Yung-Yi Wang

TST-MUSIC for joint DOA-delay estimation

A multiple signal classification (MUSIC)-based approach known as the time-space-time MUSIC (TST-MUSIC) is proposed to jointly estimate the directions of arrival (DOAs) and the propagation delays of a wireless multiray channel. The MUSIC algorithm for the DOA estimation is referred to as the spatial-MUSIC (S-MUSIC) algorithm. On the other hand, the temporal-MUSIC (T-MUSIC), which estimates the propagation delays, is introduced as well. Making use of the space-time characteristics of the multiray channel, the proposed algorithm-in a tree structure-combines the techniques of temporal filtering and of spatial beamforming with three one-dimensional (1-D) MUSIC algorithms, i.e., one S-MUSIC and two T-MUSIC algorithms. The incoming rays are thus grouped, isolated, and estimated. At the same time, the pairing of the estimated DOAs and delays is automatically determined. Furthermore, the proposed approach can resolve the incoming rays with very close DOAs or delays, and the number of antennas required by the TST-MUSIC algorithm can be made less than that of the incoming rays.

FSF MUSIC for Joint DOA and Frequency Estimation and Its Performance Analysis

In this paper, we present a tree-structured frequency-space-frequency (FSF) multiple signal classification (MUSIC)-based algorithm for joint estimation of the directions of arrival (DOAs) and frequencies in wireless communication systems. The proposed approach is a novel twist of parameter estimation and filtering processes, in which two one-dimensional (1-D) frequency (F)- and one 1-D space (S)-MUSIC algorithms are employed-in a tree structure-to estimate the DOAs and frequencies, respectively. In between every other MUSIC algorithm, a temporal filtering process or a spatial beamforming process, implemented by a set of complementary projection matrices, is incorporated to partition the incoming rays to enhance the estimation accuracy, so that the incoming rays can be well resolved even with very close DOAs or frequencies, using the 1-D MUSIC algorithms. Also, with such a tree-structured estimation scheme, the estimated DOAs and frequencies are automatically paired without extra computational overhead. Furthermore, some statistical analyses of the undesired residue signals propagating between the 1-D MUSIC algorithms and the mean square errors of the parameter estimates are derived to provide further insights into the proposed approach. Simulations show that the new approach can provide comparable performance, but with reduced complexity compared with previous works, and that there is a close match between the derived analytic expressions and simulation results

A Tree Structure One-Dimensional Based Algorithm for Estimating the Two-Dimensional Direction of Arrivals and Its Performance Analysis

A tree structure algorithm using one-dimensional (1D) multiple signal classification (MUSIC) algorithm is proposed to estimate the two-dimensional direction of arrivals (2D DOAs) of coherent signals impinging on a uniform rectangular array. The basic idea of the proposed algorithm is to successively apply several times of the 1D spatial smoothing MUSIC algorithm, in tree structure, to estimate the azimuth and the elevation angles independently. To optimally separate the receive signal, constrained spatial beamformers with adjustable null width are exploited in conjunction with the 1D spatial smoothing MUSIC algorithm to decompose the received signal into several signals each lead by its own 2D DOA. Performance analysis is provided to investigate the estimation bias caused by the residue signal propagating in the tree structure.

A baseband signal processing scheme for joint data frame synchronization and symbol decoding for RFID systems

We proposed a novel Viterbi-based algorithm using jiggling substates for joint data sequence detection, symbol boundary self-calibration, and signal frame synchronization for the EPC-Global Gen-2 system. The proposed algorithm first represents the data-encoded scheme as a trellis diagram, and then, as a consequence; the data sequence estimation can be carried out through the Viterbi algorithm. Moreover, time duration of the symbol waveform is iteratively adjusted to generate two substates in the Viterbi algorithm so as to trace and calibrate the symbol boundary on the fly. Compared with conventional approaches, the proposed Viterbi-based algorithm can significantly improve the system performance in terms of data detection accuracy due to its full exploitation of the baseband signal structure combining with the developed substate technique.

Joint estimation of DOA and delay using TST-MUSIC in a wireless channel

A multiple signal classification (MUSIC)-based approach, time-space-time MUSIC (TST-MUSIC), is proposed to jointly estimate the directions of arrival (DOAs) and the propagation delays of a wireless channel. The MUSIC for the DOA and the propagation delay estimation are referred to as the S-MUSIC and the T-MUSIC, respectively. Using the space-time characteristics of the multiray channel, the proposed algorithm in a tree structure combines the temporal filtering techniques and the spatial beamforming techniques with one S-MUSIC and two T-MUSICs. The incoming rays are thus grouped, isolated, and estimated.

