Channel Rectification and Signal Estimation Based on EIV Model in Massive MIMO System

The performance of Massive MIMO is severely limited by channel estimation error, which is caused by pilot contamination and channel aging. In this paper, we propose an estimation algorithm based on the weighted total least-squares method with errors-in-variables (EIV) model to alleviate the influence of pilot contamination and channel aging. Then, a channel rectification method has been investigated to diminish the inaccuracy of channel estimation. Comparing with the traditional methods, it not only helps to make the signal estimation more accurate, but also provides opportunities to correct the channel model with estimation error and update the aged channel statement information. Simulations are provided to verify the efficacy of this method

Doctor of Philosophy

dissertationThe use of multicarrier techniques has allowed the rapid expansion of broadband wireless communications. Orthogonal frequency division multiplexing (OFDM) has been the most dominant technology in the past decade. It has been deployed in both indoor Wi-Fi and cellular environments, and has been researched for use in underwater acoustic channels. Recent works in wireless communications include the extension of OFDM to multiple access applications. Multiple access OFDM, or orthogonal frequency division multiple access (OFDMA), has been implemented in the third generation partnership project (3GPP) long- term evolution (LTE) downlink. In order to reduce the intercarrier interference (ICI) when user's synchronization is relaxed, filterbank multicarrier communication (FBMC) systems have been proposed. The first contribution made in this dissertation is a novel study of the classical FBMC systems that were presented in 1960s. We note that two distinct methods were presented then. We show that these methods are closely related through a modulation and a time/frequency scaling step. For cellular channels, OFDM also has the weakness of relatively large peak-to-average power ratios (PAPR). A special form of OFDM for the uplink of multiple access networks, called single carrier frequency division multiple access (SC-FDMA), has been developed to mitigate this issue. In this regard, this dissertation makes two contributions. First, we develop an optimization method for designing an effective precoding method for SC-FDMA systems. Second, we show how an equivalent to SC-FDMA can be developed for systems that are based on FBMC. In underwater acoustic communications applications, researchers are investigating the use of multicarrier communication systems like OFDM in underwater channels. The movement of the communicating vehicles scales the received signal along the time axis, which is often referred to as Doppler scaling. To undo the signal degradation, researchers have investigated methods to estimate the Doppler scaling factor and restore the original signal using resampling. We investigate a method called nonuniform fast Fourier transform (NUFFT) and apply that to increase the precision in the detection and correction of the Doppler scaling factor. NUFFT is applied to both OFDM and FBMC and its performance over the experimental data obtained from at sea experiments is investigated

Advanced wireless communications using large numbers of transmit antennas and receive nodes

The concept of deploying a large number of antennas at the base station, often called massive multiple-input multiple-output (MIMO), has drawn considerable interest because of its potential ability to revolutionize current wireless communication systems. Most literature on massive MIMO systems assumes time division duplexing (TDD), although frequency division duplexing (FDD) dominates current cellular systems. Due to the large number of transmit antennas at the base station, currently standardized approaches would require a large percentage of the precious downlink and uplink resources in FDD massive MIMO be used for training signal transmissions and channel state information (CSI) feedback. First, we propose practical open-loop and closed-loop training frameworks to reduce the overhead of the downlink training phase. We then discuss efficient CSI quantization techniques using a trellis search. The proposed CSI quantization techniques can be implemented with a complexity that only grows linearly with the number of transmit antennas while the performance is close to the optimal case. We also analyze distributed reception using a large number of geographically separated nodes, a scenario that may become popular with the emergence of the Internet of Things. For distributed reception, we first propose coded distributed diversity to minimize the symbol error probability at the fusion center when the transmitter is equipped with a single antenna. Then we develop efficient receivers at the fusion center using minimal processing overhead at the receive nodes when the transmitter with multiple transmit antennas sends multiple symbols simultaneously using spatial multiplexing

Algorithms for Blind Equalization Based on Relative Gradient and Toeplitz Constraints

Blind Equalization (BE) refers to the problem of recovering the source symbol sequence from a signal received through a channel in the presence of additive noise and channel distortion, when the channel response is unknown and a training sequence is not accessible. To achieve BE, statistical or constellation properties of the source symbols are exploited. In BE algorithms, two main concerns are convergence speed and computational complexity. In this dissertation, we explore the application of relative gradient for equalizer adaptation with a structure constraint on the equalizer matrix, for fast convergence without excessive computational complexity. We model blind equalization with symbol-rate sampling as a blind source separation (BSS) problem and study two single-carrier transmission schemes, specifically block transmission with guard intervals and continuous transmission. Under either scheme, blind equalization can be achieved using independent component analysis (ICA) algorithms with a Toeplitz or circulant constraint on the structure of the separating matrix. We also develop relative gradient versions of the widely used Bussgang-type algorithms. Processing the equalizer outputs in sliding blocks, we are able to use the relative gradient for adaptation of the Toeplitz constrained equalizer matrix. The use of relative gradient makes the Bussgang condition appear explicitly in the matrix adaptation and speeds up convergence. For the ICA-based and Bussgang-type algorithms with relative gradient and matrix structure constraints, we simplify the matrix adaptations to obtain equivalent equalizer vector adaptations for reduced computational cost. Efficient implementations with fast Fourier transform, and approximation schemes for the cross-correlation terms used in the adaptation, are shown to further reduce computational cost. We also consider the use of a relative gradient algorithm for channel shortening in orthogonal frequency division multiplexing (OFDM) systems. The redundancy of the cyclic prefix symbols is used to shorten a channel with a long impulse response. We show interesting preliminary results for a shortening algorithm based on relative gradient