压缩感知与超宽带信道建模的研究
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摘要
超宽带(UWB)无线信道建模是超宽带系统设计的前提,也是评估系统性能的关键因素,最近兴起的压缩感知(CS)理论在超宽带信道估计与建模中有良好的应用潜力。本文选题来源于国家重大科技专项等项目,具有重要的理论意义及应用价值。
本文在深入研究压缩感知理论的基础上,依据超宽带信道实测数据,对压缩感知在超宽带信道建模和接收机设计方面的应用进行了深入研究,主要完成了以下具有创新性的研究成果：
针对IEEE802.15.3a/4a信道模型不完全适用于中国超宽带信道的局限性,本文依据中国超宽带频谱规范,对室内、室外和行业12个典型场景进行了大规模超宽带信道测量。基于信道实测数据和分簇信道模型,提出了一种新颖的基于小波分析的自动分簇算法,并完成了信道模型参数拟合。通过模型评测,验证了所提的超宽带信道模型比IEEE信道模型更符合中国超宽带技术的实际应用情况。
依据实测数据反映的信道特性,针对严重遮挡非视距(NLOS)情况下的办公室环境提出了一种新颖的分段双指数信道模型。与IEEE802.15.4a信道模型相比,所提的办公室NLOS模型在时延扩展和多径个数等关键信道特性上与实测数据更加符合。另外,本文还设计一种基于压缩感知的UWB信道多径分析模型。通过深入分析揭示出在可接受的信号重构概率条件下,CS所需的采样点数与UWB信道平均多径个数之间所存在的确定函数关系。
针对超宽带信道测量要求高采样速率的难题,本文提出了一种基于压缩感知的解卷积算法。对于频域和时域两种测量方式,分别利用频域窗函数对应的时域脉冲和模板信号构造出参数化的波形字典,以增强时域测量信号的稀疏表达,并通过贪婪重构算法对时域测量信号进行解卷积,最终得到离散信道响应估计。相比传统解卷积算法CLEAN算法,所提的基于CS的解卷积算法在获得相近解卷积性能的前提下,有效降低了测量系统所需的观测点数。通过对比分析,验证了在采用特定字典的情况下,匹配追踪(MP)算法等价于CLEAN算法。考虑超宽带信道所呈现出的特有的频率依赖性失真,本文设计出一种多模板压缩感知解卷积算法,进一步提高了解卷积性能。
针对经典超宽带信道估计要求高采样速率的缺陷,本文提出了一种基于部分信道状态信息的压缩感知信道估计算法。通过利用平均功率延时剖面(APDP)信息对字典原子进行加权,并依据概率式匹配追踪算法(PMP)的原理对重构算法中原子选择方式进行改进,不仅有效降低了接收机按Nyquist速率所需的采样数,同时设计的重构算法可充分利用信道先验信息,从而显著提高在低信噪比情况下的重构性能。仿真结果表明,所提出的新算法不仅能有效降低传统接收机所需的采样点数,且在复杂度保持不变的前提下,相比其他重构算法,其信道估计性能得以显著提升。
针对脉冲超宽带(UWB-IR)接收机高采样率导致的硬件复杂度过高的问题,提出了一种基于压缩感知的非相干检测算法。通过充分发掘UWB-IR接收信号分块稀疏的特点,利用分块重构算法BOMP算法,大幅降低了计算复杂度；同时,进一步利用APDP先验信息辅助设计字典以提高检测性能。仿真结果表明,相比于能量检测方法,新方案不仅提高了非相干检测的性能,还降低了接收机采样速率。
论文最后对全文进行了总结,并对压缩感知技术在超宽带系统的应用发展方向进行了展望。
Ultra Wide-band (UWB) wireless channel modeling is a prerequisite for UWB system design. Compressed sensing (CS) has a great potential in the UWB channel estimation and modeling. This thesis is supported by Important National Science&Technology Specific Projects and attempts to make a contribution to the theory and application of UWB system.
In this paper, based on UWB channel measurement data, the basic principle of compressed sensing technology and its application in UWB system channel modeling and receiver design are investigated. Several creative algorithms are developed in this thesis.
The IEEE802.15.3a/4a channel models are not applicable for the UWB channels of China. Based on the UWB frequency regulation specification of China, large scale channel measurements were performed for indoor, outdoor and industrial12scenarios. A novel aumated cluster identification based on wavelet analysis is proposed. All parameters for modified S-V channel models are obtained by fitting from the channel measurement data. The proposed channel models are verified by tesing with the measurement data and are more effective than the IEEE channel models for China environments.
