Wireless Channel Research Articles

A Breakthrough Authentication Protocol for Fortifying IoT-Cloud Security

Abstract: Physical layer key generation, leveraging the reciprocity and randomness of wireless fading channels, has garnered significant research interest in recent years. While theoretical studies have demonstrated its potential to generate informationtheoretically secure keys, challenges remain in translating theory into practice. This paper provides an overview of the physical layer key generation process and discusses its practical challenges. Various passive and active attacks are analyzed and evaluated through numerical studies. A new key generation scheme using random probing signals and combining usergenerated randomness with channel randomness is introduced to counteract active attacks. Numerical results demonstrate that the proposed scheme achieves higher security strength than existing schemes using constant probing signals under active attacks. Secret-key generation relying on wireless channel reciprocity is an intriguing solution as it can be efficiently implemented at the physical layer of emerging wireless communication networks, while providing information-theoretic security guarantees. In this paper, we investigate and compare the secret-key capacity based on the sampling of the entire complex channel state information (CSI) or only its envelope, the received signal strength (RSS).

Birth of the Cool Musical Quotes in Marty Paich Arrangements on Mel Tormé Swings Shubert Alley

In 1957, Miles Davis released Birth of the Cool (Davis, 1957) which featured Davis with a unique instrumental ensemble called a nonette. This ground-breaking collaboration between Davis and arranger Gil Evans featured works that had been released either in radio broadcast or as singles several years prior to the album’s release. In 1960, arranger Marty Paich collaborated with singer Mel Tormé and produced the album Mel Tormé Swings Shubert Alley (Tormé, 1960). This album, the fifth collaboration with Paich, featured Tormé with a 10-person ensemble. This group was inspired by the Miles Davis Nonette, and shared a common mellow vibe. Two songs that Paich arranged on Mel Tormé Swings Shubert Alley (Tormé, 1960) reference musical snippets, or quotes, from Miles Davis’ recording of “Godchild” on Birth of the Cool (Davis, 1957). This paper serves to illuminate and analyze the musical quotations from Birth of the Cool (Davis, 1957) used by Marty Paich on these two arrangements. This paper also places these works within the broader context of the evolution of American popular music and culture.

Secure transmission in a W-band RoF system based on delta-sigma modulation

In this Letter, a delta-sigma modulation (DSM) encryption technique in a W-band RoF system is proposed. By performing DSM with different over-sample ratios (OSRs) on the OFDM signal based on the controlled keys generated by the chaotic system at the transmitter and performing constellation masking to disturb the transmitting signal for encryption, a high-order QAM-OFDM-DSM encrypted signal is achieved. In order to further improve the security of the system, bit bidirectional diffusion scrambling is used to resist chosen-plaintext attacks. After experimental verification, under the same transmission power, the encrypted DSM signal can achieve better security than single OSR of DSM signals through a 50-km standard single-mode fiber (SSMF) and a 3-m wireless channel, with the gain of sensitivity increased by ∼1 dBm. From the reliability of the system, the encrypted signal of the proposed scheme can be recovered, which meets a hard decision-forward error correction (HD-FEC) threshold of 3.8 × 10−3.

A proposal for fall-risk detection of hospitalized patients from wireless channel state information using Internet of Things devices

Falls of hospitalized patients are a breach of safety and contribute to increasing hospitalization time and worsening recovery conditions, besides impacting other costs. This work explores changes in Channel State Information (CSI), available in local area wireless networks, to detect the risk of falling for hospitalized patients. In practical scenarios, it is impossible to know all fall-risk situations; therefore, one should employ one-class-based detectors since only the risk-free status can be assessed with confidence. Wireless propagation environment changes may indicate the risk of falls and are signaled using the distance from the collected CSI data to the representation learned for the risk-free class. Considering the changes caused by inpatients and nurses’ movement in the infirmary, the learning of the fall-risk-free situation must rely on a limited amount of data and be repeated several times quickly and on the fly. This prevents data-hungry algorithms from being used to learn about risk-free situations. Consequently, we design systems using k-Nearest Neighbors (kNN), Kernel Principal Component Analysis, and Autoencoder. Assuming that the negative class corresponds to falling risk-free, the decision threshold is settled according to a prescribed False Positive Ratio (FPR), and the Matthews Correlation coefficient is used to set the decision threshold. The kNN system accomplishes the highest accuracy value of 87.95%. Thus the performance of the one-class kNN-based fall-risk detector is assessed in two environments that resemble those encountered by hospitalized patients for different FPR values, achieving recall values from 67.47% to 99.40% with an accuracy greater than 79.10%.

