Search Results (210)

In Time Division Duplex (TDD) Direct-Sequence Code Division Multiple Access (DS/CDMA) architectures, Pre-Rake filtering serves as a powerful transmitter-side strategy to alleviate receiver hardware constraints by leveraging channel reciprocity. Nevertheless, rapid channel fluctuations induced by high Doppler spreads critically undermine this reciprocity assumption. This failure is primarily driven by the unavoidable latency between uplink reception and downlink transmission, leading to severe performance deterioration. To address these challenges and enhance system robustness in modern high-speed scenarios, we propose an improved hybrid transceiver architecture. This scheme integrates multiplexed Pre-Rake processing with a Matched Filter-based Rake receiver and employs a Minimum Mean Square Error (MMSE) equalizer to suppress the severe Inter-Symbol Interference (ISI) and Multi-User Interference (MUI). Furthermore, we conduct a comparative analysis of channel estimation methods tailored for a 10 Mbps high-speed transmission environment.Our investigation reveals that while complex quadratic interpolation is often prioritized in low-data-rate studies, simple averaging is sufficient and even superior in high-speed communications. This is because the shortened slot duration allows simple averaging to effectively track channel variations while avoiding the noise overfitting associated with higher-order interpolation. The simulation results demonstrate that the proposed MMSE-optimized architecture achieves superior Bit Error Rate (BER) performance, providing a practical and computationally efficient solution for next-generation mobile networks. Full article

Satellites supporting IoT connectivity may need to serve a large population of LoRa terminals, where collisions among packets using the same spreading factor (SF) can severely degrade uplink reliability. The ALOHA-based access used in LEO-IoT leads to frequent collisions under massive terminal access, which limits system capacity. Conventional signal separation methods that rely on the capture effect typically require a sufficiently large power difference between colliding signals. However, due to the channel characteristics of LEO links, this condition is often difficult to satisfy. We propose ConvLoRa, a collision demodulation method for co-SF LoRa uplink signals in LEO-IoT based on a fully convolutional neural network (FCN). To improve robustness to synchronization deviations, ConvLoRa uses an up-chirp in the preamble as a reference for feature matching, and employs data augmentation to emulate synchronization deviations during training. In addition, a multi-task design is adopted to estimate the payload length with minimal introduction of extra network parameters. Experiments show that ConvLoRa achieves lower demodulation bit error rate (BER) under collision conditions compared with baselines, including CoRa and SIC-based receivers. Under the condition of a two-signal collision with SNR = −9 dB and SF = 8, the BER of the proposed method is 21% that of CoRa and 28% that of the SIC-based method. Full article

This paper proposed a reconfigurable intelligent surface (RIS)-empowered reference-modulated differential chaos shift keying (RM-DCSK) wireless powered communication (WPC) system. As a noncoherent chaotic communication scheme, the proposed system exploits the reference reuse property of RM-DCSK, where the reference signal simultaneously carries data information, thereby improving spectral efficiency while maintaining noncoherent and channel-estimation-free reception with low receiver circuit complexity. Furthermore, RIS is utilized to reconfigure the propagation environment and mitigate the path loss effect of WPC links. At the user equipment (UE), a harvest–store–use (HSU) energy harvesting and finite-buffer model is developed, and a threshold-based on/off transmission policy is adopted to enable sustainable uplink transmission. To quantify the gain of energy buffering and management, a bufferless baseline system is further established. Closed-form bit error rate (BER) expressions are obtained under multi-path Rayleigh fading channels for both the proposed RIS-RM-DCSK-WPC system and bufferless baseline system. Finally, simulation results validate the analysis and demonstrate that the proposed system achieves superior BER performance compared with representative benchmarks, including existing RIS-aided DCSK-WPC, RM-DCSK-WPC, and bufferless RIS-RM-DCSK-WPC systems. Full article

