Search Results (274)

Ubiquitous digital array radar (UDAR) extends the integration time of moving targets by deploying a wide transmitting beam and multiple narrow receiving beams to cover the entire observed airspace. By exchanging time for energy, it effectively improves the detection ability for weak targets. Nevertheless, target motion introduces severe across-range unit (ARU), across-Doppler unit (ADU), and across-beam unit (ABU) effects, dispersing target energy across the range–Doppler-beam space. This paper proposes a beam domain angle rotation compensation and keystone-matched filtering (BARC-KTMF) algorithm to address the “three-crossing” challenge. This algorithm first corrects ABU by rotating beam–domain coordinates to align scattered energy into the final beam unit, reshaping the signal distribution pattern. Then, the KTMF method is utilized to focus target energy in the time-frequency domain. Furthermore, a special spatial windowing technique is developed to improve computational efficiency through parallel block processing. Simulation results show that the proposed approach achieves an excellent signal-to-noise ratio (SNR) gain over the typical single-beam and multi-beam long-time coherent integration (LTCI) methods under low SNR conditions. Additionally, the presented algorithm also has the capability of coarse estimation for the target incident angle. This work extends the LTCI technique to the beam domain, offering a robust framework for high-speed weak target detection. Full article

An iterative fractional Doppler shift and channel joint estimation algorithm is proposed for orthogonal time frequency space (OTFS) satellite communication systems. In the algorithm, we search the strongest path and estimate its fractional Doppler offset, and compensate the Doppler shift to the nearest integer to estimate the coefficient of the path. Then signal of the path and its inter-Doppler interference are reconstructed and canceled from the received data with these two estimated parameters. The estimation and cancel process are iteratively conducted until the strongest path in the remained paths is less than the predetermined threshold. The channel information can be reconstructed by the estimated parameters of the paths. The normalized mean squared error (NMSE) of the proposed channel estimation algorithm is less than 1/5 of the available algorithms at a high signal-to-noise ratio (SNR) region, and its BER has about 4dB SNR gain compared with those of the available algorithms when the bit error rate (BER) is ${10}^{-3}$. Full article

Compact high-frequency surface wave radars (HFSWRs) utilize miniaturized antennas, resulting in lower antenna gain and a reduced signal-to-noise ratio (SNR) for target echoes. Due to noise interference, ship echoes in the noise region often fall below the detection threshold, leading to missed detections. To address this issue, this paper proposes a cascaded detection method in the time–frequency (TF) domain to improve ship detection performance under such conditions. First, TF features are extracted from TF representations of ship and noise signals. Supervised machine learning algorithms are then employed to distinguish targets from noise, reducing false alarms. Next, a non-constant false alarm rate (CFAR) threshold is computed based on the noise mean, standard deviation, and an adjustment factor to improve detection robustness. Experiments show that the classification accuracy between the ship and noise signals exceeds 99%, and the proposed method significantly outperforms the conventional CFAR and TF-domain CFAR in terms of detection performance. Full article

This study investigates the influence of chromatic dispersion on the performance of Brillouin optical time-domain analysis (BOTDA) sensors, particularly under high-pump-power conditions, where nonlinear effects become significant. By incorporating dispersion terms into the coupled amplitude equations of stimulated Brillouin scattering (SBS), we theoretically analyzed the dispersion-induced pulse broadening effect and its impact on the Brillouin gain spectrum (BGS). Numerical simulations revealed that dispersion leads to a moderate broadening of pump pulses, resulting in slight changes to BGS characteristics, including increased peak power and reduced linewidth. To explore the interplay between dispersion and nonlinearity, we built a gain-based BOTDA experimental system and tested two types of fibers, namely standard single-mode fiber (SMF) with anomalous dispersion and dispersion-compensating fiber (DCF) with normal dispersion. Experimental results show that SMF is more prone to modulation instability (MI), which significantly degrades the signal-to-noise ratio (SNR) of the BGS. In contrast, DCF effectively suppresses MI and provides a more stable Brillouin signal. Despite SMF exhibiting narrower BGS linewidths, DCF achieves a higher SNR, aligning with theoretical predictions. These findings highlight the importance of fiber dispersion properties in BOTDA design and suggest that using normally dispersive fibers like DCF can improve sensing performance in long-range, high-power applications. Full article

