Physical Communication最新文献_第2页

A two-stage acoustic source localization algorithm incorporating frequency-dependent atmospheric absorption 结合频率相关大气吸收的两级声源定位算法

IF 2.2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2026-01-06 DOI: 10.1016/j.phycom.2026.102997

Tran Trong Tai, Truong Van Tuan

This paper presents a novel two-stage acoustic source localization algorithm that adapts to frequency-dependent atmospheric absorption in outdoor environments. The first stage leverages multiband energy distribution characteristics to efficiently obtain a coarse estimate of the source position. The second stage refines this estimate by optimizing a frequency-weighted steered response power (SRP) function. The absorption model is constructed based on the ANSI S1.26–2014 standard, incorporating realistic environmental parameters such as humidity, temperature, and pressure. Simulation results demonstrate that the proposed method achieves high localization accuracy approaching the Cramér–Rao lower bound (CRLB), while maintaining computational costs comparable to conventional SRP algorithms. The method is especially effective under long-range propagation (beyond 5 km) and strong absorption conditions, which are typical in applications such as security monitoring, UAV tracking, and gunshot detection. The main contribution lies in the proposed “absorption-aware” localization mechanism, which significantly enhances performance in real-world settings where spectral distortion due to atmospheric effects is substantial and often overlooked by existing methods.

本文提出了一种新的两级声源定位算法，该算法适应室外环境中频率相关的大气吸收。第一级利用多波段能量分布特性有效地获得源位置的粗略估计。第二阶段通过优化频率加权转向响应功率（SRP）函数来改进该估计。吸收模型是基于ANSI S1.26-2014标准构建的，纳入了现实的环境参数，如湿度、温度和压力。仿真结果表明，该方法在保持与传统SRP算法相当的计算成本的同时，获得了接近cram r - rao下界的较高定位精度。该方法在远程传播（超过5公里）和强吸收条件下特别有效，这在安全监控、无人机跟踪和枪击探测等应用中是典型的。主要贡献在于提出的“吸收感知”定位机制，该机制显著提高了实际环境中的性能，在实际环境中，由于大气效应造成的光谱失真很大，并且通常被现有方法忽略。

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引用次数: 0

Dynamic power allocation and a low-complexity deep learning based multi-user signal detection for NOMA-assisted vehicular communication 基于动态功率分配和低复杂度深度学习的noma辅助车辆通信多用户信号检测

IF 2.2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2026-01-06 DOI: 10.1016/j.phycom.2026.102993

A.O. Beena , S.M. Sameer

An investigation on the performance of a deep learning (DL)-based signal detection scheme over the traditional successive interference cancellation (SIC) method for a multi input - multi output (MIMO) based non-orthogonal multiple access (NOMA) downlink system for vehicular communication is presented in this paper. The proposed scheme is analyzed for a system with dynamic power allocation in a network experiencing Rayleigh fading channels. The DL-based MIMO-NOMA receiver detects signals from multiple users in a single-step process without explicitly estimating the channel state information (CSI). A long-short-term memory (LSTM) scheme is proposed for joint channel estimation and signal detection. Performance of the proposed DL-based detection scheme is compared with that of the traditional SIC based on least square (SIC-LS) and minimum mean square error (SIC-MMSE) estimations in terms of outage probability, sum rate, and symbol error rate (SER). Extensive simulations show that the proposed DL-based detection is more effective than conventional SIC techniques. SER studies revealed that there is 99.99 % detection accuracy at an SNR of 18 dB for the proposed DL-based detector. Also, at an SNR of 35 dB, the proposed DL-based detector with dynamic power allocation (DPA) achieves approximately 94 % lower outage probability for the weak user (Vehicle 2) and 83 % lower outage probability for the strong user (Vehicle 1) compared to the conventional SIC-based detectors. Furthermore, the SER performance of the DL-based scheme shows an improvement of up to 9 dB in SNR over the SIC-LS and SIC-MMSE schemes at a target SER of

10^{- 4}

under various channel conditions. Additionally, the proposed DL-based detector achieves a higher Jain’s fairness index compared to conventional SIC-based detection, ensuring a more equitable assurance of quality of service (QoS) among users. Also, the DL-based technique shows better adaptability to variations in cyclic prefix (CP) length and the number of pilot symbols in the frame. It has been observed that even in situations of severe inter-symbol interference (ISI) or Doppler shift, the proposed DL-based method outperforms the LS and MMSE-based conventional SIC detectors. Further, it is shown that the computational complexity of the proposed scheme is much lower than the traditional schemes.

