Journal of Communications and Information Networks

Due to the continuity of long term evolution (LTE)downlink traffic,LTE signal has been considered as a promising excitation signal for ubiquitous backscatter communication.But this continuity also brings challenges in performing self-interference cancellation. Existing backscatter designs commonly use frequency shifting to move backscattered signal away from the entire excitation band to avoid self-interference. However, due to the continuity of LTE signal, LTE bands are occupied continuously. So, there is no enough white spectrum for frequency shifting. To solve this problem, we in this paper propose a novel LTE backscatter design,which can avoid self-interference without leveraging extra spectrum. Our idea is proposed based on a full understanding of the LTE resource grid, where we find that although a band is occupied by an excitation signal, there are still reserved resource elements in the traffic. We can leverage such resource elements as in-band white space to transmit backscatter signal. Meanwhile, we address the self-cancellation issue caused by double sideband modulation,and deal with the aligning issue. Our design is evaluated using a testbed of backscatter hardware and software defined radio(SDR).The results show that our system achieves a distance of 24 m for line-of-sight(LOS) transmit-to-tag communication. Besides,we demonstrate that our system can operate on off-the-shelf eNodeB. It can achieve reliable backscatter in multi-path scenarios, with a power consumption 6.3 times less than its least counterpart.

To address the issues of unstable received signal strength indicator (RSSI) and low indoor positioning accuracy caused by walls and obstacles, the propagation conditions of the wireless communication system are categorized into two distinct environments:line-of-sight (LOS) and non-line-of-sight (NLOS). In the LOS environment, the traditional logarithmic path loss model is applied. For the NLOS environment,the impact of walls on signal transmission is considered, leading to the development of a multi-wall path loss model based on the T-RL method, with improvements made to the key parameter, the Fresnel coefficient R.The breakpoint value d = 2.3 m in the partitioned model is determined, and the positional coordinates of the unknown nodes are calculated using the trilateration algorithm.Experimental results indicate that the T-RL based multi-wall model improves localization accuracy by 47% in NLOS environments compared to the traditional logarithmic path loss model.The average localization error using the T-RL partitioned path loss model is 0.702 1 m, representing a 55.9% improvement over the logarithmic path loss model and a 16.8% enhancement over the T-RL attenuation multi-wall model,thereby providing better environmental adaptability.

The long delay spreads and significant Doppler effects of underwater acoustic (UWA) channels make the design of the UWA communication system more challenging. In this paper, we propose a learning-based end-to-end framework for UWA communications, leveraging a double feature extraction network (DFEN) for data preprocessing. The DFEN consists of an attentionbased module and a mixer-based module for channel feature extraction and data feature extraction, respectively. Considering the diverse nature of UWA channels, we propose a stack-network with a two-step training strategy to enhance generalization.By avoiding the use of pilot information, the proposed network can learn data mapping that is robust to UWA channels. Evaluation results show that our proposed algorithm outperforms the baselines by at least 2 dB under bit error rate(BER)10^-2 on the simulation channel, and surpasses the compared neural network by at least 5 dB under BER 5 × 10^-2 on the experiment channels.

Hardware fingerprint is a new dimension of security mechanisms in low power wide area networks (LPWANs). It is hard to emulate for attackers and does not increase the computing and energy burden of transmitters. long range(LoRa)is a long-range commu-nication technology designed for battery-powered devices. In practice,LoRa is vulnerable to malicious attacks such as replace attack. Therefore,the hardware fingerprint is an excellent supplementary mechanism of LoRa security. However,the variable wireless environment contaminates the extracted fingerprints.The long wireless channel adds a large amount of the environment dependent information to the hardware features extracted from LoRa devices. In this paper, we propose StableFP which is a neural network(NN)based device identifier for long range wide area network (LoRaWAN). StableFP extracts stable and representative hardware features from channel frequency response (CFR) as the fingerprint, and it eliminates the environment dependent information caused by wireless environments. We implement StableFP on a software defined radio(SDR)testbed which consists of 4 commer-cial LoRa nodes. The result demonstrates that StableFP achieves over 90% identification accuracy in unseen en-vironments under an over 5 dB signal to noise ratio(SNR).

This paper introduces a novel RISC-V processor architecture designed for ultra-low-power and energy-efficient applications, particularly for Internet of things(IoT)devices.The architecture enables runtime dynamic reconfiguration of the datapath, allowing efficient balancing between computational performance and power consumption. This is achieved through interchangeable components and clock gating mechanisms, which help the processor adapt to varying workloads. A prototype of the architecture was implemented on a Xilinx Artix 7 field programmable gate array (FPGA). Experimental results show significant improvements in power efficiency and performance. The mini configuration achieves an impressive reduction in power consumption, using only 36% of the baseline power. Meanwhile, the full configuration boosts performance by 8% over the baseline. The flexible and adaptable nature of this architecture makes it highly suitable for a wide range of low-power IoT applications, providing an effective solution to meet the growing demands for energy efficiency in modern IoT devices.

Aircraft final assembly line (AFAL) involves thousands of processes that must be completed before delivery. However, the heavy reliance on manual labor in most assembly processes affects the quality and prolongs the delivery time. While the advent of artificial intelligence of things (AIoT) technologies has introduced advancements in certain AFAL scenarios, systematically enhancing the intelligence level of the AFAL and promoting the widespread deployment of artificial intelligence(AI)technologies remain significant challenges. To address these challenges, we propose the intelligent and collaborative aircraft assembly (ICAA) framework, which integrates AI technologies within a cloud-edge-terminal architecture. The ICAA framework is designed to support AI-enabled applications in the AFAL,with the goal of improving assembly efficiency at both individual and multiple process levels. We analyze specific demands across various assembly scenarios and introduce corresponding AI technologies to meet these demands. The three-tier ICAA framework consists of the assembly field, edge data platform, and assembly cloud platform,facilitating the collection of heterogeneous terminal data and the deployment of AI technologies. The framework enhances assembly efficiency by reducing reliance on manual labor for individual processes and fostering collaboration across multiple processes. We provide detailed descriptions of how AI functions at each level of the framework. Furthermore,we apply the ICAA framework to a real AFAL, focusing explicitly on the flight control system testing process. This practical implementation demonstrates the effectiveness of the framework in improving assembly efficiency and promoting the adoption of AIoT technologies.