A new approach to layered space-time coding and signal processing

The information-theoretic capacity of multiple antenna systems has been shown to be significantly higher than that of single antenna systems in Rayleigh-fading channels. In an attempt to realize this capacity, Foschini (1996) proposed the layered space-time architecture. This scheme was argued to asymptotically achieve a lower bound on the capacity. Another line of work has focused on the design of channel codes that exploit the spatial diversity provided by multiple transmit antennas (Tarokh et al. 1998, Hammons and Gamal 2000). In this paper, we take a fresh look at the problem of designing multiple-input-multiple-output (MIMO) wireless systems. First, we develop a generalized framework for the design of layered space-time systems. Then, we present a novel layered architecture that combines efficient algebraic code design with iterative signal processing techniques. This novel layered system is referred to as the threaded space-time (TST) architecture. The TST architecture provides more flexibility in the tradeoff between power efficiency, bandwidth efficiency, and receiver complexity. It also allows for exploiting the temporal diversity provided by time-varying fading channels. Simulation results are provided for the various techniques that demonstrate the superiority of the proposed TST architecture over both the diagonal layered space-time architecture in Foschini (1996) and the multilayering approach (Tarokh et al. (1999).     

A statistical approach to subspace based blind identification

Blind identification of single input multiple output systems is considered herein. The low-rank structure of the output signal is exploited to blindly identify the channel using a subspace fitting framework. Two approaches based on a minimal linear parameterization of a subspace are presented and analyzed. The asymptotically best consistent estimate is derived for the class of blind subspace-based techniques. The asymptotic estimation error covariance of the subspace estimates is derived, and the corresponding covariance of the statistically optimal estimates provides a lower bound on the estimation error covariance of subspace methods. A two-step procedure involving only linear systems of equations is presented that asymptotically achieves the bound. Simulations and numerical examples are provided to compare the two approaches     

Application of signal subspace approach to high-resolution narrowband array processing

The paper first briefly reviews some subspace techniques for high-resolution array processing. It is shown that existing high-resolution techniques like the MUSIC algorithm are based on visual inspection of the spatial spectrum. It is not a scientifically valid means of assessing resolution of a spectrum estimator. The paper then proposes a technique based on a combination of optimal processing and signal subspace extraction for high-resolution array processing. Numerical results show that the proposed technique not only achieves superresolution of the spectrum, but also provides power estimates of the arrivals.     

A subspace rotation approach to signal parameter estimation

A new approach to Estimation of Signal Parameters by Rotational Invariance Techniques (ESPRIT) is described in the context of direction-of-arrival estimation, but is also applicable to other problems. The method relies on finding the underlying rotation between the common subspaces associated with an array of pairwise-matched and codirectional sensor doublets. ESPRIT has several remarkable advantages over earlier techniques such as MUSIC, and also provides asymptotically unbiased and efficient estimates.     

A digital signal processing approach to interpolation

In many digital signal precessing systems, e.g., vacoders, modulation systems, and digital waveform coding systems, it is necessary to alter the sampling rate of a digital signal Thus it is of considerable interest to examine the problem of interpolation of bandlimited signals from the viewpoint of digital signal processing. A frequency dmnain interpretation of the interpolation process, through which it is clear that interpolation is fundamentally a linear filtering process, is presented, An examination of the relative merits of finite duration impulse response (FIR) and infinite duration impulse response (IIR) digital filters as interpolation filters indicates that FIR filters are generally to be preferred for interpolation. It is shown that linear interpolation and classical polynomial interpolation correspond to the use of the FIR interpolation filter. The use of classical interpolation methods in signal processing applications is illustrated by a discussion of FIR interpolation filters derived from the Lagrange interpolation formula. The limitations of these filters lead us to a consideration of optimum FIR filters for interpolation that can be designed using linear programming techniques. Examples are presented to illustrate the significant improvements that are obtained using the optimum filters.     

