Search

Optical communications and their security have been got a great attention. In this work, a simple, low cost and secure optical system integrating orthogonal frequency division multiplexing-based passive optical network (OFDM-PON) and chaotic theory is proposed. In the proposed system, a novel scheme of quadrature amplitude modulation (QAM) encryption method is adopted. For privacy of image and video encryption, the process of encryption consists of two processes and a 3D chaotic system is employed. The chaotic system is used to generate the encryption sequences. In the first stage, the first generated chaotic sequence is XORed with the incoming data. While in the second stage, the QAM symbol is remapped using the other two generated chaotic sequences. Generally, the proposed system is designed to provide secure data, videos, and images transmission. The proposed system is simulated and evaluated with a transmission of a 10 Gb/s along 50 km single-mode fiber (SMF). The performance of the proposed system showed that there is a performance enhancement in terms of Peak to average power ratio (PAPR) and bit error rate (BER). The BER of unauthorized user is around 0.6147, while the PAPR is reduced to approximately half. The proposed system is simple and doesn't require complex computations. It requires simple analog multiplexers for its implementation. [ABSTRACT FROM AUTHOR]

This study presents an advanced design for a high-speed receiver tailored for the MIPI D-PHY Interface, capable of handling data rates up to 7.2 Gbps per lane. The design is developed using 18 nm fin field-effect transistor (FinFET) technology and is rigorously simulated under varying process, voltage, and temperature conditions (PVTs) to ensure robustness. The architecture of the receiver integrates several key components: differential pair sensing, a folded cascode continuous time linear equalization (CTLE), a single-ended operational amplifier, and a cross-coupled stage. Operating at a supply voltage of 0.72 V in the worst-case scenario, our CTLE achieves a peaking gain of 17.77 dB at 4.26 GHz. The design demonstrates a maximum jitter of 19.63 ps at an offset voltage of ±2 mV. Notably, the power efficiency of our receiver is optimized to 0.85 mW/Gb/s, totaling 6.1 mW, with dual supply voltages of 1.98 and 0.88 V. This work contributes to the field by offering a highly efficient solution for fast data transmission with reduced power consumption and enhanced signal integrity. [ABSTRACT FROM AUTHOR]

The data used during training in any given application space are directly tied to the performance of the system once deployed. While there are many other factors that are attributed to producing high-performance models based on the Neural Scaling Law within Machine Learning, there is no doubt that the data used to train a system provide the foundation from which to build. One of the underlying heuristics used within the Machine Learning space is that having more data leads to better models, but there is no easy answer to the question, "How much data is needed to achieve the desired level of performance?" This work examines a modulation classification problem in the Radio Frequency domain space, attempting to answer the question of how many training data are required to achieve a desired level of performance, but the procedure readily applies to classification problems across modalities. The ultimate goal is to determine an approach that requires the lowest amount of data collection to better inform a more thorough collection effort to achieve the desired performance metric. By focusing on forecasting the performance of the model rather than the loss value, this approach allows for a greater intuitive understanding of data volume requirements. While this approach will require an initial dataset, the goal is to allow for the initial data collection to be orders of magnitude smaller than what is required for delivering a system that achieves the desired performance. An additional benefit of the techniques presented here is that the quality of different datasets can be numerically evaluated and tied together with the quantity of data, and ultimately, the performance of the architecture in the problem domain. [ABSTRACT FROM AUTHOR]

The data used during training in any given application space are directly tied to the performance of the system once deployed. While there are many other factors that are attributed to producing high-performance models based on the Neural Scaling Law within Machine Learning, there is no doubt that the data used to train a system provide the foundation from which to build. One of the underlying heuristics used within the Machine Learning space is that having more data leads to better models, but there is no easy answer to the question, “How much data is needed to achieve the desired level of performance?” This work examines a modulation classification problem in the Radio Frequency domain space, attempting to answer the question of how many training data are required to achieve a desired level of performance, but the procedure readily applies to classification problems across modalities. The ultimate goal is to determine an approach that requires the lowest amount of data collection to better inform a more thorough collection effort to achieve the desired performance metric. By focusing on forecasting the performance of the model rather than the loss value, this approach allows for a greater intuitive understanding of data volume requirements. While this approach will require an initial dataset, the goal is to allow for the initial data collection to be orders of magnitude smaller than what is required for delivering a system that achieves the desired performance. An additional benefit of the techniques presented here is that the quality of different datasets can be numerically evaluated and tied together with the quantity of data, and ultimately, the performance of the architecture in the problem domain.

