Conventional Communication Systems Research Articles

Experimental demonstration of quantum encryption in phase space with displacement operator in coherent optical communications

We provide experimental validation of quantum encryption in phase space using displacement operators in coherent states (DOCS) in a conventional coherent optical communication system. The proposed encryption technique is based on displacing the information symbols in the phase space using random phases and amplitudes to achieve encryption randomly and provide security at the physical layer. We also introduce a dual polarization encryption approach where we use two different and random DOCS to encrypt the X and Y polarizations separately. The experimental results show that only authorized users can decrypt the signal correctly, and any mismatch in the displacement operator coefficients, amplitudes, or phases will lead to a bit error ratio (BER) of approximately 50%. We also compare the performance of the system with and without encryption over 80 km of standard-single mode fiber (SSMF) transmission to assess the added penalty of such encryption. The achieved net bit rates are 224, 448, and 560 Gb/s for QPSK, 16QAM, and 32QAM modulation formats, respectively. The experimental results showcase the efficacy of the DOCS encryption technique in resisting various decryption attempts, demonstrating its effectiveness in ensuring the security and confidentiality of transmitted data in a real-world transmission scenario.

A high sensitivity digital giant magneto-impedance (GMI) sensor for magnetic communication

Abstract A high sensitivity Magneto-Impedance (GMI) sensor for magnetic communication systems in harsh environments is presented. In this system, the receiving coil of a conventional magnetic communication system is replaced by a high sensitivity GMI sensor and its digital electronic conditioning as well as digital signal processing. The whole architecture of the sensor is described in detail. It achieves two digital amplitude demodulations to recover, in real time, the binary sequence of the message sent by the transmitter. The demonstration and the validation of the reliable functioning of the system are illustrated through the transmission of messages using On-Off-Keying (OOK) modulation method.
The measured equivalent magnetic noise of the sensor was about noise 2 pT/√Hz at 60 kHz, which is largely lower than the noise of other potential types of magnetic sensors such as Anisotropic Magneto-Resistive (AMR) sensors for this application. This noise level suggests a potential higher communication range. When compared with an inductive coil, the developed sensor also presented an almost flat and sufficiently wide bandwidth compatible with magnetic communication. This bandwidth could potentially be extended according to the application.

Generalized hybrid LiFi-WiFi UniPHY learning framework towards intelligent UAV-based indoor networks

Advancements in unmanned aerial vehicle (UAV) technology, along with indoor hybrid LiFi-WiFi networks (HLWN), promise the development of cost-effective, energy-efficient, adaptable, reliable, rapid, and on-demand HLWN-capable indoor flying networks (IFNs). To achieve this, a unified physical layer (UniPHY) capable of simultaneous control communication, data transfer, and sensing is crucial. However, traditional block-based decoders, designed independently for LiFi and WiFi, perform poorly in complex and hybrid LiFi-WiFi-enabled UniPHY systems. In this study, we propose an end-to-end learning framework based on convolutional neural networks (CNNs) for UniPHY, which can be trained to serve hybrid LiFi-WiFi transmissions to improve error performance and simplify UAV hardware. In this work, the performance of the proposed framework is assessed and compared with that of the conventional independent block-based communication system. Furthermore, a comprehensive summary of optimal hyper-parameters for efficient training of our learning framework has been provided. It is shown that, with optimal hyper-parameters, the proposed CNN-based framework outperforms the conventional block-based approach by providing a signal-to-noise ratio gain of approximately 7 dB for the LiFi channel and 23 dB for the WiFi channel. In addition, we evaluate the complexity and training convergence for loss and accuracy.

A review on the manufacturing techniques for textile based antennas

An antenna is a medium of communication in electronic systems and one of its branches is known as wearable antenna (embedded in clothing or other wearables). Antennas are found in a wide range of applications such as medical, military, sports, safety, fitness, satellite communication etc. Textile based antennas are considered a replacement of conventional communication systems in E-textiles as they are more flexible and comfortable as compared to their metallic counterparts and more efficient than transmission wires. Electronic wiring which is commonly used for data transmission is susceptible to damage because of bending or stretching in wearables and may cause interferences between signals and time delays. One of the most significant advantages of wearable antennas is the reduction of bulk of electronic components as they can act as a sensor and communicator at a time. The cost can also be reduced this way. The current work focuses on the review of different manufacturing techniques for wearable antennas. The scope of this review is to highlight main techniques, their advantages and limitations in comparison with each other as well as to describe the available solutions of associated problems. The findings of this review could be fruitful for researchers to find out the best manufacturing technique for antennas in their perspective. Visual Abstract [Formula: see text]