Non-orthogonal multiple access (NOMA) has been identified as one of the key technologies for 6G capacity and latency gains. However, existing implementation challenges of the NOMA technique, related to carrier, timing, and phase offsets, successive interference cancellation (SIC) error propagation, packet loss dynamics, and host to software defined radios processing jitter, create obstacles in the practical implementation of NOMA. This paper bridges the gap between theory and hardware by introducing a complete two-user NOMA transmit–receive chain on a low-cost ADALM-Pluto software defined radio (SDR) platform. The proposed implementation integrates matched filtering, offset estimation and correction, SIC with waveform reconstruction and subtraction, and reliability reinforcement via rate-1/2 convolutional coding with Viterbi decoding. We have performed a complete validation of the proposed design in both downlink and uplink modes. We collected data regarding the packet-level and system-related metrics, such as end-to-end latency, bit error rate (BER), and success rate. Moreover, we demonstrate the implementation of the uplink NOMA without need for expensive GPS-disciplined oscillators by leveraging the Pluto Rev-C dual-transmit channels that share a common oscillator. We present detailed experimental results at 915 MHz with BPSK modulation for the downlink performance, and also show a full implementation of the uplink NOMA. We observe excellent reliability for the downlink setup and good reliability for the uplink system. Full article

Affine Frequency Division Multiplexing (AFDM) has been proposed for future high-mobility communication scenarios. However, existing AFDM channel estimation methods suffer significant performance degradation under fractional Doppler conditions due to path energy dispersion. To address this issue, we propose a deep learning network that adaptively learns path energy dispersion through a 1D processing module and a Transformer block, based on the diagonal reconstruction of the AFDM effective channel matrix. 1D processing module employs convolutions with different kernel sizes to extract pilot features, and Transformer block models vary energy dispersion patterns. The proposed method does not require prior knowledge of the number of paths and the assumption of distinct path delays. Simulation results demonstrate that at a Signal-to-Noise Ratio (SNR) of 25 dB, the proposed method achieves up to a 4 dB gain in Normalized Mean Square Error (NMSE) and an 6 dB improvement in Bit Error Rate (BER) over existing traditional methods under fractional Doppler conditions. Full article

In Orthogonal Time–Frequency Space (OTFS) systems, signal detection algorithms based on convolutional neural networks (CNNs) suffer from insufficient feature extraction and are limited by local mixing. Additionally, fixed convolution kernels struggle to match the sparsity and non-stationary characteristics of OTFS signals in the delay-Doppler domain, resulting in slow convergence and high training costs. We do not stop at simply integrating more features outside the existing CNN framework. Instead, we go deeper into the network and replace the fixed convolution kernels with wavelet convolution layers that have time–frequency-adaptive capabilities. This fundamental change allows the network to more intrinsically match the physical characteristics of OTFS signals in the delay-Doppler domain, thereby achieving excellent detection performance while also gaining faster convergence efficiency. Therefore, this paper proposes a signal detection method using an adaptive wavelet convolutional neural network (AWCNN). The approach replaces the first convolutional layer of a standard CNN with an adaptive wavelet layer, which leverages the time–frequency localization properties of Sym4 wavelet kernels along with learnable scaling and translation factors. This enhances the network’s ability to extract sparse features from OTFS signals. Additionally, the model incorporates both the original received signal and preliminary estimates from the message-passing (MP) algorithm as input features, enriching the dataset and further improving detection performance. Experimental results demonstrate that the AWCNN model achieves superior convergence efficiency compared to the CNN model, which attains a bit error rate (BER) comparable to that of the CNN algorithm at a low signal-to-noise ratio of 2 dB, operating without the need for pilot-assisted channel state information acquisition. Full article