针对车载通信多输入多输出（MIMO）非正交多址（NOMA）下行系统，研究了一种基于深度学习（DL）的信号检测方案优于传统的连续干扰消除（SIC）方法的性能。针对瑞利衰落信道网络中的动态功率分配系统，对该方案进行了分析。基于dl的MIMO-NOMA接收器在单步过程中检测来自多个用户的信号，而无需显式估计信道状态信息（CSI）。提出了一种用于信道估计和信号检测的长短期记忆（LSTM）方案。在中断概率、和率和符号错误率方面，将基于dl的检测方案与基于最小二乘（SIC- ls）和最小均方误差（SIC- mmse）估计的传统SIC检测方案的性能进行了比较。大量的仿真表明，基于dl的检测比传统的SIC技术更有效。SER研究表明，在信噪比为18 dB的情况下，所提出的基于dl的检测器的检测精度为99.99%。此外，在35 dB的信噪比下，与传统的基于sic的探测器相比，基于动态功率分配（DPA）的基于dl的探测器对弱用户（车辆2）的中断概率降低了约94%，对强用户（车辆1）的中断概率降低了83%。此外，在各种信道条件下，当目标SER为10−4时，基于dl的方案的信噪比比SIC-LS和SIC-MMSE方案提高了9 dB。此外，与传统的基于sic的检测相比，本文提出的基于dl的检测器实现了更高的Jain公平性指数，从而确保了用户之间更公平的服务质量（QoS）保证。此外，基于dl的技术对帧中循环前缀（CP）长度和导频符号数量的变化具有更好的适应性。研究表明，即使在严重的码间干扰（ISI）或多普勒频移的情况下，基于dl的方法也优于基于LS和mmse的传统SIC检测器。结果表明，该算法的计算复杂度大大低于传统算法。

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引用次数: 0

Adaptive underreach protection relay using zero-mode band time-delay dynamics 基于零模带时延动态的自适应欠伸保护继电器

IF 2.2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2026-01-05 DOI: 10.1016/j.phycom.2026.102998

Fusheng Li , Fan Zhang , Bin Qian , Xiaodong Zhou , Yi Luo , Xiangyong Feng

Traditional three-stage power system protection faces inherent conflicts among its four features. When protection settings avoid mal-operation during severe external faults without boundary elements, internal fault sensitivity is compromised. This limitation causes potential over-reach or under-reach mis-operation in conventional under-reach schemes. In this paper, a novel underreach local measurement-based protection scheme is proposed using zero-mode band time-delay dynamics. First, abrupt voltage changes detected in real-time by a Kalman filter initiate the proposed protection. Second, wavelet packet decomposition processes the zero-mode reverse traveling wave signal across multiple frequency bands. Two specific bands are extracted, Hilbert-transformed, and their first wave-head arrival time difference is calculated as the adaptive characteristic criterion. This value dynamically adjusts to fault locations. The theoretical travel time difference of arrival (TTDoA) serves as the dynamic action threshold. Simulations confirm accurate identification of abrupt changes and activation criteria for single-phase-to-ground faults at varying distances. The scheme reliably protects over 80% of the line length, precisely distinguishes internal from external faults, and operates correctly under high-resistance grounding faults.

传统的三级电力系统保护在其四个特征之间面临着内在的冲突。当保护设置避免了外部严重故障而没有边界元素时的误操作时，内部故障灵敏度就会降低。这一限制在传统的欠伸方案中会导致潜在的过伸或欠伸误操作。本文提出了一种基于零模带时延动态的欠伸局部测量保护方案。首先，通过卡尔曼滤波器实时检测电压突变，启动所提出的保护。其次，小波包分解处理跨多个频带的零模反向行波信号。提取两个特定波段，进行希尔伯特变换，计算其第一波头到达时间差作为自适应特征判据。该值根据故障位置动态调整。理论到达时间差（TTDoA）作为动态动作阈值。模拟验证了在不同距离上单相接地故障突变和激活判据的准确识别。该方案能够可靠地保护80%以上的线路长度，能够准确地区分内部故障和外部故障，在高阻接地故障下也能正常工作。