A Lightweight Mobile-Anchor-Based Multi-Target Outdoor Localization Scheme Using LoRa Communication

In recent years, multi-target localization has been identified as an essential technology for delivering on the promise of the Internet of Things (IoT). Multi-target localization has been well-studied and widely deployed in conventional communications systems, but the techniques used in such systems are largely unable to meet the power, cost, and accuracy requirements needed for IoT. In this article, we propose a lightweight Mobile Anchor multi-tarGet IoT loCalization system (MAGIC), which is capable of localizing multiple targets in a low-power and cost efficient manner. MAGIC utilizes a single mobile anchor consisting of a LoRa gateway and a commercial smartphone in order to localize multiple IoT devices embedded with LoRa tags. The anchor moves and communicates with the LoRa tags from multiple locations, greatly reducing the deployment cost compared to a more traditional system with multiple stationary anchors. We formulate the path planning optimization problem of the mobile anchor to minimize the path length while fully accounting for environmental uncertainties. To efficiently solve the optimization problem, we design an online moving strategy that generates the path in real-time as the anchor moves. Through simulation and experimental studies we demonstrate the effectiveness of the proposed scheme both in an empty baseline environment and in a complicated setting around the buildings. Results indicate that our scheme can significantly enhance the localization accuracy and reduce the required path length for the anchor. Compared to the existing outdoor localization systems, the proposed approach shows advantages not only in the localization accuracy but also the overall cost including device cost, spectrum usage and energy consumption.

Performance analysis of RIS-assisted V2V communication under multipath interference

Reconfigurable Intelligent Surface (RIS) enables precise modulation and directional transmission of signals by controlling the phase and amplitude of electromagnetic waves, thus solving the problems of signal fading, multipath effects, and interference faced by conventional wireless communication systems. In this paper, the RIS-assisted vehicle-to-vehicle (V2V) communication system under multipath interference was proposed, and the precise approximation for RIS-assisted V2V communication under Nakagami-m channels was derived in detail. Then, a comprehensive analysis of the key performance metrics of the system was performed, and analytical expressions for the outage probability, average bit error rate (BER), and average channel capacity were obtained thoroughly. We also validate all the analytical results through simulations. Furthermore, through comparative analysis, it is evident that increasing the number of RIS reflecting elements and optimizing the communication channel environment can significantly reduce the outage probability, average BER, increase the average channel capacity, and mitigate the impact of interference sources on the system.

An analysis of wireless signal propagation in collapsed mine scenarios

The missing miner problem arises when a miner working in an underground mine gets trapped after a mine collapse. The ability to precisely locate a missing miner is directly linked with the availability of a communication infrastructure which can allow communication with the trapped miner. Conventional communication systems commonly used in the mines are generally unable to communicate with the miners after mine collapse due to either their limitations or damage because of the collapse. A better understanding of the wireless signal propagation in collapsed mine scenarios can help in the design of robust systems which can provide connectivity even after a mine collapse. This paper analyzes wireless channel in different collapsed mine scenarios using both simulations and real experiments in a mock mine. No such study exists to the best knowledge of the authors. Finally, suitable transmit power levels that can be used in such scenarios are also determined.

SIGN LANGUAGES AS CONVENTIONAL SYSTEMS IN COMMUNICATION OF DEAF PEOPLE

Sign languages are conventional communication systems that arise spontaneously in all deaf communities around the world. The current article offers a glimpse into the problem of the essence of a natural human language, which exists in a physical modality different from that inherent in vocal language, which has imposed its centuries-old monopoly as an object of study. The present theoretical study is aimed at building a comprehensive vision of sign languages, which, until recently, have been marginalized in research interests. A brief historical analysis of the whole cycle of arising research in the field of sign language linguistics is presented. A special focus is placed on the genealogical relationships of sign languages, the identification of which still remains in the realm of hypothetical reasoning, in contrast to spoken languages, where this is stated as a clearly defined scientific fact. A well-deserved place is given to the quantitative dimensions of sign languages in the contemporary world. Harmonized data collection in the world register of visual languages is clearly in a position of direct dependence on social and linguistic factors. The question of the non-homogeneous character of sign languages, determined by territorial and social variations, is also considered. Despite the contrasts identified at different linguistic levels, specific features inherently present in the deaf community can be found in the variations of visual languages.