Nowadays, The Universal Serial Bus (USB) is one of the most adopted communication standards. However, the ubiquity of this technology has attracted the interest of attackers. This situation is alarming, considering that the USB protocol has penetrated even into critical infrastructures. Unfortunately, the majority of the contemporary security detection and prevention mechanisms against USB-specific attacks work at the application layer of the USB protocol stack and, therefore, can only provide partial protection, assuming that the host is not itself compromised. Toward this end, we propose a USB authentication system designed to identify (and possibly block) heterogeneous USB-based attacks directly from the physical layer. Empirical observations demonstrate that any extraneous/malicious activity initiated by malicious/compromised USB peripherals tends to consume additional electrical power. Driven by this observation, our proposed solution is based on the analysis of the USB power consumption patterns. Valuable power readings can easily be obtained directly by the power lines of the USB connector with low-cost, off-the-shelf equipment. Our experiments demonstrate the ability to effectively distinguish benign from malicious USB devices, as well as USB peripherals from each other, relying on the power side channel. At the core of our analysis lies an Autoencoder model that handles the feature extraction process; this process is paired with a long short-term memory (LSTM) and a convolutional neural network (CNN) model for detecting malicious peripherals. We meticulously evaluated the effectiveness of our approach and compared its effectiveness against various other shallow machine learning (ML) methods. The results indicate that the proposed scheme can identify USB devices as benign or malicious/counterfeit with a perfect F1-score. [ABSTRACT FROM AUTHOR]

Physical layer (PHY) authentication methods provide spatial security by exploiting the unique channel between two users. In recent years, many studies focused on substituting traditional threshold-based detection mechanisms with machine/deep learning classifiers to solve the threshold selection problem and obtain better detection accuracy. However, these studies assume that receivers have access to spoofer's channel information at the training of the classifier, which is unrealistic for real-time scenarios. In this study, we propose a PHY-authentication architecture for wireless multiple access channels (W-MACs) that removes this assumption and works without any prior information about the spoofer. The proposed method is designed for multi-user systems and is suitable for any classifier model or communication protocol. The feasibility and the performance of the proposed method are investigated via computer simulations and compared with a benchmark model. The results proved the feasibility of the proposed method as it can detect spoofers successfully without requiring spoofers' channel information. [ABSTRACT FROM AUTHOR]

Sixth generation promises to have a variety of applications, as well as network entities, in terms of power, range, data rates, latency, and propagation environment. The overall heterogeneity of the network poses significant challenges in terms of physical (PHY) layer design. This article first identifies some of the areas where the 5G multinumerology orthogonal frequency division multiplexing (OFDM) falls short before discussing the possible approaches toward PHY design. In particular, we discuss a backward-compatible, forward-looking, and extendable PHY layer framework that can be extended as new requirements arise to enable a graceful and sustainable network evolution. This is illustrated with a simple case study, where users with different requirements are allowed to use the same time-frequency (T-F) resources in a cohesive manner. The article concludes with the discussion of some critical research problems/areas pertaining to the said framework. [ABSTRACT FROM AUTHOR]

The proliferation landscape of the Internet of Things (IoT) has accentuated the critical role of Authentication and Authorization (AA) mechanisms in securing interconnected devices. There is a lack of relevant datasets that can aid in building appropriate machine learning enabled security solutions focusing on authentication and authorization using physical layer characteristics. In this context, our research presents a novel dataset derived from real-world scenarios, utilizing Zigbee Zolertia Z1 nodes to capture physical layer properties in indoor environments. The dataset encompasses crucial parameters such as Received Signal Strength Indicator (RSSI), Link Quality Indicator (LQI), Device Internal Temperature, Device Battery Level, and more, providing a comprehensive foundation for advancing Machine learning enabled AA in IoT ecosystems.

