基于压缩感知的信息反馈、检测与重建研究
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摘要
压缩感知技术是针对稀疏信号或可压缩信号提出的采样和压缩同时进行的技术,引发了信号处理思想的巨大变革,是近年来国内外学术界兴起的一个研究热点。多输入多输出(MIMO)技术和信道自适应技术是未来提高频谱效率的关键技术,而发送端的信道状态信息对多用户MIMO下行链路和信道自适应技术的实现具有重要意义。压缩信号处理技术是近年来从压缩感知理论中发展而来的一种非传统性的信号处理方法,直接在压缩测量域解决信号处理问题,也是一个新的研究热点。因此,本论文主要研究了基于压缩感知的信息反馈、检测和重建问题。主要工作包含以下五个部分：
(一)研究了MIMO-OFDM系统中基于压缩感知的信道质量信息(CQI)反馈压缩问题。分别提出了MIMO空间分集和空间复用两种模式下的基于压缩感知的CQI压缩反馈方法,所提出的新方法主要利用了各子载波的信道信息与相邻子载波的信道信息具有高度的相关性。我们提供了一种CQI反馈压缩的新思路,仿真结果表明,所提出的基于压缩感知的方案在减少CQI反馈量方面是有效的。
(二)研究了多用户MIMO-OFDM系统中的基于压缩感知的信道方向信息(CDI)压缩反馈问题。提出了将压缩感知方法引入到多用户MIMO-OFDM系统的CDI反馈压缩中,该方案能够在减少反馈量的同时,给基站提供更精确的信道状态信息,从而提高系统容量。该方案利用了每根发送天线到用户端天线在相邻时频域上的信道系数是高度相关的这样一个事实。仿真结果表明,所提方案比基于码本的方案在分簇的情况下需要更少的反馈量,而且能够提供给发送端更精确的信道状态信息进行预编码来抑制多用户干扰。
(三)研究了基于分布式压缩感知(DCS)的信道信息压缩反馈问题。提出了一种基于分布式压缩感知的信道状态信息(CSI)压缩反馈方案。首先,使用子空间矩阵对CSI信息进行逼近,然后,近似的CSI信息通过设计的测量矩阵的压缩量化,最后送到反馈链路反馈到基站。在基站端,近似的CSI信息能够利用先前反馈的CSI信息,由同时正交匹配追踪(SOMP)重建算法可靠的恢复出来。仿真结果表明了所提出的基于分布式压缩感知的压缩反馈算法能够在少的吞吐量损失的前提下提高系统的可靠性。
(四)研究了压缩信号处理领域中的信号压缩检测问题。提出了一种基于粒子群(PSO)的随机矩阵的最优化方案。粒子群最优化是群体智能理论中的一种重要方法,首次用来解决信号压缩检测中的随机投影矩阵最优化问题。我们主要在两种场景下通过仿真研究了所提方案的性能：已知稀疏信号的检测和未知稀疏信号的检测。仿真结果表明基于PSO的检测器比传统的没有使用PSO的压缩检测器需要更少的测量值数目而且能够获得更好的检测性能。
(五)研究了压缩信号处理领域中期望信号在窄带干扰情况下的压缩重建问题。提出了新的测量矩阵设计方案,为了进一步减少测量数目,对测量矩阵进行了优化设计,在测量的同时有效的滤除了窄带干扰,仿真结果表明,所提方案在减少测量数目的同时提高了信号重建性能。
Compressive sensing (CS) is a novel signal sampling theory for sparse or compressible signals and it implements signal sampling and data compression at the same time. It is a revolution in signal processing and has become a hot topic in academic world. MIMO and channel adaptative transmission are key techniques to improve spectral efficiency. Channel state information at the transmitter (CSIT) plays a very important role in the multiuser MIMO system and the implementation of chananel adaptative transmission. Compressive signal processing develops from compressive sensing theory and it is a non-traditional signal processing method. It tries to solve signal processing problem in the compressive domain and is a new hot topic. Thus, this dissertation mainly focuses on information feedback, detection and reconstruction based on compressive sensing. The main work is composed of the following five parts:
1. The problem of CQI feedback compression based on compressive sensing (CS) for MIMO-OFDM system is stuied. We propose the new schemes of CQI feedback compression based on CS under two modes of MIMO diversity and multiplexing. The new methods mainly exploit the reality that the channel information of neighboring subcarriers is highly correlated. We provide a new idea of CQI feedback compression. Simulation results show that the proposed schemes are very effective to reduce the overhead of CQI feedback.