Deep Learning Enabled Semantic Communications With Speech Recognition and Synthesis

In this paper, we develop a deep learning based semantic communication system for speech transmission, named DeepSC-ST. We take the speech recognition and speech synthesis as the transmission tasks of the communication system, respectively. First, the speech recognition-related semantic features are extracted for transmission by a joint semantic-channel encoder and the text is recovered at the receiver based on the received semantic features, which significantly reduces the required amount of data transmission without performance degradation. Then, we perform speech synthesis at the receiver, which dedicates to re-generate the speech signals by feeding the recognized text and the speaker information into a neural network module. To enable the DeepSC-ST adaptive to dynamic channel environments, we identify a robust model to cope with different channel conditions. According to the simulation results, the proposed DeepSC-ST significantly outperforms conventional communication systems and existing DL-enabled communication systems, especially in the low signal-to-noise ratio (SNR) regime. A software demonstration is further developed as a proof-of-concept of the DeepSC-ST.

Programmable metasurface for front-back scattering communication

Abstract Achieving high-efficient and low-power communication is pivotal yet very challenging in the emerging technologies. Unlike conventional backscatter communication system, we propose and demonstrate an amplitude-reconfigurable metasurface loaded with PIN diodes to build a front-back scattering communication transmitter, which features the exclusive advantages of full-space secondary modulation of the ambient signals with high energy utilization efficiency. Meanwhile, this device can eliminate the interference originated from the ambient source by polarization conversion in the transmission channel. At a modulation rate of 800 kbps and a distance of 80 m, our system can achieve distortion-free transmission of a picture with size of 200 × 200 pixels. In addition, multiple amplitude-shift-keying modulation is also realized by segmenting the metasurface to further increase the communication rate. Due to the advantages of high spectral efficiency and low energy consumption, this system can be widely used in future engineering applications for the internet of things, especially for smart home, agriculture environmental monitoring, wearable sensing and others.

Reconfigurable Intelligent Surface Assisted MEC Offloading in NOMA-Enabled IoT Networks

Integrating mobile edge computing (MEC) into the Internet of Things (IoT) enables resource-limited mobile terminals to offload part or all of the computation-intensive applications to nearby edge servers. On the other hand, by introducing reconfigurable intelligent surface (RIS), it can enhance the offloading capability of MEC, such that enabling low latency and high throughput. To enhance the task offloading, we investigate the MEC non-orthogonal multiple access (MEC-NOMA) network framework for mobile edge computation offloading with the assistance of a RIS. Different from conventional communication systems, we aim at allowing multiple IoT devices to share the same channel in tasks offloading process. Specifically, the joint consideration of channel assignments, beamwidth allocation, offloading rate and power control is formulated as a multi-objective optimization problem (MOP), which includes minimizing the offloading delay of computing-oriented IoT devices (CP-IDs) and maximizing the transmission rate of communication-oriented IoT devices (CM-IDs). Since the resulting problem is non-convex, we employ ϵ-constraint approach to transform the MOP into the single-objective optimization problems (SOP), and then the RIS-assisted channel assignment algorithm is developed to tackle the fractional objective function. Simulation results corroborate the benefits of our strategy, which can outperforms the other benchmark schemes.

Deep Reinforcement Learning-Based Adaptive Beam Tracking and Resource Allocation in 6G Vehicular Networks with Switched Beam Antennas

In this paper, we propose a novel switched beam antenna system model integrated with deep reinforcement learning (DRL) for 6G vehicle-to-vehicle (V2V) communications. The proposed system model aims to address the challenges of highly dynamic V2V environments, including rapid changes in channel conditions, interference, and Doppler effects. By leveraging the beam-switching capabilities of switched beam antennas and the intelligent decision making of DRL, the proposed approach enhances the performance of 6G V2V communications in terms of throughput, latency, reliability, and spectral efficiency. The proposed work develops a comprehensive mathematical model that accounts for 6G channel modeling, beam-switching, and beam-alignment errors. The Proposed DRL framework is designed to learn optimal beam-switching decisions in real time, adapting to the complex and varying V2V communication scenarios. The integration of the proposed antenna system and DRL model results in a robust solution that is capable of maintaining reliable communication links in a highly dynamic environment. To validate the proposed approach, extensive simulations were conducted and performance analysis using various performance metrics, such as throughput, latency, reliability, energy efficiency, resource utilization, and network scalability, was analyzed. Results demonstrate that the proposed system model significantly outperforms conventional V2V communication systems and other state-of-the-art techniques. Furthermore, the proposed approach shows that the beam-switching capabilities of the switched beam antenna system and the intelligent decision making of the DRL model are essential in addressing the challenges of 6G V2V communications.

Tunable Radio Frequency Antenna Based on Phase Change Materials.

