Selective Multiple-input Multiple-output Channels Research Articles

On the BER Performance of Constant Envelope OFDM in Frequency Selective MIMO Channels With ML Detection

In this contribution, we investigate the issue of constant envelope orthogonal frequency-division multiplexing (CE-OFDM) transmission in multipath fading channels, by quantifying its bit-error rate (BER) performance with the aid of maximum likelihood (ML) detection in multiple-input multipleoutput (MIMO) configurations. Through theoretical derivation, we demonstrate the advantage of CE-OFDM over its traditional counterparts as original OFDM and discrete Fourier transformspread-OFDM (DFT-s-OFDM) in terms of BER performance, which is ultimately supported by the simulation results.

New OSTBC for Blind Channel Estimation and Tracking in MIMO-OFDM Systems

Applying orthogonal space time block coding (OSTBC) to multiple-input multiple-output (MIMO) systems helps reduce receiver complexity. However, this approach has been applied only to flat fading channels, as when the channel is a frequency selective fading MIMO channel, OSTBC cannot be used directly since its orthogonal propriety may be lost. Furthermore, the MIMO channel is not always known. To deal with this problem, many techniques were proposed to estimate the channel using a training sequence. Unfortunately, these techniques reduce the useful spectral bandwidth. This paper proposes OSTBC for blind channel estimation and data detection in the case of a MIMO frequency selective channel. The aim of this new OSTBC is twofold: to solve the ambiguity of channel estimation and to reduce the complexity of the detector. By exploiting the well-known technique of orthogonal frequency division multiplexing (OFDM), the frequency selective fading MIMO channel is split into a set of flat fading subchannels. Moreover, to accommodate the fact that a MIMO channel can be time varying, the steady state Kalman channel estimator (SS-KCE) is extended to track the channel’s fast variations. The performance of the proposed blind algorithm is related by the adequate choice of the number of subcarriers and it is compared with other existing approaches by means of Monte Carlo simulations.

MIMO Detector for LTE/LTE-A Uplink Receiver

We propose a carefully selected receiver structure, detector and detector implementation architecture for multiple-input multiple-output (MIMO) uplink base station receiver for fourth generation (4G) wireless cellular systems. First, we compare different receiver algorithms and structures for single-carrier frequency-division multiple access (SC-FDMA) uplink transmission to get a good understanding of the performance and complexity of these algorithms and their suitability for practical realization. One of those structures, namely the frequency domain MMSE equalization with sphere detection (SD), is proposed for implementation. The receiver consists of separate stages for inter-symbol interference (ISI) and inter-antenna interference (IAI) mitigation in frequency selective MIMO channels. Frame error rate (FER) performance is studied via simulations in realistic wireless channels and practical system parameters. K-best SD is selected as a detector algorithm for this receiver. There are several publications proposing a sort-free architecture for tree search type of detectors. Both a conventional K-best architecture and a sort-free architecture are implemented on a Xilinx Virtex-6 field-programmable gate array (FPGA) using High Level Synthesis (HLS) tool. Both architectures support 4 × 4 MIMO with 64-level modulation (64-QAM). Complexity results confirm that avoiding the sorter is not always recommended. The benefit of sort-free architecture depends on the system parameters.

Performance analysis of adaptive DFE using set-membership binormalized data-reusing LMS algorithm for frequency selective MIMO channels

This paper addresses the concern of complexity involved with adaptive equalization in wireless systems operating over time-varying and frequency selective multiple-input multiple-output (MIMO) channels. Here, we propose a decision feedback equalizer using binormalized data-reusing least mean square (BNLMS) algorithm with set-membership filtering for MIMO channels. The performance of the equalizer is investigated for a MIMO receiver in a multi-path fading environment as experienced in the indoor and pedestrian environment. The equalizer performance is also studied for channels having higher delay and Doppler spread. The convergence issues, BER performance and tracking capabilities are examined through computer simulations. Moreover, the computational complexity issue for this MIMO equalizer is compared with other existing data-selective algorithm based techniques.

