks. We implement our system on different commercial off-the-shelf wearables and a smartphone. Extensive experiments are conducted in a lab, apartments, malls, and outdoor areas, involving 12 volunteer subjects of different age groups, to demonstrate the robustness of our system. Results show that our system can mitigate 96.13 % of active attack attempts while triggering false alarms on merely 5.64 % of legitimate traffic.

Social Big-Data-Based Content Dissemination in Internet of Vehicles

By analogy with Internet of things, Internet of vehicles (IoV) that enables ubiquitous information exchange and content sharing among vehicles with little or no human intervention is a key enabler for the intelligent transportation industry. In this paper, we study how to combine both the physical and social layer information for realizing rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks. In the physical layer, headway distance of vehicles is modeled as a Wiener process, and the connection probability of D2D-V2V links is estimated by employing the Kolmogorov equation. In the social layer, the social relationship tightness that represents content selection similarities is obtained by Bayesian nonparametric learning based on real-world social big data, which are collected from the largest Chinese microblogging service Sina Weibo and the largest Chinese video-sharing site Youku. Then, a price-rising-based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem under various quality-of-service requirements. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains.

Security-Aware Resource Allocation With Delay Constraint for NOMA-Based Cognitive Radio Network

In this paper, a downlink security-aware resource allocation problem with delay constraint via spectrum sensing is modeled as a mixed integer non-linear problem for non-orthogonal multiple access-based cognitive radio network. The security-aware resource allocation is subject to constraints in required delay for each secondary user, maximum number of accessed secondary users at each subchannel, total interference power threshold introduced to primary users, and total power consumption at secondary BS. The security-aware resource allocation is based on channel state information at the physical layer and queue state information at the link layer. According to the queue buffer occupancy, a probability upper bound of exceeding the maximum packet delay based on M/D/1 queuing model is analyzed in terms of a required minimum secrecy transmission rate. Then, a secondary user scheduling problem and a power allocation problem are solved, separately. Finally, the secondary user scheduling problem is solved via greedy algorithm, and a power allocation algorithm is proposed by successive convex approximation method. The simulation results demonstrate that the performance of proposed algorithms can be improved significantly.

Monitoring of atopic dermatitis using leaky coaxial cable.

In our daily life, inadvertent scratching may increase the severity of skin diseases (such as atopic dermatitis etc.). However, people rarely pay attention to this matter, so the known measurement behaviour of the movement is also very little. Nevertheless, the behaviour and frequency of scratching represent the degree of itching, and the analysis of scratching frequency is helpful to the doctor's clinical dosage. In this Letter, a novel system is proposed to monitor the scratching motion of a sleeping human body at night. The core device of the system is just a leaky coaxial cable (LCX) and a router. Commonly, LCX is used in the blind field or semi-blindfield in wireless communication. The new idea is that the leaky cable is placed on the bed, and then the state information of physical layer of wireless communication channels is acquired to identify the scratching motion and other small body movements in the human sleep process. The results show that it can be used to detect the movement and its duration. Channel state information (CSI) packet is collected by card installed in the computer based on the 802.11n protocol. The characterisation of the scratch motion in the collected CSI is unique, so it can be distinguished from the wireless channel amplitude variation trend.

Joint Transmit Designs for Coexistence of MIMO Wireless Communications and Sparse Sensing Radars in Clutter

The paper proposes a cooperative scheme for the coexistence of a multiple-input-multiple-output (MIMO) communication system and a matrix completion (MC) based, collocated MIMO (MIMO-MC) radar. To facilitate the coexistence, and also deal with clutter, both the radar and the communication systems use transmit precoding. For waveform flexibility, the radar uses a random unitary waveform matrix. We prove that for such waveforms and any precoding matrix, the error performance of MC is guaranteed. The radar transmit precoder, the radar subsampling scheme, and the communication transmit covariance matrix are jointly designed in order to maximize the radar SINR, while meeting certain communication rate and power constraints. The joint design is implemented at a control center, which is a node with whom both systems share physical layer information, and which also performs data fusion for the radar. We provide efficient algorithms for the proposed optimization problem, along with insight on the feasibility and properties of the proposed design. Simulation results show that the proposed scheme significantly improves the spectrum sharing performance in various scenarios.

A joint subcarrier and power allocation technique with pro-rata fairness and unequal error protection for video transmission over OFDMA downlink

In this paper, we investigate the video quality fairness issue experienced during the transmission of video traffic from base station (BS) to users in orthogonal frequency division multiple access (OFDMA) networks. Assurance of video quality fairness is a cross-layer optimization problem when fairness is required among users in the application layer and it is achieved through resource optimization in the physical layer. For this we develop a non-iterative joint subcarrier assignment and power allocation algorithm for downlink transmission. Also we define pro-rata video quality fairness, where users are assigned video quality proportional to their demand and formulate a pro-rata fairness index to measure the fairness of the proposed algorithm. The rate compatible punctured codes (RCPC) used in the proposed work compliments video quality fairness through unequal error protection (UEP) of users based on their priority at the physical layer. The physical layer channel state information (CSI) and application layer rate distortion (RD) information of each user are used to allocate power and spectrum resources. Extensive simulation studies show that in comparison with methods which utilize only the physical layer information available at the BS, the proposed algorithm ensures significantly better video quality fairness and spectral efficiency.