A Subspace-Based CFO Estimation Algorithm for General ICI Self-Cancellation Precoded OFDM Systems

This paper presents a carrier-frequency-offset (CFO) estimation algorithm for time-dispersive orthogonal frequency division multiplexing (OFDM) system using a general inter-carrier interference (ICI) self-cancellation scheme. This study uses the time shift invariant property in the precoded signal to estimate the CFO. To achieve this, the proposed algorithm first collects the highly correlated receive time samples into snapshot vectors. The snapshot vectors can be expressed in a form having a CFO-directed response structure, which enables the proposed approach to estimate the CFO by using the multiple signal classfication (MUSIC) algorithm in the time domian. This study also develops a time sample selection scheme to mitigate the noise enhancement caused in the equalization process before the MUSIC algorithm. As compared to conventional algorithms, in addition to having an estimation error approaching to the Cramer-Rao lower bound (CRLB), the proposed algorithm has an adjustable CFO estimation range linearly proportional to the order of the ICI self-cancellation scheme.

Joint estimation of the DOA and delay based on the TST-ESPRIT in a wireless channel

This paper presents a one-dimensional (1D) estimation of signal parameter via rotational invariance technique (ESPRIT)-based approach, TST-ESPRIT, for jointly estimating the directions of arrival (DOAs) and the propagation delays in a wireless multiray channel. The DOAs and the propagation delays are estimated by the spatial-ESPRIT (S-ESPRIT) and by the temporal-ESPRIT (T-ESPRIT), respectively. Based on the spatial-temporal characteristics of the multiray channel, the proposed approach in a tree structure combines the temporal filtering technique and the spatial beamforming technique along with two T-ESPRITs and one S-ESPRIT. The incoming rays are thus grouped, isolated, and then estimated. At the same time, the pairing of the estimated DOAs and delays are automatically determined. Compared with previous works, the TST-ESPRIT can provide better performance with substantially reduced computational complexity. Some simulations are furnished to justify the proposed algorithm.

Estimation of CFO and STO for an OFDM using general ICI self-cancellation precoding☆

Abstract This study presents an efficient carrier-frequency-offset (CFO) and symbol-timing-offset (STO) estimation algorithm for time-dispersive orthogonal frequency division multiplexing (OFDM) system using a general inter-carrier interference (ICI) self-cancellation scheme. This study takes advantage of the time shift invariant property in the precoded signal to estimate the CFO. The proposed algorithm first stacks the receive time samples spaced by a pre-determined time interval into sample vectors, which can be expressed in a form having a CFO-directed response vector. This CFO-directed structure enables the proposed approach to estimate the CFO by using the multiple signal classification (MUSIC) algorithm in the time domain. Equalization is required before the MUSIC algorithm to remove the scaling factor generated in the signal stacking. Using the CFO estimate, the proposed approach compensates for the frequency error in the receive signal and then estimates the STO by invoking the MUSIC algorithm in the frequency domain. Unlike conventional algorithms, in addition to having a larger CFO estimation range linearly proportional to the precoding order, the proposed approach can easily handle the case of fractional STO. This study presents some statistical analysis of the undesired equalization residues and the mean square error of the perturbed MUSIC algorithm to provide further insights into the proposed approach.

Improved wavelet-based beamformers with dynamic subband selection

Adaptive beamformers, which can automatically adjust the weights to form prescribed spatial selectivity, have found applications in various facets of signal processing applications ranging from radar and sonar to wireless communications. To facilitate real-time implementations, it is of importance to mitigate the computational load in the weight adaptive process, especially for arrays which consist of many sensors in order to achieve better interference rejection and resolution. For this purpose, various attempts have been made for the design of partially adaptive beamformers, which only employ a subset of weights in the adaptive process. Despite their effectiveness, these approaches often call for computationally intensive eigendecomposition or nonlinear optimization. We address a simple, yet effective partially adaptive beamformer with the generalized sidelobe canceler (GSC) as the underlying structure. The new beamformer employs a set of P-regular, M-band wavelet filters in the design of the blocking matrix involved. The beamformer is applicable to the broadband scenario. Furthermore, a new subband selection scheme is addressed, which only retains the principal subband components in the adaptive process.

A kernel-based ICI self-cancellation scheme using constrained subcarrier combiners

Conventional ICI self-cancellation methods are spectral consuming because they modulate a single data on a group of subcarriers. To improve the spectral efficiency, the proposed approach uses a kernel-based precoder that maps at most L-1 data symbols to a group of L consecutive subcarriers. On the receive side, the carrier-frequency-offset-directed (CFO-directed) structure of the precoded signal enables the proposed approach to estimate the CFO in the frequency domain. Then, based on this CFO estimate, the proposed approach develops a set of constrained-subcarrier-combiners (CSC) to eliminate intra-group interference. Computer simulations show that in addition to achieving a high spectral efficiency proportional to the precoder order, the proposed approach can effectively eliminate the ICI caused by a large-frequency-error because of the CSC. HighlightsThis study presents a kernel-based precoder of high spectral efficiency.The kernel-based precoder suppresses the inter-group-interference at the transmitter.Subcarrier combiners are employed to mitigate the intra-group interference at the receiver.This study estimates the CFO in the frequency domain through a subspace-based algorithm.