77,942 research outputs found
On the Noisy Feedback Capacity of Gaussian Broadcast Channels
It is well known that, in general, feedback may enlarge the capacity region
of Gaussian broadcast channels. This has been demonstrated even when the
feedback is noisy (or partial-but-perfect) and only from one of the receivers.
The only case known where feedback has been shown not to enlarge the capacity
region is when the channel is physically degraded (El Gamal 1978, 1981). In
this paper, we show that for a class of two-user Gaussian broadcast channels
(not necessarily physically degraded), passively feeding back the stronger
user's signal over a link corrupted by Gaussian noise does not enlarge the
capacity region if the variance of feedback noise is above a certain threshold.Comment: 5 pages, 3 figures, to appear in IEEE Information Theory Workshop
2015, Jerusale
Capacity of All Nine Models of Channel Output Feedback for the Two-user Interference Channel
In this paper, we study the impact of different channel output feedback
architectures on the capacity of the two-user interference channel. For a
two-user interference channel, a feedback link can exist between receivers and
transmitters in 9 canonical architectures (see Fig. 2), ranging from only one
feedback link to four feedback links. We derive the exact capacity region for
the symmetric deterministic interference channel and the constant-gap capacity
region for the symmetric Gaussian interference channel for all of the 9
architectures. We show that for a linear deterministic symmetric interference
channel, in the weak interference regime, all models of feedback, except the
one, which has only one of the receivers feeding back to its own transmitter,
have the identical capacity region. When only one of the receivers feeds back
to its own transmitter, the capacity region is a strict subset of the capacity
region of the rest of the feedback models in the weak interference regime.
However, the sum-capacity of all feedback models is identical in the weak
interference regime. Moreover, in the strong interference regime all models of
feedback with at least one of the receivers feeding back to its own transmitter
have the identical sum-capacity. For the Gaussian interference channel, the
results of the linear deterministic model follow, where capacity is replaced
with approximate capacity.Comment: submitted to IEEE Transactions on Information Theory, results
improved by deriving capacity region of all 9 canonical feedback models in
two-user interference channe
On the Capacity of Symmetric Gaussian Interference Channels with Feedback
In this paper, we propose a new coding scheme for symmetric Gaussian
interference channels with feedback based on the ideas of time-varying coding
schemes. The proposed scheme improves the Suh-Tse and Kramer inner bounds of
the channel capacity for the cases of weak and not very strong interference.
This improvement is more significant when the signal-to-noise ratio (SNR) is
not very high. It is shown theoretically and numerically that our coding scheme
can outperform the Kramer code. In addition, the generalized degrees-of-freedom
of our proposed coding scheme is equal to the Suh-Tse scheme in the strong
interference case. The numerical results show that our coding scheme can attain
better performance than the Suh-Tse coding scheme for all channel parameters.
Furthermore, the simplicity of the encoding/decoding algorithms is another
strong point of our proposed coding scheme compared with the Suh-Tse coding
scheme. More importantly, our results show that an optimal coding scheme for
the symmetric Gaussian interference channels with feedback can be achieved by
using only marginal posterior distributions under a better cooperation strategy
between transmitters.Comment: To appear in Proc. of IEEE International Symposium on Information
Theory (ISIT), Hong Kong, June 14-19, 201
Dependence Balance Based Outer Bounds for Gaussian Networks with Cooperation and Feedback
We obtain new outer bounds on the capacity regions of the two-user multiple
access channel with generalized feedback (MAC-GF) and the two-user interference
channel with generalized feedback (IC-GF). These outer bounds are based on the
idea of dependence balance which was proposed by Hekstra and Willems [1]. To
illustrate the usefulness of our outer bounds, we investigate three different
channel models. We first consider a Gaussian MAC with noisy feedback (MAC-NF),
where transmitter , , receives a feedback , which is the
channel output corrupted with additive white Gaussian noise . As the
feedback noise variances become large, one would expect the feedback to become
useless, which is not reflected by the cut-set bound. We demonstrate that our
outer bound improves upon the cut-set bound for all non-zero values of the
feedback noise variances. Moreover, in the limit as , , our outer bound collapses to the capacity region of the
Gaussian MAC without feedback. Secondly, we investigate a Gaussian MAC with
user-cooperation (MAC-UC), where each transmitter receives an additive white
Gaussian noise corrupted version of the channel input of the other transmitter
[2]. For this channel model, the cut-set bound is sensitive to the cooperation
noises, but not sensitive enough. For all non-zero values of cooperation noise
variances, our outer bound strictly improves upon the cut-set outer bound.
Thirdly, we investigate a Gaussian IC with user-cooperation (IC-UC). For this
channel model, the cut-set bound is again sensitive to cooperation noise
variances but not sensitive enough. We demonstrate that our outer bound
strictly improves upon the cut-set bound for all non-zero values of cooperation
noise variances.Comment: Submitted to IEEE Transactions on Information Theor
A Counterexample to Cover's 2P Conjecture on Gaussian Feedback Capacity
We provide a counterexample to Cover's conjecture that the feedback capacity
of an additive Gaussian noise channel under power constraint
be no greater than the nonfeedback capacity of the same channel under
power constraint , i.e., .Comment: 2 pages, submitted to IEEE Transactions on Information Theor
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