This study assesses the clinical deployment of SubtlePET™, a commercial AI-based denoising algorithm, across three radiotracers—¹⁸F-FDG, ⁶⁸Ga-PSMA-11, and ¹⁸F-FDOPA—with the goal of improving image quality while reducing injected activity, technologist radiation exposure, and scan time. A retrospective analysis on a digital PET/CT system showed that SubtlePET™ enabled dose reductions exceeding 33% and time savings of over 25%. AI-enhanced images were rated interpretable in 100% of cases versus 65% for standard low-dose reconstructions. Notably, 85% of AI-enhanced scans received the maximum Likert quality score (5/5), indicating excellent diagnostic confidence and noise suppression, compared to only 50% with conventional reconstruction. The quantitative image quality improved significantly across all tracers, with SNR and CNR gains of 50–70%. Radiotracer dose reductions were particularly substantial in low-BMI patients (up to 41% for FDG), and the technologist exposure decreased for high-exposure roles. The daily patient throughput increased by an average of 4.84 cases. These findings support the robust integration of SubtlePET™ into routine clinical PET practice, offering improved efficiency, safety, and image quality without compromising lesion detectability. Full article

This paper presents a low-power data acquisition (DAQ) chip tailored for impulsive charge sensing, featuring a comparator-gated dynamic acquisition (CG-DAQ) architecture. A dynamic comparator triggers both the gain stage and a 12-bit successive-approximation register (SAR) analog-to-digital converter (ADC) through a shared timing path, enabling event-driven amplification and digitization. Programmable conversion gain ranging from 5 to 40 mV/pC is achieved by switching the sampling capacitance. Fabricated in TSMC 65 nm CMOS, the chip detects input charges from 0.01 to 36 pC, supports a signal bandwidth of 10 kHz to 100 kHz, and enables sampling rates up to 1 MS/s. It achieves an input-referred noise of 5.5 fC_rms and a peak signal-to-noise ratio (SNR) of 67 dB, all within a 54 μW power envelope and a compact 0.03 mm² core area. The proposed architecture facilitates accurate and energy-efficient charge-domain sensing for capacitive and piezoelectric sensor applications. Full article

This paper presents an analytical study of the bit error rate (BER) and signal-to-noise ratio (SNR) performance in simultaneous wireless information and power transfer (SWIPT) amplify-and-forward (AF) relaying systems over Rayleigh fading channels. A power-splitting (PS) protocol is employed at the energy-constrained relay to divide the received signal for concurrent energy harvesting and information processing. Closed-form and asymptotic BER expressions are derived based on exact and bounded moment-generating functions (MGFs), offering insights into how the SNR balance between the source–relay (SR) and relay–destination (RD) links influences system performance. An asymptotic BER expression further reveals that a SWIPT AF relay system can be interpreted as a generalized AF relaying model, sharing the same diversity order as conventional AF systems. Based on this interpretation, an optimization method for the PS factor is proposed, effectively reducing the BER by reinforcing the weaker link. Simulation results confirm the tightness of the derived expressions and the effectiveness of the optimization strategy. Moreover, the analytical framework is extended to multiple SWIPT relaying systems, where multiple relays operate with individually optimized PS ratios. For such configurations, approximations for the system BER, outage probability, and channel capacity are derived and validated. Results demonstrate that increasing the number of relays significantly improves system performance, and the proposed analysis accurately captures these performance gains under varying channel conditions. Full article

Large language models (LLMs) have recently demonstrated state-of-the-art performance in various natural language processing (NLP) tasks, achieving near-human levels in multiple language understanding challenges and aligning closely with the core principles of semantic communication Inspired by LLMs’ advancements in semantic processing, we propose LLM-SC, an innovative LLM-enabled semantic communication system framework which applies LLMs directly to the physical layer coding and decoding for the first time. By analyzing the relationship between the training process of LLMs and the optimization objectives of semantic communication, we propose training a semantic encoder through LLMs’ tokenizer training and establishing a semantic knowledge base via the LLMs’ unsupervised pre-training process. This knowledge base facilitates the creation of optimal decoder by providing the prior probability of the transmitted language sequence. Based on this, we derive the optimal decoding criteria for the receiver and introduce beam search algorithm to further reduce complexity. Furthermore, we assert that existing LLMs can be employed directly for LLM-SC without extra re-training or fine-tuning. Simulation results reveal that LLM-SC outperforms conventional DeepSC at signal-to-noise ratios (SNRs) exceeding 3 dB, as it enables error-free transmissions of semantic information under high SNRs while DeepSC fails to do so. In addition to semantic-level performance, LLM-SC demonstrates compatibility with technical-level performance, achieving approximately an 8 dB coding gain for a bit error ratio (BER) of ${10}^{-3}$ without any channel coding while maintaining the same joint source–channel coding rate as traditional communication systems. Full article