A new piecewise double exponential channel model for office non-line of sight (NLOS)case is proposed based on the measurement data. Compared to the IEEE802.15.4a channel model, the proposed office NLOS channel model for has more similar channel characteristic on delay spread and number of paths with measurement data. Another compressed sensing based average number of multipath model of UWB channel is proposed. It reveals that there exists a functional relation between the number of compressive samples required by CS and the average number of UWB channel.
To solve the high sampling rate problem in UWB channel model measurement, this thesis proposed deconvolution algorithm based on compressed sensing. For frequency-domain and time-domain measurement approach, the time-domain window pulse corresponding with the frequency domain window function and the template signal is used to construct a parameterized waveform dictionary respectively. The dictionary can enhance the sparse representation of time-domain measurement data. Greedy restruction algorithms can be used to perform deconvolution. Discrete channel impulse response are obtained. Compared to the traditional CLEAN deconvolution algorithm, the CS based deconvolution algorithm can achieve similar deconvolution performance with much fewer samples required by Nyquist sampling rate. We also demonstrated that with a dictionary designed specifically, MP algorithm is an equivalent of CLEAN algorithm. When the frequency dependent distortion of UWB channel is considered, a multi-template CS-based deconvolution is proposed, which improves deconvolution performance.
Consider the high sampling rate of traditional UWB channel estimation, we present a compressed channel estimation algorithm with channel state a priori information. By using the partial channel state information Average Power Delay Profile (APDP) to weight the atoms of dictionary, modifying the atoms selection procedure based on the principle of Probablistic MP (PMP), the proposed reconstruction algorithm not only reduces the requirement of highg sampling rate, but also improves the performance under low Signal-to-Noise Ratio (SNR) using channel a priori information. The simulation results show that the prosed CS-SWMP algorithm achieves higher channel estimation accuracy than other reconstruction algorithm, with almost identical complexity.
Considering the high sampling rate of traditional UWB noncoherent detection, we present a compressed channel estimation algorithm with channel state a priori information APDP. To make the CS-based noncoherent detection feasible for real-time application, the block sparse reconstruction algorithm Block Orthogonal Mathcing Pursuit (BOMP) is exploited for the block sparsity characteristic of received UWB-IR signal. The CS-based noncoherent detection algorithm outperforms the energy detection algorithm and reduces sampling rate.
Finally, the content of the whole dissertation is summarized, and several valuable research directions of compressed sensing and UWB channel modeling are discussed.
本文在深入研究压缩感知理论的基础上,依据超宽带信道实测数据,对压缩感知在超宽带信道建模和接收机设计方面的应用进行了深入研究,主要完成了以下具有创新性的研究成果：
针对IEEE802.15.3a/4a信道模型不完全适用于中国超宽带信道的局限性,本文依据中国超宽带频谱规范,对室内、室外和行业12个典型场景进行了大规模超宽带信道测量。基于信道实测数据和分簇信道模型,提出了一种新颖的基于小波分析的自动分簇算法,并完成了信道模型参数拟合。通过模型评测,验证了所提的超宽带信道模型比IEEE信道模型更符合中国超宽带技术的实际应用情况。
依据实测数据反映的信道特性,针对严重遮挡非视距(NLOS)情况下的办公室环境提出了一种新颖的分段双指数信道模型。与IEEE802.15.4a信道模型相比,所提的办公室NLOS模型在时延扩展和多径个数等关键信道特性上与实测数据更加符合。另外,本文还设计一种基于压缩感知的UWB信道多径分析模型。通过深入分析揭示出在可接受的信号重构概率条件下,CS所需的采样点数与UWB信道平均多径个数之间所存在的确定函数关系。
针对超宽带信道测量要求高采样速率的难题,本文提出了一种基于压缩感知的解卷积算法。对于频域和时域两种测量方式,分别利用频域窗函数对应的时域脉冲和模板信号构造出参数化的波形字典,以增强时域测量信号的稀疏表达,并通过贪婪重构算法对时域测量信号进行解卷积,最终得到离散信道响应估计。相比传统解卷积算法CLEAN算法,所提的基于CS的解卷积算法在获得相近解卷积性能的前提下,有效降低了测量系统所需的观测点数。通过对比分析,验证了在采用特定字典的情况下,匹配追踪(MP)算法等价于CLEAN算法。考虑超宽带信道所呈现出的特有的频率依赖性失真,本文设计出一种多模板压缩感知解卷积算法,进一步提高了解卷积性能。
针对经典超宽带信道估计要求高采样速率的缺陷,本文提出了一种基于部分信道状态信息的压缩感知信道估计算法。通过利用平均功率延时剖面(APDP)信息对字典原子进行加权,并依据概率式匹配追踪算法(PMP)的原理对重构算法中原子选择方式进行改进,不仅有效降低了接收机按Nyquist速率所需的采样数,同时设计的重构算法可充分利用信道先验信息,从而显著提高在低信噪比情况下的重构性能。仿真结果表明,所提出的新算法不仅能有效降低传统接收机所需的采样点数,且在复杂度保持不变的前提下,相比其他重构算法,其信道估计性能得以显著提升。
针对脉冲超宽带(UWB-IR)接收机高采样率导致的硬件复杂度过高的问题,提出了一种基于压缩感知的非相干检测算法。通过充分发掘UWB-IR接收信号分块稀疏的特点,利用分块重构算法BOMP算法,大幅降低了计算复杂度；同时,进一步利用APDP先验信息辅助设计字典以提高检测性能。仿真结果表明,相比于能量检测方法,新方案不仅提高了非相干检测的性能,还降低了接收机采样速率。
论文最后对全文进行了总结,并对压缩感知技术在超宽带系统的应用发展方向进行了展望。