Game Theoretic Approaches to Modelling Cooperative and Competitive Dynamics for Enhanced Routing and Resource Allocation

The goal of the field of game theory is to simulate scenarios in which decision-makers must choose between courses of action that may or may not conflict and in which the choices made by one element may affect the choices made by other elements. Techniques from game theory have been widely used to solve a variety of engineering design issues. Game theory may be applied to wireless networks to create cooperative strategies between nodes, terminals and network providers, among other organizations. The majority of the time, game theory has been used in networking to address routing and resource allocation issues in competitive settings. Because radio communication channels are typically shared in wireless networks, the actions of one wireless device may have an impact on the communication capabilities of a neighboring device. Game theory may be used to simulate situations such as these. A multi-layered viewpoint is presented in Applications of Game Theory in Wireless Networking, highlighting the domains in which game theory might find practical application. This explains how different wireless network interactions may be represented as games and how game-theoretical methods can accurately mimic or forecast genuine user behavior in cooperative or competitive settings. Because of these similarities, it is possible to grasp the intricate relationships between nodes in this highly dynamic and dispersed environment by using a strong mapping between classical game theory components and network parts

Enhanced High-Definition Video Transmission for Unmanned Driving in Mining Environments

In the development of intelligent mines, unmanned driving transportation has emerged as a key technology to reduce human involvement and enable unmanned operations. The operation of unmanned vehicles in mining environments relies on remote operation, which necessitates the low-latency transmission of high-definition video data across multiple channels for comprehensive monitoring and precise remote control. To address the challenges associated with unmanned driving in mines, we propose a comprehensive scheme that leverages the capabilities of 5G super uplink, edge collaborative computing, and advanced video transmission strategies. This approach utilizes dual-frequency bands, specifically 3.5 GHz and 2.1 GHz, within the 5G super uplink framework to establish an infrastructure designed for high-bandwidth and low-latency information transmission, crucial for real-time autonomous operations. To overcome limitations due to computational resources at terminal devices, our scheme incorporates task offloading and edge computing methodologies to effectively reduce latency and enhance decision-making speed for real-time autonomous activities. Additionally, to consolidate the benefits of low latency, we implement several video transmission strategies, such as optimized network usage, service-specific wireless channel identification, and dynamic frame allocation. An experimental evaluation demonstrates that our approach achieves an uplink peak rate of 418.5 Mbps with an average latency of 18.3 ms during the parallel transmission of seven channels of 4K video, meeting the stringent requirements for remote control of unmanned mining vehicles.

Machine Learning Strategies for Reconfigurable Intelligent Surface-Assisted Communication Systems—A Review

Machine learning (ML) algorithms have been widely used to improve the performance of telecommunications systems, including reconfigurable intelligent surface (RIS)-assisted wireless communication systems. The RIS can be considered a key part of the backbone of sixth-generation (6G) communication mainly due to its electromagnetic properties for controlling the propagation of the signals in the wireless channel. The ML-optimized (RIS)-assisted wireless communication systems can be an effective alternative to mitigate the degradation suffered by the signal in the wireless channel, providing significant advantages in the system’s performance. However, the variety of approaches, system configurations, and channel conditions make it difficult to determine the best technique or group of techniques for effectively implementing an optimal solution. This paper presents a comprehensive review of the reported frameworks in the literature that apply ML and RISs to improve the overall performance of the wireless communication system. This paper compares the ML strategies that can be used to address the RIS-assisted system design. The systems are classified according to the ML method, the databases used, the implementation complexity, and the reported performance gains. Finally, we shed light on the challenges and opportunities in designing and implementing future RIS-assisted wireless communication systems based on ML strategies.