2. A novel channel direction information (CDI) feedback compression scheme based on the recently proposed compressive sensing (CS) theory to be used in multi-user MIMO-OFDM system is proposed. The new method mainly exploits the reality that the channel coefficients of neighboring subcarriers from every transmit antenna to every receiver antenna are highly correlated. Simulation results show that our proposed scheme has the potential of reducing the CSI feedback overhead and providing more accurate CSI at the transmitter than the codebook-based method to suppress multi-user interference.
3. The problem of channel compression feedback based on distributed compressed sensing (DCS) is studied. A new compressive feedback (CF) scheme based on distributed compressed sensing for time-corrected MIMO channel is proposed. First, the channel state information (CSI) is approximated by using a subspace matrix, then, the approximated CSI is compressed using a compressive matrix. At the base station, the approximated CSI can be robustly recovered with simultaneous orthogonal matching pursuit (SOMP) algorithm by using forgone CSIs. Simulation results show our proposed DCS-CF method can improve the reliability of system without creating a large performance loss.
4. The problem of signal compressive detection which belongs to compressive signal processing domain is studied. The random matrix in the compressive and subspace compressive detectors is optimized based on the particle swarm optimization (PSO). The PSO, which belongs to swarm intelligent theory, is used for the first time to solve the optimization problem of the random projection matrix, leading to an improved version of the conventional compressive and subspace compressive detectors. Simulation results show the proposed PSO-based detectors can achieve a better detection performance and require less number of measurements than the traditional compressive detectors without using PSO.
5. The problem of signal compression reconstruction with narrow-band interference is studied. The new scheme is proposed. To further reduce the number of measurement values, measurement matrix is optimated. The narrow-band interference information is filitered out at the measurement stage. Simulation results show that the proposed schemes reduce the required measurement values and also improve the signal reconstruction performance.
(一)研究了MIMO-OFDM系统中基于压缩感知的信道质量信息(CQI)反馈压缩问题。分别提出了MIMO空间分集和空间复用两种模式下的基于压缩感知的CQI压缩反馈方法,所提出的新方法主要利用了各子载波的信道信息与相邻子载波的信道信息具有高度的相关性。我们提供了一种CQI反馈压缩的新思路,仿真结果表明,所提出的基于压缩感知的方案在减少CQI反馈量方面是有效的。
(二)研究了多用户MIMO-OFDM系统中的基于压缩感知的信道方向信息(CDI)压缩反馈问题。提出了将压缩感知方法引入到多用户MIMO-OFDM系统的CDI反馈压缩中,该方案能够在减少反馈量的同时,给基站提供更精确的信道状态信息,从而提高系统容量。该方案利用了每根发送天线到用户端天线在相邻时频域上的信道系数是高度相关的这样一个事实。仿真结果表明,所提方案比基于码本的方案在分簇的情况下需要更少的反馈量,而且能够提供给发送端更精确的信道状态信息进行预编码来抑制多用户干扰。
(三)研究了基于分布式压缩感知(DCS)的信道信息压缩反馈问题。提出了一种基于分布式压缩感知的信道状态信息(CSI)压缩反馈方案。首先,使用子空间矩阵对CSI信息进行逼近,然后,近似的CSI信息通过设计的测量矩阵的压缩量化,最后送到反馈链路反馈到基站。在基站端,近似的CSI信息能够利用先前反馈的CSI信息,由同时正交匹配追踪(SOMP)重建算法可靠的恢复出来。仿真结果表明了所提出的基于分布式压缩感知的压缩反馈算法能够在少的吞吐量损失的前提下提高系统的可靠性。
(四)研究了压缩信号处理领域中的信号压缩检测问题。提出了一种基于粒子群(PSO)的随机矩阵的最优化方案。粒子群最优化是群体智能理论中的一种重要方法,首次用来解决信号压缩检测中的随机投影矩阵最优化问题。我们主要在两种场景下通过仿真研究了所提方案的性能：已知稀疏信号的检测和未知稀疏信号的检测。仿真结果表明基于PSO的检测器比传统的没有使用PSO的压缩检测器需要更少的测量值数目而且能够获得更好的检测性能。
(五)研究了压缩信号处理领域中期望信号在窄带干扰情况下的压缩重建问题。提出了新的测量矩阵设计方案,为了进一步减少测量数目,对测量矩阵进行了优化设计,在测量的同时有效的滤除了窄带干扰,仿真结果表明,所提方案在减少测量数目的同时提高了信号重建性能。
