173,415 research outputs found
How to Solve the Fronthaul Traffic Congestion Problem in H-CRAN?
The design of efficient wireless fronthaul connections for future heterogeneous networks incorporating emerging paradigms such as heterogeneous cloud radio access network (H-CRAN) has become a challenging task that requires the most effective utilization of fronthaul network resources. In this paper, we propose and analyze possible solutions to facilitate the fronthaul traffic congestion in the scenario of Coordinated Multi-Point (CoMP) for 5G cellular traffic which is expected to reach ZetaByte by 2017. In particular, we propose to use distributed compression to reduce the fronthaul traffic for H-CRAN. Unlike the conventional approach where each coordinating point quantizes and forwards its own observation to the processing centre, these observations are compressed before forwarding. At the processing centre, the decompression of the observations and the decoding of the user messages are conducted in a joint manner. Our results reveal that, in both dense and ultra-dense urban small cell deployment scenarios, the usage of distributed compression can efficiently reduce the required fronthaul rate by more than 50% via joint operation
Bayesian Lower Bounds for Dense or Sparse (Outlier) Noise in the RMT Framework
Robust estimation is an important and timely research subject. In this paper,
we investigate performance lower bounds on the mean-square-error (MSE) of any
estimator for the Bayesian linear model, corrupted by a noise distributed
according to an i.i.d. Student's t-distribution. This class of prior
parametrized by its degree of freedom is relevant to modelize either dense or
sparse (accounting for outliers) noise. Using the hierarchical Normal-Gamma
representation of the Student's t-distribution, the Van Trees' Bayesian
Cram\'er-Rao bound (BCRB) on the amplitude parameters is derived. Furthermore,
the random matrix theory (RMT) framework is assumed, i.e., the number of
measurements and the number of unknown parameters grow jointly to infinity with
an asymptotic finite ratio. Using some powerful results from the RMT,
closed-form expressions of the BCRB are derived and studied. Finally, we
propose a framework to fairly compare two models corrupted by noises with
different degrees of freedom for a fixed common target signal-to-noise ratio
(SNR). In particular, we focus our effort on the comparison of the BCRBs
associated with two models corrupted by a sparse noise promoting outliers and a
dense (Gaussian) noise, respectively
Online Monaural Speech Enhancement Using Delayed Subband LSTM
This paper proposes a delayed subband LSTM network for online monaural
(single-channel) speech enhancement. The proposed method is developed in the
short time Fourier transform (STFT) domain. Online processing requires
frame-by-frame signal reception and processing. A paramount feature of the
proposed method is that the same LSTM is used across frequencies, which
drastically reduces the number of network parameters, the amount of training
data and the computational burden. Training is performed in a subband manner:
the input consists of one frequency, together with a few context frequencies.
The network learns a speech-to-noise discriminative function relying on the
signal stationarity and on the local spectral pattern, based on which it
predicts a clean-speech mask at each frequency. To exploit future information,
i.e. look-ahead, we propose an output-delayed subband architecture, which
allows the unidirectional forward network to process a few future frames in
addition to the current frame. We leverage the proposed method to participate
to the DNS real-time speech enhancement challenge. Experiments with the DNS
dataset show that the proposed method achieves better performance-measuring
scores than the DNS baseline method, which learns the full-band spectra using a
gated recurrent unit network.Comment: Paper submitted to Interspeech 202
Multi-scale Multi-band DenseNets for Audio Source Separation
This paper deals with the problem of audio source separation. To handle the
complex and ill-posed nature of the problems of audio source separation, the
current state-of-the-art approaches employ deep neural networks to obtain
instrumental spectra from a mixture. In this study, we propose a novel network
architecture that extends the recently developed densely connected
convolutional network (DenseNet), which has shown excellent results on image
classification tasks. To deal with the specific problem of audio source
separation, an up-sampling layer, block skip connection and band-dedicated
dense blocks are incorporated on top of DenseNet. The proposed approach takes
advantage of long contextual information and outperforms state-of-the-art
results on SiSEC 2016 competition by a large margin in terms of
signal-to-distortion ratio. Moreover, the proposed architecture requires
significantly fewer parameters and considerably less training time compared
with other methods.Comment: to appear at WASPAA 201
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