In communication-centric integrated sensing and communication (ISAC) systems, passive radars exploit existing communication signals of opportunity for sensing. To compute delay-Doppler or range–velocity maps (DDMs and RVMs, respectively), modern orthogonal frequency division multiplexing (OFDM)-based sensing systems use the channel frequency response (CFR) originally estimated in communication receivers for equalization. In OFDM-based passive radars utilizing 4G LTE or 5G NR waveforms, CFR estimation typically relies only on reference signals. However, simulation-based studies that assume a priori knowledge of user data symbols indicate potential performance gains when incorporating user data and other downlink channels. In this work, we present an experimental evaluation of an OFDM-based passive radar that jointly utilizes all commonly present components of the 5G NR downlink waveform: synchronization signals (PSS and SSS), broadcast and control channels (PBCHs and PDCCHs, respectively), data channels (PDSCHs), and reference signals (PBCH DM-RSs, PDCCH DM-RSs, PDSCH DM-RSs, and CSI-RSs). Our results show that utilizing user data from fully occupied 5G downlink signals, under the assumption of full knowledge of PDSCH locations, significantly improves both the probability of detection (POD) and the peak height, measured by the peak-to-noise-floor ratio (PNFR), compared with pilot-only sensing. Since perfect knowledge of the user data payload is not assumed, we estimate the transmission bit error rate (BER) and analyze its impact on sensing performance. Finally, we investigate more realistic scenarios in which only a subset of PDSCH resource element locations is known, as in practical 5G deployments, and evaluate how partial data location knowledge affects the POD and PNFR under different BER conditions. Full article

In shallow-water high-speed mobile acoustic channels, severe non-uniform Doppler effects pose significant challenges to traditional equalization methods based on linear and time-invariant channel assumptions. Existing approaches typically rely on inverse compensation strategies, which are inadequate for handling path-dependent nonlinear Doppler distortions and fail to accurately reflect the underlying physical propagation process. To address these limitations, this paper proposes a forward reference-sample equalization (FRSE) method. Based on estimated channel parameters, forward channel modeling is performed for all possible transmitted symbols to generate a reference-sample matrix that is consistent with channel-induced distortions. At the receiver, a least-squares decision criterion is employed to match the received signal with the closest reference sample, thereby enabling reliable demodulation. Simulation results demonstrate that, at a high relative speed of 30 kn and a signal-to-noise ratio (SNR) of 8 dB, the proposed method achieves a bit error rate (BER) of $1.75\times {10}^{-4}$, significantly outperforming conventional equalization methods. Furthermore, sea trial experiments validate the robustness of the proposed approach in real shallow-water environments. By avoiding signal inversion, FRSE achieves improved detection reliability and strong robustness against non-uniform Doppler effects, highlighting its potential for practical underwater acoustic communication applications. Full article

To address the phase noise issue in terahertz OFDM system, this paper proposes a dual-branch deep learning phase noise compensation network named AdaPhaseNet. The Transformer branch of this network leverages the powerful modeling capability of Transformers for long-range dependencies to achieve long-range phase noise estimation and compensation, while the CNN branch is employed for local signal enhancement. Finally, an optimized signal is output through a confidence-driven adaptive fusion module. For experimental validation of the algorithm, we constructed a photonic terahertz communication system comprising 10 km of fiber and 5 m of wireless transmission. Experimental results show that, compared with multiple baseline models, AdaPhaseNet achieves relative BER reductions ranging from 37.0% to 57.9% and EVM gains ranging from 1.4 dB to 3.2 dB. Full article

This work presents the design and experimental validation of a 2 × 2 MIMO communication system assisted by a directly modulated analog radio-over-fiber (A-RoF) fronthaul, targeting low-complexity connectivity solutions for underserved/remote regions. The study details the complete end-to-end architecture, including a wireless access segment to complement the 20-km optical fronthaul link. The system is implemented on an software defined radio (SDR) platform using GNU Radio 3.7.11, running on Ubuntu 18.04 with kernel 4.15.0-213-generic. It also employs adaptive modulation driven by real-time signal-to-noise ratio (SNR) estimation to keep bit error rate (BER) close to zero while maximizing throughput. Performance is characterized over 20 km of single-mode fiber (SMF) using coarse wavelength division multiplexing (WDM) and assessed through root mean square error vector magnitude (${\mathrm{EVM}}_{\mathrm{RMS}}$), throughput, and spectral integrity. The results identify an optimum radio-frequency drive region around $-16$ dBm enabling high-order modulation (e.g., 256-QAM), whereas RF input powers above approximately $-10$ dBm increase ${\mathrm{EVM}}_{\mathrm{RMS}}$ due to nonlinearity in the RF front-end/low-noise amplifier (LNA) and direct modulation stage, forcing the adaptive scheme to reduce modulation order and throughput. Over the optical-power sweep, when the incident optical power exceeds approximately $-8$ dBm, the system reaches ∼130 Mbps (24-MHz channel) with ${\mathrm{EVM}}_{\mathrm{RMS}}$ approaching ∼1%, highlighting the need for careful joint tuning of RF drive, optical launch power, and wavelength allocation across transceivers. Finally, the integrated access link employs diplexers for transmitter/receiver separation in a 2 × 2 configuration with 2.8 m antenna separation and low channel correlation, demonstrating a 10 m proof-of-concept range and enabling end-to-end spectrum/EVM/throughput observations across the full communication chain. Full article

