Study Of Information Feedback, Detection And Reconstruction Based On Compressive Sensing

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.