Search Results (186)

In complex urban electromagnetic environments, wireless positioning signals are subject to various types of interference, including narrowband, chirp, and pulse jamming. Traditional generic suppression methods struggle to achieve global optimization tailored to specific interference mechanisms. This paper proposes a classification-driven differentiated jamming suppression (CDDJ) method, which adaptively selects the optimal mitigation strategy by pre-identifying interference types and integrating classification confidence levels. First, the theoretical bounds of the output carrier-to-noise ratio ($C/N_{0_{out}}$) under typical interference scenarios are derived, characterizing the performance distribution of anti-jamming efficiency ($\mathsf{\Gamma }$). Then, a mapping relationship between interference categories and their corresponding suppression strategies is established, along with decision criteria for strategy switching based on signal quality evaluation metrics. Finally, an OpenMax-Lite rejection layer is designed to handle low-confidence inputs, identify unknown jamming using the Weibull distribution, and implement a broadband conservative suppression policy. Simulation results demonstrate that the proposed method exhibits significant advantages across different interference types. Under high JSR conditions, the signal recovery rate improves by over 10% and 8% compared to that of the WPT and KLT methods, respectively. In terms of SINR performance, the proposed approach outperforms the AFF, TDPB, and FDPB methods by 1.5 dB, 1.1 dB, and 5.3 dB, respectively, thereby enhancing the reliability of wireless positioning in complex environments. Full article

This paper presents hardware-efficient phase demodulation schemes for FPGA-based digital phase-sensitive optical time-domain reflectometry ($\varphi$-OTDR) receivers. We first derive a signal model for the heterodyne $\varphi$-OTDR frontend, then propose and analyze three demodulation methods: (1) a baseband reconstruction approach via zero-IF downconversion, (2) an analytic signal generation technique using the Hilbert transform (HT), and (3) a wavelet transform (WT)-based alternative for analytic signal extraction. Algorithm-hardware co-design implementations are detailed for both RFSoC and conventional FPGA platforms, with resource utilization comparisons. Additionally, we introduce an incremental DC-rejected phase unwrapper (IDRPU) algorithm to jointly address phase unwrapping and DC drift removal, minimizing computational overhead while avoiding numerical overflow. Experiments on simulated and real-world $\varphi$-OTDR data show that the HT method matches the performance of zero-IF demodulation with simpler hardware and lower resource usage, while the WT method offers enhanced robustness against fading noise (3.35–22.47 dB SNR improvement in fading conditions), albeit with slightly ambiguous event boundaries and higher hardware utilization. These findings provide actionable insights for demodulator design in distributed acoustic sensing (DAS) applications and advance the development of single-chip DAS systems. Full article

In high-capacity and short-reach applications, double-sideband self-coherent detection (DSB-SCD) has garnered significant attention due to its ability to recover optical fields of DSB signals without requiring a local oscillator. However, DSB-SCD is fundamentally constrained by the non-ideal receiver transfer function, necessitating a guard band between the carrier and signal. While the conventional twin-single-sideband (twin-SSB) modulation scheme addresses this requirement, it incurs substantial implementation complexity. In this paper, we propose a spectrum inversion-based double-sideband (SI-DSB) modulation scheme, where spectral inversion shifts the DSB signal to the high-frequency region, creating a guard band around the zero frequency. After photodetector detection, baseband signal recovery is achieved through subsequent spectral inversion. Compared with the twin-SSB modulation scheme, this approach significantly reduces DSP complexity. The simulation exploration two modulation formats of pulse–amplitude modulation and quadrature-amplitude modulation, demonstrating a comparable system performance between SI-DSB and twin-SSB modulation schemes. We also illustrate the parameter optimization process for the SI-DSB modulation scheme, including carrier-to-signal power ratio and guard band. Furthermore, validation with three FADD receivers further demonstrates the superior performance of the proposed SI-DSB modulation in DSB-SCD systems. Full article

