An improved wavelet packet domain least mean square （IWPD-LMS） based adaptive muhiuser detection algorithm is proposed. The algorithm employs the wavelet packet transform to rewhiten the input data, and chooses the ...An improved wavelet packet domain least mean square （IWPD-LMS） based adaptive muhiuser detection algorithm is proposed. The algorithm employs the wavelet packet transform to rewhiten the input data, and chooses the best wavelet packet basis according to a novel convergence contribution function rather than the conventional Shannon entropy. The theoretic analyses show that the inadequacy of the eigenvalue spread of the tap-input correlation matrix is ameliorated, thus the convergence performance is improved greatly. The simulation result of convergence performance and bit error rate（BER） performance as a function of the signal power to noise power ratio（SNR） are presented finally to prove the validity of the proposed algorithm.展开更多
The QR-RLS-CMOE algorithm which was applied to synchronous DS/CDMA systems in AWGN channel, is modified and applied to asynchronous DS/CDMA systems in multi-path fading channel in this paper. Computer simulation exper...The QR-RLS-CMOE algorithm which was applied to synchronous DS/CDMA systems in AWGN channel, is modified and applied to asynchronous DS/CDMA systems in multi-path fading channel in this paper. Computer simulation experiences show that the asynchronous QR-RLS-CMOE (A-QR-RLS-CMOE) blind multiuser detection algorithm can well cancel multiple access interference and overcome multipath fading, and has a good anti-near-far effect in the case of τ<<bT .展开更多
This paper proposes some low complexity algorithms for active user detection(AUD),channel estimation(CE)and multi-user detection(MUD)in uplink non-orthogonal multiple access(NOMA)systems,including single-carrier and m...This paper proposes some low complexity algorithms for active user detection(AUD),channel estimation(CE)and multi-user detection(MUD)in uplink non-orthogonal multiple access(NOMA)systems,including single-carrier and multi-carrier cases.In particular,we first propose a novel algorithm to estimate the active users and the channels for single-carrier based on complex alternating direction method of multipliers(ADMM),where fast decaying feature of non-zero components in sparse signal is considered.More importantly,the reliable estimated information is used for AUD,and the unreliable information will be further handled based on estimated symbol energy and total accurate or approximate number of active users.Then,the proposed algorithm for AUD in single-carrier model can be extended to multi-carrier case by exploiting the block sparse structure.Besides,we propose a low complexity MUD detection algorithm based on alternating minimization to estimate the active users’data,which avoids the Hessian matrix inverse.The convergence and the complexity of proposed algorithms are analyzed and discussed finally.Simulation results show that the proposed algorithms have better performance in terms of AUD,CE and MUD.Moreover,we can detect active users perfectly for multi-carrier NOMA system.展开更多
Without any prior information about related wireless transmitting nodes,joint estimation of the position and power of a blind signal combined with multiple co-frequency radio waves is a challenging task.Measuring the ...Without any prior information about related wireless transmitting nodes,joint estimation of the position and power of a blind signal combined with multiple co-frequency radio waves is a challenging task.Measuring the signal related data based on a group distributed sensor is an efficient way to infer the various characteristics of the signal sources.In this paper,we propose a particle swarm optimization to estimate multiple co-frequency"blind"source nodes,which is based on the received power data measured by the sensors.To distract the mix signals precisely,a genetic algorithm is applied,and it further improves the estimation performance of the system.The simulation results show the efficiency of the proposed algorithm.展开更多
盲道和盲道障碍物是影响盲人出行安全的重要因素,现有算法只对盲道分割和盲道障碍物检测单独处理,效率低且计算量大。针对上述问题,文中提出了一种基于深度学习的多任务识别算法。该算法通过骨干网络提取公共特征,将提取的特征经过SPP(S...盲道和盲道障碍物是影响盲人出行安全的重要因素,现有算法只对盲道分割和盲道障碍物检测单独处理,效率低且计算量大。针对上述问题,文中提出了一种基于深度学习的多任务识别算法。该算法通过骨干网络提取公共特征,将提取的特征经过SPP(Spatial Pyramid Pooling)和FPN(Feature Pyramid Networks)网络融合特征后,分别传入分割网络和检测网络完成盲道分割和盲道障碍物检测的任务。为了让盲道分割更平整,引入修正损失函数。为了提高障碍物检测召回率,将检测网络的NMS(Non Maximum Suppression)替换为Soft-NMS。实验结果表明,该算法分割部分MIoU(Mean Intersection over Union)、MPA(Mean Pixel Accuracy)分别达到了93.52%、95.29%,检测部分mAP(mean Average Precision)、mAP@0.5以及mAP@0.75分别达到了75.58%、91.58%和74.82%。相较于使用SegFormer网络进行盲道分割和RetinaNet网络进行盲道障碍物检测,该算法在精度提升的同时速度也提升73.72%,FPS(Frames Per Secon)达到了18.52。相比于其他对比算法,该算法在速度和精度上也有一定的提升。展开更多
基金Sponsored by the National"863"Program Projects (2007AA012293)
文摘An improved wavelet packet domain least mean square （IWPD-LMS） based adaptive muhiuser detection algorithm is proposed. The algorithm employs the wavelet packet transform to rewhiten the input data, and chooses the best wavelet packet basis according to a novel convergence contribution function rather than the conventional Shannon entropy. The theoretic analyses show that the inadequacy of the eigenvalue spread of the tap-input correlation matrix is ameliorated, thus the convergence performance is improved greatly. The simulation result of convergence performance and bit error rate（BER） performance as a function of the signal power to noise power ratio（SNR） are presented finally to prove the validity of the proposed algorithm.