Compressive sensing (CS) is a novel signal sampling theory for sparse or compressible signals and it implements signal sampling and data compression at the same time. It is a revolution in signal processing and has become a hot topic in academic world. MIMO and channel adaptative transmission are key techniques to improve spectral efficiency. Channel state information at the transmitter (CSIT) plays a very important role in the multiuser MIMO system and the implementation of chananel adaptative transmission. Compressive signal processing develops from compressive sensing theory and it is a non-traditional signal processing method. It tries to solve signal processing problem in the compressive domain and is a new hot topic. Thus, this dissertation mainly focuses on information feedback, detection and reconstruction based on compressive sensing. The main work is composed of the following five parts:
1. The problem of CQI feedback compression based on compressive sensing (CS) for MIMO-OFDM system is stuied. We propose the new schemes of CQI feedback compression based on CS under two modes of MIMO diversity and multiplexing. The new methods mainly exploit the reality that the channel information of neighboring subcarriers is highly correlated. We provide a new idea of CQI feedback compression. Simulation results show that the proposed schemes are very effective to reduce the overhead of CQI feedback.
2. A novel channel direction information (CDI) feedback compression scheme based on the recently proposed compressive sensing (CS) theory to be used in multi-user MIMO-OFDM system is proposed. The new method mainly exploits the reality that the channel coefficients of neighboring subcarriers from every transmit antenna to every receiver antenna are highly correlated. Simulation results show that our proposed scheme has the potential of reducing the CSI feedback overhead and providing more accurate CSI at the transmitter than the codebook-based method to suppress multi-user interference.
3. The problem of channel compression feedback based on distributed compressed sensing (DCS) is studied. A new compressive feedback (CF) scheme based on distributed compressed sensing for time-corrected MIMO channel is proposed. First, the channel state information (CSI) is approximated by using a subspace matrix, then, the approximated CSI is compressed using a compressive matrix. At the base station, the approximated CSI can be robustly recovered with simultaneous orthogonal matching pursuit (SOMP) algorithm by using forgone CSIs. Simulation results show our proposed DCS-CF method can improve the reliability of system without creating a large performance loss.
4. The problem of signal compressive detection which belongs to compressive signal processing domain is studied. The random matrix in the compressive and subspace compressive detectors is optimized based on the particle swarm optimization (PSO). The PSO, which belongs to swarm intelligent theory, is used for the first time to solve the optimization problem of the random projection matrix, leading to an improved version of the conventional compressive and subspace compressive detectors. Simulation results show the proposed PSO-based detectors can achieve a better detection performance and require less number of measurements than the traditional compressive detectors without using PSO.
5. The problem of signal compression reconstruction with narrow-band interference is studied. The new scheme is proposed. To further reduce the number of measurement values, measurement matrix is optimated. The narrow-band interference information is filitered out at the measurement stage. Simulation results show that the proposed schemes reduce the required measurement values and also improve the signal reconstruction performance.
引文
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