In conventional communication systems, dedicated tunable circuit elements are used to realize different functions and achieve performance metrics. For example, tuning the center frequency or the input impedance of an antenna in a radio frequency (RF) system is performed by complex impedance-matching circuits. In this paper, the antenna utilizes the temperature-induced irreversible mechanical deformation of a shape memory alloy (SMA) as a natural way to tune the antenna's shape and configuration, thereby providing inherent tunability without bulky circuit elements. This paradigm of material programming for impedance tuning of an SMA-based antenna is validated by both numerical simulation and measurements.

Https://sifisheriessciences.com/journal/index.php/journal/article/view/1294

Wireless experts worldwide have become interested in using Autoencoders (AEs) for modelling communication systems as an end-to-end reconstruction task. This approach optimizes both the transmitter and receiver components simultaneously, offering flexibility and convenience for representing complex channel models. Traditional communication systems rely on conventional models and assumptions that limit their utilization of limited frequency resources and hinder their ability to adapt to new wireless applications. However, with the rise of Artificial Intelligence, new wireless systems are capable of learning from wireless spectrum data and optimizing their performance. In this paper, the use of deep learning with autoencoders is explored to create an end-to-end communication system that replaces traditional transmitter and receiver activities. The autoencoder architecture effectively addresses channel impairments and enhances overall performance. Simulation results indicate that autoencoders surpass conventional communication systems in terms of Block Error Rate performance, even when facing impairments in the autoencoder's channel layer and using different neural network optimization algorithms.

End-to-End Underwater Acoustic Communication Based on Autoencoder with Dense Convolution

To address the problems of the high complexity and poor bit error rate (BER) performance of traditional communication systems in underwater acoustic environments, this paper incorporates the theory of deep learning into a conventional communication system and proposes data-driven underwater acoustic filter bank multicarrier (FBMC) communications based on convolutional autoencoder networks. The proposed system is globally optimized by two one-dimensional convolutional (Conv1D) modules at the transmitter and receiver, it realizes signal reconstruction through end-to-end training, it effectively avoids the inherent imaginary interference of the system, and it improves the reliability of the communication system. Furthermore, dense-block modules are constructed between Conv1D layers and are connected across layers to achieve feature reuse in the network. Simulation results show that the BER performance of the proposed method outperforms that of the conventional FBMC system with channel equalization algorithms such as least squares (LS) estimation and virtual time reversal mirrors (VTRM) under the measured channel conditions at a certain moment in the Qingjiang River.

Multi-message multi-receiver signcryption scheme based on blockchain.

In conventional message communication systems, the practice of multi-message multi-receiver signcryption communication encounters several challenges, including the vulnerability to Key Generation Center (KGC) attacks, privacy breaches and excessive communication data volume. The KGC necessitates a secure channel to transmit partial private keys, thereby rendering the security of these partial private keys reliant on the integrity of the interaction channel. This dependence introduces concerns regarding the confidentiality of the private keys. Our proposal advocates for the substitution of the KGC in traditional certificateless schemes with blockchain and smart contract technology. Parameters are publicly disclosed on the blockchain, leveraging its tamper-proof property to ensure security. Furthermore, this scheme introduces conventional encryption techniques to achieve user identity privacy in the absence of a secure channel, effectively resolving the issue of user identity disclosure inherent in blockchain-based schemes and enhancing communication privacy. Moreover, users utilize smart contract algorithms to generate a portion of the encrypted private key, thereby minimizing the possibility of third-party attacks. In this paper, the scheme exhibits resilience against various attacks, including KGC leakage attacks, internal privilege attacks, replay attacks, distributed denial of service attacks and Man-in-the-Middle (MITM) attacks. Additionally, it possesses desirable security attributes such as key escrow security and non-repudiation. The proposed scheme has been theoretically and experimentally analyzed under the random oracle model, based on the computational Diffie-Hellman problem and the discrete logarithm problem. It has been proven to possess confidentiality and unforgeability. Compared with similar schemes, our scheme has lower computational cost and shorter ciphertext length. It has obvious advantages in communication and time overhead.

Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface Assisted NOMA Networks

Simultaneously transmitting/refracting and reflecting reconfigurable intelligent surface (STAR-RIS) has been introduced to achieve full coverage area. This paper investigate the performance of STAR-RIS assisted non-orthogonal multiple access (NOMA) networks over Rician fading channels, where the incidence signals sent by base station are reflected and transmitted to the nearby user and distant user, respectively. To evaluate the performance of STAR-RIS-NOMA networks, we derive new approximate expressions of outage probability and ergodic rate for a pair of users, in which the imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC) schemes are taken into consideration. Based on the asymptotic expressions, the diversity orders of the nearby user with ipSIC/pSIC and distant user are achieved carefully.The high signal-to-noise ratio slopes of ergodic rates for nearby user with pSIC and distant user are equal to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$one$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$zero$ </tex-math></inline-formula> , respectively. In addition, the system throughput of STAR-RIS-NOMA is discussed in delay-limited and delay-tolerant modes. Simulation results are provided to verify the accuracy of the theoretical analyses and demonstrate that: 1) The outage probability of STAR-RIS-NOMA outperforms that of STAR-RIS assisted orthogonal multiple access (OMA) and conventional cooperative communication systems; 2) With the increasing of reflecting elements <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> and Rician factor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\kappa $ </tex-math></inline-formula> , the STAR-RIS-NOMA networks are capable of attaining the enhanced performance; and 3) The ergodic rates of STAR-RIS-NOMA are superior to that of STAR-RIS-OMA.

A Learning-Based End-to-End Wireless Communication System Utilizing a Deep Neural Network Channel Module

The existing end-to-end (E2E) wireless communication systems require fewer communication modules and have a simple processing signal flow, compared to conventional wireless communication systems. However, in the absence of a differentiable channel model, it is impossible to train transmitters, used in such systems, which makes impossible to get optimal system performance. To solve this problem, an E2E wireless communication system, learned with conditional generative adversarial networks (CGANs) for channel modeling, has been proposed recently. Unfortunately, the CGAN training is prone to instability, slow convergence, and inaccurate channel modeling, which affects the system performance. To this end, a learning-based E2E wireless communication system, utilizing a deep neural network (DNN) channel module to model an unknown channel, is proposed in this paper. Simulation results show that the proposed DNN channel modeling has faster convergence, simpler network structure, and can reflect the behavior of real channels more accurately. In addition, the proposed E2E wireless communication system performs better, in terms of bit error rate (BER) and block error rate (BLER), than the E2E wireless communication system, using CGAN as unknown channel, and a traditional communication system, designed based on the prior knowledge of the channel. Compared to these two systems, at high signal-to-noise ratio (SNR) values, the proposed system can achieve a SNR gain of at least 2 dB, in communication scenarios involving frequency-selective multi-path channels.

Wireless End-to-End Image Transmission System Using Semantic Communications

Semantic communication is considered the future of mobile communication, which aims to transmit data beyond Shannon’s theorem of communications by transmitting the semantic meaning of the data rather than the bit-by-bit reconstruction of the data at the receiver’s end. The semantic communication paradigm aims to bridge the gap of limited bandwidth problems in modern high-volume multimedia application content transmission. Integrating AI technologies with the 6G communications networks paved the way to develop semantic communication-based end-to-end communication systems. In this study, we have implemented a semantic communication-based end-to-end image transmission system, and we discuss potential design considerations in developing semantic communication systems in conjunction with physical channel characteristics. A Pre-trained GAN network is used at the receiver as the transmission task to reconstruct the realistic image based on the Semantic segmented image at the receiver input. The semantic segmentation task at the transmitter (encoder) and the GAN network at the receiver (decoder) is trained on a common knowledge base, the COCO-Stuff dataset. The research shows that the resource gain in the form of bandwidth saving is immense when transmitting the semantic segmentation map through the physical channel instead of the ground truth image in contrast to conventional communication systems. Furthermore, the research studies the effect of physical channel distortions and quantization noise on semantic communication-based multimedia content transmission.

Engineering Semantic Communication: A Survey

As the global demand for data has continued to rise exponentially, some have begun turning to the idea of semantic communication as a means of efficiently meeting this demand. Pushing beyond the boundaries of conventional communication systems, semantic communication focuses on the accurate recovery of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">meaning</i> conveyed from source to receiver, as opposed to the accurate recovery of transmitted symbols. In this survey, we aim to provide a comprehensive view of the history and current state of semantic communication and the techniques for engineering this higher level of communication. A survey of the current literature reveals four broad approaches to engineering semantic communication. We term the earliest of these approaches classical semantic information, which seeks to extend information-theoretic results to include semantic information. A second approach makes use of knowledge graphs to achieve semantic communication, and a third utilizes the power of modern deep learning techniques to facilitate this communication. The fourth approach focuses on the significance of information, rather than its meaning, to achieve efficient, goal-oriented communication. We discuss each of these four approaches and their corresponding studies in detail, and provide some challenges and opportunities that pertain to each approach. Finally, we introduce a novel approach to semantic communication, which we term context-based semantic communication. Inspired by the way in which humans naturally communicate with one another, this context-based approach provides a general, optimization-based design framework for semantic communication systems. Together, this survey provides a useful guide for the design and implementation of semantic communication systems.