Radio frequency fingerprint identification (RFFI) offers a promising physical-layer method to authenticate devices based on unique hardware impairments. However, existing RFFI systems use deep learning (DL) models that are resource- intensive. Training is particularly demanding, requiring repeated updates to a large number of parameters. In this paper, we introduce a quantum-assisted training (QAST) framework to address training inefficiencies in RFFI. QAST integrates a quantum neural network (QNN) with a mapping network to generate parameters for a classical DL model. This indirect training strategy substantially reduces the number of trainable parameters and the overall memory requirements compared to direct training of the DL model. We achieve this by introducing a multimodal mapping network that effectively learns the QNN output. This network generates multiple classical parameters from a shared quantum representation, thereby reducing qubit requirements and lowering the risk of barren-plateau-related trainability degradation. We also propose a new embedding method that reduces the size of the embedding matrix and yields a 15% to 30% reduction in training time. The tailored QAST framework trains the RFFI model while requiring only 10% of the original number of parameters while maintaining comparable accuracy, thereby substantially reducing memory and computational overhead and enabling efficient training or retraining in resource-limited environments.

The vision of sustainable 6G connectivity infrastructure, from space to ground, critically relies on green communication protocols that can efficiently operate within the constraints of wireless power transfer (WPT). Backscatter communication emerges as a cornerstone for such protocols, yet its potential is hindered by the inability to adapt to the dynamic spectrum and energy conditions inherent to WPT-powered networks. This paper presents ChannelDance, an edge-assisted adaptive hopping system for Bluetooth Low Energy (BLE) backscatter, architected specifically for green and sustainable connectivity in WPT-enabled environments. By leveraging real-time excitation channel intelligence from a low-latency edge server, ChannelDance dynamically configures the tag modulation clock, enabling robust and spectrally agile frequency hopping. This agility is paramount for maintaining reliable communication links amidst the interference and intermittent energy supply characteristic of integrated WPT systems. Our prototype demonstrates a median hopping success rate of 93% across 40 channels, a 3.5× goodput gain with channel optimization, and the ability to establish connections with commodity BLE devices under hopping conditions. ChannelDance thus establishes a foundational green communication primitive for future sustainable 6G networks, where seamless coexistence with energy transfer signals is not a feature but a fundamental requirement.

Convolutional Neural Networks (CNNs) achieve remarkable accuracy in vision tasks, yet their computational complexity challenges low-power edge deployment. In this work, we present COMET, a framework of CNN models that employ efficient hardware offset-binary coding (OBC) techniques to enable co-optimization of performance and resource utilization. The approach formulates CNN inference using OBC representations applied separately to inputs (Scheme A) and weights (Scheme B), enabling exploitation of bit-width asymmetry. The shift–accumulate operation is modified by incorporating offset-term with the pre-scaled bias. Leveraging symmetries in Schemes A and B, we introduce four look-up table (LUT) techniques—parallel, shared, split, and hybrid—and evaluate their efficiency. Building on this foundation, we develop a general matrix multiplication core using theim2coltransformation for efficient CNN acceleration. We consider LeNet-5 and All-CNN-C to demonstrate that the OBC-GEMM core efficiently supports modern workloads. Evaluation shows that COMET enables efficient FPGA deployment compared to state-of-the-art designs, with negligible accuracy loss, demonstrating its efficiency and scalability across diverse network architectures.

The computational demands of next-generation (Next-Gen) communication systems pose major challenges for real-time signal processing, particularly in digital predistortion (DPD), which is essential for linearizing power amplifier (PA) nonlinearities. While traditional DPD methods-such as polynomial and Volterra series models-remain prevalent, neural network (NN)-based approaches offer superior modeling accuracy and adaptability. However, their deployment is hindered by high computational complexity, limited scalability, and hardware integration challenges. This review presents a comprehensive analysis of NN-based DPD techniques and hardware acceleration strategies for efficient real-time implementation. We assess the strengths of various NN architectures-deep, convolutional, recurrent, and hybrid-and evaluate their tradeoffs across graphics processing unit (GPU), field-programmable gate arrays (FPGA), and application-specific integrated circuits (ASIC) platforms. We also examine key challenges, including fragmented evaluation standards and limited real-world validation. Finally, we outline future directions emphasizing model-hardware codesign, reconfigurable computing, and on-chip learning to enable scalable, energy-efficient DPD for 5G, 6G, and beyond.

