486 research outputs found
Interference Channel with Intermittent Feedback
We investigate how to exploit intermittent feedback for interference
management. Focusing on the two-user linear deterministic interference channel,
we completely characterize the capacity region. We find that the
characterization only depends on the forward channel parameters and the
marginal probability distribution of each feedback link. The scheme we propose
makes use of block Markov encoding and quantize-map-and-forward at the
transmitters, and backward decoding at the receivers. Matching outer bounds are
derived based on novel genie-aided techniques. As a consequence, the
perfect-feedback capacity can be achieved once the two feedback links are
active with large enough probabilities.Comment: Extended version of the same-titled paper that appears in IEEE
International Symposium on Information Theory (ISIT) 201
Achievable Regions for Interference Channels with Generalized and Intermittent Feedback
In this paper, we first study a two-user interference channel with
generalized feedback. We establish an inner bound on its capacity region. The
coding scheme that we employ for the inner bound is based on an appropriate
combination of Han-Kobayash rate splitting and compress-and-forward at the
senders. Each sender compresses the channel output that is observes using a
compression scheme that is \`a-la Lim et al. noisy network coding and
Avestimeher et al. quantize-map-and-forward. Next, we study an injective
deterministic model in which the senders obtain output feedback only
intermittently. Specializing the coding scheme of the model with generalized
feedback to this scenario, we obtain useful insights onto effective ways of
combining noisy network coding with interference alignment techniques. We also
apply our results to linear deterministic interference channels with
intermittent feedback.Comment: To appear in Proc. of the 2014 IEEE International Symposium on
Information Theory, 6 pages, 2 figure
Delay Minimization for Instantly Decodable Network Coding in Persistent Channels with Feedback Intermittence
In this paper, we consider the problem of minimizing the multicast decoding
delay of generalized instantly decodable network coding (G-IDNC) over
persistent forward and feedback erasure channels with feedback intermittence.
In such an environment, the sender does not always receive acknowledgement from
the receivers after each transmission. Moreover, both the forward and feedback
channels are subject to persistent erasures, which can be modelled by a two
state (good and bad states) Markov chain known as Gilbert-Elliott channel
(GEC). Due to such feedback imperfections, the sender is unable to determine
subsequent instantly decodable packets combination for all receivers. Given
this harsh channel and feedback model, we first derive expressions for the
probability distributions of decoding delay increments and then employ these
expressions in formulating the minimum decoding problem in such environment as
a maximum weight clique problem in the G-IDNC graph. We also show that the
problem formulations in simpler channel and feedback models are special cases
of our generalized formulation. Since this problem is NP-hard, we design a
greedy algorithm to solve it and compare it to blind approaches proposed in
literature. Through extensive simulations, our adaptive algorithm is shown to
outperform the blind approaches in all situations and to achieve significant
improvement in the decoding delay, especially when the channel is highly
persisten
Does the motor system need intermittent control?
Explanation of motor control is dominated by continuous neurophysiological pathways (e.g. trans-cortical, spinal) and the continuous control paradigm. Using new theoretical development, methodology and evidence, we propose intermittent control, which incorporates a serial ballistic process within the main feedback loop, provides a more general and more accurate paradigm necessary to explain attributes highly advantageous for competitive survival and performance
On Minimizing the Maximum Broadcast Decoding Delay for Instantly Decodable Network Coding
In this paper, we consider the problem of minimizing the maximum broadcast
decoding delay experienced by all the receivers of generalized instantly
decodable network coding (IDNC). Unlike the sum decoding delay, the maximum
decoding delay as a definition of delay for IDNC allows a more equitable
distribution of the delays between the different receivers and thus a better
Quality of Service (QoS). In order to solve this problem, we first derive the
expressions for the probability distributions of maximum decoding delay
increments. Given these expressions, we formulate the problem as a maximum
weight clique problem in the IDNC graph. Although this problem is known to be
NP-hard, we design a greedy algorithm to perform effective packet selection.
Through extensive simulations, we compare the sum decoding delay and the max
decoding delay experienced when applying the policies to minimize the sum
decoding delay [1] and our policy to reduce the max decoding delay. Simulations
results show that our policy gives a good agreement among all the delay aspects
in all situations and outperforms the sum decoding delay policy to effectively
minimize the sum decoding delay when the channel conditions become harsher.
They also show that our definition of delay significantly improve the number of
served receivers when they are subject to strict delay constraints
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