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引用次数: 0

Range estimation in FMCW SDR radars with unsynchronized receivers 非同步接收机FMCW SDR雷达的距离估计

IF 2.2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2026-01-05 DOI: 10.1016/j.phycom.2026.103000

Juan Carlos Martinez Quintero, Edward Paul Guillen Pinto

Frequency-modulated continuous wave (FMCW) radars are widely used in applications such as automotive sensing and remote monitoring. Implementing these systems on software-defined radio (SDR) platforms offers flexibility but also introduces challenges, such as synchronization and isolation between the transmitter and receiver, processing large volumes of data, and digital signal processing. This paper proposes two methods for range detection in FMCW radars using unsynchronized SDR platforms for the transmitter and receiver. In both approaches, a direct signal between the transmitting and receiving antennas, positioned a few centimeters apart, serves as a reference. The first method employs FFT on the squared magnitude of the received signal to estimate the range, while the second method adapts the classical matched filter technique. The proposed methods also enable the receiver to operate with a lower sampling rate than the transmitter. Tests were conducted using LimeSDR platforms with a frequency deviation of 58 MHz (corresponding to the sweep bandwidth), an output power of 10 dBm, and a central frequency of 2.4 GHz, targeting distances between 5.27 m and 28.45 m. The first method achieved an average error of 3.5% with matching sampling rates, while the second method reduced the error to 2.15%. When halving the receiver's sampling rate, the average errors increased to 4.28% and 2.33%, respectively. These results demonstrate the potential of the proposed methods for flexible and efficient radar implementations on SDR platforms.

调频连续波（FMCW）雷达广泛应用于汽车传感和远程监控等领域。在软件定义无线电（SDR）平台上实现这些系统提供了灵活性，但也带来了挑战，例如发射器和接收器之间的同步和隔离，处理大量数据和数字信号处理。本文提出了两种基于非同步SDR平台的FMCW雷达距离检测方法。在这两种方法中，发射天线和接收天线之间相隔几厘米的直接信号作为参考。第一种方法采用FFT对接收信号的幅值平方估计距离，第二种方法采用经典的匹配滤波技术。所提出的方法还使接收机能够以比发射机更低的采样率工作。测试使用LimeSDR平台进行，频率偏差为58 MHz（对应于扫描带宽），输出功率为10 dBm，中心频率为2.4 GHz，目标距离为5.27 m至28.45 m。在采样率匹配的情况下，第一种方法的平均误差为3.5%，第二种方法的平均误差为2.15%。当接收机采样率减半时，平均误差分别增加到4.28%和2.33%。这些结果证明了所提出的方法在SDR平台上灵活有效地实现雷达的潜力。

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引用次数: 0

Fading parameter estimation for backscattering signals using method of moments 基于矩量法的后向散射信号衰落参数估计

IF 2.2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2026-01-02 DOI: 10.1016/j.phycom.2025.102983

Ge Song, Yunfei Chen, Zhuangkun Wei

Backscattering communication has gained significant interest due to its low power consumption and wide applicability in the Internet of Things (IoT) and wireless sensing systems. Accurate parameter estimation of backscattering signals is essential for improving system performance and optimizing communication efficiency. This article focuses on the channel model parameter estimation using the method of moments for backscattering signals in three different fading channels: Nakagami-m, Rayleigh, and Rician. A total of five different cases are considered using these distributions. Moment-based estimators are derived for key channel model parameters in each case, and their accuracy is verified through MATLAB simulation by comparing them with the case without backscattering and the case without noise. The results indicate that the moment-based estimators provide accurate parameter estimation in various fading conditions.

后向散射通信由于其低功耗和在物联网（IoT）和无线传感系统中的广泛适用性而引起了人们的极大兴趣。准确的后向散射信号参数估计是提高系统性能和优化通信效率的关键。本文重点研究了在Nakagami-m、Rayleigh和ricar三种不同衰落信道中，利用矩量法对后向散射信号进行信道模型参数估计。使用这些分布总共考虑了五种不同的情况。对每种情况下的关键信道模型参数推导了基于矩量的估计，并通过MATLAB仿真与无后向散射和无噪声情况进行了比较，验证了其准确性。结果表明，基于矩的估计器在各种衰落条件下都能提供准确的参数估计。