The efficiency of the current cellular network is limited due to the imbalance between resource availability and traffic demand. To overcome these limitations, baseband units (BBUs) are deployed on virtual machines (VMs) to form a virtual pool of BBUs. This setup enables the pooling of hardware resources, reducing the costs associated with building base stations (BSs) and simplifying both management and control. However, extreme levels of server resource use within the pool can increase physical maintenance costs and impact virtual BBU performance. This study introduces an adaptive, threshold-based dynamic migration strategy for virtual BBUs within the iCanCloud framework by setting upper and lower limits on the servers’ resource usage in the pool. The proposed method determines whether to initiate a migration by evaluating resource usage on each compute node and identifies the target node for migration if required. This aims to balance server load and cut energy consumption, and also to avoid unnecessary migration because of too high or too low server load, and effectively determine the time to trigger migration and not depend only on a certain instantaneous peak of server resource utilization. This paper used a deep Q-network learning method to predict resource utilization and make an accurate migration decision based on a history dataset. Experimental results show that as compared with Kalman filter prediction and other traditional methods, this model can effectively lower the cost of VM migration by decreasing the migration time and occurrence of it to enhance overall performance while reducing energy consumption. Full article

Frequency-modulated continuous wave (FMCW) and orthogonal frequency-division multiplexing (OFDM) technologies play significant roles in millimeter-wave radar and communication. Their combinations, however, are understudied in the literature. This paper introduces a novel OFDM-FMCW dual-functional radar-communications (DFRC) system that takes advantage of the merits of both technologies. Specifically, we introduce a baseband modulation to the traditional FMCW radar system architecture. This integration combines the advantages of both waveforms, enhancing the diversity of radar transmission waveforms without compromising high-resolution distance detection and enjoying the communication capabilities of OFDM in the meantime. We establish the system and signal models for the proposed DFRC and develop holistic methods for both sensing and communications to accommodate the integration. For radar, we develop an efficient radar sensing scheme, with the impacts of adding OFDM also being analyzed. A communication scheme is also proposed, utilizing the undersampling theory to recover the OFDM baseband signals modulated by FMCW. The theoretical model of the communication receive signal is analyzed, and a coarse estimation combined with a fine estimation method for Carrier Frequency Offset (CFO) estimation is proposed. System simulations validate the feasibility of radar detection and communication demodulation. Full article

Wireless communication plays an important role in the digitization of industries. A 5G cellular communication system enables several industrial automation use cases. Fifth-generation deployments in industrial use cases have mainly been carried out in the sub-7 GHz frequency range. In this work, we empirically study 5G system performance in the millimeter wavelength (mmW) range for industrial use cases: additive manufacturing processes and precision manufacturing robotics. We carry out an experimental performance evaluation of a commercially available non-public 5G mmW system to assess its latency, reliability and throughput for uplink and downlink data traffic in a real industrial environment. We also investigate the impact of various 5G configurations on 5G performance characteristics with insights from the baseband log information as well as unidirectional latency measurements. Our empirical results indicate that 5G mmW system can achieve low latency with high reliability in both one-way traffic directions. The throughput is observed to be high for line-of-sight (LOS) scenarios, making the use of the 5G mmW system appealing especially for data rate-intensive and time-critical industrial use cases. We also observe that industrial environments with lots of metal and reflective surfaces provide favorable propagation conditions for non-LOS transmissions. Our results indicate that static industrial use cases with low mobility can leverage the performance benefits of 5G mmW systems. Full article

It may be helpful to integrate multiple aircraft communication and navigation functions into a single software-defined radio (SDR) platform. To transmit these multiple signals, the SDR would first sum the baseband version of the signals. This outgoing composite signal would be passed through a digital-to-analog converter (DAC) before being up-converted and passed through a radio frequency (RF) amplifier. To prevent non-linear distortion in the RF amplifier, it is important to know the peak voltage of the composite. While this is reasonably straightforward when a single modulation is used, it is more challenging when working with composite signals. This paper describes a machine learning solution to this problem. We demonstrate that a generalized gamma distribution (GGD) is a good fit for the distribution of the instantaneous voltage of the composite waveform. A deep neural network was trained to estimate the GGD parameters based on the parameters of the modulators. This allows the SDR to accurately estimate the peak of the composite voltage and set the gain of the DAC and RF amplifier, without having to generate or directly observe the composite signal. Full article