Due to its ability to adapt to a variety of electromagnetic (EM) environments, the sensing‐enabled metasurface has garnered significant attention. However, large‐scale EM‐field sensing to obtain more information is still very challenging. Here, an adaptive information metasurface is proposed to enable intelligent sensing and wave manipulating simultaneously or more specifically, to realize intelligent target localization and beam tracking adaptively. The metasurface is composed of an array of meta‐atoms, and each is loaded with two PIN diodes and a sensing‐channel structure, for polarization‐insensitive and programmable beamforming and sensing. By controlling the state of the PIN diode, the proposed meta‐atom has 1‐bit phase response in the designed frequency band, while the sensing loss keeps higher than ‐10 dB for both "ON" and "OFF" states. Hence there is nearly no interaction between the beamforming and sensing modes. Experiments are conducted to show multiple functions of the metasurface, including intelligent target sensing and self‐adaptive beamforming, and the measured results are in good agreement with the numerical simulations and theoretical calculations. [ABSTRACT FROM AUTHOR]

For wireless communication systems with large-scale multiple-input–multiple-output array in the presence of an active eavesdropper, the eavesdropper masquerades as a legitimate user sending the identical pilot sequence to manipulate channel estimation in the uplink training phase, which misleads precoding at the transmitter. If such pilot spoofing attack is not handled effectively, it will not only reduce system capacity, but also cause much signal power leak to the spoofer, and so it seriously threatens the security transmission in wireless communication. Nevertheless, how to detect such potential pilot spoofing and recover the contaminated channel plays a significant role in designing combating schemes and ensuring security transmission. In this paper, the impact of pilot spoofing attack on the system security capacity is investigated, and it is shown that pilot spoofing attack will degrade system capacity greatly and the transmitted information will be intercepted by the spoofer. Then, a novel detection scheme based on pilot-random-block-encryption is proposed, through which not only the detection probability on the potential pilot spoofing can be improved greatly, but also the channel state information of the legal user and the spoofer are obtained. Simulation results show that the detection probability of the proposed detection scheme reaches 93% even the transmit power of pilot spoofing is as low as − 10 dB, which is much greater than other existing schemes, and the system security capacity is also enhanced with the proposed channel estimation strategy. [ABSTRACT FROM AUTHOR]

Security and privacy of data are important factors in the Internet of Things (IoT), which enables safe communication with emerging objects. The non-standardization of security protocols on the internet of things makes them vulnerable to intruders and compromises the process of communication. Conventional cryptography approaches face certain limitations in the generation of keys for authentication and authorization of communication systems. The limitations of the cryptography approach are explored in terms of computational overhead and hardware requirements. Singal processing is an alternative cryptography approach for key generation in physical-layer wireless communication systems. The wireless channel's reciprocity principle is used to generate keys. The important factor in signal-based key generation is the process of quantization. In this manuscript, we propose modified discrete wavelet transform (DWT) methods for key generation in IoT. The modification of discrete wavelet transform methods encapsulates the process of common spatial patterning (CSP). The CSP is another signal processing method for the formation of signal patterns. The proposed method reduces DWT discrepancies and yields a key generation technique that is efficient. Key generation processing used multi-bit quantization to quantify the product of the modified discrete wavelet transform. The proposed algorithm for key generation simulates different numbers of device nodes in indoor and outdoor scenarios. Use MATLAB tools to simulate the process and measure standard parameters such as AKL and KDR. According to the results, the suggested technique outperforms existing key generation algorithms by 3%. [ABSTRACT FROM AUTHOR]

