基于OFDM的雷达通信一体化共波形设计

为了减小通信信息对一体化波形模糊函数的影响,以连续波信号为基础,提出了一种基于扩频的多OFDM符号调制的雷达通信一体化信号模型,并分别从波形以及雷达模糊函数的角度对其进行分析。通过对通信信息预扩频后采用OFDM调制的方法,可显著改善一体化信号的模糊函数性能,降低距离维旁瓣。理论分析表明,一体化信号的距离旁瓣由所用伪随机编码序列的自相关、互相关特性及调制的通信信息共同决定,为波形的性能改进提供了指导方向。仿真结果表明扩频因子、子载波数目、数据长度的不同取值对雷达通信一体化信号的距离维模糊函数的旁瓣的影响,为优化一体化信号的性能提供依据。设计的雷达通信一体化信号模型可有效改善对通信信息和多普勒频移的敏感性,满足雷达探测波形要求。     

Waveform Design for Multi-Target Detection Based on Two-Stage Information Criterion

Parameter estimation accuracy and average sample number (ASN) reduction are important to improving target detection performance in sequential hypothesis tests. Multiple-input multiple-output (MIMO) radar can balance between parameter estimation accuracy and ASN reduction through waveform diversity. In this study, we propose a waveform design method based on a two-stage information criterion to improve multi-target detection performance. In the first stage, the waveform is designed to estimate the target parameters based on the criterion of single-hypothesis mutual information (MI) maximization under the constraint of the signal-to-noise ratio (SNR). In the second stage, the objective function is designed based on the criterion of MI minimization and Kullback–Leibler divergence (KLD) maximization between multi-hypothesis posterior probabilities, and the waveform is chosen from the waveform library of the first-stage parameter estimation. Furthermore, an adaptive waveform design algorithm framework for multi-target detection is proposed. The simulation results reveal that the waveform design based on the two-stage information criterion can rapidly detect the target direction. In addition, the waveform design based on the criterion of dual-hypothesis MI minimization can improve the parameter estimation performance, whereas the design based on the criterion of dual-hypothesis KLD maximization can improve the target detection performance.     

一种裕量最大的高速载波比特功率分配算法

在国家电网公司Q/GDW 1379.4-2013技术规范对智能电能表载波通信单元功率消耗的限定要求下,为保障低压远程电力集抄台区本地通信链路的传输速率稳定可靠,给智能用电实时交互平台提供带宽支持,提出了一种裕量最大的高速载波比特功率分配算法。该算法以兼并窄带载波和宽带载波优势的G3-PLC技术为应用对象,使用约束注水法限定各子载波的传输比特数,根据时变的电力信道增益自适应调整比特功率分配方案,并响应通信服务质量QoS对系统误码率和目标传输速率的要求,结合信噪比门限算法对约束注水法进行界定和优化,得到了功率裕量最大化的表达式。仿真结果表明：提出的算法优于低反馈开销算法,能够适应低压电力线信道。在系统目标传输速率为200kbps时,达到了最大裕量值20,极大程度地降低了通信单元调制解调芯片实际的功率消耗。     

机载多脉冲激光雷达目标信号模拟器的研究

研究多脉冲激光雷达目标信号模拟器,用以评估回波数字信号处理算法及其实现平台的性能。结合信号能量与波形关系指出由激光雷达方程建立回波信号波形模型是不充分的。对空中目标回波脉冲展宽进行建模及数值仿真,得到不同倾角、目标距离及目标尺寸下的波形展宽时间数据。根据展宽前后能量保持不变的性质,利用高斯脉冲函数特征参数求解建立回波波形数学模型。基于目标距离误差及目标回波噪声统计特性建立回波仿真模型,提出了依信噪比发生回波观测信号的方法。对比两种模拟器的信噪比误差、最小模拟信噪比、连续工作时间等性能指标,总结了新型模拟器的优势。     

非线性调频信号激励下非线性系统的最优共振响应

非线性调频(NLFM)信号在雷达、通信、信号处理中应用广泛,该类信号所激励下的非线性系统响应有着丰富的信息,通过共振来增强NLFM信号具有一定实际意义与价值.本文主要研究了受到不同类型NLFM信号所激励的非线性系统共振现象,提出了实时尺度变换方法来处理高频NLFM信号,克服人为选择造成信号输出响应较差的缺点.同时,提出实时谱放大因子作为共振评价指标,准确评价NLFM信号激励下的系统共振响应,讨论系统参数对系统最优共振响应的影响,参数选择在合理的区间内即可实现最优共振响应,不仅实现信号特征的大幅增强,还保持信号时频特征的连续性.最后,将实时尺度变换方法和固定尺度变换方法进行对比,说明所提方法处理NLFM信号的优越性.     

