86,678 research outputs found
Bit-Interleaved Coded Modulation Revisited: A Mismatched Decoding Perspective
We revisit the information-theoretic analysis of bit-interleaved coded
modulation (BICM) by modeling the BICM decoder as a mismatched decoder. The
mismatched decoding model is well-defined for finite, yet arbitrary, block
lengths, and naturally captures the channel memory among the bits belonging to
the same symbol. We give two independent proofs of the achievability of the
BICM capacity calculated by Caire et al. where BICM was modeled as a set of
independent parallel binary-input channels whose output is the bitwise
log-likelihood ratio. Our first achievability proof uses typical sequences, and
shows that due to the random coding construction, the interleaver is not
required. The second proof is based on the random coding error exponents with
mismatched decoding, where the largest achievable rate is the generalized
mutual information. We show that the generalized mutual information of the
mismatched decoder coincides with the infinite-interleaver BICM capacity. We
also show that the error exponent -and hence the cutoff rate- of the BICM
mismatched decoder is upper bounded by that of coded modulation and may thus be
lower than in the infinite-interleaved model. We also consider the mutual
information appearing in the analysis of iterative decoding of BICM with EXIT
charts. We show that the corresponding symbol metric has knowledge of the
transmitted symbol and the EXIT mutual information admits a representation as a
pseudo-generalized mutual information, which is in general not achievable. A
different symbol decoding metric, for which the extrinsic side information
refers to the hypothesized symbol, induces a generalized mutual information
lower than the coded modulation capacity.Comment: submitted to the IEEE Transactions on Information Theory. Conference
version in 2008 IEEE International Symposium on Information Theory, Toronto,
Canada, July 200
The Binary Energy Harvesting Channel with a Unit-Sized Battery
We consider a binary energy harvesting communication channel with a
finite-sized battery at the transmitter. In this model, the channel input is
constrained by the available energy at each channel use, which is driven by an
external energy harvesting process, the size of the battery, and the previous
channel inputs. We consider an abstraction where energy is harvested in binary
units and stored in a battery with the capacity of a single unit, and the
channel inputs are binary. Viewing the available energy in the battery as a
state, this is a state-dependent channel with input-dependent states, memory in
the states, and causal state information available at the transmitter only. We
find an equivalent representation for this channel based on the timings of the
symbols, and determine the capacity of the resulting equivalent timing channel
via an auxiliary random variable. We give achievable rates based on certain
selections of this auxiliary random variable which resemble lattice coding for
the timing channel. We develop upper bounds for the capacity by using a
genie-aided method, and also by quantifying the leakage of the state
information to the receiver. We show that the proposed achievable rates are
asymptotically capacity achieving for small energy harvesting rates. We extend
the results to the case of ternary channel inputs. Our achievable rates give
the capacity of the binary channel within 0.03 bits/channel use, the ternary
channel within 0.05 bits/channel use, and outperform basic Shannon strategies
that only consider instantaneous battery states, for all parameter values.Comment: Submitted to IEEE Transactions on Information Theory, August 201
Channels with block interference
A new class of channel models with memory is presented in order to study various kinds of interference phenomena. It is shown, among other things, that when all other parameters are held fixed, channel capacity C is an increasing function of the memory length, while the cutoff rate R0 generally is a decreasing function. Calculations with various explicit coding schemes indicate that C is better than R0 as a performance measure for these channel models. As a partial resolution of this C versus R0 paradox, the conjecture is offered that R0 is more properly a measure of coding delay rather than of coding complexity
Write Channel Model for Bit-Patterned Media Recording
We propose a new write channel model for bit-patterned media recording that
reflects the data dependence of write synchronization errors. It is shown that
this model accommodates both substitution-like errors and insertion-deletion
errors whose statistics are determined by an underlying channel state process.
We study information theoretic properties of the write channel model, including
the capacity, symmetric information rate, Markov-1 rate and the zero-error
capacity.Comment: 11 pages, 12 figures, journa
YodaNN: An Architecture for Ultra-Low Power Binary-Weight CNN Acceleration
Convolutional neural networks (CNNs) have revolutionized the world of
computer vision over the last few years, pushing image classification beyond
human accuracy. The computational effort of today's CNNs requires power-hungry
parallel processors or GP-GPUs. Recent developments in CNN accelerators for
system-on-chip integration have reduced energy consumption significantly.
Unfortunately, even these highly optimized devices are above the power envelope
imposed by mobile and deeply embedded applications and face hard limitations
caused by CNN weight I/O and storage. This prevents the adoption of CNNs in
future ultra-low power Internet of Things end-nodes for near-sensor analytics.
Recent algorithmic and theoretical advancements enable competitive
classification accuracy even when limiting CNNs to binary (+1/-1) weights
during training. These new findings bring major optimization opportunities in
the arithmetic core by removing the need for expensive multiplications, as well
as reducing I/O bandwidth and storage. In this work, we present an accelerator
optimized for binary-weight CNNs that achieves 1510 GOp/s at 1.2 V on a core
area of only 1.33 MGE (Million Gate Equivalent) or 0.19 mm and with a power
dissipation of 895 {\mu}W in UMC 65 nm technology at 0.6 V. Our accelerator
significantly outperforms the state-of-the-art in terms of energy and area
efficiency achieving 61.2 TOp/s/[email protected] V and 1135 GOp/s/[email protected] V, respectively
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