2,632 research outputs found
Joint Unitary Triangularization for MIMO Networks
This work considers communication networks where individual links can be
described as MIMO channels. Unlike orthogonal modulation methods (such as the
singular-value decomposition), we allow interference between sub-channels,
which can be removed by the receivers via successive cancellation. The degrees
of freedom earned by this relaxation are used for obtaining a basis which is
simultaneously good for more than one link. Specifically, we derive necessary
and sufficient conditions for shaping the ratio vector of sub-channel gains of
two broadcast-channel receivers. We then apply this to two scenarios: First, in
digital multicasting we present a practical capacity-achieving scheme which
only uses scalar codes and linear processing. Then, we consider the joint
source-channel problem of transmitting a Gaussian source over a two-user MIMO
channel, where we show the existence of non-trivial cases, where the optimal
distortion pair (which for high signal-to-noise ratios equals the optimal
point-to-point distortions of the individual users) may be achieved by
employing a hybrid digital-analog scheme over the induced equivalent channel.
These scenarios demonstrate the advantage of choosing a modulation basis based
upon multiple links in the network, thus we coin the approach "network
modulation".Comment: Submitted to IEEE Tran. Signal Processing. Revised versio
Constellation Shaping for WDM systems using 256QAM/1024QAM with Probabilistic Optimization
In this paper, probabilistic shaping is numerically and experimentally
investigated for increasing the transmission reach of wavelength division
multiplexed (WDM) optical communication system employing quadrature amplitude
modulation (QAM). An optimized probability mass function (PMF) of the QAM
symbols is first found from a modified Blahut-Arimoto algorithm for the optical
channel. A turbo coded bit interleaved coded modulation system is then applied,
which relies on many-to-one labeling to achieve the desired PMF, thereby
achieving shaping gain. Pilot symbols at rate at most 2% are used for
synchronization and equalization, making it possible to receive input
constellations as large as 1024QAM. The system is evaluated experimentally on a
10 GBaud, 5 channels WDM setup. The maximum system reach is increased w.r.t.
standard 1024QAM by 20% at input data rate of 4.65 bits/symbol and up to 75% at
5.46 bits/symbol. It is shown that rate adaptation does not require changing of
the modulation format. The performance of the proposed 1024QAM shaped system is
validated on all 5 channels of the WDM signal for selected distances and rates.
Finally, it was shown via EXIT charts and BER analysis that iterative
demapping, while generally beneficial to the system, is not a requirement for
achieving the shaping gain.Comment: 10 pages, 12 figures, Journal of Lightwave Technology, 201
A Continuous-Time Recurrent Neural Network for Joint Equalization and Decoding â Analog Hardware Implementation Aspects
Equalization and channel decoding are âtraditionallyâ two cascade processes at the receiver side of a digital transmission. They aim to achieve a reliable and efficient transmission. For high data rates, the energy consumption of their corresponding algorithms is expected to become a limiting factor. For mobile devices with limited batteryâs size, the energy consumption, mirrored in the lifetime of the battery, becomes even more crucial. Therefore, an energy-efficient implementation of equalization and decoding algorithms is desirable. The prevailing way is by increasing the energy efficiency of the underlying digital circuits. However, we address here promising alternatives offered by mixed (analog/digital) circuits. We are concerned with modeling joint equalization and decoding as a whole in a continuous-time framework. In doing so, continuous-time recurrent neural networks play an essential role because of their nonlinear characteristic and special suitability for analog very-large-scale integration (VLSI). Based on the proposed model, we show that the superiority of joint equalization and decoding (a well-known fact from the discrete-time case) preserves in analog. Additionally, analog circuit design related aspects such as adaptivity, connectivity and accuracy are discussed and linked to theoretical aspects of recurrent neural networks such as Lyapunov stability and simulated annealing
Integer-Forcing Source Coding
Integer-Forcing (IF) is a new framework, based on compute-and-forward, for
decoding multiple integer linear combinations from the output of a Gaussian
multiple-input multiple-output channel. This work applies the IF approach to
arrive at a new low-complexity scheme, IF source coding, for distributed lossy
compression of correlated Gaussian sources under a minimum mean squared error
distortion measure. All encoders use the same nested lattice codebook. Each
encoder quantizes its observation using the fine lattice as a quantizer and
reduces the result modulo the coarse lattice, which plays the role of binning.
Rather than directly recovering the individual quantized signals, the decoder
first recovers a full-rank set of judiciously chosen integer linear
combinations of the quantized signals, and then inverts it. In general, the
linear combinations have smaller average powers than the original signals. This
allows to increase the density of the coarse lattice, which in turn translates
to smaller compression rates. We also propose and analyze a one-shot version of
IF source coding, that is simple enough to potentially lead to a new design
principle for analog-to-digital converters that can exploit spatial
correlations between the sampled signals.Comment: Submitted to IEEE Transactions on Information Theor
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