1,439 research outputs found
Enhanced Compressive Wideband Frequency Spectrum Sensing for Dynamic Spectrum Access
Wideband spectrum sensing detects the unused spectrum holes for dynamic
spectrum access (DSA). Too high sampling rate is the main problem. Compressive
sensing (CS) can reconstruct sparse signal with much fewer randomized samples
than Nyquist sampling with high probability. Since survey shows that the
monitored signal is sparse in frequency domain, CS can deal with the sampling
burden. Random samples can be obtained by the analog-to-information converter.
Signal recovery can be formulated as an L0 norm minimization and a linear
measurement fitting constraint. In DSA, the static spectrum allocation of
primary radios means the bounds between different types of primary radios are
known in advance. To incorporate this a priori information, we divide the whole
spectrum into subsections according to the spectrum allocation policy. In the
new optimization model, the minimization of the L2 norm of each subsection is
used to encourage the cluster distribution locally, while the L0 norm of the L2
norms is minimized to give sparse distribution globally. Because the L0/L2
optimization is not convex, an iteratively re-weighted L1/L2 optimization is
proposed to approximate it. Simulations demonstrate the proposed method
outperforms others in accuracy, denoising ability, etc.Comment: 23 pages, 6 figures, 4 table. arXiv admin note: substantial text
overlap with arXiv:1005.180
Linear Precoding with Low-Resolution DACs for Massive MU-MIMO-OFDM Downlink
We consider the downlink of a massive multiuser (MU) multiple-input
multiple-output (MIMO) system in which the base station (BS) is equipped with
low-resolution digital-to-analog converters (DACs). In contrast to most
existing results, we assume that the system operates over a frequency-selective
wideband channel and uses orthogonal frequency division multiplexing (OFDM) to
simplify equalization at the user equipments (UEs). Furthermore, we consider
the practically relevant case of oversampling DACs. We theoretically analyze
the uncoded bit error rate (BER) performance with linear precoders (e.g., zero
forcing) and quadrature phase-shift keying using Bussgang's theorem. We also
develop a lower bound on the information-theoretic sum-rate throughput
achievable with Gaussian inputs, which can be evaluated in closed form for the
case of 1-bit DACs. For the case of multi-bit DACs, we derive approximate, yet
accurate, expressions for the distortion caused by low-precision DACs, which
can be used to establish lower bounds on the corresponding sum-rate throughput.
Our results demonstrate that, for a massive MU-MIMO-OFDM system with a
128-antenna BS serving 16 UEs, only 3--4 DAC bits are required to achieve an
uncoded BER of 10^-4 with a negligible performance loss compared to the
infinite-resolution case at the cost of additional out-of-band emissions.
Furthermore, our results highlight the importance of taking into account the
inherent spatial and temporal correlations caused by low-precision DACs
The Pros and Cons of Compressive Sensing for Wideband Signal Acquisition: Noise Folding vs. Dynamic Range
Compressive sensing (CS) exploits the sparsity present in many signals to
reduce the number of measurements needed for digital acquisition. With this
reduction would come, in theory, commensurate reductions in the size, weight,
power consumption, and/or monetary cost of both signal sensors and any
associated communication links. This paper examines the use of CS in the design
of a wideband radio receiver in a noisy environment. We formulate the problem
statement for such a receiver and establish a reasonable set of requirements
that a receiver should meet to be practically useful. We then evaluate the
performance of a CS-based receiver in two ways: via a theoretical analysis of
its expected performance, with a particular emphasis on noise and dynamic
range, and via simulations that compare the CS receiver against the performance
expected from a conventional implementation. On the one hand, we show that
CS-based systems that aim to reduce the number of acquired measurements are
somewhat sensitive to signal noise, exhibiting a 3dB SNR loss per octave of
subsampling, which parallels the classic noise-folding phenomenon. On the other
hand, we demonstrate that since they sample at a lower rate, CS-based systems
can potentially attain a significantly larger dynamic range. Hence, we conclude
that while a CS-based system has inherent limitations that do impose some
restrictions on its potential applications, it also has attributes that make it
highly desirable in a number of important practical settings
Combining frontend-based memory with MFCC features for Bandwidth Extension of narrowband speech
In this paper, we continue our previous work on improving Band-width Extension (BWE) of narrowband speech. We have shown that including memory into the parametrization frontend (through delta features) results in higher highband certainty irrespective of feature type, with MFCCs exhibiting higher correlation, in general, between both bands, reaching twice that using LSFs. By incorpo-rating memory into the frontend of a conventional LP-based BWE system, we were able to translate the higher correlation due to mem-ory into BWE performance improvement. Using high-resolution inverse DCT, we also achieved high quality speech reconstruction from MFCCs, thus enabling MFCC-based BWE with improved performance compared to conventional static LP-based BWE. We continue this work by incorporating the superior correlation prop-erties of frontend memory into our MFCC-based BWE system. Log-Spectral Distortion as well as the more perceptually-correlated Itakura-based measures show that incorporating memory into our MFCC-based BWE system results in BWE performance superior to that of our dynamic LP-based BWE system. Index Terms — Bandwidth extension, memory inclusion, high-resolution IDCT, highband certainty, mutual informatio
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