Sphere Packings, Lattices and Groups

In mobile communications, coherent detection requires the estimate of an increasing number of channel parameters due to the promising performance of systems that deploy multiple antennas. However, the estimate of space-time channels requires a number of training symbols that grows with the number of unknowns. To overcome this problem, we propose a subspace-based estimation method that exploits the different varying rates in the structure of the space-time channel for moving terminals. Since the channel has some fast-varying (faded amplitudes of the paths) and slowly varying (delays and directions of arrival) features, the multislot (MS) estimate is composed of two terms: the slowly varying spatial and temporal bases estimated from L consecutive slots and the fast-varying amplitudes estimated on a slot-by-slot basis. Performance analysis and simulations confirm the expected benefits of the multislot approach and demonstrate that for large L, the mean square error (MSE) on the channel estimate depends only on the number of fast-varying parameters.

Multi-slot estimation of fast-varying space-time communication channels

It is shown how expectation maximization (EM) may be viewed as a message passing algorithm in factor graphs. In particular, a general EM message computation rule is identified. As a factor graph tool, EM may be used to break cycles in a factor graph, and tractable messages may in some cases be obtained where the sum-product messages are unwieldy. 
As an exemplary application, the paper considers linear Gaussian state space models. Unknown coefficients in such models give rise to multipliers in the corresponding factor graph. A main attraction of EM in such cases is that it results in purely Gaussian message passing algorithms. These Gaussian EM messages are tabulated for several (scalar, vector, matrix) multipliers that frequently appear in applications.

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Expectation Maximization as Message Passing - Part I: Principles and Gaussian Messages

A complete solution to joint channel tracking and symbol detection for bit-interleaved MIMO-OFDM systems in a time-varying frequency-selective fading environment is proposed in this research. In the training mode, the Rao-Blackwellised particle filter (RBPF) is used to estimate the phase offset (PO) and the carrier frequency offset (CFO). In the data mode, based on a recursive EM procedure in conjunction with soft decoding, we develop an iterative algorithm that performs the minimum mean squared error (MMSE) channel estimation and the maximum a posterior (MAP) probability symbol detection jointly. The soft decoding module is composed of the list sphere decoder (LSD) and the soft-in-soft-out (SISO) channel decoder based on the BCJR algorithm. The performance of the proposed algorithm is evaluated via simulation. It is demonstrated that the new algorithm has robust performance in the presence of phase noise and the carrier frequency offset.

Receiver design for bit-interleaved MIMO-OFDM systems over time-varying channels

In this paper, we investigates the effects of two different modulation schemes for image transmission using an OFDM (Orthogonal Frequency Division Multiplexing) transceiver. The main aim is to evaluate the performance between 16PSK (Phase Shift Keying) and 16QAM (Quadrature amplitude modulation) in context of BER (Bit Error Rate) in AWGN (Additive White Gaussian Noise) channel. We evaluate the performance of these modulation schemes using MATLAB simulations. The results show that QAM is a better modulation scheme than PSK for the image transfer using OFDM transceiver. The data modulated by PSK modulation technique gets more affected by the channel effects and more number of errors occur, as compared to QAM.

Evaluating the performance of OFDM transceiver for image transfer using 16PSK and 16QAM modulation schemes

An expectation maximization (EM) algorithm for joint channel tracking and symbol detection in a multi-input multi-output (MIMO) time-varying frequency-selective fading environment is proposed in this research. Based on the recursive EM procedure in conjunction with soft decoding, we develop an iterative algorithm that performs the minimum mean squared error (MMSE) channel estimation and the maximum a posterior (MAP) probability symbol detection jointly. Two soft decoders are examined; namely, the BCJR algorithm and the soft sphere decoder. The performance of the proposed algorithm is evaluated via simulation and compared with that of Kalman filtering with hard decision feedback. It is demonstrated by numerical simulation that the proposed algorithm has robust performance in the presence of a severe channel model mismatch

Joint MIMO channel tracking and symbol detection with EM algorithm and soft decoding

An expectation maximization (EM) algorithm for joint channel tracking and symbol detection at the receiver in a multi-input multi-output (MIMO) frequency-selective fading environment is proposed in this research. Based on the recursive EM procedure in conjunction with the BCJR algorithm, we develop an iterative algorithm that performs the minimum mean squared error (MMSE) channel estimation and the maximum a posterior (MAP) probability symbol detection jointly. The per- formance of the proposed algorithm is evaluated via simulation and compared with that of Kalmanfiltering with hard decision feedback. It is also demonstrated that the new algorithm has robust performance in the presence of a severe channel model mismatch. I. INTRODUCTION

Robust joint channel estimation and symbol detection over MIMO channels using EM algorithm

The performance analysis and transceiver design of an adaptive bit-interleaved-coded multi-input multi-output orthogonal frequency division multiplexing (BIC-MIMO-OFDM) system without channel station information (CSI) in priori are investigated in this research. First, the noncoherent capacity of a MIMO-OFDM system with TV subcarriers, nt transmit antennas and nr receive antennas is analyzed. It is shown that the lower bound of noncoherent capacity in the high signal-to-noise ratio (SNR) region is asymptotically equal to n*(1 - n*L/N) log rho, where n* = min(nl,nr), L is the number of channel taps and rho is the SNR value. Then, an adaptive BIC-MIMO-OFDM system based on transmitter beamforming and variable-rate-variable-power (VRVP) QAM is proposed. The throughput of adaptive scheme can achieve the noncoherent capacity, however, the gap to the coherent capacity cannot be totally compensated due to the overhead of training. The effect of imperfect channel state information (CSI) feedback is studied. It is demonstrated by numerical simulation that the proposed BIC-MIMO-OFDM scheme has better performance than adaptive trellis coded modulation (TCM) when outdated channel estimation is used.

Performance Analysis and Transceiver Design for Adaptive BIC-MIMO-OFDM Systems

A reduced-rank space-time channel estimation scheme is presented for uplink multiuser DS-CDMA (direct-sequence code-division multiple access) systems in dispersive MIMO (multiple input and multiple output) channels. The proposed method is based on the alternative power (AP) method with the aid of pilot symbols. It is shown that the mean squared error (MSE) of channel estimation can be improved via rank order selection where optimal performance can be achieved using the generalized likelihood ratio test (GLRT). Both analytical and simulated results are provided to demonstrate the performance of the proposed scheme with various measurement criteria.

Fu-Hsuan Chiu

Papers

Receiver design for bit-interleaved MIMO-OFDM systems over time-varying channels

Joint MIMO channel tracking and symbol detection with EM algorithm and soft decoding

Robust joint channel estimation and symbol detection over MIMO channels using EM algorithm

Performance Analysis and Transceiver Design for Adaptive BIC-MIMO-OFDM Systems

Reduced-rank space-time channel estimation for DS-CDMA in MIMO dispersive channels