Journal of Communications and Information Networks

For the waveform design and research of wireless powered and backscatter hybrid communication system, it is crucial to balance energy harvesting with ensuring communication rate performance. Considering the communication needs of traditional users in the hybrid communication system is particularly practical. In this paper, we study the model of wireless powered and backscatter hybrid communication system, while also taking the traditional orthogonal frequency division multiplexing (OFDM) user system into account. We jointly design the transmitting signal waveform and the backscatter signal waveform to ensure the communication performance of the traditional user while simultaneously enhancing the communication and energy transmission performance of the hybrid communication system. We maximize the signal-to-noise ratio(SNR)at the receiver by jointly optimizing the amplitude and phase of the transmitted signal waveform from the radio frequency (RF) source and the reflection coefficient of the backscatter device(BD).Furthermore, we use geometric programming methods to solve the problem. The simulation results confirm the effectiveness of the proposed scheme.

Sonar locates underwater targets by receiving reflected sound waves. However, the complex marine environment makes it difficult to set targets in appropriate locations and conditions. The sonar echo simulator has the function of simulating sonar detection target echo signals. As a cutting-edge technology,underwater backscatter has led to the emergence of array based acoustic reflection systems. The research on sonar echo simulators based on backscatter technology has promoted the solution of problems such as target echo modeling, sonar arrival direction estimation, and echo directional transmission. In response to the above problems, this paper designs an end-to-end sonar echo simulator system array phase estimation sonar echo simulation system (APE-SESS), which can independently complete highresolution real-time direction of arrival(DOA)estimation and generate directional simulation echo based on the array structure. Dual branch convolutional neural network(DB-CNN)is proposed in the system to estimate the direction of the signal array and directly obtain the phase weights containing azimuth information. Comparing DBCNN with conventional methods and classic underwater DOA network models based on classification problems, the results show that DB-CNN exhibits stability, small error, and high real-time performance under different signal-to-noise ratio(SNR).The proposed APE-SESS has end-to-end characteristics, real-time angle estimation, and azimuth simulation functions.

In the realm of the Internet of things(IoT), radio frequency identification (RFID) technology is essential for linking physical objects to digital data. The introduction of harmonic technology has expanded RFID’s applications by improving sensitivity and enabling communication with wireless fidelity (Wi-Fi) networks. However,this trend faces challenges,notably interference from strong Wi-Fi signals, which impacts RFID-based sensing systems. This paper proposes the WiFID(Wi-Fi and RFID) algorithm, which enables Wi-Fi devices to sense weak RFID harmonic signals. Adaptive subcarrier deallocation minimally reduces Wi-Fi throughput while effectively limiting interference with RFID harmonic sensing. Experimental validation on the universal software radio peripheral (USRP) platform demonstrates that WiFID successfully detects 90% of RFID harmonics at-30 dBm,with only a minimal 4% decrease in Wi-Fi throughput.

Zero-day malware refers to a previously unknown or newly discovered type of malware. While most existing studies rely on large malware sample sets, their performance is unknown when dealing with a limited number of samples. This paper addresses this challenge by proposing a novel approach for effective zero-day malware detection,even with a scarcity of known samples. The proposed method begins by visualizing the malware binary and converting it into an entropy image. Subsequently,a deep convolutional generative adversarial network (DCGAN) is employed to learn from the available samples and generate new, highly similar synthetic samples. By combining these generated samples with the real ones, a comprehensive training set is constructed for a convolutional neural network (CNN) classification model.The randomness introduced by DCGAN facilitates the generation of new features, even in the presence of a small sample size. This enables the classifier to learn the characteristics of unknown zero-day malware and enhance its detection capabilities. Extensive experiments validate the effectiveness of the proposed approach, demonstrating that leveraging entropy images as features and applying DCGAN for data augmentation leads to a robust zero-day malware detection system, capable of achieving promising results even with a limited number of samples.

The rapid increase in the number of Internet of things (IoT) devices has led to significant access pressure, making network energy consumption and communication load key challenges. Edge caching, cooperative communication, and energy management technologies have proven to be effective in alleviating these issues. This paper investigates a unmanned aerial vehicle (UAV)-assisted Internet of everything (IoE) architecture that integrates caching, communication, and energy management. A collaborative communicationcaching-energy optimization scheme is proposed, which involves the joint operation of the UAV and base station (BS)to pre-cache content required by ground users,thus minimizing system energy consumption. We model the joint optimization of content caching, communication, and energy consumption as a Markov decision process (MDP), transforming it into a long-term optimization problem solvable by deep reinforcement learning. Based on the simple deep Q-network (DQN), we design a dynamic content placement strategy that jointly optimizes communication, caching, and energy consumption. Simulation results demonstrate that the proposed method, compared to branch and bound (B＆amp;B), particle swarm optimization(PSO),genetic algorithm(GA),and random algorithms,not only approaches the optimal solution most closely, effectively reducing system energy consumption, but also exhibits the lowest time complexity.

Digital twin (DT) technology is currently pervasive in industrial Internet of things (IoT) applications, notably in predictive maintenance scenarios. Prevailing digital twin-based predictive maintenance methodologies are constrained by a narrow focus on singular physical modeling paradigms, impeding comprehensive analysis of diverse factory data at scale. This paper introduces an improved method,federated continual learning with authentication for distributed digital twin-based industrial IoT(FCLA-DT).This decentralized strategy ensures the continual learning capability vital for adaptive and real-time decision-making in complex industrial predictive maintenance systems. An authentication scheme based on group signature is introduced to enable the verification of digital twin identities during inter-twin collaborations, avoiding unauthorized access and potential model theft. Security analysis shows that FCLA-DT can enable numerous nodes to collaborate learning without compromising individual twin privacy, thereby ensuring group authentication in the cooperative distributed industrial IoT. Performance analysis shows that FCLA-DT outperforms traditional federated learning methods with over 95% fault diagnosis accuracy and ensures the privacy and authentication of digital twins in multi-client task learning.