引用次数: 0

Deep reinforcement learning-based computation offloading and resource allocation in user-centered UAV-MEC 基于深度强化学习的以用户为中心的无人机- mec计算卸载与资源分配

IF 2.2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2026-01-02 DOI: 10.1016/j.phycom.2026.102992

Zhongqiang Luo , Wenjie Wu , Xiang Dai , Qiang Han

Using unmanned aerial vehicles (UAVs) equipped with servers to assist multi-access edge computing (MEC) can provide computing support in areas with insufficient network coverage or hotspots. However, UAV-MEC systems under traditional cellular network support are susceptible to inter-cell interference and shadow fading, resulting in increased task processing delays and higher energy consumption. To address these challenges, this paper proposes a user-centric UAV-MEC architecture (UCUAV-MEC). This architecture integrates a user-centered transmission method, dynamically adjusting the UAV and access point (AP) to provide flexible computing and communication support for user equipment (UE). Additionally, dual connectivity (DC) technology is employed to enable parallel processing, alleviating resource competition and transmission interference. The delay and energy minimization problem is then formulated by jointly optimizing the UAV position, offloading decision, power allocation, and computing resource allocation within the UCUAV-MEC framework. To solve this problem, this paper proposes a multi-agent collaborative optimization scheme based on deep reinforcement learning (DRL) and convex optimization. Simulation results demonstrate that, compared to traditional UAV-MEC, the proposed optimization scheme based on UCUAV-MEC can reduce delay by up to 72.26% and energy consumption by 73.29%.

使用配备服务器的无人机（uav）来辅助多接入边缘计算（MEC），可以在网络覆盖不足或热点地区提供计算支持。然而，在传统蜂窝网络支持下的无人机- mec系统容易受到蜂窝间干扰和阴影衰落的影响，导致任务处理延迟增加和能耗增加。为了解决这些挑战，本文提出了一种以用户为中心的无人机- mec架构（UCUAV-MEC）。该架构集成了以用户为中心的传输方式，动态调整无人机和接入点（AP），为用户设备（UE）提供灵活的计算和通信支持。此外，采用双连接（DC）技术实现并行处理，减轻资源竞争和传输干扰。在UCUAV-MEC框架下，通过联合优化无人机位置、卸载决策、功率分配和计算资源分配，制定时延和能量最小化问题。针对这一问题，本文提出了一种基于深度强化学习（DRL）和凸优化的多智能体协同优化方案。仿真结果表明，与传统的无人机- mec相比，基于UCUAV-MEC的优化方案可将时延降低72.26%，能耗降低73.29%。

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引用次数: 0

CSI-Based Location-Independent human activity recognition using spatiotemporal feature alignment 基于csi的时空特征定位人类活动识别

IF 2.2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2026-01-02 DOI: 10.1016/j.phycom.2025.102991

Yong Zhang , Changchuang Zhao , Weiwei Jiang , Yiqi Zhou

Addressing cross-location human activity recognition (HAR) using Channel State Information (CSI), existing methods have made progress in spatiotemporal feature extraction and global coarse-grained fusion. However, they generally overlook spatiotemporal feature alignment, failing to provide classifiers with consistent spatiotemporal representations across locations, which ultimately limits system generalization. To address this challenge, this paper proposes a CSI-based Spatiotemporal Feature Aligned system (CSI-STA). The system employs a dual-branch architecture to extract spatial and temporal features and incorporates a novel Spatiotemporal Feature Alignment (SFA) module to enhance feature consistency modeling. This module performs alignment at both spatial and channel levels, effectively reducing structural discrepancies between feature types. Subsequently, a multi-branch attention-based feature fusion module performs fine-grained integration of the aligned features, strengthening complementary representations and thereby improving cross-location HAR capability. Systematic evaluation on the Widar3.0 dataset demonstrates that our model achieves a superior average accuracy of 92.04% in recognizing six activity categories under cross-location settings, confirming its superior generalization performance.