Securing Internet of Things (IoT) devices presents increasing challenges due to their limited computational and energy resources. Radio Frequency Fingerprint Identification (RFFI) emerges as a promising authentication technique to identify wireless devices through hardware impairments. RFFI performance under low signal-to-noise ratio (SNR) scenarios is significantly degraded because the minute hardware features can be easily swamped in noise. In this paper, we leveraged the diffusion model to effectively restore the RFF under low SNR scenarios. Specifically, we trained a powerful noise predictor and tailored a noise removal algorithm to effectively reduce the noise level in the received signal and restore the device fingerprints. We used Wi-Fi as a case study and created a testbed involving 6 commercial off-the-shelf Wi-Fi dongles and a USRP N210 software-defined radio (SDR) platform. We conducted experimental evaluations on various SNR scenarios. The experimental results show that the proposed algorithm can improve the classification accuracy by up to 34.9%.

Radio frequency fingerprint identification (RFFI) is a promising technique for authenticating Internet of Things (IoT) devices by leveraging unique RF hardware impairments. However, RFFI is vulnerable to channel variations and low signal- to- noise ratio (SNR) conditions. In this paper, we proposed a robust RFFI system specifically designed to tackle these issues for Bluetooth Low Energy (BLE), which is a popular IoT technology. Our system integrated a denoising autoencoder (DAE) to enhance feature robustness under low SNR conditions and employed data augmentation to mitigate the impact of channel and noise effects. We created a testbed consisting of 18 commercial off-the-shelf (COTS) BLE devices and a USRP N210 software-defined radio (SDR) platform and then carried out extensive experimental evaluation under various channel conditions. The experiments involved line-of-sight (LOS) and non-line-of-sight (NLOS) propagation as well as dynamic and static channels. The results demonstrated that our approach consistently achieved over 95 % accuracy in high SNR environments and maintained strong performance with over 75% accuracy at low SNR levels (10 dB).

Directional modulation (DM) is a physical layer security (PLS) technique implemented at the transmitter, leveraging antenna arrays to ensure secure communications. Through a process of spatial precoding between transceivers to transmit signals in specific directions, DM is capable of disrupting communications in unintended directions to prevent eavesdropping. In general, recent progress in the development of multiple-input multiple-output (MIMO) systems, including advanced radio frequency (RF), antenna technologies, along with innovative precoding algorithms, has enhanced the capabilities of DM techniques, leading to a multitude of robust DM variants. Hence, this survey aims to offer a comprehensive overview of DM, covering its fundamentals, promising variants, applications, hardware implementations, and future trends. Initially, the basic principle of DM is outlined in a general manner for subsequent comprehension. Subsequently, the large family of DM techniques is categorized into distinct variants based on the types of transmitting arrays. Next, we give a comprehensive survey of DM in common wireless scenarios, including multi-user (MU), relay, Internet of Things (IoT), and non-orthogonal access (NOMA) networks. Furthermore, we provide an illustration of DM system implementations, encompassing foundational architectures and cost-effective hardware realizations. Finally, concerning the unresolved challenges and current research focal points in DM, we present future research directions that merit further exploration and reference.

Radio frequency fingerprint identification is a promising technique for device authentication that relies on the unique radio frequency fingerprint features caused by hardware impairments. Existing radio frequency fingerprint identification models usually contain a significant number of trainable parameters, making them undesirable for Internet of Things applications. In this paper, we augment a classical neural network by introducing an intermediary quantum neural network stage to enhance the authentication of Internet of Things devices using radio frequency fingerprint features. The model is based on the combination of quantum and classical machine learning and benefits from a significantly smaller number of trainable parameters. Empirical results show that our proposed model not only achieves a much smaller footprint (in terms of device memory) but also delivers competitive accuracy to conventional deep learning approaches. It therefore shows much promise as a solution for securing networks which feature resource-constrained Internet of Things devices.

Existing IoT systems suffer from restricted communication distances, high deployment costs, and frequent battery replacements, making them ineffective for managing healthcare data. This paper presents Prometheus, a self-powered wristband for reporting personal health status over long distances and intelligently managing healthcare data. Prometheus backscatters ambient BLE and ZigBee signals for low-power communication while incorporating a multi-source energy harvester to convert ambient RF, light, and heat into electricity. It also features a biochemical sensor array for monitoring sweat biochemical markers. Prototyped on a flexible PCB, Prometheus demonstrates impressive efficiency, consuming only 5.8 mW for sweat sensing, with BLE and ZigBee transmission energies significantly lower than standard electrochemical workstations and commercial alternatives. Our experiments show consistent signal quality at distances up to 20 meters. In summary, Prometheus emerges as a convenient, efficient, and self-powered wristband, promising to provide ubiquitous healthcare data management in our lives.