Analysis of MIMO-OFDM system impaired by nonlinear dual-band power amplifiers

This paper presents a theoretical analysis of multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing in the presence of nonlinear dual-band power amplifiers. A two-dimensional memory polynomial model is used to describe the dual-band power amplifier. It is analytically shown that the effect of nonlinearity impairment in the transmitter can be modeled by a complex coefficient and a nonlinear additive noise. An analytical formulation is provided for symbol error rate (SER) of this system considering a frequency selective MIMO channel. The experimental results have been deployed to extract a realistic model for a dual-band amplifier. Also, the model of the linearized power amplifier has been extracted after applying digital pre-distortion (DPD). The proposed model is validated by simulation for 2 × 2 and 2 × 4 MIMO systems. The results show that the analytical model maps well onto the simulation. Also, it is shown that the DPD is able to compensate for the nonlinearity effect of the dual-band power amplifier as the SER is improved.

PAPR Reduction in STBC MIMO OFDM System using AMS and PTS Schemes

data rates and reliability are the two key factors required to support emerging multimedia applications and new communications technologies. The two techniques used in high data rate transmission are orthogonal frequency division multiplexing (OFDM) and multiple-input multiple- output (MIMO) scheme. The OFDM is used to mitigate the problem of inter symbol interference (ISI) and provides good protection against co-channel interference and noise. MIMO system helps to reduce fading and can be used for decreasing bit error rate that is spatial diversity or to increase the data rate that is spatial multiplexing. The combination of MIMO and OFDM is MIMO OFDM system. MIMO-OFDM system converts frequency selective MIMO channel into multiple parallel flat fading channels. One of the major drawbacks of in MIMO-OFDM systems is that the transmitted signal exhibits a high PAPR when the input sequences are correlated. In this paper, AMS and PTS schemes have been used to reduce peak to average power ratio (PAPR) in multiple input multiple output orthogonal frequency division multiplexing (MIMO OFDM) system with Alamouti's space time block coding (STBC). The AMS scheme reduces the computational complexity and when AMS scheme is used with quadrature amplitude modulation (QAM). Simulation and results show that the AMS scheme reduces PAPR more efficiently than the PTS scheme.

Unifying eigen-mode MIMO transmission

Multiple-input multiple-output (MIMO) technique plays a key role in improving the spectrum and power efficiency in future mobile communication systems. Exploiting a unified MIMO transmission scheme that can adapt with various channel conditions is well motivated both in theory and practical applications. An eigen-mode based closed-loop MIMO transmission over frequency selective fading MIMO channels, which considers receive correlation, transmit correlation and line of sight (LOS) components, is investigated by maximizing the upper bound of channel capacity under the assumption that the channel is partially known at the transmitter and perfectly known at the receiver. Based on the eigen-mode transmission, several key techniques including linear precoding, stream selection, virtual spatial hopping and online capacity estimation are proposed, and a unified MIMO transmission scheme is established. Both computer simulation and field test results show that the proposed scheme can significantly improve the spectral efficiency and link reliability under various channel conditions.

Zero-forcing precoding for frequency selective MIMO channels with [formula omitted] criterion and causality constraint

We consider zero-forcing equalization of frequency selective multiple-input multiple-output (MIMO) channels by causal and linear time-invariant precoders in the presence of intersymbol interference. Our motivation is twofold. First, we are concerned with the optimal performance of causal precoders from a worst case point of view. Therefore we construct an optimal causal precoder, where contrary to other works our construction is not limited to finite or rational impulse responses. Moreover, we derive a novel numerical approach to computation of the optimal performance index achievable by causal precoders. This quantity is important in the numerical determination of optimal precoders.

A new framework for soft decision equalization in frequency selective MIMO channels

We introduce a novel framework for soft-input, soft-output (SISO) equalization in frequency selective multipleinput multiple-output (MIMO) channels based on the well-known belief propagation (BP) algorithm. As in the BP equalizer, we model the multipath channels using factor graphs (FGs) where the transmitted and received signals are represented by the function and variable nodes respectively. The edges connecting the function and variable nodes illustrate the dependencies of the multipath channel and soft decisions are developed by exchanging information on these edges iteratively. We incorporate powerful techniques such as groupwise iterative multiuser detection (IMUD), probabilistic data association (PDA) and sphere decoding (SD) in order to reduce the computational complexity of BP equalizer with relatively small degradation in performance. The computational complexity of this new reduced-complexity BP (RCBP) equalizer grows linearly with block size and memory length of the channel. The proposed framework has a flexible structure that allows for parallel as well as serial detection. We will illustrate through simulations that the RCBP equalizer can even handle overloaded scenarios where the channel matrix is rank deficient, and it can achieve excellent performance by applying iterative equalization using the low-density parity check codes (LDPC).