The popularity of concurrent transmissions (CT) has soared after recent studies have shown their feasibility on the four physical layers specified by BLE 5, hence providing an alternative to the use of IEEE 802.15.4 for the design of reliable and efficient low-power wireless protocols. However, to date, the extent to which physical layer properties affect the performance of CT has not yet been investigated in detail. This article fills this gap and provides an extensive study on the impact of the physical layer on CT-based solutions using IEEE 802.15.4 and BLE 5. We first highlight through simulation how the impact of errors induced by relative carrier frequency offsets on the performance of CT highly depends on the choice of the underlying physical layer. We then confirm these observations experimentally on real hardware and with varying environmental conditions through an analysis of the bit error distribution across received packets, unveiling possible techniques to effectively handle these errors. We further study the performance of CT-based data collection and dissemination protocols in the presence of RF interference on a large-scale testbed, deriving insights on how the employed physical layer affects their dependability. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless communication technology, intelligent communication has become one of the mainstream research directions after the fifth generation (5G). In particular, deep learning has emerged as a significant artificial intelligence technology widely applied in the physical layer of wireless communication for achieving intelligent receiving processing. Channel estimation, a crucial component of physical layer communication, is essential for further information recovery. As a motivation, this paper aims to review the relevant research on applying deep learning methods in channel estimation. Firstly, this paper briefly introduces the conventional channel estimation methods and then analyzes their respective merits and drawbacks. Subsequently, this paper introduces several common types of neural networks and describes the application of deep learning in channel estimation according to data-driven and model-driven approaches, respectively. Then, this paper extends to emerging communication scenarios and discusses the existing research on channel estimation based on deep learning for reconfigurable intelligent surface (RIS)-aided communication systems. Finally, to meet the demands of next-generation wireless communication, challenges and future research trends in deep-learning-based channel estimation are discussed. [ABSTRACT FROM AUTHOR]

This paper presents a downlink channel estimation method intended for a Narrowband Internet of Things (NB-IoT) access link. Due to its low computational complexity, this method is well suited for energy-efficient IoT devices, still providing acceptable reception quality in terms of signal-to-noise (SNR) performance. This paper describes the physical layer of NB-IoT within the scope of channel estimation, and also reviews existing channel estimation methods for OFDM signals. The proposed method, based on linear interpolation of channel coefficients, is described as a three-step procedure. Next, indicators of channel quality assessment, which may be determined without prior knowledge about the transmitted signal, are defined. Two variants of channel estimation, differing in the frequency domain processing, are evaluated to assess the significance of frequency selectivity in an NB-IoT downlink. The chosen method is compared with another method implemented in MATLAB LTE ToolboxTM. An analysis of the computation time is conducted, subsequently demonstrating the definite advantage of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract With the rapid development of the communication industry in the fifth generation and the advance towards the intelligent society of the sixth generation wireless networks, traditional methods are unable to meet the ever‐growing demands for higher data rates and improved quality of service. Deep learning (DL) has achieved unprecedented success in various fields such as computer vision, large language model processing, and speech recognition due to its powerful representation capabilities and computational convenience. It has also made significant progress in the communication field in meeting stringent demands and overcoming deficiencies in existing technologies. The main purpose of this article is to uncover the latest advancements in the field of DL‐based algorithm methods in the physical layer of wireless communication, introduce their potential applications in the next generation of communication mechanisms, and finally summarize the open research questions.

2D and 3D digital multimedia files offer numerous benefits like excellent quality, compression, editing, reliable copying, etc. These qualities of the multimedia files, on the other hand, are the cause of fear including the fear of getting access to data during communication. Steganography plays an important role in providing security to the data in communication. Changing the type of cover file from digital multimedia files to protocols improve the security of the communication system. Protocols are an integral part of the communication system and these protocols can also be used to hide secret data resulting in low chances of detection. This paper is intended to help improve existing network steganography techniques by enhancing bandwidth and decreasing detection rates through reviewing previous related work. Recent papers of the last 21 years on network steganography techniques have been studied, analyzed, and summarized. This review can help researchers to understand the existing trends in network steganography techniques to pursue further work in this area for algorithms’ improvement. The paper is divided according to the layers of the OSI model.

Abstract Due to its ability to adapt to a variety of electromagnetic (EM) environments, the sensing‐enabled metasurface has garnered significant attention. However, large‐scale EM‐field sensing to obtain more information is still very challenging. Here, an adaptive information metasurface is proposed to enable intelligent sensing and wave manipulating simultaneously or more specifically, to realize intelligent target localization and beam tracking adaptively. The metasurface is composed of an array of meta‐atoms, and each is loaded with two PIN diodes and a sensing‐channel structure, for polarization‐insensitive and programmable beamforming and sensing. By controlling the state of the PIN diode, the proposed meta‐atom has 1‐bit phase response in the designed frequency band, while the sensing loss keeps higher than ‐10 dB for both “ON” and “OFF” states. Hence there is nearly no interaction between the beamforming and sensing modes. Experiments are conducted to show multiple functions of the metasurface, including intelligent target sensing and self‐adaptive beamforming, and the measured results are in good agreement with the numerical simulations and theoretical calculations.