Mitigating Large Errors in WiFi-Based Indoor Localization for Smartphones

Although WiFi fingerprint-based indoor localization is attractive, its accuracy remains a primary challenge, especially in mobile environments. Existing approaches either appeal to physical layer information or rely on extra wireless signals for high accuracy. In this paper, we revisit the received signal strength (RSS) fingerprint-based localization scheme and reveal crucial observations that act as the root causes of localization errors, yet are surprisingly overlooked or not adequately addressed in previous works. Specifically, we recognize access points’ (APs) diverse discrimination for fingerprinting a specific location, observe the RSS inconsistency caused by signal fluctuations and human body blockages, and uncover the transitional fingerprint problem on commodity smartphones. Inspired by these insights, we devise a discrimination factor to quantify different APs’ discrimination, incorporate robust regression to tolerate outlier measurements, and reassemble different normal fingerprints to cope with transitional fingerprints. Integrating these techniques in a unified system, we propose DorFin, i.e., a novel scheme of fingerprint generation, representation, and matching, which yields remarkable accuracy without incurring extra cost. Extensive experiments in three campus buildings demonstrate that DorFin achieves a mean error of 2.5 m and, more importantly, decreases the 95th percentile error under 6.2 m, both significantly outperforming existing approaches.

Harnessing Partial Packets in Wireless Networks: Throughput and Energy Benefits

This paper proposes a partial packet recovery scheme called packetized rateless algebraic consistency (PRAC). PRAC exploits intra- and inter-packet consistency to identify and recover erroneous packet segments, without recourse to soft physical layer (PHY) or detailed feedback information. PRAC uses a rateless linear code for data encoding and an iterative decoding process for data reconstruction. It allows, but does not rely upon, the use of any PHY forward error correction code, and requires no feedback other than a notification of completion and, in the absence of partial packets, incurs no overhead. In order to quantify PRAC’s performance in terms of both throughput and energy efficiency, experiments are conducted using commercial transceivers in two different scenarios. Our implementation results reveal that PRAC offers an average throughput gain of 35% compared with a baseline ARQ scheme discarding partial packets, and 13% compared with an ideal hybrid-ARQ scheme. On high PER links, throughput is improved by 148% and 34%, respectively. In addition, PRAC reduces on average the total energy consumption of the transmitting nodes by 16%, while, on high PER links, savings can be up to 50%.

Social Network-Based Content Delivery in Device-to-Device Underlay Cellular Networks Using Matching Theory

With the popularity of social network-based services, the unprecedented growth of mobile date traffic has brought a heavy burden on the traditional cellular networks. Device-to-device (D2D) communication, as a promising solution to overcome wireless spectrum crisis, can enable fast content delivery based on user activities in social networks. In this paper, we address the content delivery problem related to optimization of peer discovery and resource allocation by combining both the social and physical layer information in D2D underlay networks. The social relationship, which is modeled as the probability of selecting similar contents and estimated by using the Bayesian nonparametric models, is used as a weight to characterize the impact of social features on D2D pair formation and content sharing. Next, we propose a 3-D iterative matching algorithm to maximize the sum rate of D2D pairs weighted by the intensity of social relationships while guaranteeing the quality of service requirements of both cellular and D2D links simultaneously. Moreover, we prove that the proposed algorithm converges to a stable matching and is weak Pareto optimal, and also provide the theoretical complexity. Simulation results show that the algorithm is able to achieve more than 90% of the optimum performance with a computation complexity 1000 times lower than the exhaustive matching algorithm. It is also demonstrated that the satisfaction performance of D2D receivers can be increased significantly by incorporating social relationships into the resource allocation design.

Privacy-Preserving Location Authentication in Wi-Fi Networks Using Fine-Grained Physical Layer Signatures

A recent measurement reveals that a large portion of the reported locations is either forged or superfluous, which raises security issues such as bogus alibis and illegal usage of restricted resources. However, most prior approaches leak users’ location information or rely on external devices. To overcome these limitations, we propose PriLA , a privacy-preserving location authentication system that verifies users’ location information based on physical layer (PHY) information available in legacy Wi-Fi preambles. The crux of PriLA is to turn detrimental features in wireless systems, namely carrier frequency offset (CFO) and multipath, into useful signatures for privacy protection and authentication. In particular, PriLA exploits CFO and channel state information (CSI) to secure wireless transmissions starting from the handshake phase between mobile users and the access point (AP), and meanwhile verify the truthfulness of users’ reported locations based on users’ multipath profiles. We have implemented PriLA on GNURadio/USRP platform and commercial off-the-shelf Intel 5300 NICs, and the experimental results show that PriLA achieves the authentication accuracy of 93.2% on average, while leaking merely 45.7% information in comparison with the state-of-the-art approach.