674 research outputs found
A Belief Propagation Based Framework for Soft Multiple-Symbol Differential Detection
Soft noncoherent detection, which relies on calculating the \textit{a
posteriori} probabilities (APPs) of the bits transmitted with no channel
estimation, is imperative for achieving excellent detection performance in
high-dimensional wireless communications. In this paper, a high-performance
belief propagation (BP)-based soft multiple-symbol differential detection
(MSDD) framework, dubbed BP-MSDD, is proposed with its illustrative application
in differential space-time block-code (DSTBC)-aided ultra-wideband impulse
radio (UWB-IR) systems. Firstly, we revisit the signal sampling with the aid of
a trellis structure and decompose the trellis into multiple subtrellises.
Furthermore, we derive an APP calculation algorithm, in which the
forward-and-backward message passing mechanism of BP operates on the
subtrellises. The proposed BP-MSDD is capable of significantly outperforming
the conventional hard-decision MSDDs. However, the computational complexity of
the BP-MSDD increases exponentially with the number of MSDD trellis states. To
circumvent this excessive complexity for practical implementations, we
reformulate the BP-MSDD, and additionally propose a Viterbi algorithm
(VA)-based hard-decision MSDD (VA-HMSDD) and a VA-based soft-decision MSDD
(VA-SMSDD). Moreover, both the proposed BP-MSDD and VA-SMSDD can be exploited
in conjunction with soft channel decoding to obtain powerful iterative
detection and decoding based receivers. Simulation results demonstrate the
effectiveness of the proposed algorithms in DSTBC-aided UWB-IR systems.Comment: 14 pages, 12 figures, 3 tables, accepted to appear on IEEE
Transactions on Wireless Communications, Aug. 201
Echo Cancellation : the generalized likelihood ratio test for double-talk vs. channel change
Echo cancellers are required in both electrical (impedance mismatch) and acoustic (speaker-microphone coupling) applications. One of the main design problems is the control logic for adaptation. Basically, the algorithm weights should be frozen in the presence of double-talk and adapt quickly in the absence of double-talk. The optimum likelihood ratio test (LRT) for this problem was studied in a recent paper. The LRT requires a priori knowledge of the background noise and double-talk power levels. Instead, this paper derives a generalized log likelihood ratio test (GLRT) that does not require this knowledge. The probability density function of a sufficient statistic under each hypothesis is obtained and the performance of the test is evaluated as a function of the system parameters. The receiver operating characteristics (ROCs) indicate that it is difficult to correctly decide between double-talk and a channel change, based upon a single look. However, detection based on about 200 successive samples yields a detection probability close to unity (0.99) with a small false alarm probability (0.01) for the theoretical GLRT model. Application of a GLRT-based echo canceller (EC) to real voice data shows comparable performance to that of the LRT-based EC given in a recent paper
Detection in the presence of surprise or undernulled interference
We consider the problem of detecting a signal of interest
in the presence of colored noise, in the case of a covariance mismatch between the test cell and the training samples. More precisely, we consider a situation where an interfering signal (e.g., a sidelobe target or an undernulled interference) is present in the test cell and not in the secondary data. We show that the adaptive coherence estimator (ACE) is the generalized likelihood ratio test for such a problem, which may explain the previously observed fact that theACE has excellent sidelobe rejection capability, at the price of low mainlobe target sensitivity
Efficient Direct Detection of M-PAM Sequences with Implicit CSI Acquisition for The FSO System
Compared to on-off keying (OOK), M-ary pulse amplitude modulation (M-PAM,
M>2) is more spectrally efficient. However, to detect M-PAM signals reliably,
the requirement of accurate channel state information is more stringent.
Previously, for OOK systems, we have developed a receiver that requires few
pilot symbols and can jointly detect the data sequence and estimate the unknown
channel gain implicitly. In this paper, using the same approach, we extend our
previous work and derive a generalized receiver for M-PAM systems. A
Viterbi-type trellis-search algorithm coupled with a selective-store strategy
is adopted, resulting in a low implementation complexity and a low memory
requirement. Therefore, the receiver is efficient in terms of energy, spectra,
implementation complexity and memory. Using theoretical analysis, we show that
its error performance approaches that of maximum likelihood detection with
perfect knowledge of the channel gain, as the observation window length
increases. Also, simulation results are presented to justify the theoretical
analysis.Comment: 6 page
Coherent network analysis for continuous gravitational wave signals in a pulsar timing array: Pulsar phases as extrinsic parameters
Supermassive black hole binaries are one of the primary targets for
gravitational wave searches using pulsar timing arrays. Gravitational wave
signals from such systems are well represented by parametrized models, allowing
the standard Generalized Likelihood Ratio Test (GLRT) to be used for their
detection and estimation. However, there is a dichotomy in how the GLRT can be
implemented for pulsar timing arrays: there are two possible ways in which one
can split the set of signal parameters for semi-analytical and numerical
extremization. The straightforward extension of the method used for continuous
signals in ground-based gravitational wave searches, where the so-called pulsar
phase parameters are maximized numerically, was addressed in an earlier paper
(Wang et al. 2014). In this paper, we report the first study of the performance
of the second approach where the pulsar phases are maximized semi-analytically.
This approach is scalable since the number of parameters left over for
numerical optimization does not depend on the size of the pulsar timing array.
Our results show that, for the same array size (9 pulsars), the new method
performs somewhat worse in parameter estimation, but not in detection, than the
previous method where the pulsar phases were maximized numerically. The origin
of the performance discrepancy is likely to be in the ill-posedness that is
intrinsic to any network analysis method. However, scalability of the new
method allows the ill-posedness to be mitigated by simply adding more pulsars
to the array. This is shown explicitly by taking a larger array of pulsars.Comment: 30 pages, 11 figures, revised version, published in Ap
Adaptive detection of distributed targets in compound-Gaussian noise without secondary data: A Bayesian approach
In this paper, we deal with the problem of adaptive detection of distributed targets embedded in colored noise modeled in terms of a compound-Gaussian process and without assuming that a set of secondary data is available.The covariance matrices of the data under test share a common structure while having different power levels. A Bayesian approach is proposed here, where the structure and possibly the power levels are assumed to be random, with appropriate distributions. Within this framework we propose GLRT-based and ad-hoc detectors. Some simulation studies are presented to illustrate the performances of the proposed algorithms. The analysis indicates that the Bayesian framework could be a viable means to alleviate the need for secondary data, a critical issue in heterogeneous scenarios
Adaptive detection with bounded steering vectors mismatch angle
We address the problem of detecting a signal of interest (SOI), using multiple observations in the primary data, in a background of noise with unknown covariance matrix. We consider a situation where the signal signature is not known perfectly, but its angle with a nominal and known signature is bounded. Furthermore, we consider a possible scaling
inhomogeneity between the primary and the secondary noise covariance matrix. First, assuming that the noise covariance matrix is known, we derive the generalized-likelihood ratio test (GLRT), which involves solving a semidefinite programming problem. Next, we substitute the unknown
noise covariance matrix for its estimate obtained from secondary data, to yield the final detector. The latter is compared with a detector that assumes a known signal signature
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