A gesture recognition method is proposed in this paper, which leverages millimeter-wave radar point clouds, primarily for identifying six basic human gestures. First, the raw radar signals collected by the MIMO millimeter-wave radar are converted into 3D point cloud sequences using a microcontroller integrated into the radar’s baseband processor. Next, based on the SequentialPointNet network, a multiscale feature extraction module is proposed in this paper, which enhances the network’s ability to extract local and global features through convolutional layers at different scales. This compensates for the lack of feature understanding capability caused by single-scale convolution kernels. Moreover, the CBAM in the network is replaced with GAM, which effectively enhances the extraction of global features by more precisely modeling global contextual information, thereby increasing the network’s focus on global features. A separable MLP structure is introduced into the network. The separable MLP operation is used to separately extract local point cloud features and neighborhood features, and then fuse these features, significantly improving the model’s performance. The effectiveness of the proposed method is confirmed through experiments, achieving a 99.5% accuracy in recognizing six fundamental human gestures, effectively distinguishing between gesture categories, and confirming the potential of millimeter-wave radar 3D point clouds in recognizing gestures. Full article

Specific emitter identification (SEI) is a highly active research area in physical layer security. In this paper, we propose a SEI scheme based on time-frequency domain channel, spatial, and self-attention mechanisms (TF-CSS) for deep networks with few-shot learning. The scheme first uses an asymmetric masked auto-encoder (AMAE) with attention mechanisms for unsupervised learning, then removes the decoder and adds a linear layer as a classifier, and finally fine-tunes the whole network to achieve effective recognition. The scheme improves the feature representation and identification performance of complex-value neural network (CVNN)-based AMAE by adding channel, spatial, and self-attention mechanisms in the time-frequency domain, respectively. Experimental results show that this scheme outperforms the recognition accuracy of contrastive learning and other MAE/AMAE-based methods in 30 classes of LoRa baseband signal transmitter recognition with different few-shot scenarios and observation lengths. Full article

This paper presents a low power wideband dB-linear analog baseband (ABB) circuit for a millimeter-wave (mmW) wireless receiver in 40 nm CMOS technology. The proposed ABB system consists of a multi-stage variable gain amplifier (VGA) and a low-pass filter (LPF). The 5-stage VGA is composed of two variable gain units followed by three fixed gain units with DC offset cancellation (DCOC). The first variable gain unit with a self-compensated transistor pair and compact active inductor load is designed for dB-linear functionality and bandwidth extension, respectively. Moreover, a proposed error compensation method is applied to the second cascaded variable gain unit for further dB-linear gain error correction. A 4th-order Butterworth transconductance-capacitance (Gm-C) LPF with flipped source follower (FSF) as an input transconductance stage for linearity enhancement is designed after the VGA stage. The prototype chip is implemented, and measurement results show a dB-linear gain range from −18 to 26 dB with less than 0.5 dB-linear gain error with a bandwidth of 4 GHz. The VGA and LPF consume 8.3 mW and 3 mW, respectively, under a 1 V power supply, while the entire ABB occupies an area of 0.94 mm² with an active core area of only 0.045 mm². Full article

We propose and demonstrate a microwave photonic system to generate multiband and multi-format microwave waveforms based on an actively mode-locked optoelectronic oscillator (AML-OEO) using a dual-polarization dual-drive Mach–Zehnder modulator (DPol-DDMZM). In the proposed system, the upper DDMZM is injected by two baseband single-chirped signals, and the lower DDMZM is biased to realize single-sideband (SSB) modulation using a 90° hybrid coupler. The lower DDMZM is also used to construct an AML-OEO loop, which outputs microwave frequency comb signals. By setting the phase difference in the applied two single-chirped signals, multiband up-, down-, and dual-chirped microwave signals are successfully generated. Furthermore, the tunability of the system can be realized by adjusting the frequency and power of the injection signal in the AML-OEO loop, the passband of the electrical filter, and the chirp rate of the single-chirped signal. The proposed scheme is theoretically analyzed and experimentally verified. Full article

In this paper, the texture evolution of the Ni-5%W alloy baseband with different strain variables (ε_vM = 3.9, 4.9, and 5.1) during rolling and annealing was studied using the electron back scattering diffraction (EBSD) technique. The results indicate that after high-temperature annealing at 1150 °C, all three strain levels of the alloy substrates can achieve a strong cubic texture, with a content exceeding 99% (<10°). However, the texture evolution trajectory is significantly influenced by the strain level. When the content of cubic texture in the alloy substrates under strain levels of 3.9 and 5.1 is the same, significant temperature differences exist. Additionally, the different strain levels result in varying nucleation rates and growth rates of cubic texture in the Ni-5%W alloy substrates. The study reveals that in the alloy substrates under strain levels of 3.9 and 4.9, recrystallized cubic grain nuclei grow within a layered structure, resulting in larger grain sizes and lower nucleation rates. In contrast, in the alloy substrates under a strain level of 5.1, recrystallized cubic grain nuclei form from small equiaxed grains, leading to higher nucleation rates but smaller grain sizes, competing with random orientations. In the later stages of nucleation, recrystallized grains in the alloy substrates under a strain level of 5.1 exhibit a significant size advantage, rapidly growing by engulfing randomly oriented grains. Compared to the alloy substrates with lower strain levels, the recrystallized cubic grains in the alloy substrates under a strain level of 5.1 have higher nucleation rates and faster growth rates. Full article