利用通道状态信息（CSI）解决跨位置人类活动识别问题，现有方法在时空特征提取和全局粗粒度融合方面取得了进展。然而，它们通常忽略了时空特征对齐，无法为分类器提供跨位置一致的时空表征，最终限制了系统的泛化。为了解决这一问题，本文提出了一种基于csi的时空特征对齐系统（CSI-STA）。该系统采用双分支结构提取时空特征，并引入了一种新颖的时空特征对齐（spatial - temporal Feature Alignment， SFA）模块来增强特征一致性建模。该模块在空间和通道级别进行对齐，有效减少特征类型之间的结构差异。随后，基于多分支注意力的特征融合模块对对齐的特征进行细粒度集成，增强互补表示，从而提高跨位置HAR能力。在Widar3.0数据集上的系统评价表明，该模型在跨位置设置下识别6种活动类别的平均准确率达到了92.04%，证实了其优越的泛化性能。

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引用次数: 0

Channel estimation in RIS-NOMA enabled 5G/B5G systems using meta-learned spatio-temporal graph neural networks 基于元学习时空图神经网络的基于RIS-NOMA的5G/B5G系统信道估计

IF 2.2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2026-01-01 DOI: 10.1016/j.phycom.2025.102984

Aryan Mittal , Sandeep Kumar Singh , Amit Agarwal

Accurate and adaptive channel estimation is essential for realizing intelligent and energy-efficient reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) systems. However, conventional learning-based channel estimation (CE) models often struggle to generalize across varying SNR, frequency, and environmental dynamics. This paper proposes a novel First-Order Model-Agnostic Meta-Learning (FOMAML)-based Spatio-Temporal Graph Neural Network (STGNN) framework for robust and frequency-adaptive CE in RIS-assisted multi-user systems. The proposed model jointly captures spatial dependencies among RIS elements and temporal channel variations using edge-conditioned graph convolutions and gated recurrent units (GRUs), while meta-learning enables rapid adaptation to unseen conditions with minimal fine-tuning. A synthetic yet realistic dataset covering multiple carrier frequencies (3.5 GHz, 28 GHz, and 60 GHz) and SNR ranges (10-30 dB) is generated to evaluate performance. Simulation results demonstrate that the proposed FOMAML-STGNN achieves superior generalization, attaining up to 15% lower NRMSE and higher R² scores compared to conventional CNN-BiLSTM and full MAML-GNN baselines, while maintaining reduced computational complexity. Moreover, system-level evaluations confirm that RIS-NOMA consistently outperforms RIS-OMA in achievable sum rate, and controlled ablation studies validate the scalability and robustness of the proposed framework under varying SNRs, RIS sizes, and user densities. These results highlight the suitability of the proposed approach for real-time and scalable CE in beyond-5G (B5G) networks.

准确的自适应信道估计是实现可重构智能曲面（RIS）辅助非正交多址（NOMA）系统智能化、高能效的关键。然而，传统的基于学习的信道估计（CE）模型往往难以在不同的信噪比、频率和环境动态中进行泛化。本文提出了一种新的基于一阶模型不可知元学习（faml）的时空图神经网络（STGNN）框架，用于ris辅助多用户系统的鲁棒和频率自适应CE。所提出的模型使用边缘条件图卷积和门控循环单元（gru）共同捕获RIS元素之间的空间依赖关系和时间通道变化，而元学习能够以最小的微调快速适应未知条件。生成了一个合成但现实的数据集，涵盖多个载波频率（3.5 GHz， 28 GHz和60 GHz）和信噪比范围（10-30 dB），以评估性能。仿真结果表明，与传统的CNN-BiLSTM和完整的mail - gnn基线相比，所提出的FOMAML-STGNN实现了卓越的泛化，在保持较低的计算复杂度的同时，NRMSE降低了15%，R2得分更高。此外，系统级评估证实，RIS- noma在可实现的求和速率方面始终优于RIS- oma，控制消融研究验证了所提出框架在不同信噪比、RIS大小和用户密度下的可扩展性和鲁棒性。这些结果突出了所提出的方法在超5g （B5G）网络中实时和可扩展CE的适用性。

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引用次数: 0

CFO-based physical-layer authentication for integrated sensing and communication under dynamic time resources 动态时间资源下基于cfo的集成传感与通信物理层认证

IF 2.2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2025-12-31 DOI: 10.1016/j.phycom.2025.102977

Tuanwei Tian , Ke Shao , Jing Yang , Jinlong Zhang , Hao Deng

This study proposes an integrated sensing and communication (ISAC) framework that incorporates physical-layer authentication through a time-slotted protocol with delayed authentication. To begin with, we propose a time-slotted framework in which the carrier frequency offset (CFO) features obtained during the previous authentication phase are used as a reference for current real-time transmitter verification. This structure effectively decouples the sensing and security operations while ensuring both high-precision target localization and robust authentication. Then, we establish a unified analytical model that quantitatively connects the Cramér-Rao Bound (CRB) for time-of-arrival and direction-of-arrival estimation with CFO-based authentication performance, explicitly characterizing the trade-off between sensing accuracy and authentication security under joint time-power resource constraints. Finally, extensive simulations validate the analytical framework and demonstrate significant performance improvements over conventional schemes in terms of authentication probability, detection accuracy, and localization error across various signal-to-noise ratios and mobility conditions. The robustness of the proposed scheme under practical hardware imperfections is also verified.