Ultra Wide-band (UWB) wireless channel modeling is a prerequisite for UWB system design. Compressed sensing (CS) has a great potential in the UWB channel estimation and modeling. This thesis is supported by Important National Science&Technology Specific Projects and attempts to make a contribution to the theory and application of UWB system.
In this paper, based on UWB channel measurement data, the basic principle of compressed sensing technology and its application in UWB system channel modeling and receiver design are investigated. Several creative algorithms are developed in this thesis.
The IEEE802.15.3a/4a channel models are not applicable for the UWB channels of China. Based on the UWB frequency regulation specification of China, large scale channel measurements were performed for indoor, outdoor and industrial12scenarios. A novel aumated cluster identification based on wavelet analysis is proposed. All parameters for modified S-V channel models are obtained by fitting from the channel measurement data. The proposed channel models are verified by tesing with the measurement data and are more effective than the IEEE channel models for China environments.
A new piecewise double exponential channel model for office non-line of sight (NLOS)case is proposed based on the measurement data. Compared to the IEEE802.15.4a channel model, the proposed office NLOS channel model for has more similar channel characteristic on delay spread and number of paths with measurement data. Another compressed sensing based average number of multipath model of UWB channel is proposed. It reveals that there exists a functional relation between the number of compressive samples required by CS and the average number of UWB channel.
To solve the high sampling rate problem in UWB channel model measurement, this thesis proposed deconvolution algorithm based on compressed sensing. For frequency-domain and time-domain measurement approach, the time-domain window pulse corresponding with the frequency domain window function and the template signal is used to construct a parameterized waveform dictionary respectively. The dictionary can enhance the sparse representation of time-domain measurement data. Greedy restruction algorithms can be used to perform deconvolution. Discrete channel impulse response are obtained. Compared to the traditional CLEAN deconvolution algorithm, the CS based deconvolution algorithm can achieve similar deconvolution performance with much fewer samples required by Nyquist sampling rate. We also demonstrated that with a dictionary designed specifically, MP algorithm is an equivalent of CLEAN algorithm. When the frequency dependent distortion of UWB channel is considered, a multi-template CS-based deconvolution is proposed, which improves deconvolution performance.
Consider the high sampling rate of traditional UWB channel estimation, we present a compressed channel estimation algorithm with channel state a priori information. By using the partial channel state information Average Power Delay Profile (APDP) to weight the atoms of dictionary, modifying the atoms selection procedure based on the principle of Probablistic MP (PMP), the proposed reconstruction algorithm not only reduces the requirement of highg sampling rate, but also improves the performance under low Signal-to-Noise Ratio (SNR) using channel a priori information. The simulation results show that the prosed CS-SWMP algorithm achieves higher channel estimation accuracy than other reconstruction algorithm, with almost identical complexity.
Considering the high sampling rate of traditional UWB noncoherent detection, we present a compressed channel estimation algorithm with channel state a priori information APDP. To make the CS-based noncoherent detection feasible for real-time application, the block sparse reconstruction algorithm Block Orthogonal Mathcing Pursuit (BOMP) is exploited for the block sparsity characteristic of received UWB-IR signal. The CS-based noncoherent detection algorithm outperforms the energy detection algorithm and reduces sampling rate.
Finally, the content of the whole dissertation is summarized, and several valuable research directions of compressed sensing and UWB channel modeling are discussed.
引文
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