Frequency-selective multiple-input multiple-output channel estimation with transmitter non-linearities

The authors propose an algorithm based on the knowledge of training sequences to obtain an asymptotically unbiased estimator of non-linear multiple-input multiple-output (MIMO) channels, which involves the radio frequency front-end non-linearity and linear frequency selective MIMO channels. Although the impact of non-linearity in the transmitter side has been widely studied, most work on the channel estimation assumes linear channel models and ignores the non-linear effects. In this study, we develop a non-linear channel estimator that can simultaneously estimate the linear MIMO channel model and non-linearity of the transmitter is developed. With these two sets of parameters, the non-linear channel model can be fully described. This channel estimation algorithm is implemented over an empirical MIMO channel model using an orthogonal frequency division multiplexing system.

Spatial correlation and capacity measurements for wideband MIMO channels in indoor office environment

This paper describes a broadband multiple input, multiple output (MIMO) channel characterization platform and capacity measurement results in indoor office environment. The MIMO testbed has been designed for broadband MIMO channel sounding, capacity measurements and for characterizing the directional-multipaths of the radio propagation channel. The MIMO channel data have been collected in 5 GHz band inside a modern office environment. Capacity results from these experiments are discussed for different propagation conditions, including non line-of-sight (NLOS) and LOS propagations with various spacings between array elements. Spatial correlations are analyzed from the measured data and a frequency selective MIMO channel model based on the correlation statistics is validated. Post-processing of the measured data with a sequential ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) algorithm is utilized to extract the directions of departures and arrivals of multipath components at the transmitting and receiving arrays, respectively. A method of obtaining broadband MIMO capacity, indirectly, from a single directional measurement of MIMO propagation channel is also proposed and verified from the measurement results.

Near-Optimum Soft Decision Equalization for Frequency Selective MIMO Channels

In this paper, we develop soft decision equalization (SDE) techniques for frequency selective multiple-input multiple-output (MIMO) channels in the quest for low-complexity equalizers with error performance competitive to that of maximum likelihood (ML) sequence detection. We demonstrate that decision feedback equalization (DFE) based on soft-decisions, expressed via the posterior probabilities associated with feedback symbols, is able to outperform hard-decision DFE, with a low computational cost that is polynomial in the number of symbols to be recovered and linear in the signal constellation size. Building on the probabilistic data association (PDA) multiuser detector, we present two new MIMO equalization solutions to handle the distinctive channel memory. The first SDE algorithm adopts a zero-padded transmission structure to convert the challenging sequence detection problem into a block-by-block least-square formulation. It introduces key enhancement to the original PDA to enable applications in rank-deficient channels and for higher level modulations. The second SDE algorithm takes advantage of the Toeplitz channel matrix structure embodied in an equalization problem. It processes the data samples through a series of overlapping sliding windows to reduce complexity and, at the same time, performs implicit noise tracking to maintain near-optimum performance. With their low complexity, simple implementations, and impressive near-optimum performance offered by iterative soft-decision processing, the proposed SDE methods are attractive candidates to deliver efficient reception solutions to practical high-capacity MIMO systems. Simulation comparisons of our SDE methods with minimum-mean-square error (MMSE)-based MIMO DFE and sphere decoded quasi-ML detection are presented.

Low-Complexity Iterative Receiver for Space-Time Coded Signals over Frequency Selective Channels

We propose a low-complexity turbo-detector scheme for frequency selective multiple-input multiple-output channels. The detection part of the receiver is based on a List-type MAP equalizer which is a state-reduction algorithm of the MAP algorithm using per-survivor technique. This alternative achieves a good tradeoff between performance and complexity provided a small amount of the channel is neglected. In order to induce the good performance of this equalizer, we propose to use a whitened matched filter (WMF) which leads to a white-noise "minimum phase" channel model. Simulation results show that the use of the WMF yields significant improvement, particularly over severe channels. Thanks to the iterative turbo processing (detection and decoding are iterated several times), the performance loss due to the use of the suboptimum List-type equalizer is recovered.