Multi-task learning approach for modulation and wireless signal classification for 5G and beyond: Edge deployment via model compression

Future communication networks must address the scarce spectrum to accommodate extensive growth of heterogeneous wireless devices. Efforts are underway to address spectrum coexistence, enhance spectrum awareness, and bolster authentication schemes. Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, secure communications, among others. Consequently, comprehensive spectrum awareness on the edge has the potential to serve as a key enabler for the emerging beyond 5G (fifth generation) networks. State-of-the-art studies in this domain have (i) only focused on a single task – modulation or signal (protocol) classification – which in many cases is insufficient information for a system to act on, (ii) consider either radar or communication waveforms (homogeneous waveform category), and (iii) does not address edge deployment during neural network design phase. In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks based multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks while considering heterogeneous wireless signals such as radar and communication waveforms in the electromagnetic spectrum. The proposed MTL architecture benefits from the mutual relation between the two tasks in improving the classification accuracy as well as the learning efficiency with a lightweight neural network model. We additionally include experimental evaluations of the model with over-the-air collected samples and demonstrate first-hand insight on model compression along with deep learning pipeline for deployment on resource-constrained edge devices. We demonstrate significant computational, memory, and accuracy improvement of the proposed model over two reference architectures. In addition to modeling a lightweight MTL model suitable for resource-constrained embedded radio platforms, we provide a comprehensive heterogeneous wireless signals dataset for public use.     

Covert underwater acoustic communications

Low probability of detection (LPD) communications are conducted at a low received signal-to-noise ratio (SNR) to deter eavesdroppers to sense the presence of the transmitted signal. Successful detection at intended receiver heavily relies on the processing gain achieved by employing the direct-sequence spread-spectrum (DSSS) technique. For scenarios that lack a sufficiently low SNR to maintain LPD, another metric, referred to as low probability of interception (LPI), is of interest to protect the privacy of the transmitted information. If covert communications take place in underwater acoustic (UWA) environments, then additional challenges are present. The time-varying nature of the UWA channel prevents the employment of a long spreading waveform. Furthermore, UWA environments are frequency-selective channels with long memory, which imposes challenges to the design of the spreading waveform. In this paper, a covert UWA communication system that adopts the DSSS technique and a coherent RAKE receiver is investigated. Emphasis is placed on the design of a spreading waveform that not only accounts for the transceiver structure and frequency-selective nature of the UWA channel, but also possesses a superior LPI. The proposed techniques are evaluated using both simulated and SPACE'08 in-water experimental data.     

声矢量锥形阵的高阶累积量波达方向估计

为解决信源在较低信噪比情况下的测向分辨率问题,提出阵列可扩展的声矢量锥形阵测向算法。算法基于四阶累积量的阵列扩展和高斯噪声抑制特性,计算声矢量传声器不同输出分量的四阶累积量,使其在三维方向上扩展与原阵型结构相同的虚拟阵,从而构造包含角度信息的旋转不变矩阵进行测向。推导给出了算法的克拉美罗界,理论分析了算法性能受信噪比、采样快拍以及入射声源俯仰角的影响。仿真实验验证了该算法较常规声矢量阵ESPRIT算法有更优的噪声抑制能力及更高分辨的DOA估计性能。      

Improved light uniformity and SNR employing new LED distribution pattern for indoor applications in VLC system

In this paper, a new LED arrangement design using 16 LED array in an indoor VLC system is introduced. The performance of the proposed design is investigated and compared with other different (previously introduced) designs using the same number of LED arrays. Our aim is to increase light uniformity and decrease power and SNR fluctuations across the room without increasing number of LED arrays. The received power and SNR were calculated to evaluate the system performance, while the root mean square delay spread was calculated to analyze the intersymbol interference impact on the system performance. The power standard deviation (STD) was calculated as well to measure uniformity of distributed power. The received power fluctuations under our proposed LED lamp arrangement were reduced from 4.46 to 0.93 dBm, the STD is decreased from 1.01 to 0.23, and the SNR fluctuations were reduced from 9 to 1.8 dB leading to almost identical communication across the entire room with high quality.     

Deep learning-based energy-efficient relay precoder design in MIMO-CRNs

This paper investigates the problem of energy efficient relay precoder design in multiple-input multiple-output cognitive relay networks (MIMO-CRNs). This is a non-convex fractional programming problem, which is traditionally solved using computationally expensive optimization methods. In this paper, we propose a deep learning (DL) based approach to compute an approximate solution. Specifically, a deep neural network (DNN) is employed and trained using offline computed optimal solution. The proposed scheme consists of an offline data generation phase, an offline training phase, and an online deployment phase. The numerical results show that the proposed DNN provides comparable performance at significantly lower computational complexity as compared to the conventional optimization-based algorithm that makes the proposed approach suitable for real-time implementation.     

