Wireless Communication Domain Research Articles

Metasurface-based wireless communication technology and its applications

Metasurfaces, due to their outstanding ability to control electromagnetic waves, have great application prospects in the field of wireless communication. This paper provides a comprehensive review of research work based on metasurface in three aspects: wireless power transfer, wireless information transmission, and novel wireless transceiver architectures. In the domain of wireless power transfer, several focusing metasurfaces and systems with unique performance are presented along with a new formula for calculating wireless power transfer. Concerning wireless information transmission section, the direct digital information transmission based on metasurface and the information transmission based on space-time-coding digital metasurface are introduced. Lastly, a simplified wireless transceiver with metasurfaces was introduced. The paper concludes with a discussion on the future directions of metasurfaces in the wireless communication domain.

Dynamic resource allocation in IRS-assisted UAV wideband cognitive radio networks: A DDQN-TD3 approach

Intelligent reflecting surface (IRS) technology has found extensive application in the wireless communication domain, offering significant enhancements in communication performance by manipulating the reflection of electromagnetic waves. This research article delves into the domain of UAV-assisted wideband cognitive radio networks augmented by IRS and underpinned by sensing-based spectrum sharing techniques. We introduce UAVs as alternative solutions for both primary and secondary base stations to optimize the management of spectral resources. Our primary objective centers around the joint optimization of both the trajectories of the primary and secondary UAVs, power allocation at the secondary UAVs, reflection coefficients of intelligent reflecting surfaces, sensing time, and subcarrier allocation, all aimed at maximizing the achievable rate of secondary users. Given that the problem at hand is non-convex, we employ deep reinforcement learning algorithms for resolution. To address the challenge of mixed-action spaces, we implement a novel Dueling DQN-Twin Delayed Deep Deterministic policy gradient (DDQN-TD3) algorithm. The simulation outcomes illustrate that the methodology introduced in this paper substantially amplifies the performance of CR system when contrasted with benchmark methods. This is evident in the improved spectrum efficiency, elevated data transmission rates, and the minimization of interference with the primary network.

Exploring the applications and security threats of Internet of Thing in the cloud computing paradigm: A comprehensive study on the cloud of things

AbstractThe term “Internet of Things” (IoT) represents a vast interconnected network comprising ordinary objects enhanced with electronics like sensors, actuators, and wireless connectivity. These augmentations enable seamless communication and data sharing among devices. IoT constitutes an ecosystem of programs, systems, and technologies that operate across diverse communication services. In our rapidly evolving world, cutting‐edge technologies are swiftly gaining prominence. IoT, in particular, strives to proliferate innovative applications, programs, and communication amalgamating the virtual and physical realms. The bedrock of this communication is the machine‐to‐machine communication paradigm. IoT encompasses a spectrum of technologies encompassing smart vehicles, efficient vehicle parking management systems, and roadway‐embedded sensors, culminating in the vision of a “smart city.” Such integration has the potential to alleviate traffic congestion and curb energy consumption. The impact of IoT extends to power generation and business operations, promising transformative outcomes. Furthermore, the synergy between IoT and cloud computing plays a pivotal role in the wireless communication domain. This paper offers a comprehensive insight into IoT's functionalities and underscores the associated security threats. The study aims to equip academia with an enhanced understanding of diverse IoT applications, particularly their integration with cloud computing, referred to as the “Cloud of Things.”

Transfer Learning in Natural Language Processing (NLP)