Orthogonal frequency division multiplexing (OFDM) signals have been widely studied as a potential waveform used in integrated sensing and communication (ISAC) systems. High computational effort, however, is required to estimate the azimuth of the target and suppress the interference from the non-target directions. Moreover, along the non-target directions, the transmitted confidential information can be easily intercepted by the eavesdroppers. In this paper, directional modulation (DM) technology combined with OFDM waveforms namely, OFDM-DM, is proposed for ISAC systems. From the sensing perspective, the interference from the non-target direction can be suppressed, and three-dimensional (3-D) radar images can be calculated without consuming extra computational resources. From a communication perspective, the OFDM-DM signals provide a secured physical-layer wireless transmission link and thus the confidential information can be securely delivered to the target. The efficacy of the proposed OFDM-DM ISAC waveforms is validated via numerical results for both sensing and communication functionalities by comparison with the traditional OFDM ISAC waveforms.

This paper presents a novel radio frequency fingerprint (RFF) enhancement strategy by exploiting the physical unclonable function (PUF) to tune the RF hardware impairments in a unique and secure manner, which is exemplified by taking power amplifiers (PAs) in RF chains as an example. This is achieved by intentionally and slightly tuning the PA non-linearity characteristics using the active load-pulling technique. The motivation driving the proposed research is to enlarge the RFF feature differences among wireless devices of same vendor, in order to massively improve their RFF classification accuracy in low to medium signal to noise ratio (SNR) channel conditions. PUF is employed to dynamically tune the PA’s RFF feature which guarantees the security since the PUF response cannot be cloned. Specifically, a ring oscillator (RO)-based PUF is implemented to control the PA non-linearity by selecting unique but random configuration parameters. This approach is proposed to amplify the distinctions across same model PAs, thereby enhancing the RFF classification performance. In the meantime, our innovative strategy of PUF-assisted RFF does not noticeably compromise communication link performance which is experimentally tested. The resulting RFF features can be extracted from the received distorted constellation diagrams with the help of image recognition-based machine learning classification algorithms. Extensive experimental evaluations are carried out using both cable-connected and over-the-air (OTA) measurements. Our proposed approach, when classifying eight PAs from a same vendor, achieves 11% to 24% average classification accuracy improvement by enlarging the RFF feature differences arising from the PA non-linearity.

In the past decade, backscatter communications have drawn significant attention as they are an ultra-low-power solution to transmit IoT sensor data, including video and audio. However, most of the state-of-the-art backscatter systems that are fully compatible with commodity radios suffer from poor synchronization accuracy and low throughput, being unable to support various multimedia sensors. In this paper, we propose Bitalign, a Bluetooth backscatter system that can make use of uncontrolled ambient signals as excitations and deliver high throughput for multimedia streaming applications. To do so, we identify several backscatter bottlenecks and employ a set of techniques to considerably boost backscatter throughput. In particular, we introduce an identification-based synchronization method that can efficiently distinguish various ambient signals and accurately decide where to modulate. We further propose a matching-based synchronization method with higher synchronization accuracy. In addition, we propose header reconstruction to make Bitalign truly compatible with commercial Bluetooth devices. Finally, we implement a tag prototype using FPGAs and conduct extensive experiments. Results show that the minimum bit error rate of Bitalign is 0.5%, which is 60 times better than that of FreeRider, a state-of-the-art Bluetooth system that features uncontrolled excitors. The maximal theoretical throughput of Bitalign is 1.98 Mbps.

With the large-scale commercialization of 5G networks, the era of Internet of Things (IoT), which is oriented toward the Internet of Everything (IoE), is coming. Under the circumstance, reliable and secure communication are the great challenges for future wireless network because of the broadcasting characteristics of electromagnetic wave. Physical-layer security (PLS) is an effective way to ensure secure communication by exploiting random nature of fading channels. Motivated by this, we investigate PLS of the wireless-powered cooperative multirelaying for IoT networks in the presence of eavesdropper with the estimation errors of channel (EEC) and imbalance between in-phase and quadrature-phase (IIQ). Specifically, the relays can be charged by the source with the aid of energy harvesters, and a novel more secure modify-and-forward (MF) relay protocol is proposed. For further improving energy efficiency and reducing extra interference, the$K$th superior relay selection scheme is proposed since some best ones are not available due to some scheduling or failure. Based on the system under study, we derive the analytical expressions for the outage probability (OP), intercept probability (IP), and secrecy OP (SOP) to evaluate the reliable and secure performance of this consideration system. Particularly, we then analyze the asymptotic behaviors of the OP, IP, and SOP, respectively. Through computer simulation, we show that: 1) with EEC, the error floors of the OP and SOP are of presence; 2) multiple relays lead to better OP and SOP performance; and 3) IIQ improves the security and is detrimental to the system reliability.