Scheme for nonlinear frequency modulated signal cancelling system

A cancelling system for the four nonlinear frequency modulated (NLFM) signal (i.e. Taylor window, Tangent-based, Combination linear frequency modulation (LFM) and tan-FM, Stepped NLFM) is presented. It is mainly composed of a digital radio frequency memory (DRFM) and a field programmable gate array (FPGA) chip etc. The received signals are stored and reproduced by DRFM, and the system delay time is controlled by the FPGA chip. According to the target's radar cross section (RCS), the radar echo cancelling wave will be generated by the FPGA and DRFM on signal processing. The effect of error on the cancelling wave is analysed and the method for reducing nonlinear phase errors is presented. Theoretical analysis and simulation show that the system effectively reduces the signal power received by the radar receiver. Numerical simulation results show that about 11.3 dB target gain reduction can be achieved under the condition of large deviation.     

基于多项式调频Fourier变换的信号分量提取方法

为了从含有噪声的混合信号中有效提取各个信号分量, 提出一种基于多项式调频Fourier变换的分量提取方法. 通过研究Fourier变换和分数阶Fourier变换的信号能量积累方式及变换基函数的时频表示, 提出利用时频平面上的多项式调频曲线族代替Fourier变换和分数阶Fourier 变换的调频直线族, 将变换的适用范围扩展到非线性调频信号. 采用粒子群智能优化算法搜索调频曲线族的最优多项式参数, 使混合信号中的某一分量在多项式调频Fourier域上能量谱集中. 最后对能量谱集中的分量进行窄带滤波, 并利用多项式调频逆Fourier变换重构信号分量. 仿真实验结果表明, 该方法不仅能够提取混合信号中的线性调频分量, 还能够实现非线性调频分量的能量谱集中、信号分离和时频特征提取.     

A signal optimization strategy for next generation navigation and communication integration applications

For 6G and future communication technologies, integration of navigation and communication has become a hot research topic, which can greatly improve the transmission data rate and can be applied to self-driving vehicles. In former research, a novel integrated navigation and communication signal has been designed by combining traditional global navigation satellite system (GNSS) and cyclic code shift keying (CCSK) signals. In this paper, we design an enhancement strategy for this signal, optimizing the number of cyclic shifts of the CCSK signal, which can make larger values of cross-correlation function align with the peak value of self-correlation function to overcome the cross-correlation interference and improve communication performance. Simulation results validate that the proposal not only can avoid interference between navigation and communication signals, but also can improve the signal detection probability and reduce the transmit error rate. Taking Beidou B1I signal as an example, when the signal to noise ratio (SNR) is set to -27 dB, the detection probabilities for the navigation and communication signals increase about 11.4% and 13.5%, respectively, while the symbol error rate (SER) for the navigation and communication signals decrease about 16.3% and 17.8%, respectively.     

Cross-layer design based hybrid MAC protocol for cognitive radio network

A minimal interrupted communication link setup is the primary objective of the MAC layer. The MAC layer is responsible for accessing the communication channel. At MAC, a control channel is used in the selection of collision free paths for data transfer. Therefore, the design of the control channel plays a pivotal role in achieving desired QoS in cognitive radio (CR) technology. Various schemes of control channel design help the CR network (CRN) to obtain better performance. The reported work focuses on a hybrid MAC protocol. The novelty of the scheme lies in the process of hybridization. A cross-layer framework is proposed for hybridization. The cross-layering has been done between network and MAC layer to achieve hybridization between different control channel design approaches. The broad categorization of control channel designs is between licensed in-band and dedicated unlicensed out-band approaches. In the in-band control channel design approach, the opportunistic use of data channel as control channel fulfills the decorum of CR technology. As soon as the primary user activity rises in the data channels, the in-band approach suffers from poor performance. On the other hand, the dedicated unlicensed out-band control channel design approach provides global coverage and all-time availability but suffers from channel saturation and intruder attacks. Interference in the control channel limits the use of out-band design. This motivates authors to develop a hybrid MAC protocol that can float between licensed in-band design and unlicensed out-band design to access the control channel. The hybridization is possible by sharing a primary user free channel list (PCL) among CR nodes. In conventional hybrid MAC protocols, the PCL is shared as a control beacon in the channel. Extra packet requirement as control beacon affects the performance of CR scenario. The proposed cross-layer design based hybrid MAC protocol avoids the need of an extra control beacon for PCL transmission. Further, the hybridization helps in achieving advantages of both in-band and out-band control channel design approaches. The simulation results show that the proposed hybrid MAC protocol performs satisfactorily in terms of packet delivery ratio, average throughput, average delay and control overhead. The performances are also tested in the worst scenarios.     