With the rapid development of the communication industry in the fifth generation and the advance towards the intelligent society of the sixth generation wireless networks, traditional methods are unable to meet the ever‐growing demands for higher data rates and improved quality of service. Deep learning (DL) has achieved unprecedented success in various fields such as computer vision, large language model processing, and speech recognition due to its powerful representation capabilities and computational convenience. It has also made significant progress in the communication field in meeting stringent demands and overcoming deficiencies in existing technologies. The main purpose of this article is to uncover the latest advancements in the field of DL‐based algorithm methods in the physical layer of wireless communication, introduce their potential applications in the next generation of communication mechanisms, and finally summarize the open research questions. [ABSTRACT FROM AUTHOR]

Train real‐time ethernet network (TREN) is responsible for the transmission of train control commands and equipment status information. Due to the harsh and changeable climatic environments, complex electromagnetic environments, and long service, the TREN has a high incidence of physical layer failures, which affects train operation safety. This paper presents a novel fault diagnosis method in which waveform electrical signal features are extracted from the physical layer signal of TREN. Random forest is used for identifying typical failure modes of actual TREN, and the model parameters are optimized to improve diagnostic performance. Finally, the physical layer signals of actual TREN are collected. The results show that the performance of the proposed fault diagnosis method is better than other advanced methods. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

We consider in this work a physical layer authentication method in which a message authentication code, referred to as a tag, is transmitted along with the data message to provide a robust authentication method. This work diverges from previous work in the area when it comes to the tag generation method. While the previous works use methods based on cryptographic hash functions, our system employs unidimensional chaotic maps to generate these tags. Due to the loss of information about the initial condition of chaotic maps, we show that they are strong candidates for the tag generation process. We employ an information-theoretic approach to show that chaotic tags provide a positive lower bound on the unconditional security of the system even in a noiseless environment. To the best of our knowledge this is the first work where unconditional security is obtained independently of the noise power. Additionally, we calculate the probability of success for two active attacks to the authentication system: impersonation, substitution.

JCRS applications are currently being hailed as the innovation of the next generation of mobile communications. The combination of communications and sensor technology on one hardware platform offers various advantages, such as significant savings in space and costs. The two technologies are no longer being optimized and developed side by side, as has been the case in the past, but jointly. The physical channel is used by both simultaneously, including the transmitter and receiver structures, which must be optimized for the combined application. This inevitably leads to an influence on the performance of both systems. In this work, this influence is considered and analyzed with respect to the effects of the radio frequency components. For this purpose, our previously published code-division multiple access (CDMA)-based and vehicle-to-vehicle-focused model is extended to a joint communications and radar sensing (JCRS) model and evaluated according to the achieved bit error rate as well as the mean deviation of the detected distance and velocity in Simulink. The influence of the non-ideal hardware components (mixers, power amplifier, low noise amplifier, filter, antenna) and characteristics (S-parameters, non-linearity of the amplifiers and the noise) on the sensing and communications are analyzed and compared in this automotive context. The results reveal that the noise of the low noise amplifier at the receiver side has the most decisive influence. In contrast, the noise generated by the power amplifier at the transmitter has no effect due to the relatively high signal power. The non-linearity of amplifiers at the transmitter also significantly impacts the available sensing range by limiting and compressing signal power. Besides, the phase noise with a frequency offset higher than 10MHz can increase the communications and sensing error rate. In contrast, the influence of S-parameters is negligible, as the performance of communications and sensing is almost unchanged with different S-parameters. In the future, the proposed single-target scene will be further extended to a multi-target one.