Purpose: The purpose of this study is to address the limited use of transfer learning techniques in radio frequency machine learning and to propose a customized taxonomy for radio frequency applications. The aim is to enable performance gains, improved generalization, and cost-effective training data solutions in this specific domain.
 Methodology: The research design employed in this study involves a comprehensive review of existing literature on transfer learning in radio frequency machine learning. The researchers collected relevant papers from reputable sources and analyzed them to identify patterns, trends, and insights. The method of data collection primarily relied on examining and synthesizing existing literature. Data analysis involved identifying key findings and developing a customized taxonomy for radio frequency applications.
 Findings: The study's findings highlight the limited utilization of transfer learning techniques in radio frequency machine learning. While transfer learning has shown significant performance improvements in computer vision and natural language processing, its potential in the wireless communications domain has yet to be fully explored. The customized taxonomy proposed in this study provides a consistent framework for analyzing and comparing existing and future efforts in this field.
 Recommendations: Based on the findings, the study recommends further research and experimentation to explore the potential of transfer learning techniques in radio frequency machine learning. This includes investigating performance gains, improving generalization capabilities, and addressing concerns related to training data costs. Additionally, collaborations between researchers and practitioners in the field are encouraged to facilitate knowledge exchange and foster innovation. Practice: To practitioners in the field of radio frequency machine learning, this study emphasizes the potential benefits of incorporating transfer learning techniques. It encourages practitioners to explore the application of transfer learning in their specific domain, leveraging prior knowledge to enhance performance and address training data challenges. It also highlights the importance of staying informed about the latest developments and collaborating with experts in the field. Policy: To policy makers, the study underscores the need for supportive policies that promote research and development in radio frequency machine learning. It recommends creating an environment that fosters innovation, encourages collaborations between academia and industry, and provides resources and incentives for further exploration of transfer learning techniques. Policy makers should consider the potential impact of transfer learning on the wireless communications industry and support initiatives that enhance its adoption and implementation.

The new trends in networking: IOT, 5G and cognitive radio

The domain of networking and wireless communication is rapidly expanding to the point where traditional legacy networks research is nearly complete, and they are supposed to be an elder. IoT, the Internet of Things, and the 5G radio communications are the newest networking advancements. These two technologies fulfil all of the researchers' goals of improving people's quality of life. IoT is a framework that allows everyday devices to become smarter, processing to become more intelligent, and communication to become more relevant. The Internet of Things (IoT) has already made huge progress as a ubiquitous solution platform for the linked world. We also discussed the Radio Cognitive RC, which allows people to connect to networks as secondary users at no cost while the service provided is not as good as that provided to primary users who pay a license to use the network. RC can provide a solution for best-effort IoT applications that do not require constant connectivity and high QoS requirements. This study describes all of these technologies and concludes with a recommendation for using IoT and RC in our further works.

Adversarial Machine Learning in Wireless Communications Using RF Data: A Review

Machine learning (ML) provides effective means to learn from spectrum data and solve complex tasks involved in wireless communications. Supported by recent advances in computational resources and algorithmic designs, deep learning (DL) has found success in performing various wireless communication tasks such as signal recognition, spectrum sensing and waveform design. However, ML in general and DL in particular have been found vulnerable to manipulations thus giving rise to a field of study called adversarial machine learning (AML). Although AML has been extensively studied in other data domains such as computer vision and natural language processing, research for AML in the wireless communications domain is still in its early stage. This paper presents a comprehensive review of the latest research efforts focused on AML in wireless communications while accounting for the unique characteristics of wireless systems. First, the background of AML attacks on deep neural networks is discussed and a taxonomy of AML attack types is provided. Various methods of generating adversarial examples and attack mechanisms are also described. In addition, an holistic survey of existing research on AML attacks for various wireless communication problems as well as the corresponding defense mechanisms in the wireless domain are presented. Finally, as new attacks and defense techniques are developed, recent research trends and the overarching future outlook for AML in next-generation wireless communications are discussed.

THz-enabled UAV communications: Motivations, results, applications, challenges, and future considerations

Unmanned aerial vehicles (UAVs) and Terahertz (THz) communication are promising technologies envisioned to support the race towards enhanced performance with respect to data rate, reliability, coverage, etc. Research on the merger of these two technologies for meeting the different application requirements in future wireless communication is gaining momentum, which could facilitate its deployment for applications envisioned for the sixth-generation wireless systems and beyond. Enabling UAV communication in the THz band is quite challenging owing to the highly mobile nature of UAVs and the strict line-of-sight requirements of communication in the high frequency bands. Nevertheless, very recent research in terms of experimentation and modelling has shown the merits of THz-enabled UAV communication in several critical and peculiar cases which reveals its great potential. Thus, this paper presents a survey of the literature on the experimental results, numerous potential use cases and the corresponding research challenges of THz-UAV with a view to providing insights into the myriad of opportunities in this domain of aerial wireless communications.