This paper proposes a complex fast recursive least-squares (FRLS) channel-estimation algorithm for single-carrier electromagnetic (EM) communications across the seawater–air interface, where severe attenuation and multipath cause strong SNR fluctuations. By redesigning the input-data structure and using forward–backward joint estimation, FRLS reduces the per-iteration complexity from the quadratic cost of classical RLS to a linear form (14L + 20 operations per iteration, where L is the channel length). Simulations under representative one- to three-path channels show that FRLS achieves the lowest steady-state mean-square deviation (MSD) at low SNR, outperforming LMS, IPNLMS, RLS, and PRLS. Offshore experiments further validate the practicality: after MMSE equalization, FRLS yields higher OSNR and improves the BER distribution, demonstrating an effective accuracy–complexity trade-off for hardware-constrained cross-medium EM links. Full article

Millimeter-wave (mmWave) massive multiple-input, multiple-output (MIMO) systems are a cornerstone technology for integrated sensing and communication (ISAC) in sixth-generation (6G) mobile networks. These systems provide high-capacity backhaul while simultaneously enabling high-resolution environmental sensing. However, accurate channel estimation remains highly challenging due to intrinsic noise sensitivity and clustered sparse multipath structures. These challenges are particularly severe under limited pilot resources and low signal-to-noise ratio (SNR) conditions. To address these difficulties, this paper proposes HASwinNet, a deep learning (DL) framework designed for mmWave channel denoising. The framework integrates a hierarchical Swin Transformer encoder for structured representation learning. It further incorporates two complementary branches. The first branch performs sparse token extraction guided by angular-domain significance. The second branch focuses on angular-domain refinement by applying discrete Fourier transform (DFT), squeeze-and-excitation (SE), and inverse DFT (IDFT) operations. This generates a mask that highlights angularly coherent features. A decoder combines the outputs of both branches with a residual projection from the input to yield refined channel estimates. Additionally, we introduce an angular-domain perceptual loss during training. This enforces spectral consistency and preserves clustered multipath structures. Simulation results based on the Saleh–Valenzuela (S–V) channel model demonstrate that HASwinNet achieves significant improvements in normalized mean squared error (NMSE) and bit error rate (BER). It consistently outperforms convolutional neural network (CNN), long short-term memory (LSTM), and U-Net baselines. Furthermore, experiments with reduced pilot symbols confirm that HASwinNet effectively exploits angular sparsity. The model retains a consistent advantage over baselines even under pilot-limited conditions. These findings validate the scalability of HASwinNet for practical 6G mmWave backhaul applications. They also highlight its potential in ISAC scenarios where accurate channel recovery supports both communication and sensing. Full article