An intelligent wireless channel corrupted image-denoising framework using symmetric convolution-based heuristic assisted residual attention network

ABSTRACT Image denoising is one of the significant approaches for extracting valuable information in the required images without any errors. During the process of image transmission in the wireless medium, a wide variety of noise is presented to affect the image quality. For efficient analysis, an effective denoising approach is needed to enhance the quality of the images. The main scope of this research paper is to correct errors and remove the effects of channel degradation. A corrupted image denoising approach is developed in wireless channels to eliminate the bugs. The required images are gathered from wireless channels at the receiver end. Initially, the collected images are decomposed into several regions using Adaptive Lifting Wavelet Transform (ALWT) and then the “Symmetric Convolution-based Residual Attention Network (SC-RAN)” is employed, where the residual images are obtained by separating the clean image from the noisy images. The parameters present are optimized using Hybrid Energy Golden Tortoise Beetle Optimizer (HEGTBO) to maximize efficiency. The image denoising is performed over the obtained residual images and noisy images to get the final denoised images. The numerical findings of the developed model attain 31.69% regarding PSNR metrics. Thus, the analysis of the developed model shows significant improvement.

Spatial sensitivity synthesis based on alternate projection for the machine‐learning‐based coding digital receiving array

AbstractRecently, a novel low‐cost coding digital receiving array based on machine learning (ML‐CDRA) has been proposed to reduce the required radio frequency channels in modern wireless systems. The spatial sensitivity of ML‐CDRA is studied which describes the spatial accumulation gain in different directions. It is demonstrated that the spatial sensitivity is determined by the encoding network, decoding network, and beamforming criterion. To obtain the desired spatial sensitivity, a spatial sensitivity synthesis method is proposed based on the alternate projection by optimising the encoding network with the constraint of amplitude‐phase quantisation. Simulation results show that the proposed method can significantly improve the spatial sensitivity of ML‐CDRA. Furthermore, in the directions of interest, the spatial accumulation gain of ML‐CDRA can exceed the full‐channel digital receiving array.

An Optimized Fraud Identification Framework Using DCNN For Wireless Communication

Convolution Neural Network (Cnn) Is The Powerful Framework To Solve The Lot Of Issues Of Link Detection In Unlabeled Wireless Communication. Last Few Decades Lot Of Fraud Detection Strategies Has Been Projected Bit That All Are Inefficient For Detecting The Frauds. Therefore, The Great Need For Effective And Fast Fraud Detection Scheme With Higher Detection Accuracy. In This Research, Spider Based Convolution Neural Network (Sbcnn) Model To Detect The Frauds In The Wireless Communication. Initially, Create The Wireless Channel To Transmit The Messages From Source To Destination. Here, The Fraudulent Activities Are Detected Based On The Packet Delivery Time Of The Source To Destination Of The Wireless Medium. Moreover, The Proposed System Implementation Is Done In The Matlab Frame Work Additionally; The Obtained Results Are Validated With Prevailing Methods For Evaluating The Efficiency Of The Proposed Sbcnn Approach. Wireless communication fraud poses a significant threat as unauthorized use of services, compromising the security of cellular networks and infrastructure. With the surge in online services and users, the reliance on wireless communication for high-speed internet applications has grown. Despite the convenience brought by technologies like net banking, credit cards, and online services, financial frauds and unauthorized payments remain substantial risks. The intricate nature of wireless communication, illustrated in Fig. 1, where devices use signals between source and destination nodes, leads to challenges like network interference and loss rate, often exacerbated by fraudulent activities. Numerous techniques, including Artificial Intelligence (AI), hybrid ensemble models, oversampling, and machine learning, have been explored but haven't provided satisfactory solutions. In this study, an optimization-assisted intelligent framework is proposed to maximize communication performance, addressing the limitations of existing approaches. The subsequent sections analyse related research, identify issues with conventional methods, elaborate on the functioning of the proposed framework, discuss results, and conclude with research inferences