Underwater wireless optical communications links: perspectives, challenges and recent trends

AbstractUnderwater wireless optical communication (UOWC) systems have lately garnered a significant amount of attention for both academic purposes and trial applications. Although the idea is not new, the fact that seawater has a smaller window of absorption for blue-green light has reawakened interest in it, and it has grown an essential attraction because of its high bandwidth, it can cover a wide variety of underwater activities as compared to radio frequency and acoustic technologies. To monitor pollution, maintain oil pipelines, monitor climate change, conduct offshore investigations, and conduct oceanography research, the wireless transmission of information underwater technology is of importance to the military, industrial, and scientific organizations all around the world. The use of wavelengths of visible light to transmit secure data across point-to-point connections in underwater optical wireless communication (UOWC) compares well with the usage of free-space optical (FSO) communications. However, UWOC systems also have a huge amount of absorption and scattering introduced by the aquatic channels. Different from standard terrestrial free-space optical communication, many unique system design strategies have been investigated in recent years to solve these technological issues. This article presents a vision as well as various obstacles in the domain of underwater optical wireless communication, a detailed overview, and comparison of underwater communications techniques (UOWC) links, basic modulation technique techniques, and c pursuits on UWOC.

Multi-task learning approach for modulation and wireless signal classification for 5G and beyond: Edge deployment via model compression

Future communication networks must address the scarce spectrum to accommodate extensive growth of heterogeneous wireless devices. Efforts are underway to address spectrum coexistence, enhance spectrum awareness, and bolster authentication schemes. Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, secure communications, among others. Consequently, comprehensive spectrum awareness on the edge has the potential to serve as a key enabler for the emerging beyond 5G (fifth generation) networks. State-of-the-art studies in this domain have (i) only focused on a single task – modulation or signal (protocol) classification – which in many cases is insufficient information for a system to act on, (ii) consider either radar or communication waveforms (homogeneous waveform category), and (iii) does not address edge deployment during neural network design phase. In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks based multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks while considering heterogeneous wireless signals such as radar and communication waveforms in the electromagnetic spectrum. The proposed MTL architecture benefits from the mutual relation between the two tasks in improving the classification accuracy as well as the learning efficiency with a lightweight neural network model. We additionally include experimental evaluations of the model with over-the-air collected samples and demonstrate first-hand insight on model compression along with deep learning pipeline for deployment on resource-constrained edge devices. We demonstrate significant computational, memory, and accuracy improvement of the proposed model over two reference architectures. In addition to modeling a lightweight MTL model suitable for resource-constrained embedded radio platforms, we provide a comprehensive heterogeneous wireless signals dataset for public use.

Recent Trends in Underwater Visible Light Communication (UVLC) Systems

In recent years, underwater visible light communication (UVLC) has become a potential wireless carrier candidate for signal transmission in highly critical, unknown, and acrimonious water mediums such as oceans. Unfortunately, the oceans are the least explored reservoirs in oceanogeographical history. However, natural disasters have aroused significant interest in observing and monitoring oceanic environments for the last couple of decades. Therefore, UVLC has drawn attention as a reliable digital carrier and claims a futuristic optical media in the wireless communication domain. Counterparts of traditional communications, the green, clean, and safe UVLC support high capacity data-rate and bandwidth with minimal delay. Nevertheless, the deployment of UVLC is challenging rather than terrestrial basis communication over long ranges. In addition, UVLC systems have severe signal attenuation and strong turbulence channel conditions. Due to the fact that, this study provides an exhaustive and comprehensive survey of recent advancements in UVLC implementations to cope with the optical signal propagation issues. In this regard, a wide detailed summary and future perspectives of underwater optical signaling towards 5G and beyond (5GB) networks along with the current project schemes, channel impairments, various optical signal modulation techniques, underwater sensor network (UWSN) architectures with energy harvesting approaches, hybrid communication possibilities, and advancements of Internet of underwater things (IoUTs) are concluded in this research.