The sixth generation of mobile communication (6G) systems is recently rising a lot of interest, introducing new futuristic and challenging use cases that will demand much more than just communications to become a reality. Higher throughput, lower latencies, higher number of connections will push the requirement of the future mobile networks to a new level, but also sensing, positioning and imaging will play an important role in the new foreseen use cases. The integration of techniques developed for wireless communications with those conceived for optical links will be essential to provide the infrastructure for the 6G networks. In this context, this paper presents a review on wireless and optical convergent access solutions towards the 6G systems. The manuscript brings the use cases, requirements and enablers for 6G networks including a discussion about the state-of-the-art on THz and sub-THz communications, wireless and optical convergence, visible light communication, integrated and free-space optics, new antenna designs, power-over-fiber deployments and the use of machine learning in the physical layer of future networks. By reviewing the most relevant contributions available in the literature for wireless and optical communications and presenting their main contributions, this paper clearly shows that, more than a technological trend, the convergence of wireless and optical technologies is a fundamental step towards the development of the 6G network infrastructure.

Reconfigurable intelligent surface (RIS) is one of the most promising technologies for constructing sixth-generation (6G) wireless networks. It has the capability of controlling the electromagnetic response, thereby improving the communication performance. Current studies about physical (PHY) layer analysis of RIS assisted WLC systems neglect the influence of retransmission protocols. Furthermore, the impacts of traffic rate and limited transit buffer on packet transmission and energy consumption are not considered for RIS assisted WLC systems. Focusing on the above problems, we put forward an insightful model to analyze the PHY layer performance of RIS assisted WLC systems using retransmission protocols. In the modeling process, we firstly provide an E2E (end to end) transmission model for the RIS assisted WLC system. The influence of typical retransmission protocols, containing ARQ (automatic repeat request) and hybrid ARQ (HARQ), channel fading types, frame duration on data transmission are comprehensively taken into consideration. Then, we establish an accurate energy consumption model through analyzing the queueing state of the transmitter with finite buffer size and traffic rate. Based on the above two models, we derive the closed-form expressions of PHY layer metrics for RIS assisted WLC systems considering the impact of retransmission protocols. In addition, we also analyze the bound performance of coverage range and expected power efficiency of RIS assisted WLC system with retransmission protocols. Finally, we evaluate the performance of RIS assisted WLC systems under different retransmission protocols and system parameters, and verify the proposed theoretical model.

The broadcast nature of radio propagation in wireless communication has been suspected as the loopholes of passive or active attacks by unauthorized users (eavesdroppers). The physical layer security techniques operate at the lowest stack of OSI layer against conventional cryptographic approaches, operating at the upper layer. However, techniques such as channel coding, power (directional antenna and artificial noise), and spread spectrum have been (and continuously) deployed to safeguard against sophisticated attacks. Most of these deployments are theoretical, and a few are enhanced for efficient security against an intruder. In this article, a boundary technique approach is proposed and applied to the physical layer to improve its secrecy‐capacity and subdue adversary effects at the legitimate receiver. Hybrid performance metrics were adopted, and a Monte Carlo simulation was performed. The result obtained using secrecy outage probability, secrecy‐capacity, and intercept‐probability show that our proposed techniques enhance the secret transmission between the main transmitter and legitimate receiver. The simulation results were compared with the analytical methods. It was found that the channel between the transmitter and the main receiver has a better signal‐to‐noise ratio than the corresponding eavesdropper's channel. Conclusively, performance of the proposed technique is validated for applications in emerging wireless communication systems. [ABSTRACT FROM AUTHOR]

The concept of labeling-based recipient identification (LABRID) for bit-interleaved coded modulation with iterative decoding (BICM-ID) is revisited. LABRID allows addressing a message recipient station in a wireless network by using an individual labeling map without compromising error performance. This eliminates the need to use any byte of the data frame to carry the recipient address explicitly. In addition, the destination of the frame can be determined in parallel with a BICM-ID decoding procedure in the receiver's physical layer. Therefore, the MAC layer is not involved in processing the vast majority of frames transmitted in a network. Previously, it was shown that LABRID works fine if there are only LABRID-compatible stations within the network, and every receiver can reject frames destined for other receivers. This paper considers a scenario in which LABRID-compatible BICM-ID stations and legacy BICM stations coexist in the same network. A few experiments show that the LABRID receiver can reject an old-fashioned BICM frame by judging the convergence of the iterative decoding process. It also turns out that the legacy BICM receiver can identify and dismiss the LABRID-type frames thanks to the standard cyclic redundancy check (CRC) procedure. [ABSTRACT FROM AUTHOR]