Factors Influencing Learners Discontinuation in the Alternative Learning System

This study looked into identified reasons for enrolling in the Alternative Learning System to determine what factors lead to ALS learners dropping out of the program. This study utilized a descriptive correlational design employing a researcher-made survey to gather data. The survey underwent validation by three experts in the field of research and Alternative Learning Systems and passed the validity test conducted. The responses of the participants were analyzed to determine if there is a significant difference in their profiles based on the reasons, they reported for enrolling in ALS using the ANOVA test. The investigation also looked into whether there is a correlation between the reasons reported by the participants for enrolling in ALS and the factors affecting their discontinuation. It was found that there is no significant difference in the profiles of the respondents such as age, gender, civil status, level taken, and monthly income in the reasons they reported for enrolling in ALS. However, it was discovered that there is a significant correlation between the reasons reported by the students for enrolling in ALS and the factors influencing their discontinuation. In response to the challenge of student discontinuation, it is proposed to initiate and effectively implement Radio-Based Instruction (RBI) as an alternative mode of delivery for ALS programs. Leveraging radio broadcasting, this innovative approach to distance learning has the potential to significantly enhance access to education by reaching students in diverse geographic locations, thereby mitigating the barriers posed by distance and geographical isolation. Moreover, RBI allows learners the flexibility to engage in their studies even while occupied with other activities, further facilitating their educational journey. Keywords: Alternative Learning System, Influencing Factors, Learners Discontinuation, ALS Learners

Lightweight Privacy Preserving Scheme for IoT based Smart Home

Background: The Internet of Things (IoT) is the interconnection of physical devices, controllers, sensors and actuators that monitor and share data to another end. In a smart home network, users can remotely access and control home appliances/devices via wireless channels. Due to the increasing demand for smart IoT devices, secure communication also becomes the biggest challenge. Hence, a lightweight authentication scheme is required to secure these devices and maintain user privacy. The protocol proposed is secure against different kinds of attacks and as well as is efficient. Methods: The proposed protocol offers mutual authentication using shared session key establishment. The shared session key is established between the smart device and the home gateway, ensuring that the communication between the smart devices, home gateway, and the user is secure and no third party can access the information shared. Results: Informal and formal analysis of the proposed scheme is done using the AVISPA tool. Finally, the results of the proposed scheme also compare with existing security schemes in terms of computation and communication performance cost. The results show that the proposed scheme is more efficient and robust against different types of attacks than the existing protocols. Conclusion: In the upcoming years, there will be a dedicated network system built inside the home so that the user can have access to the home from anywhere. The proposed scheme offers secure communication between the user, the smart home, and different smart devices. The proposed protocol makes sure that security and privacy are maintained since the smart devices lack computation power which makes them vulnerable to different attacks.

Energy-efficient mobile edge computing assisted by layered UAVs based on convex optimization

The utilization of unmanned aerial vehicles (UAVs) as aerial mobile edge computing (MEC) servers presents an effective approach for mitigating the computational limitations of intelligent mobile devices. However, the limited battery capacity of UAVs poses a significant challenge in ensuring sustained service provision. To counter this obstacle and enhance energy efficiency, we introduce a partial offloading MEC scheme assisted by layered UAVs (POAL). Our objective is to minimize the total energy consumption of UAVs by jointly optimizing several factors, including wireless channels, transmit power, task partition, computing power, and UAV trajectories. Due to the non-convexity of the problem, we decompose it into three sub-problems and address them iteratively. Firstly, we employ the CVX optimization tool to allocate wireless channels and utilize Lagrangian duality to determine the optimal transmit power. Secondly, leveraging the monotonicity of the optimization objective, we derive the optimal computing power. Lastly, we employ the successive convex approximation (SCA) technique to tackle the trajectory planning sub-problem. Numerical results demonstrate that the proposed algorithm can effectively position UAVs within 15 iterations. Furthermore, our layered scheme significantly reduces overall energy consumption.

