8,921 research outputs found
Degrees of Freedom of Time Correlated MISO Broadcast Channel with Delayed CSIT
We consider the time correlated multiple-input single-output (MISO) broadcast
channel where the transmitter has imperfect knowledge on the current channel
state, in addition to delayed channel state information. By representing the
quality of the current channel state information as P^-{\alpha} for the
signal-to-noise ratio P and some constant {\alpha} \geq 0, we characterize the
optimal degree of freedom region for this more general two-user MISO broadcast
correlated channel. The essential ingredients of the proposed scheme lie in the
quantization and multicasting of the overheard interferences, while
broadcasting new private messages. Our proposed scheme smoothly bridges between
the scheme recently proposed by Maddah-Ali and Tse with no current state
information and a simple zero-forcing beamforming with perfect current state
information.Comment: revised and final version, to appear in IEEE transactions on
Information Theor
Deep Long Short-Term Memory Adaptive Beamforming Networks For Multichannel Robust Speech Recognition
Far-field speech recognition in noisy and reverberant conditions remains a
challenging problem despite recent deep learning breakthroughs. This problem is
commonly addressed by acquiring a speech signal from multiple microphones and
performing beamforming over them. In this paper, we propose to use a recurrent
neural network with long short-term memory (LSTM) architecture to adaptively
estimate real-time beamforming filter coefficients to cope with non-stationary
environmental noise and dynamic nature of source and microphones positions
which results in a set of timevarying room impulse responses. The LSTM adaptive
beamformer is jointly trained with a deep LSTM acoustic model to predict senone
labels. Further, we use hidden units in the deep LSTM acoustic model to assist
in predicting the beamforming filter coefficients. The proposed system achieves
7.97% absolute gain over baseline systems with no beamforming on CHiME-3 real
evaluation set.Comment: in 2017 IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Successive Integer-Forcing and its Sum-Rate Optimality
Integer-forcing receivers generalize traditional linear receivers for the
multiple-input multiple-output channel by decoding integer-linear combinations
of the transmitted streams, rather then the streams themselves. Previous works
have shown that the additional degree of freedom in choosing the integer
coefficients enables this receiver to approach the performance of
maximum-likelihood decoding in various scenarios. Nonetheless, even for the
optimal choice of integer coefficients, the additive noise at the equalizer's
output is still correlated. In this work we study a variant of integer-forcing,
termed successive integer-forcing, that exploits these noise correlations to
improve performance. This scheme is the integer-forcing counterpart of
successive interference cancellation for traditional linear receivers.
Similarly to the latter, we show that successive integer-forcing is capacity
achieving when it is possible to optimize the rate allocation to the different
streams. In comparison to standard successive interference cancellation
receivers, the successive integer-forcing receiver offers more possibilities
for capacity achieving rate tuples, and in particular, ones that are more
balanced.Comment: A shorter version was submitted to the 51st Allerton Conferenc
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