The rapid development of the Internet of Things (IoT) has imposed increasingly stringent power consumption requirements on receiver design. Unlike phase-locked loops (PLLs), free-running oscillators eliminate power-hungry loop circuitry. However, the inherent frequency offset of free-running oscillators introduces uncertainty in the intermediate frequency (IF), preventing the receiver from aligning with the desired channel. To address this, we present a reconfigurable channel receiver employing a free-running oscillator and frequency estimation for low-power IoT applications. The proposed receiver first captures a specific preamble sequence corresponding to the desired channel through multiple parallel sub-channels implemented in the digital baseband (DBB), which collectively cover the expected IF frequency range. The desired IF frequency is estimated using the proposed preamble-based frequency estimation (PBFE) algorithm. After frequency estimation, the receiver switches to a single-channel mode and tunes its passband center frequency to the estimated IF frequency, enabling high-sensitivity data reception. Measurement results demonstrate that the PBFE algorithm achieves reliable frequency estimation with a minimum IF signal-to-noise ratio (SNR) of 2 dB and an estimation error below 22 kHz. In single-channel mode, with a residual frequency offset of 30 kHz, an 8-point energy accumulation decoding scheme achieves a bit error rate (BER) of 10⁻³ at an IF SNR of 5.2 dB. Compared with the case of the original 50 kHz IF frequency offset, the required SNR is improved by 4.1 dB. Full article

In-band full-duplex (IBFD) communication systems offer a promising means of improving spectral efficiency by enabling simultaneous transmission and reception on the same frequency channel. Despite this advantage, self-interference (SI) remains a major challenge to their practical deployment. Among the different SI cancellation (SIC) techniques, this paper focuses on digital SIC methodologies tailored for multiple-input multiple-output (MIMO) wireless transceivers operating under digital beamforming architectures. Two distinct digital SIC approaches are evaluated, employing a generalized memory polynomial (GMP) model augmented with Itô–Hermite polynomial basis functions and a phase-normalized neural network (PNN) to effectively model the nonlinearities and memory effects introduced by transmitter and receiver hardware impairments. The robustness of the SIC is further evaluated under both single off-line training and closed-loop real-time adaptation, employing estimation techniques such as least squares (LS), least mean squares (LMS), and fast Kalman (FK) for model coefficient estimation. The performance of the proposed digital SIC techniques is evaluated through detailed simulations that incorporate realistic power amplifier (PA) characteristics, channel conditions, and high-order modulation schemes. Metrics such as error vector magnitude (EVM) and total bit error rate (BER) are used to assess the quality of the received signal after SIC under different signal-to-interference ratio (SIR) and signal-to-noise ratio (SNR) conditions. The results show that, for time-variant scenarios, a low-complexity adaptive SIC can be realized using a GMP model with FK parameter estimation. However, in time-invariant scenarios, an open-loop SIC approach based on PNN offers superior performance and maintains robustness across various modulation schemes. Full article

This paper presents an orthogonal time–frequency space (OTFS)-based integrated sensing and communication (ISAC) framework for vehicular-to-base-station (V2B) scenarios, where a synthetic road environment models vehicular mobility and multipath propagation with explicit ground truth. In the sensing stage, OTFS probing signals with Gray-coded quadrature amplitude modulation (QAM) are processed via inverse symplectic finite Fourier transform (ISFFT) and cyclic prefix orthogonal frequency-division multiplexing (CP-OFDM). The receiver applies cyclic prefix (CP) removal, fast Fourier transform (FFT), and symplectic finite Fourier transform (SFFT) to extract delay–Doppler (DD) responses. Channel estimation uses time–frequency least squares (TF-LS), robust background suppression, constant false alarm rate (CFAR) detection, and non-maximum suppression (NMS), yielding Precision = 0.79, Recall = 0.84, and F1 = 0.82. Communication decoding employs per-bin least squares, minimum mean-squared error (MMSE) equalization, and Gray-mapped QAM demapping. Across ten frames at 20 dB SNR, the system decoded $1.887\times {10}^8$ bits with $1.575\times {10}^5$ errors, producing a bit error rate (BER) of $8.34\times {10}^{-4}$. Error vector magnitude (EVM) analysis reports mean = 0.30%, median = 0.06%, confirming constellation stability. Random Forest (RF) and imbalanced RF (IRF) classifiers trained on augmented DD payloads achieve Precision = 0.94, Recall = 0.87, and F1 = 0.92. Results validate OTFS-ISAC as a robust framework for V2B communication and sensing. Full article