This paper is devoted to software-defined radio (SDR) implementation of frequency modulated antipodal chaos shift keying (FM-ACSK) transceiver and presents results of prototype testing in real conditions. This novel and perspective class of spread-spectrum communication systems employs chaotic synchronization for the acquisition and tracking of the analog chaotic spreading code and does not need resource-demanding cross-correlation. The main motivation of the given work is to assess the performance of FM-ACSK in real conditions and demonstrate that chaotic synchronization can be considered an efficient spread-spectrum demodulation method. The work focuses on the real-time implementation aspects of the modulation-demodulation algorithms, forward error correction (FEC) and symbol timing synchronization approach in MATLAB Simulink. The performance of the presented prototype is assessed via extensive testing, which includes measurement of bit error ratio (BER) in single-user and multi-user scenarios, estimation of carrier frequency offset (CFO) impact and image transmission over-the-air between two independent sites and comparison with classical frequency hopping spread spectrum (FHSS). The paper shows that the presented class of the spread spectrum communication systems demonstrates good performance in low signal-to-noise ratio (SNR) conditions and in terms of BER significantly outperforms the classic spread-spectrum modulation schemes which employ correlation-based detection. [ABSTRACT FROM AUTHOR]

In this work, we mainly focus on low probability detection (LPD) and low probability interception (LPI) wireless communication in cyber-physical systems. An LPD signal waveform based on multi-carrier modulation and an under-sampling method for signal detection is introduced. The application of the proposed LPD signal for physical layer security is discussed in a typical wireless-tap channel model, which consists of a transmitter (Alice), an intended receiver (Bob), and an eavesdropper (Eve). Since the under-sampling method at Bob's end depends very sensitively on accurate sampling clock and channel state information (CSI), which can hardly be obtained by Eve, the security transmission is initialized as Bob transmits a pilot for Alice to perform channel sounding and clock synchronization by invoking the channel reciprocal principle. Then, Alice sends a multi-carrier information-bearing signal constructed according to Bob's actual sampling clock and the CSI between the two. Consequently, Bob can coherently combine the sub-band signals after sampling, while Eve can only obtain a destructive combination. Finally, we derived the closed-form expressions of detection probability at Bob's and Eve's ends when the energy detector is employed. Simulation results show that the bit error rate (BER) at Alice's end is gradually decreased with the increase in the signal-to-noise ratio (SNR) in both the AWGN and fading channels. Meanwhile, the BER at Eve's end is always unacceptably high no matter how the SNR changes. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ethernet circuit based on IEEE 802.3 has been widely used in the field of rail transit. Open systems interconnection (OSI) model divides Ethernet into seven layers, of which the physical layer (PHY) belongs to the first layer, which is mainly responsible for converting Ethernet signals into specific electrical signals and transmitting them to Ethernet cables, and plays a very important role in the interconnection between different network interfaces. In order to improve the reliability of train network system based on Ethernet and improve the quality of train communication, this paper designs and studies the physical layer circuit of train Ethernet, including PHY chip filtering and impedance matching, the application of different xMII (medium independent interface) interfaces, EMC protection, factors in the consistency test of network interface and PCB layout and wiring rules, and designs an Ethernet physical layer circuit according to the research results. The application results show that the designed circuit is suitable for the field of train Ethernet communication, and has strong EMC protection ability and high reliability.

A quantum noise stream cipher (QNSC) is a physical layer encryption technology based on quantum noise. Bit error rate loopback measurement (BER-LM) is a method to measure the BER of a loopback channel and extract channel characteristics. Then, channel characteristics can be extracted, and consensus keys can be obtained through negotiation. In previous studies, encryption and key distribution were implemented in independent channels. In this paper, we propose a scheme that combines these two technologies in a single fiber channel to achieve encrypted transmission and key distribution. We verified a 20 Gbps QPSK coherent optical transmission system with a PSK/QNSC scheme. The results show that by reasonably setting the negotiation bit position, the consensus key could be obtained through negotiation, and the requirements of transmission performance could be met. When the negotiation bit position was set to seven, the Q-factor of the system was nine, which met the error-free condition of the 7% forward error correction (FEC) limit. [ABSTRACT FROM AUTHOR]

