Proc. SPIE. 10211, Compressive Sensing VI: From Diverse Modalities to Big Data Analytics
KEYWORDS: Transmitters, Modulation, Data hiding, Matrices, Receivers, Phase shift keying, Linear filtering, Telecommunications, Information technology, High dynamic range imaging, Picosecond phenomena, Binary data
We introduce maximum-SINR, sparse-binary waveforms that modulate data information symbols over the entire continuum of the available/device-accessible spectrum. We present an optimal algorithm that designs the proposed waveforms by maximizing the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum- SINR linear filter at the receiver. In addition, we propose a suboptimal, computationally-efficient algorithm. Simulation studies compare the proposed sparse-binary waveforms with their conventional non-sparse binary counterparts and demonstrate their superior SINR performance. The post-filtering SINR and bit-error rate (BER) improvements attained by the proposed waveforms are also experimentally verified in a software-defined radio testbed operating in multipath laboratory environment, in the presence of colored interference.
For any given digital host image or audio file (or group of hosts) and any (block) transform domain of interest,
we find an orthogonal set of signatures that achieves maximum sum-signal-to-interference-plus-noise ratio (sum-
SINR) spread-spectrum message embedding for any fixed embedding amplitude values. We also find the sumcapacity
optimal amplitude allocation scheme for any given total distortion budget under the assumption of
(colored) Gaussian transform-domain host data. The practical implication of the results is sum-SINR, sumcapacity
optimal multiuser/multisignature spread-spectrum data hiding in the same medium. Theoretically,
the findings establish optimality of the recently presented Gkizeli-Pados-Medley multisignature eigen-design
We consider the problem of signature waveform design for code division medium-access-control (MAC) of wireless
sensor networks (WSN). In contract to conventional randomly chosen orthogonal codes, an adaptive signature
design strategy is developed under the maximum pre-detection SINR (signal to interference plus noise ratio)
criterion. The proposed algorithm utilizes slowest descent cords of the optimization surface to move toward the
optimum solution and exhibits, upon eigenvector decomposition, linear computational complexity with respect
to signature length. Numerical and simulation studies demonstrate the performance of the proposed method
and offer comparisons with conventional signature code sets.
In this work, we develop a new second-order statistics based multiuser multipath channel estimation algorithm for uplink wireless space-time coded CDMA systems. The estimation procedure is based on the parameterization, with respect to the multiuser channel response vector, of the received data covariance matrix. As a side result, we also obtain an improved covariance matrix estimator. Then we utilize both the channel and the covariance matrix estimates to obtain an estimate of the linear MMSE receiver. Simulation studies illustrate the performance improvements of the proposed estimators relative to existing methods in terms of channel estimation mean-square error as well as receiver filter output SINR and receiver BER.
In this work we consider the problem of detecting the information bit of a direct-sequence code-division-multiple-access (DS-CDMA) user in the presence of spread spectrum interference and AWGN using a multi-layer perceptron neural network receiver. We develop a fast converging adaptive training algorithm that minimizes the mean square error (MSE) at the output of the receiver. The proposed adaptive algorithm has two key features: (i) it utilizes constraints that are derived from properties of the optimum single-user decision boundary for AWGN multiple-access channels, and (ii) it embeds importance sampling principles directly into the receiver optimization process. Simulation studies illustrate the BER performance of the proposed scheme.
In this paper we quantify theoretically the effect of the desired-signal power level on the mean square filter estimation error and the normalized output signal-to-interference-plus-noise-ratio (SINR) of sample matrix inversion (SMI)-type estimates of the minimum mean-square-error (MMSE) and the linearly constrained minimum variance (LCMV) filters. We prove that in finite data support situations filters that utilize a sample average estimate of the desired-signal-absent input correlation matrix exhibit superior normalized filter output SINR and mean square filter estimation error when compared to filters that utilize a sample average estimate of the desired-signal-present input correlation matrix. Finally, we investigate pilot-assisted and decision-directed adaptive filter implementations that exhibit near desired-signal-absent SMI-filtering performance while they are trained using desired-signal-present data/observations.
n this paper, we consider the transmission of video over wireless direct-sequence code-division multiple access (DS-CDMA) channels. A layered (scalable) video source codec is used and each layer is transmitted over a different CDMA channel. Spreading codes with different lengths are allowed for each CDMA channel (multirate CDMA). Thus, a different number of chips per bit can be used for the transmission of each scalable layer. For a given fixed energy value per chip and chip rate, the selection of a spreading code length affects the transmitted energy per bit and bit rate for each scalable layer. An MPEG-4 source encoder is used to provide a two-layer SNR scalable bitstream. Each of the two layers is channel-coded using Rate-Compatible Punctured Convolutional (RCPC) codes. Then, the data are interleaved, spread, carrier-modulated and transmitted over the wireless channel. A multipath Rayleigh fading channel is assumed.
At the other end, we assume the presence of an antenna array receiver. After carrier demodulation, multiple-access-interference suppressing despreading is performed using space-time auxiliary vector (AV) filtering. The choice of the AV receiver is dictated by
realistic channel fading rates that limit the data record available for receiver adaptation and redesign. Indeed, AV filter short-data-record estimators have been shown to exhibit superior bit-error-rate performance in comparison with LMS, RLS, SMI, or 'multistage nested Wiener' adaptive filter implementations. Our experimental results demonstrate the effectiveness of multirate DS-CDMA systems for wireless video transmission.
We investigate the data-record-size requirements to meet a given performance objective in interference suppression and direction-of-arrival (DoA) estimation problems. For interference suppression problems we consider the MVDR (minimum-variance-distortionless-response) beamformer evaluated under desired-signal-present and desired-signal- absent conditions. For the former case we adopt as the figure of merit the ratio between the output variance of the ideal and the estimated SMI (sample-matrix-inversion) MVDR filter, while for the latter we examine the inverse of the corresponding ratio of the output SINRs (signal-to- interference-plus-noise ratio). For DoA estimation problems we consider the conventional and the MVDR DoA estimation algorithm and we adopt a spectrum-based performance measure that is given as a function of the ratio between the estimated and the ideal spectrum. For all cases, we derive closed form expressions that provide the data record size that is necessary to achieve a given performance confidence level in a neighborhood of the optimal performance point as well as expressions that identify the performance level that can be reached for a given data record size. This is done by utilizing close approximations of the involved probability density functions (pdfs) and Markoff-type inequalities. The practical significance of the derived expressions lies in the fact that they are functions of the number of antenna elements only while they are independent of the ideal covariance matrix which is not known in most realistic applications. As a byproduct of the above developments we derive close approximations to the pdf of the output SINR of the MVDR beamformer when the latter is estimated in the presence or in the absence of the desired signal.
We consider the problem of recovering a spread-spectrum signal in the presence of unknown highly correlated spread- spectrum interference and impulsive noise. In terms of basic system and signal model considerations, we assume availability of a narrowband adaptive antenna array that experiences additive white Gaussian noise in time and across elements, as well as impulsive disturbance that is correlated across elements. The space-time receiver design developed in this work is characterized by the following attributes: (1) Adaptive interference suppression is pursued in the joint space-time domain. (2) An adaptive parametric non-linear front-end offers effective suppression of impulsive disturbances at low computational cost. (3) An adaptive auxiliary-vector linear filter post-processor offers effective, low-complexity suppression of SS interferes that avoids eigen-decomposition and/or matrix inversion operations and leads to superior BER performance under rapid, short-data-record system adaptation. Numerical and simulation comparisons with plain and outlier resistant space-time MVDR filtering procedures are included to illustrate and support the theoretical developments.