Interrupted sampling repeater jamming (ISRJ) is characterized by its coherent processing gains and flexible modulation techniques. ISRJ generates spurious targets along the range, which presents significant challenges to the radar systems. However, existing ISRJ countermeasure methods struggle to eliminate ISRJ signals without compromising the integrity of the real target signal, especially under low-signal-to-noise-ratio (SNR) conditions, resulting in a deteriorated sidelobe and diminished detection performance. We propose a complex-valued encoder–decoder network (CVEDNet) to address these challenges based on signal decomposition. This network offers an end-to-end ISRJ suppression approach, working on complex-valued time-domain signals without the need for additional preprocessing. The encoding and decoding structure suppresses noise components and obtains more compact echo feature representations through layer-by-layer compression and reconstruction. A stacked dual-branch structure and multi-scale dilated convolutions are adopted to further separate the echo signal and ISRJ based on high-dimensional features. A multi-domain combined loss function integrates the waveform and range-pulse-compression information to ensure the amplitude and phase integrity of the reconstructed echo waveform during the training process. The effectiveness of the proposed method was validated in terms of its jamming suppression capability, echo fidelity, and detection performance indicators under low-SNR conditions compared to conventional methods. Full article

Millimeter wave (mmWave) technology, with its ultra-high bandwidth and low latency, holds significant promise for vehicle-to-everything (V2X) communications. However, it faces challenges such as high propagation losses and limited coverage in dense urban vehicular environments. Intelligent Reflecting Surfaces (IRSs) help address these issues by enhancing mmWave signal paths around obstacles, thereby maintaining reliable communication. This paper introduces a novel Contextual Multi-Armed Bandit (C-MAB) algorithm designed to dynamically adapt beam and IRS selections based on real-time environmental context. Simulation results demonstrate that the proposed C-MAB approach significantly improves link stability, doubling average beam sojourn times compared to traditional SNR-based strategies and standard MAB methods, and achieving gains of up to four times the performance in scenarios with IRS assistance. This approach enables optimized resource allocation and significantly improves coverage, data rate, and resource utilization compared to conventional methods. Full article

This paper proposes a tree-based regression model for hybrid channel estimation in wireless sensor networks (WSNs) in generalized frequency division multiplexing (GFDM) over both visible light communication (VLC) and radio frequency (RF) links. The hybrid channel incorporates both additive white Gaussian noise (AWGN) and Rayleigh fading to mimic realistic environments. Traditional estimators, such as MMSE and LMMSE, often underperform in such heterogeneous and nonlinear conditions due to their analytical rigidity. To overcome these limitations, we introduce a data-driven approach using a decision tree regressor trained on 18,000 signal samples across 36 SNR levels. Simulation results show that support vector machine (SVM) achieved 91.34% accuracy and a BER of 0.0866 at 10 dB, as well as 96.77% accuracy with a BER of 0.0323 at 30 dB. Random forest achieved 91.01% accuracy and a BER of 0.0899 at 10 dB, as well as 97.88% accuracy with a BER of 0.0212 at 30 dB. The proposed tree model attained 90.83% and 97.63% accuracy with BERs of 0.0917 and 0.0237, respectively, at the corresponding SNR values. The distinguishing advantage of the tree model lies in its inference efficiency. It completes predictions on the test dataset in just 45.53 s, making it over three times faster than random forest (140.09 s) and more than four times faster than SVM (189.35 s). This significant reduction in inference time makes the proposed tree model particularly well suited for real-time and resource-constrained WSN scenarios, where fast and efficient estimation is often more critical than marginal gains in accuracy. The results also highlight a trade-off, where the tree model provides sub-optimal predictive performance while significantly reducing computational overhead, making it an attractive choice for low-power and latency-sensitive wireless systems. Full article

