Fangjiong Chen

Low-Complexity ML Detector and Performance Analysis for OFDM With In-Phase/Quadrature Index Modulation

A novel variant of orthogonal frequency division multiplexing (OFDM) techniques, which carries additional information bits through the index domain including in-phase and quadrature dimensions, is recently proposed. For its nature, we refer to this technique as OFDM with in-phase/quadrature index modulation (OFDM-I/Q-IM). In this letter, we propose a novel low-complexity detector based on the maximum-likelihood (ML) criterion for OFDM-I/Q-IM, which does not need to know the variance of the noise and the possible realizations of the active subcarrier indices. With the proposed ML detector, the asymptotic average bit error probability (ABEP) and the exact coding gain achieved by OFDM-I/Q-IM are also derived.

ESPRIT-Like Two-Dimensional DOA Estimation for Coherent Signals

A second-order statistics-based algorithm is proposed for two-dimensional (2D) direction-of-arrival (DOA) estimation of coherent signals. The problem is solved by arranging the elements of the correlation matrix of the signal received from a uniform rectangular array to a block Hankel matrix. In noiseless cases, it is shown that the rank of the block Hankel matrix equals the number of the DOAs and is independent of the coherency of the incoming waves. Therefore, the signal subspace of the block Hankel matrix can be estimated properly and spans the same column space as the array response matrix. Two matrix pencil pairs containing the DOA parameters are extracted from the signal subspace. This matrix pencil-based estimation problem is then resolved using our previously proposed pairing-free 2D parameter estimation algorithm. Simulation results show that the proposed algorithm outperforms the spatial smoothing method in terms of mean square error (MSE).

Estimation of Two-Dimensional Frequencies Using Modified Matrix Pencil Method

The problem of multiple two-dimensional (2-D) sinusoidal frequency estimation is considered. A modified matrix pencil method is proposed to simultaneously estimate the frequency pairs in the signal, thereby bypassing the computationally expensive pairing operation as seen in the literature. Simulation results show that the accuracy of the estimates for each frequency from our technique is better than or comparable to that of existing methods and the variance of the estimates are close to the Crameacuter-Rao bound. Simulation results also show that our method provides accurate and consistent frequency estimation results that other methods cannot provide with less or comparable computational complexity

Multiple-Input Multiple-Output OFDM With Index Modulation: Low-Complexity Detector Design

Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM), which provides a flexible trade-off between spectral efficiency and error performance, is recently proposed as a promising transmission technique for energy-efficient 5G wireless communications systems. However, due to the dependence of subcarrier symbols within each subblock and the strong interchannel interference, it is challenging to detect the transmitted data effectively while imposing low computational burden to the receiver. In this paper, we propose two types of low-complexity detectors based on the sequential Monte Carlo (SMC) theory for the detection of MIMO-OFDM-IM signals. The first detector draws samples independently at the subblock level, while the second detector draws samples at the subcarrier level with further reduced complexity. To meet the constraint of the subcarrier combinations within each subblock, the second detector is further coupled with a carefully designed legality examination method. Attributed to the effectiveness of legality examination and deterministic SMC sampling, both proposed detectors achieve near-optimal error performance for the MIMO-OFDM-IM system.

A closed-form expanded autocorrelation method for frequency estimation of a sinusoid

A closed-form expanded autocorrelation method for real single-tone frequency estimation is proposed. Firstly, the modified covariance (MC) method based on multiple autocorrelation lags is applied to provide a coarse frequency estimate. Then, a closed-form adjustment term based on a least square cost function is derived to get the fine frequency estimate. Simulation results show that the performance of the proposed algorithm, when compared with several existing closed-form time-domain estimators, is closer to the Cramer-Rao bound (CRB). Moreover, the proposed method has lower computation complexity than other autocorrelation-based approaches, which also use multiple autocorrelation lags.

Normalized Adaptive Channel Equalizer Based on Minimal Symbol-Error-Rate

Existing minimum-symbol-error-rate equalizers were derived based on the symbol-error-rate objective function. Due to the complexity of the objective function the derivation is not straightforward. In this paper we present a new approach to derive the minimum-symbol-error-rate adaptive equalizers. The problem is formulated as minimizing the norm between two subsequent parameter vectors under the constraint of symbol-error-rate minimization. The constrained optimization problem then is solved with the Lagrange multiplier method, which results in an adaptive algorithm with normalization. Simulation results show that the proposed algorithm outperforms the existing adaptive minimum-symbol-error-rate equalizer in convergence speed and steady-state performance.

Closed-form frequency estimator based on narrow-band approximation under noisy environment

A new method for single sinusoidal frequency estimation in closed-form formula is proposed. Since sinusoidal signals are narrow-banded and white noise distribution is statistically equal in the whole spectrum, a narrow-band signal extracted from the Fourier transform of the original signal can be used to approximate the noise-corrupted sinusoidal signal. A concise closed-form formula is then deduced to estimate the frequency based on the narrow-band signal. Performance analysis and simulation results are presented, showing that the new algorithm has close performance to the Cramer-Rao bound, especially under low SNRs. It is also demonstrated that the method can be easily generalized to multi-sinusoidal signals.

Blind linear channel estimation using genetic algorithm and SIMO model

In this paper, we propose to use genetic algorithm (GA) to solve the blind infinite-impulse-response (IIR) channel estimation problem. The contributions of this paper are three-fold: (1) We prove that by oversampling the output of a single-input-single-output IIR channel, one can build a single-input-multiple-output (SIMO) model in which the subchannels are IIR channels with the same Autoregressive (AR) order and coefficients. (2) Based on this SIMO model, we further develop a second-order statistics based objective function that includes the unknown model order and parameters whereas most of the existing work must assume the channel order is known in advance. (3) A GA is proposed to deal with this optimisation problem in that we encode the model order and parameters into one single chromosome. Therefore the order and parameters can be estimated simultaneously. Computer simulation results indicate the effectiveness of the proposed algorithms.

Equiprobable Subcarrier Activation Method for OFDM With Index Modulation

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) conveys additional information bits via the indices of active subcarriers. Consequently, the determination of the active subcarriers according to the incoming bits arises as a challenging problem. The existing solution resorts to the lexicographic ordering, which leads to a low implementation complexity but an unequal subcarrier activation probability. This letter proposes a distinct solution that allows the equiprobable activation of all OFDM-IM subcarriers with comparable implementation complexity. The signal-to-noise ratio gain achieved by the proposed solution is also analyzed. Computer simulations reveal the advantages of the proposed solution.

Robust Timing Estimation Method for OFDM Systems With Reduced Complexity

As a modification of Hamed and Mahrokhs (HM) method, a preamble based timing offset estimation method for orthogonal frequency division multiplexing (OFDM) systems is presented. To make the estimator simpler in computation and keep its immunity to carrier frequency offset (CFO), the local autocorrelation sequence of the known preamble is mapped into an elaborately simplified one. The performance of the proposed estimator is evaluated by mean square error (MSE). Computer simulations under three different Rayleigh fading channels show that the proposed method achieves the same performance as HMs method with only half of its computational complexity.