A signal subspace-based subband approach to space-time adaptiveprocessing for mobile communications

In this paper, we present a novel space-time signal subspace-based subband approach to space-time adaptive processing (STAP) that has been shown to be an effective method to suppress both the intersymbol interference (ISI) and the cochannel interference (CCI) in mobile communications. We first study the performance of STAP and make clear the conditions of perfect processing (i.e., perfect equalization of the desired user signal and perfect suppression of CCI signals). Based on the polyphase representation and the subspace analysis of the signal channels, we propose a space-time signal subspace-based subband approach to STAP, namely the subband STAP, which highly improves the convergence rate without loss of the steady-state performance. Simulation results show its effectiveness under the procedure of signal subspace estimation and detection     

A robust adaptive array based on signal subspace approach

In this note, we propose a signal subspace approach that improves the performance of a beam steered adaptive array in the presence of steering errors due to look-direction error (LDE) and/or random steering error (RSE). In the method, the degrees of freedom (DOF) are reduced so as not to cancel the desired signal while preserving the optimal characteristic of the array, and thus the weights of the array are determined by a linear combination of the eigenvectors of the signal subspace. The proposed method works as far as the eigen decomposition of the input covariance matrix into signal and noise subspaces is possible. The proposed method improves noticeably the array performance of the beam steered array in the presence of steering errors and provides the optimum array performance in the absence of steering errors     

A signal processing approach to symmetry detection.

We present an algorithm that detects rotational and reflectional symmetries of two-dimensional objects. Both symmetry types are effectively detected and analyzed using the angular correlation (AC), which measures the correlation between images in the angular direction. The AC is accurately computed using the pseudopolar Fourier transform, which rapidly computes the Fourier transform of an image on a near-polar grid. We prove that the AC of symmetric images is a periodic signal whose frequency is related to the order of the symmetry. This frequency is recovered via spectrum estimation, which is a proven technique in signal processing with a variety of efficient solutions. We also provide a novel approach for finding the center of symmetry and demonstrate the applicability of our scheme to the analysis of real images.     

Image enhancement based on signal subspace approach

This paper describes a block-by-block basis image enhancement algorithm which uses the signal subspace method to enhance images corrupted by uncorrelated additive noise. The enhancement is performed by eliminating the noise components in the noise subspace and estimating the clean image from the remaining components in the signal subspace.     

A subspace blind channel estimation method for OFDM systems without cyclic prefix

We propose a subspace based blind channel estimation method for orthogonal frequency-division multiplexing (OFDM) systems over a time-dispersive channel. Our approach is motivated by the resemblance of the multichannel signal model resulting from oversampling (or use of multiple receive sensors) of the received OFDM signal to that in conventional single carrier system. The proposed algorithm distinguishes itself from many previously reported channel estimation methods by the elimination of the cyclic prefix, thereby leading to higher channel utilization. Comparison of the proposed method with other two reported subspace channel estimation methods is presented by computer simulations to support its effectiveness.     

Ultrafast optical signal processing based upon space-time dualities

The last two decades have seen a wealth of optical instrumentation based upon the concepts of space-time duality. A historical overview of how this beautiful framework has been exploited to develop instruments for optical signal processing is presented. The power of this framework is then demonstrated by reviewing four devices in detail based upon space-time dualities that have been experimentally demonstrated: 1) a time-lens timing-jitter compensator for ultralong-haul dense-wavelength-division-multiplexed dispersion-managed soliton transmission, 2) a multiwavelength pulse generator using time-lens compression, 3) a programmable ultrafast optical delay line by use of a time-prism pair, and 4) an enhanced ultrafast optical delay line by use of soliton propagation between a time-prism pair.     

Zero-forcing blind equalization based on subspace estimation formultiuser systems

Input signal recovery from frequency-selective fading channels is a problem of great theoretical and practical importance. We present several new blind algorithms that utilize second-order statistics for direct multichannel equalization. The algorithms are based on the subspace extraction of a preselected block column of the channel convolution matrix. For a multiuser system, user signal separation can be achieved based on partial information of the composite channel response. These equalization algorithms do not rely on the precise separation of signal and noise subspaces and therefore tend to be less sensitive to channel order (or column rank) estimation errors. Equalization is directly achieved without channel identification. Furthermore, the equalizability conditions of these algorithms are discussed     

RF photonics signal processing in subcarrier multiplexed optical-label switching communication systems