Surface Waves Analysis of Efficient Underwater Radio-Based Wireless Link

The domain of underwater wireless communication (UWC) link is gaining much attention due to an increase in various underwater activities such as offshore hydrocarbon exploration, underwater unmanned vehicles (UUV), and military practices. Increased bandwidth and a reliable data link are mainly required for such activities. Both requirements of the domain are heavily affected by the highly conductive property of the seawater. This paper demonstrates the performance evaluation of radiofrequency-UWC, focusing on surface wave analysis, to propose a reliable solution for offshore activities. A constructive interference scheme can be useful due to the sharp difference in the properties of the two mediums (air and seawater). To that end, an experimental setup is created, and a corresponding finite element method (FEM) based simulation of the radio-based wireless link is run. This is because it has higher bandwidth and speed than acoustic and optical approaches. A conduction current mechanism transmits and receives data in a synthetic water tank containing a prepared conductive media (saltwater). The study of changing depths of transmitter-receiver nodes in saltwater shows that surface waves cause significant noise reception in shallow water (less than dipole length, below water level). During a series of experiments in the tank, the lowest bit error rate (BER) is observed only when the node’s submerged height was greater than dipole length. As a result, it is meant to provide a genuine data channel model. The discovery and analysis will aid in the development of a dependable underwater data link, with applications including short-range diver-to-diver communication, and UUV capability.

Next generation optical wireless communication: a comprehensive review

Abstract Next generation wireless networks are the need of the hour with reliable, high bandwidth, low Bit Error Rate (BER), high Signal to Noise Ratio (SNR), large network capacity, etc. for a paradigm shift to all optical networks from the prevailing conventional electro optic network due to the various benefits of optical links such as enormous bandwidth and extremely low losses. This paper presents a primitive overview of the foregoing technologies and then move on to encapsulating the contemporary techniques to enhance the system performance. For this, preceding limitation and their respective improvement measures from literature of fiber communication and its integration with the next generation networks mainly based upon Radio over Fiber (RoF), Fiber to the Home (FTTH) and Free Space Optics (FSO) are discussed. The advancement in the domain of optical wireless communication utilizing various methodologies is summarized. Finally, we conclude by incorporating various cost reduction methodologies developed which may further be explored to make next generation networks more promising.

Joint Antenna Selection and Power Allocation for Secure Co-Time Co-Frequency Full-Duplex Massive MIMO Systems

In this paper, we design a co-time co-frequency full-duplex (CCFD) massive multiple-input multiple-output (MIMO) network to improve the utilization of time–frequency resources and physical layer security. Simultaneous information transmissions of the base station and the users are regarded as same-frequency interference against interception by eavesdroppers. To promote energy conservation and improve utilization of system resources, we propose a joint antenna selection and power allocation (JASPA) strategy for CCFD massive MIMO networks. Considering the trade-off between secrecy performance and system communications quality, we formulate optimization functions based on JASPA for two security concerns. We design a quantum-inspired backtracking search algorithm (QBSA) to obtain the optimal JASPA scheme. Simulation results show that the performance of the proposed QBSA is superior to previous intelligent algorithms in CCFD massive MIMO systems. The effectiveness of the proposed JASPA scheme is demonstrated under various communications scenarios, and can be considered a cornerstone for future research in wireless communications domain.