Physical layer secret key generation (PLKG) is a promising technology for establishing effective secret keys. Current works for PLKG mostly study key generation schemes in ideal communication environments with little or even no signal interference. In terms of this issue, exploiting the reconfigurable intelligent reflecting surface (IRS) to assist PLKG has caused an increasing interest. Most IRS-assisted PLKG schemes focus on the single-input-single-output (SISO), which is limited in future communications with multi-input-multi-output (MIMO). However, MIMO could bring a serious overhead of channel reciprocity extraction. To fill the gap, this paper proposes a novel low-overhead IRS-assisted PLKG scheme with deep learning in the MIMO communications environments. We first combine the direct channel and the reflecting channel established by the IRS to construct the channel response function, and we propose a theoretically optimal interaction matrix to approach the optimal achievable rate. Then we design a channel reciprocity-learning neural network with an IRS introduced (IRS-CRNet), which is exploited to extract the channel reciprocity in time division duplexing (TDD) systems. Moreover, a PLKG scheme based on the IRS-CRNet is proposed. Final simulation results verify the performance of the PLKG scheme based on the IRS-CRNet in terms of key generation rate, key error rate and randomness. [ABSTRACT FROM AUTHOR]

This paper proposes a novel key distribution method based on the masking effect of fiber channel noise in a power transmission system. The two physical layer signal processing technologies used in this solution are frame synchronization and resampling. The random data sent and received is digitally processed by these two techniques. An experimental platform is built, and the key performance is assessed by analyzing the measured bit error rate (BER) dynamic curve, setting different decision thresholds and the amplitude of the state basis. Experimental results show that the key distribution rate can reach 10 kbit/s with a key consistency rate of 98%. This is of great significance to the development of safe optical communication technology in power transmission systems. In the power communication system, a large number of optical fiber communication devices are used. The key distribution method proposed in this paper can effectively improve the security of optical fiber communication, which is of great significance. [ABSTRACT FROM AUTHOR]

Ultra-wideband (UWB) technology has been applied in many fields, such as radar and indoor positioning, because of its advantages of having a high transmission rate, anti-multipath interference, and good concealment. In the UWB physical layer, the transmitting link, including an encoder and a pulse generator, is used to improve the anti-interference ability of the signal, while the receiving link, including a receiver and a decoder, can correct the error signal. Therefore, the performance of the UWB physical layer can obviously affect the speed and quality of UWB signal transmission. In this paper, the structure and performance of the codec and transceiver of the UWB physical layer are introduced and compared. In addition, some typical architectures and features are summarized and discussed, which provides a valuable reference and suggestions for the design of the UWB physical layer. Finally, the outlook of the UWB physical layer is presented: its development direction mainly includes high speed, low power consumption, and fewer hardware resources. [ABSTRACT FROM AUTHOR]

The IEEE 802.15.4 physical layer (PHY) standard is one of the communication standards with wireless features by providing low-power and low-data rates in wireless personal area network (WPAN) applications. In this paper, an efficient IEEE 802.15.4 digital transceiver hardware architecture is designed using the binary phase-shift keying (BPSK) technique. The transceiver mainly has transmitter and receiver modules along with the error calculation unit. The BPSK modulation and demodulation are designed using a digital frequency synthesizer (DFS). The DFS is used to generate the in-phase (I) and quadrature-phase (Q) signals and also provides better system performance than the conventional voltage-controlled oscillator (VCO) and look up table (LUT) based memory methods. The differential encoding-decoding mechanism is incorporated to recover the bits effectively and to reduce the hardware complexity. The simulation results are illustrated and used to find the error bits. The design utilizes less chip area, works at 268.2 MHz, and consumes 108 mW of total power. The IEEE 802.15.4 transceiver provides a latency of 3.5 clock cycles and works with a throughput of 76.62 Mbps. The bit error rate (BER) of 2x10-5 is achieved by the proposed digital transceiver and is suitable for real-time applications. The work is compared with existing similar approaches with better improvement in performance parameters. [ABSTRACT FROM AUTHOR]