Hongya Ge

A new full-rate full-diversity orthogonal space-time block coding scheme

In this work, we present a new space-time orthogonal coding scheme with full-rate and full-diversity. The proposed space-time coding scheme can be used on quaternary phase-shift keyed (QPSK) transceiver systems with four transmit antennas and any number of receivers. An additional feature is that the coded signals transmitted through all four transmit antennas do not experience any constellation expansion. The performance of the proposed coding scheme is studied in comparison with that of 1/2-rate full-diversity orthogonal space-time code, quasi-orthogonal code, as well as constellation-rotated quasi-orthogonal code. Our study shows that the proposed coding scheme offers full rate and outperforms the 1/2-rate orthogonal codes as well as full-rate quasi-orthogonal codes when the signal-to-noise increases. Compared to the constellation-rotated quasi-orthogonal codes (the improved QO scheme), the newly proposed code has the advantage of not expanding the signal constellation at each transmit antenna. The performance of the newly proposed code is comparable to that of the improved QO scheme.

Low-complexity multiuser detection and reduced-rank Wiener filters for ultra-wideband multiple access

Realizing the large user capacity planned for ultra-wideband (UWB) systems motivates multiuser detection (MUD). However, it is impractical to implement conventional chip-rate MUD methods, because UWB signaling gives rise to high detection complexity and difficulty in capturing energy scattered by dense multipath. In this paper, we develop a reception model for UWB multiple access based on frame-rate sampled signals in lieu of chip-rate samples. This model enables low-complexity MUD, of which we examine a reduced-rank Wiener filter for blind symbol detection. We show that frame-rate UWB samples have a small number of distinct eigenvalues in the data covariance matrix, resulting in warp convergence of reduced-rank filtering. The proposed MUD method exhibits good performance at low complexity, even in the presence of strong frequency-selective multipath fading.

Fast maximum likelihood estimation of signal parameters using the shape of the compressed likelihood function

A computationally efficient fast maximum-likelihood (FML) estimation scheme, which makes use of the shape of the surface of the compressed likelihood function (CLF), is proposed. The scheme uses only multiple one-dimensional searches oriented along appropriate ridges on the surface of the CLF. Simulations indicate that the performances of the proposed estimators match those of the corresponding maximum-likelihood estimators with very high probability. The approach is demonstrated by applying it to two different problems. The first problem involves the estimation of time of arrival and Doppler compression of a wideband hyperbolic frequency modulated (HFM) active sonar signal buried in reverberation. The second problem deals with estimating the frequencies of sinusoids. A threshold analysis of the proposed scheme is carried out to predict the signal-to-noise ratio (SNR) at which large estimation errors begin to occur, i.e., the threshold SNR, and its computational complexity is discussed. >

Data Dimension Reduction Using Krylov Subspaces: Making Adaptive Beamformers Robust to Model Order-Determination

In this work, we present a class of low-complexity reduced-dimension adaptive beamformers constructed from expanding Krylov subspaces. We demonstrate how the data dimensionality reduction obtained from Krylov pre-processing decreases the sensitivity of reduced-rank adaptive beamforming techniques to incorrect model-order selection and lessens the computational complexity of systems involving large arrays with many elements. An important advantage of the proposed dimensionality reduction scheme is that it relieves reduced-rank methods from the stringent requirement on the precise model order determination

Warp convergence in conjugate gradient Wiener filters

In this work, we present interesting case studies that lead to new and deeper results on fast convergence of reduced-rank conjugate gradient (RRCG) Wiener filters (WF), for applications in communications and sensor array signal processing. We discover that for signal modes with a specially structured Gram matrix, which induces L groups of distinct eigenvalues in the data covariance matrix, a fast and predictable convergence, in at most L steps, can be achieved when the RRCG WF is used to detect, and/or to focus on, the desired signal mode. For such applications, given knowledge of the repeated eigenstructure of the Gram matrix of signal modes or of the measurement covariance matrix, a RRCG Wiener filter, of at most rank L, delivers the same performance as the full-rank Wiener filter. Typically L is much less than the rank of the Gram matrix.

Statistical characterization of multiple-input multiple-output (MIMO) channel capacity

We derive precise as well as good approximate statistical characterizations, such as probability density functions and statistical moments of capacity, for different MIMO wireless channels. The characterizations help us to understand and predict the capacity gain expected from the MIMO technique, in terms of system parameters and channel dimension. It also helps us to design space-time modulation schemes that can take full advantage of the MIMO link for various wireless channels.

Reduced-rank multiuser detectors based on vector and matrix conjugate gradient Wiener filters

Using the notion of expanding subspace and the framework of reduced-rank signal processing, we present our latest discovery on applying the vector and matrix conjugate gradient (CG) methods to design reduced-rank linear MMSE multiuser detectors (MUD) for code division multiple access (CDMA) systems. We show that for a synchronous CDMA system with K users, each using a distinct length N spreading code, the vector CG method converges to the full-rank linear MUD in at most K steps (K/spl les/N).The matrix CG method converges to the full-rank linear MUD in one step. Furthermore, when the Gold codes are used as spreading codes in combination with a groupwise power control scheme, early convergence in the vector CG Wiener filter can be reached in just L steps (L/spl Lt/K/spl les/N), typically L=2/spl sim/4 independent of the user number K and the spreading length N.

Performance analysis of Krylov space adaptive beamformers

The performance of Krylov subspace-based dimensionality reduction for adaptive beamforming is analyzed using a simple second-order Taylor series approximation to the mean output signal-to-noise ratio (SNR). It is shown that the predicted SNRs accurately follow the experimentally measured SNR and explain the threshold effects when the angles or spacing are varied between the signal mode (subspace) and interference modes (subspace). Furthermore, we discuss how the SNR approximation can be applied to calculating the deflection of a Krylov subspace dimension-reduced Capons test statistic.

Resolving ambiguities in estimating spatial frequencies in sparse linear array

The authors extend previous work on ambiguity resolution in sparse linear prediction to array signal processing. A non-uniformly spaced linear array with minimum number of sensors is introduced in order to obtain high resolution spatial frequency estimates at reduced computation and at minimum hardware cost. By properly choosing inter-element spacing, combining and processing subarray outputs in parallel, a computationally simple algorithm is proposed for unambiguously estimating spatial frequencies of the source signals.<<ETX>>

Space-time coding for wireless sensor networks with cooperative routing diversity

In this work, the idea of cross-layer design for wireless sensor networks is exploited to improve the network performance. We present a new energy efficient cooperative routing scheme with space diversity using space-time block codes (STBCs) as well as the link quality. In our solution, the selected multiple nodes act as multiple transmitting and receiving antennas. Full diversity from the orthogonal STBC is utilized to overcome multipath fading and to enhance power efficiency. The steady state network performance measures, such as, network throughput and delay are analyzed via Markov chain modelling. Compared with the traditional single relay routing method and the single receiving diversity routing method, our proposed method outperforms the other two in low SNR environments and provides higher throughput and similar delay in high SNR environments.