Abstract Recent works have shown that by using time‐modulated arrays (TMAs), directional modulation (DM) physical‐layer secured transmitters for orthogonal frequency‐division multiplexing (OFDM) wireless data transfer can be constructed. In this paper, three‐state TMAs are introduced for OFDM DM systems which allow more flexible manipulation of the injected orthogonal artificial noise and hence improve security. In particular, this paper presents for the first time both static and dynamic three‐state time‐modulated OFDM DM systems. Simulated bit error rate (BER) spatial distributions are shown for various system configurations in order to illustrate representative examples of secrecy performance enhancement that can be achieved by the proposed transmitter arrangement.

Backscatter communication is an emerging paradigm for pervasive connectivity of low-power communication devices. Wirelessly powered backscattering wireless sensor networks (WSNs) become particularly important to meet the upcoming era of the Internet of Things (IoT), which requires the massive deployment of self-sustainable and maintenance-free low-cost sensing and communication devices. This article will introduce the state-of-the-art antenna design and radio frequency (RF) system integration for wirelessly powered backscatter communications, covering both the node and the base unit. We capture the latest development in ultralow-power RF front ends and coding schemes for$\mu \text{W}$-level backscatter modulators, as well as the latest progress in wireless power transfer (WPT) and energy harvesting (EH) techniques. Newly emerged rectenna system, waveform design, and channel optimization are reviewed in light of the opportunities for adaptively optimizing the WPT/EH efficiency for low-power signals with varying conditions. In addition, advanced device packaging and integration technologies in, e.g., additively manufactured RF components and modules for microwave and millimeter-wave ubiquitous sensing and backscattering energy-autonomous RF structures are reported. Inkjet printing for the sustainable and ultralow-cost fabrication of flexible RF devices and sensors will be reviewed to provide a prospective insight into the future packaging of backscatter communications from the chip-level design to complete system integration. Finally, this article will also address the challenges in fully wireless powered backscatter radio networks and discuss the future directions of backscatter communication in terms of “Green IoT” and “Low Carbon” smart home, smart city, smart skin, and machine-to-machine (M2M) applications.

The integration of artificial intelligence and Internet of Things (IoT) has promoted the rapid development of artificial IoT (AIoT) networks. A wide range of AIoT applications have generated a great deal of data. The fifth-generation (5G) mobile communication has powerful data processing capabilities, and it is a key technology to enable AIoT big data processing. The explosive growth of the 5G users has made information security in AIoT networks a significant issue. Real-time security evaluation in AIoT networks is difficult due to user mobility and dynamic wireless environments. Thus, the evaluation and prediction of secrecy performance is a very critical research. In this article, new expressions for the nonzero secrecy capacity probability (NSCP) are derived to evaluate the mobile collaborative secrecy performance. An improved convolutional neural network (CNN) model, named as SI-CNN in this article, is proposed to predict the NSCP performance. The SI-CNN model combines the SqueezeNet and InceptionNet, and it has four convolution layers, which all adopt the same convolution model. For the first two layers, they employ a 2 × 1 convolution and a three-branch convolution, which not only increase the number of channels but also extract more features. For the last two layers, they employ the same structure, but different convolution kernels. The proposed SI-CNN prediction algorithm is shown to provide better NSCP performance prediction than other state-of-the-art methods. In particular, compared with wavelet neural network, the prediction precision of SI-CNN is improved by 26.8%.

In this study, a new hybrid secondary transmitter (ST) mode is proposed for cognitive radio Internet of Things (CR‐IoT) networks with energy harvesting (EH) and ambient backscatter (AmBack) communication capabilities. The authors firstly describe the proposed hybrid mode with wireless‐powered ST. The ST, consisting of EH, Amback, and conventional active transmission modules, adapts itself under different primary user operation states, i.e. active or idle. The overall throughput of the secondary system is formulated and the system settings for achieving optimal performance are derived and validated through numerical simulations.

In this study, the secrecy performance of the classic Wyner's wiretap model over shadowed fading channels is studied. More specifically, the authors derive two analytical expressions for the lower bound of secure outage probability at high signal‐to‐noise ratio regime and the probability of strictly positive secrecy capacity over shadowed fading channels, respectively. As there exist infinite series in the two derived expressions, they further obtain two simple and explicit approximate expressions for the lower bound of secure outage probability and the probability of strictly positive secrecy capacity with the aid of the moment matching method. It is shown that the match between the analytical results and simulations is very excellent for all parameters under considerations.

In this paper, a new type of architecture for secure wireless key establishment is proposed. A retrodirective array (RDA) that is configured to receive and re-transmit at different frequencies is utilized as a relay node. The RDA is able to respond in “real time,” reducing the required number of time slots to two. More importantly, in this architecture, equivalent reciprocal wireless channels between legitimate keying nodes can be randomly updated within one channel coherence time period, leading to greatly increased key generation rates in slow fading environment. The secrecy performance of this RDA-assisted key generation system is evaluated under several eavesdropping strategies and it is shown that it outperforms previous relay key generation systems.