Mathematical Model of the Process of Data Transmission over the Radio Channel of Cyber-Physical Systems

This article introduces a refined mathematical model to evaluate the quality of mobile radio channels within cyber-physical systems, employing the topological transformation of stochastic networks. The operation of the radio channel is conceptualized as a stochastic network, enabling the derivation of critical metrics such as an equivalent function, mathematical expectation, variance, and the time distribution function of the implemented processes. The model uses the Gamma distribution for the initial distribution functions of random variables, enhancing its analytical precision. A significant advancement of this study is the development of a comprehensive model that describes the data transmission process through phases of connection establishment, information transmission, and connection maintenance. The innovative aspect of this research lies in applying an equivalent function to a stochastic network that includes a logical “AND” node with gamma-distributed incoming branches. The stochastic network presented in the article, which includes a logical “AND” node, helps to elucidate the mechanism for obtaining an equivalent function for such networks, allowing the application area of the GERT method to be expanded. This methodological enhancement extends the previously limited scope of topological transformation methods, which only applied to exponential distribution models for the timing of branch inputs. By integrating a Gamma distribution, the model simplifies the equivalent function and reduces the computational complexity required to assess the radio channel’s quality, ensuring the accuracy needed for engineering calculations. Moreover, the proposed method requires 25–40% fewer series members than the traditional Taylor series decomposition, while maintaining comparable computational complexity for the typical series members. Furthermore, the maximum absolute error in the calculations is capped at 9 × 10−3, which is well within acceptable limits for engineering purposes. Primarily designed for radio channels in cyber-physical systems, the model’s applicability extends to wireless communications, providing a valuable tool for evaluating channel efficiency and security in the face of increasing cyber threats.

UAV-Mounted RIS-Aided Mobile Edge Computing System: A DDQN-Based Optimization Approach

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are increasingly employed in mobile edge computing (MEC) systems to flexibly modify the signal transmission environment. This is achieved through the active manipulation of the wireless channel facilitated by the mobile deployment of UAVs and the intelligent reflection of signals by RISs. However, these technologies are subject to inherent limitations such as the restricted range of UAVs and limited RIS coverage, which hinder their broader application. The integration of UAVs and RISs into UAV–RIS schemes presents a promising approach to surmounting these limitations by leveraging the strengths of both technologies. Motivated by the above observations, we contemplate a novel UAV–RIS-aided MEC system, wherein UAV–RIS plays a pivotal role in facilitating communication between terrestrial vehicle users and MEC servers. To address this challenging non-convex problem, we propose an energy-constrained approach to maximize the system’s energy efficiency based on a double-deep Q-network (DDQN), which is employed to realize joint control of the UAVs, passive beamforming, and resource allocation for MEC. Numerical results demonstrate that the proposed optimization scheme significantly enhances the system efficiency of the UAV–RIS-aided time division multiple access (TDMA) network.

Issues of state regulation of television and radio broadcasting in border areas

Issues of state regulation of television and radio broadcasting in border areas

Hybrid intelligent system for channel allocation and packet transmission in CR-IoT networks

The proliferation of interconnected devices is driving a surge in the demand for wireless spectrum. Meeting the need for wireless channel access for every device, while also ensuring consistent quality of service (QoS), poses significant challenges. This is particularly true for resource-limited heterogeneous devices within Internet of Things (IoT) networks. Cognitive radio (CR) technology addresses the shortcomings of traditional fixed channel allocation policies by enabling unlicensed users to opportunistically access unused spectrum belonging to licensed users. This facilitates timely and reliable transmission of mission-critical data packets. A cognitive radio-enabled IoT (CR-IoT) network is poised to better accommodate the growing demands of diverse applications and services within the smart city framework, spanning areas such as healthcare, agriculture, manufacturing, logistics, transportation, environment, public safety, and pharmaceuticals. To minimize switching delays and ensure energy and spectral efficiency, this study proposes a hybrid intelligent system for efficient channel allocation and packet transmission in CR-IoT networks. Leveraging Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS), the system dynamically manages spectrum resources to minimize handoffs while upholding QoS. A Java-based simulation integrates system outputs with interference temperature data to accommodate service demands across 2G–4G spectrums. Evaluation reveals SVM’s 98.8% accuracy in detecting spectrum holes and ANFIS’s 90.4% accuracy in channel allocation. These results demonstrate significant potential for enhancing spectrum utilization in various IoT applications.