Towards Enhancing Spectrum Sensing: Signal Classification Using Autoencoders

The demand for technologies relying on the radio spectrum, such as mobile communications and IoT, has been growing exponentially. As a consequence, providing access to the radio spectrum is becoming increasingly more important. The ever-growing wireless traffic and the increasing scarcity of available spectrum warrants efficient management of the radio spectrum. At the same time, machine learning (ML) is becoming ubiquitous and has found applications in many fields for its ability to identify patterns and assist with decision-making processes. Recently, machine learning algorithms have been used to address challenges in the wireless communications domain, such as radio spectrum sensing, and have shown better performance than traditional sensing methods, such as energy detection. Spectrum sensing, a method for detecting and identifying different wireless signals being transmitted in the same band of the radio spectrum, is crucial for improving dynamic spectrum sharing, which has the potential to enhance sharing and coexistence of different wireless technologies in the same frequency band and ultimately improve spectrum efficiency. To this end, this research evaluates different types of autoencoders, such as deep, variational and Long Short-Term Memory (LSTM) autoencoders, to identify and differentiate between LTE and Wi-Fi transmissions. The goal is to investigate the performance of the different types of autoencoders on an I/Q dataset consisting of LTE and a combination of Wi-Fi signals (IEEE 802.11ax and IEEE 802.11ac) for the classification task in terms of complexity, precision, and recall to identify the best algorithm. Our models have achieved up to 99.9% precision and 88.1% recall for this classification task. Additionally, with a shortest training time of approximately 47 seconds, the models are suitable for online learning and deployment in a dynamic RF environment.

Deep Learning as Applied to Wireless Networks: A Review

The rapid development in the domain of wireless communication and high traffic of mobile data led to introduce an advanced model called Deep Learning (DL). It has been proposed in the literature, which is a subset of Machine learning. It is a technology that is considered to be an evolution for mobile data traffic management. A large amount of heterogeneous data is generated by wireless systems which is difficult to compute so to control such data various optimization tools are discussed. DL makes wireless communication more intelligent and versatile. Here, a review of the DL platform and different techniques to enable DL in wireless communications is summarized. There are different domains in wireless networking like networking, routing, and scheduling in which DL is employed. All such domains are discussed in this paper. Further different optimization techniques and recent advancement in this area were discussed.

Stationary queue and server content distribution of a batch-size-dependent service queue with batch Markovian arrival process:

Queueing systems with batch Markovian arrival process (BMAP) have paramount applications in the domain of wireless communication. The BMAP has been used to model the superposition of video sources and to approximate the super-position of data, voice and video traffic. This article analyzes an infinite-buffer generally distributed batch-service queue with BMAP, general bulk service (a, b) rule and batch-size-dependent service time. In this proposed analysis, we mainly focus on deriving the bivariate vector generating function of queue and server content distribution together at departure epoch using supplementary variable technique. The mathematical procedure for the complete extraction of distribution at departure epoch has been discussed and using those extracted probabilities, we achieve the queue and server content distribution at arbitrary epoch. Finally, numerical illustrations have been carried out in order to make a deep insight to the readers which contains deterministic as well as phase-type service time distributions.

Antenna-Independent Frequency- and Time-Domain Representation of Wireless Channel

Propagation channel characterization and modeling is an important aspect in the wireless communication domain prior to the design and implementation of any RF system, especially in the millimeter-wave band for the 5G ecosystem. Several methods have been proposed in the literature for different environments where the antenna effects present inherently in the measured results caused by ambiguous channel characterization and model. The results are antenna specific and unreliable for other antennas. In this letter, the standalone channel response (frequency and time domain) excluding the antenna effects has been derived using a two-port network approach. Identical horn antennas operating in the C -band have been used to measure the channel transfer function and impulse response. A comparative investigation of the measured results with and without the antenna effects is presented. It is observed that measuring the channel characteristics without excluding the transmitting and receiving antenna effects adds additional time delays and reduced coherence bandwidth of the channel.