文摘The QR-RLS-CMOE algorithm which was applied to synchronous DS/CDMA systems in AWGN channel, is modified and applied to asynchronous DS/CDMA systems in multi-path fading channel in this paper. Computer simulation experiences show that the asynchronous QR-RLS-CMOE (A-QR-RLS-CMOE) blind multiuser detection algorithm can well cancel multiple access interference and overcome multipath fading, and has a good anti-near-far effect in the case of τ<<bT .
基金supported by National Natural Science Foundation of China(NSFC)under Grant No.62001190The work of J.Wen was supported by NSFC(Nos.11871248,61932010,61932011)+3 种基金the Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme(2019),Guangdong Major Project of Basic and Applied Basic Research(2019B030302008)the Fundamental Research Funds for the Central Universities(No.21618329)The work of P.Fan was supported by National Key R&D Project(No.2018YFB1801104)NSFC Project(No.6202010600).
文摘This paper proposes some low complexity algorithms for active user detection(AUD),channel estimation(CE)and multi-user detection(MUD)in uplink non-orthogonal multiple access(NOMA)systems,including single-carrier and multi-carrier cases.In particular,we first propose a novel algorithm to estimate the active users and the channels for single-carrier based on complex alternating direction method of multipliers(ADMM),where fast decaying feature of non-zero components in sparse signal is considered.More importantly,the reliable estimated information is used for AUD,and the unreliable information will be further handled based on estimated symbol energy and total accurate or approximate number of active users.Then,the proposed algorithm for AUD in single-carrier model can be extended to multi-carrier case by exploiting the block sparse structure.Besides,we propose a low complexity MUD detection algorithm based on alternating minimization to estimate the active users’data,which avoids the Hessian matrix inverse.The convergence and the complexity of proposed algorithms are analyzed and discussed finally.Simulation results show that the proposed algorithms have better performance in terms of AUD,CE and MUD.Moreover,we can detect active users perfectly for multi-carrier NOMA system.
文摘Without any prior information about related wireless transmitting nodes,joint estimation of the position and power of a blind signal combined with multiple co-frequency radio waves is a challenging task.Measuring the signal related data based on a group distributed sensor is an efficient way to infer the various characteristics of the signal sources.In this paper,we propose a particle swarm optimization to estimate multiple co-frequency"blind"source nodes,which is based on the received power data measured by the sensors.To distract the mix signals precisely,a genetic algorithm is applied,and it further improves the estimation performance of the system.The simulation results show the efficiency of the proposed algorithm.
文摘盲道和盲道障碍物是影响盲人出行安全的重要因素,现有算法只对盲道分割和盲道障碍物检测单独处理,效率低且计算量大。针对上述问题,文中提出了一种基于深度学习的多任务识别算法。该算法通过骨干网络提取公共特征,将提取的特征经过SPP(Spatial Pyramid Pooling)和FPN(Feature Pyramid Networks)网络融合特征后,分别传入分割网络和检测网络完成盲道分割和盲道障碍物检测的任务。为了让盲道分割更平整,引入修正损失函数。为了提高障碍物检测召回率,将检测网络的NMS(Non Maximum Suppression)替换为Soft-NMS。实验结果表明,该算法分割部分MIoU(Mean Intersection over Union)、MPA(Mean Pixel Accuracy)分别达到了93.52%、95.29%,检测部分mAP(mean Average Precision)、mAP@0.5以及mAP@0.75分别达到了75.58%、91.58%和74.82%。相较于使用SegFormer网络进行盲道分割和RetinaNet网络进行盲道障碍物检测,该算法在精度提升的同时速度也提升73.72%,FPS(Frames Per Secon)达到了18.52。相比于其他对比算法,该算法在速度和精度上也有一定的提升。