This research introduces and studies the performance of the $\alpha$-Fluctuating Nakagami-m model, which addresses the limitations of conventional models for wireless communications. For the assumed channel model, the research presents a complete first-order statistical description (including the probability density function (PDF), cumulative distribution function (CDF), moment generating function (MGF), and raw moments) and provides closed-form results for system performance (assessed in terms of outage probability, average bit error rate (ABER), and channel capacity). All of the expressions have the same numerical complexity as the base-line Fluctuating Nakagami-m model, and are accompanied by their high signal-to-noise ratio (SNR) asymptotics. The derived results helped to identify the amount of fading (AoF) and diversity/coding gain of the proposed channel model. In-depth analysis of the system performance was carried out for all possible fading channel parameter values. Numerical analysis of the proposed solutions demonstrated their high computational efficiency. The comparison with experimental results demonstrated that the model offers enhanced flexibility and better characterization of fading regimes. Numerical analysis and simulation results show a high degree of correspondence with the analytical work and help study the dependence of channel nonlinearity effects on overall system performance. Full article

Gastrointestinal (GI) tract diseases are among the most common diseases in the world, resulting in more than 8 million deaths. The majority of these deaths occur due to cancer or tumors. Early detection of these tumors can greatly lower the mortality rate. In this work, an implantable multiple-input-multiple-output (MIMO) antenna sensor is constructed for GI tract devices to detect the tumor. The implantable MIMO antenna sensor has two embedded antennas, each operating at 915 MHz. Both elements of the system are placed 0.6 mm apart from each other (edge-to-edge). The volume consumed by this design is measured to be 7 × 7 × 0.25 = 12.25 mm³. It occupies a very small volume due to miniaturization achieved using meandered resonating structures and a high-permittivity substrate. It maintains stable radiation performance (gain = −26.2 dBi at resonance). The antenna units are decoupled by maintaining a proper gap between them and adding a slot on the bottom side. An isolation level greater than 28.7 dB is achieved using these approaches. Since the MIMO system utilizes two antenna elements, its effectiveness is verified using MIMO parameters. At SNR = 20 dB, the channel capacity reaches 8.75 bps/Hz. The proposed antenna ensures high channel capacity and enables seamless communication while simultaneously acting as a sensor to monitor internal changes in the observed region. The frequency response change with variations in the permittivity of human tissue, enabling its sensing capability. Moreover, the antenna sensor maintains stable radiation and S-parameter performance throughout the sensing mechanism. Thus, the proposed solution is suitable for biomedical implants requiring both high-data-rate communication and sensing. Full article

In this paper, the uplink spectral efficiency performance of a massive MIMO system based on full-duplex relay communication is investigated in Rician fading channels. The relay station is equipped with a large number of antennas, while multiple source and destination nodes are located at both ends of the transceiver. Each source and destination node is equipped with a single antenna. The relay station adopts Maximum Ratio Combining/Maximum Ratio Transmission (MRC/MRT) and Equal Gain Combining/Equal Gain Transmission (EGC/EGT) schemes to perform linear preprocessing on the received signals. Approximate expressions for uplink spectral efficiency under both MRC/MRT and EGC/EGT schemes are derived, and the effects of antenna number, signal-to-noise ratio (SNR), and loop interference on spectral efficiency are analyzed. In addition, the impact of full-duplex and half-duplex modes on system performance is compared, and a hybrid relay scheme is proposed to maximize the total spectral efficiency by dynamically switching between full-duplex and half-duplex modes based on varying levels of loop interference. Finally, a novel power allocation scheme is proposed to maximize energy efficiency under given total spectral efficiency and peak power constraints at both the relay and source nodes. The results show that the impact of loop interference can be eliminated by using a massive receive antenna array, leading to the disappearance of inter-pair interference and noise. Under these conditions, the spectral efficiency of the system can be improved up to $2N$ times, while the transmission power of the user and relay nodes can be reduced to $1/N_{\mathrm{rx}}$ and $1/N_{\mathrm{tx}}$, respectively. Full article

This study focuses on a 4FSK-modulated ultraviolet (UV) communication system, introducing an innovative symbol-level maximum likelihood decoding approach based on Poisson statistics. A forward error correction (FEC) coding mechanism is integrated to enhance system robustness. Through Monte Carlo simulations, the proposed decoding scheme and the error correction performances of Reed–Solomon (RS) and Low-Density Parity-Check (LDPC) codes are evaluated in UV channels. Both RS and LDPC codes significantly improve the Bit Error Rate (BER), with LDPC codes achieving superior gains under low SNR conditions. Hardware implementation and field tests validate the decoding algorithm and LDPC-optimized 4FSK system. Under non-line-of-sight (NLOS) conditions (10–45° transmit elevation angle), stable 60 m communication with BER < 10⁻³ is achieved. In line-of-sight (LOS) scenarios, the system demonstrates 900 m range with BER < 10⁻³, highlighting practical applicability in challenging atmospheric environments. Full article