The new trends in networking: IoT, 5G, and cognitive radio

The domain of networking and wireless communication is rapidly expanding to the point where traditional legacy networks research is nearly complete, and they are supposed to be an elder. IoT, the Internet of Things, and the 5G radio communications are the newest networking advancements. These two technologies fulfill all of the researchers' goals of improving people's quality of life. IoT is a framework that allows everyday devices to become smarter, processing to become more intelligent, and communication to become more relevant. The Internet of Things (IoT) has already made huge progress as a ubiquitous solution platform for the linked world. We also discussed the Radio Cognitive RC, which allows people to connect to networks as secondary users at no cost while the service provided is not as good as that provided to primary users who pay a license to use the network. RC can provide a solution for best-effort IoT applications that do not require constant connectivity and high QoS requirements. This study describes all of these technologies and concludes with a recommendation for using IoT and RC in our further works.

Secure and Opportunistic Routing in WSN using Associative Secure Optimized Routing Algorithm

Under wireless communication domain optimized routing for wireless networks is the trending scenario for research. Various older schemes faces performance issues such as consistency, alongside ensuring routing security which significantly causes performance degradation. Although the current mechanisms designed for routing are low-cost and precise positioning is also not essential but security still requires emphasis to be given during design. In this paper a secure and optimized routing technique is proposed for WSN mentioned as Associative Secure Optimized Routing (ASOR) Algorithm. Optimized data progression is the prime focus of ASOR alongside ensuring security to it. ASOR operates in two phases, in first phase it identifies and club the associative nodes based on the defined indulgent constant(IC) based on explicit trust, strength of link and link_quality parameters. Explicit trust and strength of link are directly measurable however to measure quality, survival time of link and the incurred delay are taken into consideration. Secondly the selection of optimized nodes using proposed ASOR is carried out through defined aptness function(AF) which is dependent on explicit_trust(ET), strength_of_link(SOL), node_quality(NQ) and distance. Proposed associative secure optimized routing scheme’s performance is evaluated based on metrics for adhoc sensor network of 100 dynamic nodes in the presence of worm_hole and black_hole attacks. Comparatively higher throughput and detection_rate whereas lower distance and delay are indicated by ASOR, which are relatively better than opposing methods. ASOR shows significantly high detection_rate and throughput as 55.7 and 44.1 respectively and comparatively less delay and distance as 13 and 168.2 which are significantly better than opposing methods. ASOR can be effectively used in WSN for real time application such as in agriculture, industries etc.

A Trust Framework to Detect Malicious Nodes in Cognitive Radio Networks

Cognitive radio is considered as a pioneering technique in the domain of wireless communication as it enables and permits the Cognitive Users (CU) to exploit the unused channels of the Primary Users (PU) for communication and networking. The CU nodes access the vacant bands/channels through the Cognitive Radio Network (CRN) cycle by executing its different phases, which are comprised of sensing, decision making, sharing (accessing) and hand-off (mobility). Among these phases, hand-off is the most critical phase as the CU needs to switch its current data transmissions to another available channel by recalling all the previous functions upon the emergence of a PU. Further, from the security perspective, a Malicious User (MU) may imitate the PU signal with the intention to never allow the CU to use its idle band, which ultimately degrades the overall network performance. Attacks such as the Cognitive User Emulation Attack (CUEA) and Primary User Emulation Attack (PUEA) may be encountered by the handoff procedure, which need to be resolved. To address this issue, a secure and trusted routing and handoff mechanism is proposed specifically for the CRN environment, where malicious devices are identified at the lower layers, thus prohibiting them from being part of the communication network. Further, at the network layer, users need to secure their data that are transmitted through various intermediate nodes. To ensure a secure handoff and routing mechanism, a Trust Analyser (TA) is introduced between the CU nodes and network layer. The TA maintains the record of all the communicating nodes at the network layer while also computing the rating and trust value of the Handoff Cognitive User (HCUs) using the Social Impact Theory Optimizer (SITO). The simulation results suggest that the proposed solution leads to 88% efficiency in terms of better throughput of CRN during data communication, the packet loss ratio, the packet delivery ratio and the maximum and average authentication delay and clearly outperforms the prevailing mechanisms in all the parameters.