Hybrid Multiple Access Techniques Performance Analysis Of Dynamic Resource Allocation

The Non-Orthogonal Multiple Access (NOMA) and the Orthogonal Frequency Division Multiple Access (OFDMA) are promising techniques for next-generation wireless systems. Although much attention in the literature considered these techniques, an effective combination between such systems as hybrid multiple access (HMA) needs more attention. For this reason, this paper devotes to proposing two different scenarios of HMA using multi-user OFDM (MU-OFDM) and power domain (PD) NOMA for downlink transmission to overcome the limitations of both Orthogonal Multiple Access (OMA) and NOMA. Due to the randomness of a wireless channel, different user grouping strategies and dynamic power allocation (DPA) strategies are employed to satisfy users’ requirements. The proposed systems give high flexibility utilizing bitrate allocation and user fairness. System results show superior performance to traditional OMA and NOMA systems. The achieved variety of fairness is helpful for the diversity of applications which is a principal requirement for beyond-fifth-generation (B5G) networks. The next step in this analysis is to enhance the proposed systems’ spectral efficiency (SE) by introducing beamforming for massive Multiple-Input Multiple-Output (MIMO) systems. The bit error rate (BER) result of the proposed system achieves almost the same error floor with a benefit of approximately 10 dB signalto-noise ratio (SNR) when 90 resource blocks (RBs) are used.

Artificial Broadcasts as Galactic Populations. II. Comparing Individualist and Collective Bounds on Broadcast Populations in Single Galaxies

The search for extraterrestrial intelligence includes efforts to constrain populations of artificial broadcasts in other galaxies. Previous efforts use individualist methods, searching for single broadcasts with high signal-to-noise ratio. These would be detected as observables with extreme values. This approach is limited to very bright broadcasts and also is subject to confusion, where a large number of broadcasts blend together to form a noise continuum. The mean value of the total emission provides an additional collective bound: the luminosity of the transmitters is no higher than the galaxy’s observed luminosity. Using the framework developed in Paper I, I evaluate how confusion affects individualist searches. I then compare individualist and collective approaches for radio broadcasts from the Milky Way, M31, and three Virgo Cluster elliptical galaxies. For current observations, confusion blurs narrowband radio broadcasts together in the Virgo ellipticals when there is one broadcast per gigahertz per 1000 stars. The collective bound implies fewer than ∼106(ℓ¯/1013W)−1 L-band broadcasts per star per gigahertz in the Milky Way wideband and is about 10 and 400 times stronger in M31 and M59, respectively. Applying the collective bound to the far-infrared–radio correlation yields constraints on radio broadcast populations in star-forming galaxies throughout the Universe. The collective bound allows us to rule out large regions of broadcast population parameter space even for distant galaxies. It also imposes constraints on gamma-ray, neutrino, and gravitational-wave broadcasts in the nearest galaxies.

Analyzing the blockchain mining strategy in Industrial Internet of Things: A game theoretical approach

Deploying Proof-of-Work (PoW) consensus based blockchain systems in Industrial Internet of Things (IIoT) scenarios that rely on wireless channels for data transmission faces new challenges. An IIoT device working in industrial production may participate in the operation of blockchain as a miner. These devices not only need to consume their own computation capability to compete for the right to generate blocks, but also need to transmit newly generated blocks to other devices through wireless channel to continuously maintain the global ledger. Asynchronous mining and data transmission by IIoT devices in wireless environments may significantly interfere with the propagation of blocks in the system and result the reduction of mining payoff, thereby affecting the willingness of IIoT devices to participate in maintaining blockchain system. This paper considers the behavior of independent mining between multiple IIoT devices as a non-cooperative game, which we call the Wireless Mining Game (WMG). Markov chain is adopted to derive the block propagation model under the wireless channel, and then the payoff model of IIoT device is formulated. IIoT devices can make reasonable mining decisions by evaluating the computation capability of other IIoT devices and the probability of successful block transmission. We analyze the Nash equilibrium of the WMG and the corresponding mining behavior set between two-players scenario through theoretical modeling and experiments. We also analyzed the expected mining payoff of the IIoT devices in the multiple devices scenario.