Gradient-Based Markov Chain Monte Carlo for MIMO Detection

Accurately detecting symbols transmitted over multiple-input multiple-output (MIMO) wireless channels is crucial in realizing the benefits of MIMO techniques. However, optimal MIMO detection is associated with a complexity that grows exponentially with the MIMO dimensions and quickly becomes impractical. Recently, stochastic sampling-based Bayesian inference techniques, such as Markov chain Monte Carlo (MCMC), have been combined with the gradient descent (GD) method to provide a promising framework for MIMO detection. In this work, we propose to efficiently approach optimal detection by exploring the discrete search space via MCMC random walk accelerated by Nesterov's gradient method. Nesterov's GD guides MCMC to make efficient searches without the computationally expensive matrix inversion and line search. Our proposed method operates using multiple GDs per random walk, achieving sufficient descent towards important regions of the search space before adding random perturbations, guaranteeing high sampling efficiency. To provide augmented exploration, extra samples are derived through the trajectory of Nesterov's GD by simple operations, effectively supplementing the sample list for statistical inference and boosting the overall MIMO detection performance. Furthermore, we design an early stopping tactic to terminate unnecessary further searches, remarkably reducing the complexity. Simulation results and complexity analysis reveal that the proposed method achieves near-optimal performance in both uncoded and coded MIMO systems, adapts to realistic channel models, and scales well to large MIMO dimensions.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Multimodal methods for blind source separation of audio sources

The enhancement of the performance of frequency domain convolutive blind source separation (FDCBSS) techniques when applied to the problem of separating audio sources recorded in a room environment is the focus of this thesis. This challenging application is termed the cocktail party problem and the ultimate aim would be to build a machine which matches the ability of a human being to solve this task. Human beings exploit both their eyes and their ears in solving this task and hence they adopt a multimodal approach, i.e. they exploit both audio and video modalities. New multimodal methods for blind source separation of audio sources are therefore proposed in this work as a step towards realizing such a machine. The geometry of the room environment is initially exploited to improve the separation performance of a FDCBSS algorithm. The positions of the human speakers are monitored by video cameras and this information is incorporated within the FDCBSS algorithm in the form of constraints added to the underlying cross-power spectral density matrix-based cost function which measures separation performance. [Continues.

Efficient low-complexity data detection for multiple-input multiple-output wireless communication systems

The tradeoff between the computational complexity and system performance in multipleinput multiple-output (MIMO) wireless communication systems is critical to practical applications. In this dissertation, we investigate efficient low-complexity data detection schemes from conventional small-scale to recent large-scale MIMO systems, with the targeted applications in terrestrial wireless communication systems, vehicular networks, and underwater acoustic communication systems. In the small-scale MIMO scenario, we study turbo equalization schemes for multipleinput multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) and multipleinput multiple-output single-carrier frequency division multiple access (MIMO SC-FDMA) systems. For the MIMO-OFDM system, we propose a soft-input soft-output sorted QR decomposition (SQRD) based turbo equalization scheme under imperfect channel estimation. We demonstrate the performance enhancement of the proposed scheme over the conventional minimum mean-square error (MMSE) based turbo equalization scheme in terms of system bit error rate (BER) and convergence performance. Furthermore, by jointly considering channel estimation error and the a priori information from the channel decoder, we develop low-complexity turbo equalization schemes conditioned on channel estimate for MIMO systems. Our proposed methods generalize the expressions used for MMSE and MMSE-SQRD based turbo equalizers, where the existing methods can be viewed as special cases. In addition, we extend the SQRD-based soft interference cancelation scheme to MIMO SC-FDMA systems where a multi-user MIMO scenario is considered. We show an improved system BER performance of the proposed turbo detection scheme over the conventional MMSE-based detection scheme. In the large-scale MIMO scenario, we focus on low-complexity detection schemes because computational complexity becomes critical issue for massive MIMO applications. We first propose an innovative approach of using the stair matrix in the development of massive MIMO detection schemes. We demonstrate the applicability of the stair matrix through the study of the convergence conditions. We then investigate the system performance and demonstrate that the convergence rate and the system BER are much improved over the diagonal matrix based approach with the same system configuration. We further investigate low-complexity and fast processing detection schemes for massive MIMO systems where a block diagonal matrix is utilized in the development. Using a parallel processing structure, the processing time can be much reduced. We investigate the convergence performance through both the probability that the convergence conditions are satisfied and the convergence rate, and evaluate the system performance in terms of computational complexity, system BER, and the overall processing time. Using our proposed approach, we extend the block Gauss-Seidel method to large-scale array signal detection in underwater acoustic (UWA) communications. By utilizing a recently proposed computational efficient statistic UWA channel model, we show that the proposed scheme can effectively approach the system performance of the original Gauss-Seidel method, but with much reduced processing delay

Modelling, Dimensioning and Optimization of 5G Communication Networks, Resources and Services

This reprint aims to collect state-of-the-art research contributions that address challenges in the emerging 5G networks design, dimensioning and optimization. Designing, dimensioning and optimization of communication networks resources and services have been an inseparable part of telecom network development. The latter must convey a large volume of traffic, providing service to traffic streams with highly differentiated requirements in terms of bit-rate and service time, required quality of service and quality of experience parameters. Such a communication infrastructure presents many important challenges, such as the study of necessary multi-layer cooperation, new protocols, performance evaluation of different network parts, low layer network design, network management and security issues, and new technologies in general, which will be discussed in this book

5G無線通信における誤り訂正符号化方式の評価に関する研究

早大学位記番号:新8267早稲田大