一种快速有效的正弦波信号频率估计方法*

针对I-Rife算法和Fang算法计算量增大,低信噪比时性能下降,该文提出了一种快速有效的正弦信号频率精确估计方法,利用两点细化的频谱值估计频率偏移量,不需要判别频率修正方向,降低了算法复杂度和计算量;分析了频偏估计的偏差。仿真结果表明,该算法的整体性能优于I-Rife算法和改进的Fang算法,在保证性能的同时,提高了算法的稳定性和实用性。     

扩频原理在聚合物光纤性能测量中的应用

提出了一种将扩频通信原理用于聚合物光纤性能测量的新方法,使用伪随机序列对注入测量系统的光信号进行调制,实现输入光信号的扩展频带,输出光信号经光电转换后,用伪随机序列对信号进行解扩处理,根据扩频原理,解扩后的信号的信噪比会有较大提高.对测量系统的性能进行了仿真分析并与实验结果进行了比较,结果表明该方法可以精确地测量信噪比低于-20 dB的有用信号波形.     

Parameter allocation of parallel array bistable stochastic resonance and its application in communication systems

In this paper, we propose a parameter allocation scheme in a parallel array bistable stochastic resonance-based communication system(P-BSR-CS) to improve the performance of weak binary pulse amplitude modulated(BPAM) signal transmissions. The optimal parameter allocation policy of the P-BSR-CS is provided to minimize the bit error rate(BER)and maximize the channel capacity(CC) under the adiabatic approximation condition. On this basis, we further derive the best parameter selection theorem in realistic communication scenarios via variable transformation. Specifically, the P-BSR structure design not only brings the robustness of parameter selection optimization, where the optimal parameter pair is not fixed but variable in quite a wide range, but also produces outstanding system performance. Theoretical analysis and simulation results indicate that in the P-BSR-CS the proposed parameter allocation scheme yields considerable performance improvement, particularly in very low signal-to-noise ratio(SNR) environments.     

Bound values for Hall conductivity of heterogeneous medium under quantum Hall effect conditions

Bound values for Hall conductivity under quantum Hall effect (QHE) conditions in inhomogeneous medium has been studied. It is shown that bound values for Hall conductivity differ from bound values for metallic conductivity. This is due to the unusual character of current percolation under quantum Hall effect conditions.      

水声通信低复杂度最大似然联合均衡译码方法

现有Turbo均衡采用迭代联合均衡译码结构,这一迭代过程在信噪比高于某一门限后才能产生正向的迭代增益,当信道码间干扰较为严重或者采用高阶调制方式时,该信噪比门限较高;同时,现有Turbo均衡的性能还受限于其交织器长度,故当码块长度较短时,其性能并不理想。针对以上问题,提出了一种基于Spinal码的联合均衡译码方法。首先,该方法采用非迭代的最大似然联合均衡译码结构,有效避免了Turbo均衡迭代结构所带来的信噪比门限高问题;其次,该方法采用信息混合能力更强的Spinal码,有助于提高码块长度受限时的误码率性能;最后,该方法利用Spinal码可以有效增加相似信息序列间汉明距离的特点,将联合均衡译码时间复杂度降低为仅随信息序列长度与信道响应长度呈线性增长,解决了本方法难以实际应用于实时水声通信的难题。在抚仙湖湖试中,同样实现速率为3.34kbps数据通信,该方法的误码率优于Turbo均衡方法两个数量级以上。时变水声信道下的仿真以及湖试结果表明,该方法在码长受限的情况下仍在复杂水声信道中表现出更好性能,其所需的最低输入信噪比显著低于现有Turbo均衡方法。      

Swarmtrust: A swarm optimization-based approach to enhance trustworthiness in smart homes

As smart home networks become increasingly complex and dynamic, maintaining trust between the various devices and services is crucial. Existing trust management approaches face challenges such as high computational overhead and difficulty in selecting optimal parameters. To address these challenges, we propose a new method for trust management in smart home networks based on swarm optimization, a principle of swarm intelligence. Our proposed approach is based on swarm optimization, which is a principle of swarm intelligence that optimizes communication patterns and manages trust values within the network. In SwarmTrust, the nodes in the smart home network are represented by particles, and the positions and velocities of these particles are adjusted to optimize communication patterns and reduce the computational burden on nodes. This optimization is achieved by evaluating the trust values of each node and adjusting the communication patterns accordingly. The proposed method integrates trust management with several distinct parameters and selects the parameters optimally to maintain efficiency. The proposed approach also addresses optimization challenges by monitoring communication patterns and managing trust values in a distributed manner, which reduces the computational burden on nodes and maintains efficiency. To evaluate the effectiveness of our proposed approach, we conducted simulation experiments and compared the performance of our approach with existing methods. Our results showed notable improvements in terms of computational efficiency and optimized computation.