This paper provides theoretical and experimental studies of radio-frequency (RF) photonics processing techniques applicable in subcarrier-multiplexed optical-label switching (OLS) communications systems. The paper provides an overview of various label-coding technologies and introduces subcarrier multiplexing (SCM) as an attractive technology for OLS networks. All-optical-label extraction using optical filters, such as fiber Bragg gratings (FBGs), provides an effective means to demodulate the SCM labels without inducing RF fading effects caused by fiber dispersion. Furthermore, the role of fiber nonlinearities in the RF fading effects are theoretically and experimentally verified. The all-optical label extraction and rewriting processes constitute optical-label swapping, wherein 2R data regeneration can take place. Scalable and cascadable OLS systems are feasible by applying viable RF photonics technologies in all-optical-label processing.     

Subspace approach to blind and semi-blind channel estimation for space-time block codes

A new approach to blind and semi-blind channel estimation for space-time block codes (STBCs) is presented. By exploiting the recently proposed generalized space-time block code framework, an STBC system is reduced from multiple-input multiple-output to a single-input multiple-output system. A conventional blind algorithm based on the subspace algorithm is then used to identify the channel impulse responses and achieve equalization. Simulation results demonstrate the performance of the proposed algorithm and a comparable scheme.     

A multichannel subspace approach with signal presence probability for speech enhancement

Multidimensional Systems and Signal Processing - For the last few decades, speech enhancement based on microphone arrays has primarily utilized prior information about system models, e.g., array...     

A digital signal processing approach to real-time AC motor modeling

Discrete-time computational models of squirrel-cage AC motors are derived and analyzed for the purpose of advanced motor control. The starting point is a continuous-time model defined by a pair of simultaneous complex-coefficient differential equations. Discrete-time models are derived using both the bilinear transformation and the forward-difference approximation. The exact dependence between stability and the minimum sampling rate in the forward-difference approach is shown. The responses of the two discretized models are compared using real-world motor parameters. Using the forward difference method, a more compact implementation is obtained (although a higher sampling rate is required) as compared to the bilinear transformed model of equivalent performance and 2.7 times higher computational complexity. A 16/24-b DSP-ASIC coprocessor prototype implementing the AC motor model is introduced     

Ordered array processing for space-time coded systems

The zero-forcing interference suppression and cancellation scheme proposed by Tarokh et al., in 1999, for grouped space time coded system suffers performance loss due to limited diversity gains for the first detected antenna groups, and power allocation was suggested to overcome this problem. In this letter, we propose an ordered detection algorithm which compensates the diversity loss by always detecting the group with the strongest post-ing signal-to-noise ratio. Compared with the power allocation scheme, our proposed scheme is more robust to varying channel conditions and when used with iterative interference cancellation, a performance gain of 2.4 dB can be obtained at a frame-error rate rate of 10/sup -2/.     

The SpectrumWare approach to wireless signal processing

The SpectrumWare project is applying a software oriented approach to wireless communication and distributed signal processing. Advances in processor and analog-to-digital conversion technology have made it possible to implementvirtual radios that directly sample wide bands of the RF spectrum and process these samples in application software. The elimination of dedicated hardware introduces tremendous flexibility into a wireless communication system. Our approach goes further than the software implementation of traditional signal processing functions. We use processor and network memory to temporally decouple the sample streams from the software modules so that the bulk of the processing can be realized within virtual time programming environments. Decoupling relaxes the temporal constraints on the processing algorithms and their execution. This paper describes the SpectrumWare concept, our experimental approach, and the implications that this approach has for wireless signal processing algorithms.A previous version of this work was presented at Mobicom '95, Berkeley, CA, USA.     

On subspace methods for blind identification of single-inputmultiple-output FIR systems

Blind identification of single-input multiple-output (SIMO) FIR systems based on second-order statistics has attracted a great deal of research effort. We focus on subspace estimation procedures, which exploit the structure of the range space of certain matrix-valued statistics constructed by arranging in a prescribed order the covariance coefficients of the observations. General subspace identifiability results are obtained, based on properties of minimal polynomial bases of rational subspaces. Several subspace estimation procedures are then derived. These estimators are all based on a weighted least-square solution of an overdetermined system of linear equations. An asymptotic statistical analysis of these estimators is carried out to evaluate the potential of these methods and the impact of the weighting