The multifunctionality feature of graphene field-effect transistors (GFETs) is exploited here to design circuit building blocks of high-data-rate modulators by using a physics-based compact model. Educated device performance projections are obtained with the experimentally calibrated model and used to choose an appropriate improved feasible GFET for these applications. Phase-shift and frequency-shift keying (PSK and FSK) modulation schemes are obtained with $0.6$ $\mathrm{G}$$\mathrm{Hz}$ GFET-based multifunctional circuits used alternatively in different operation modes: inverting and in-phase amplification and frequency multiplication. An adequate baseband signal applied to the transistors’ input also serves to enhance the device and circuit performance reproducibility since the impact of traps is diminished. Quadrature PSK is also achieved by combining two GFET-based multifunctional circuits. This device circuit co-design proposal intends to boost the heterogeneous implementation of graphene devices with incumbent technologies into a single chip: the baseband pulses can be generated with CMOS technology as a front end of line and the multifunctional GFET-based circuits as a back end of line. Full article

Visible light communication (VLC) has been viewed as one promising candidate to mitigate the challenging spectrum crisis and radio frequency interference in future 6G mobile communications and networking. Due to the relatively limited baseband modulation bandwidth of VLC light sources—typically, light-emitting diodes—non-orthogonal multiple access (NOMA) techniques have been proposed and explored to enhance the spectral efficiency (SE) of VLC systems. However, almost all reported NOMA VLC schemes focus on well-discussed applications employing a Lambertian light beam configuration and ignore the potential applications with distinct non-Lambertian optical beam configurations. To address this issue, in this work, the performance of non-Lambertian optical beam configuration-based NOMA VLC is comparatively investigated for future 6G mobile networks. The numerical results demonstrate that, for a fundamental two-user application scenario with the far user located at the corner position, a maximum sum rate gain of about 15.6 Mbps could be provided by the investigated distinct non-Lambertian beam-based NOMA VLC, compared with the maximum sum rate of about 93.3 Mbps for the conventional Lambertian configuration with the same power splitting factor. Full article

Microwave Doppler sensors are used extensively in motion detection as they are energy-efficient, small-size and relatively low-cost sensors. Common applications of microwave Doppler sensors are for detecting intrusion behind a car roof liner inside an automotive vehicle and to detect moving objects. These applications require a millisecond response from the target for effective detection. A Doppler microwave sensor is ideally suited to the task, as we are only interested in movement of a large water-based mass (i.e., a person) (FMCW Radar also detect static objects). Although microwave components at $2.45$ $\mathrm{G}$$\mathrm{Hz}$ are now relatively cheap due to mass production of other Industrial Scientific and Medical application (ISM) devices, they do require tuning for temperature compensation, dielectric, and manufacturing variability. A digital solution would be ideal, as chip solutions are known to be more repeatable, but Application-Specific Integrated Circuits (ASICs) are expensive to initially prototype. This paper presents the first completely digital Doppler motion sensor solution at $2.45$ $\mathrm{G}$$\mathrm{Hz}$, implemented on the new RFSoC from Xilinx without the need to up/downconvert the frequency externally. Our proposed system uses a completely digital approach bringing the benefits of product repeatability, better overtemperature performance and softwarisation, without compromising any performance metric associated with a comparable analogue motion sensor. The RFSoC shows to give superior distance versus false detection, as the Signal-to-Noise Ratio (SNR) is better than a typical analogue system. This is mainly due to the high gain amplification requirement of an analogue system, making it susceptible to electrical noise appearing in the intermediate-frequency (IF) baseband. The proposed RFSoC-based Doppler sensor shows how digital technology can replace traditional analogue radio frequency (RF). A case study is presented showing how we can use a novel method of using multiple Doppler channels to provide range discrimination, which can be performed in both analogue and in a digital implementation (RFSoC). Full article