本研究提出了一种集成传感和通信（ISAC）框架，该框架通过具有延迟身份验证的时隙协议合并了物理层身份验证。首先，我们提出了一个时隙框架，其中在前一个认证阶段获得的载波频率偏移（CFO）特征被用作当前实时发射机验证的参考。该结构有效地解耦了传感和安全操作，同时保证了高精度的目标定位和鲁棒性认证。然后，我们建立了一个统一的分析模型，定量地将到达时间和到达方向估计的cram r- rao边界（CRB）与基于cfo的认证性能联系起来，明确表征了在联合时间-功率资源约束下感知精度和认证安全性之间的权衡。最后，广泛的仿真验证了分析框架，并证明了在各种信噪比和移动条件下，在认证概率、检测精度和定位误差方面比传统方案有显著的性能改进。验证了该方案在实际硬件缺陷下的鲁棒性。

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引用次数: 0

A low-complexity expectation propagation symbol detector for DCT-OTFS systems DCT-OTFS系统的低复杂度期望传播符号检测器

IF 2.2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2025-12-31 DOI: 10.1016/j.phycom.2025.102989

Ruijie Jiang, Kaixuan Qiao, Chao Liu, Weina Yuan

Orthogonal Time-Frequency Space (OTFS) modulation has emerged as a robust solution for high-mobility wireless communication scenarios, owing to its delay-Doppler domain representation and resilience to time-varying channels. Among OTFS variants, the Discrete Cosine Transform-based OTFS (DCT-OTFS) system offers improved energy compaction, reduced interference spread, and enhanced sparsity compared to conventional OTFS, which facilitates more efficient low-complexity detection. In this letter, we propose a novel Expectation Propagation (EP) symbol detection algorithm tailored to the DCT-OTFS framework. To the best of our knowledge, this is the first work to investigate EP detection within DCT-OTFS. By leveraging the structural advantages of the DCT-precoded delay-Doppler domain, the proposed detector achieves more accurate posterior approximations through moment matching while maintaining reduced per-iteration complexity. Simulation results under the Extended Vehicular A (EVA) channel model show that the proposed EP algorithm significantly outperforms traditional OTFS-based detection schemes-including Linear Minimum Mean Square Error (LMMSE), Message Passing (MP), and standard EP-in terms of bit error rate (BER), especially in high-mobility and high-dimensional scenarios. These results confirm that incorporating DCT into the OTFS framework not only enhances EP detection accuracy, but also exploits energy compaction and sparsity to improve robustness and efficiency across diverse system configurations.

正交时频空间（OTFS）调制由于其延迟多普勒域表示和对时变信道的弹性而成为高移动性无线通信场景的强大解决方案。在OTFS变体中，与传统OTFS相比，基于离散余弦变换的OTFS （DCT-OTFS）系统提供了更好的能量压缩，减少了干扰传播，增强了稀疏性，从而实现了更高效的低复杂度检测。在这封信中，我们提出了一种针对DCT-OTFS框架的新颖期望传播（EP）符号检测算法。据我们所知，这是第一个在DCT-OTFS中研究EP检测的工作。利用dct预编码延迟多普勒域的结构优势，该检测器通过矩匹配获得更精确的后验近似，同时保持较低的每次迭代复杂度。在扩展车辆A （EVA）信道模型下的仿真结果表明，所提出的EP算法在误码率（BER）方面显著优于传统的基于otfs的检测方案，包括线性最小均方误差（LMMSE）、消息传递（MP）和标准EP，特别是在高移动和高维场景下。这些结果证实，将DCT纳入OTFS框架不仅可以提高EP检测精度，还可以利用能量压缩和稀疏性来提高不同系统配置的鲁棒性和效率。

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引用次数: 0