203 research outputs found
Noisy Network Coding with Partial DF
In this paper, we propose a noisy network coding integrated with partial
decode-and-forward relaying for single-source multicast discrete memoryless
networks (DMN's). Our coding scheme generalizes the
partial-decode-compress-and-forward scheme (Theorem 7) by Cover and El Gamal.
This is the first time the theorem is generalized for DMN's such that each
relay performs both partial decode-and-forward and compress-and-forward
simultaneously. Our coding scheme simultaneously generalizes both noisy network
coding by Lim, Kim, El Gamal, and Chung and distributed decode-and-forward by
Lim, Kim, and Kim. It is not trivial to combine the two schemes because of
inherent incompatibility in their encoding and decoding strategies. We solve
this problem by sending the same long message over multiple blocks at the
source and at the same time by letting the source find the auxiliary covering
indices that carry information about the message simultaneously over all
blocks.Comment: 5 pages, 1 figure, to appear in Proc. IEEE 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
Achievable rates for relay networks using superposition coding
We investigate the superposition strategy and its usefulness in terms of achievable information theoretic rates. The achievable rate of the superposition of block Markov encoding (decode-forward) and side information encoding (compress-forward) for the three-node Gaussian relay channel is analyzed. It is generally believed that superposition can out perform decode-forward or compress-forward due to its generality. We prove that within the class of Gaussian distributions, this is not the case: the superposition scheme only achieves a rate that is equal to the maximum of the rates achieved by decode-forward or compress-forward individually.
We use the insight gathered on superposition forward scheme and devise a new coding scheme. The superposition coding scheme for communication over a network, combines partial decode-forward with noisy network coding. This hybrid scheme is termed as superposition noisy network coding. The novel coding scheme is designed and analyzed for a single relay channel, single source multicast network and multiple source multicast network. The special cases of Gaussian single relay channel and two way relay channel are analyzed for superposition noisy network coding. The achievable rate of the proposed scheme is higher than the existing schemes of noisy network coding, compress-forward and binning
Wyner-Ziv Type Versus Noisy Network Coding For a State-Dependent MAC
We consider a two-user state-dependent multiaccess channel in which the
states of the channel are known non-causally to one of the encoders and only
strictly causally to the other encoder. Both encoders transmit a common message
and, in addition, the encoder that knows the states non-causally transmits an
individual message. We find explicit characterizations of the capacity region
of this communication model in both discrete memoryless and memoryless Gaussian
cases. The analysis also reveals optimal ways of exploiting the knowledge of
the state only strictly causally at the encoder that sends only the common
message when such a knowledge is beneficial. The encoders collaborate to convey
to the decoder a lossy version of the state, in addition to transmitting the
information messages through a generalized Gel'fand-Pinsker binning.
Particularly important in this problem are the questions of 1) optimal ways of
performing the state compression and 2) whether or not the compression indices
should be decoded uniquely. We show that both compression \`a-la noisy network
coding, i.e., with no binning, and compression using Wyner-Ziv binning are
optimal. The scheme that uses Wyner-Ziv binning shares elements with Cover and
El Gamal original compress-and-forward, but differs from it mainly in that
backward decoding is employed instead of forward decoding and the compression
indices are not decoded uniquely. Finally, by exploring the properties of our
outer bound, we show that, although not required in general, the compression
indices can in fact be decoded uniquely essentially without altering the
capacity region, but at the expense of larger alphabets sizes for the auxiliary
random variables.Comment: Submitted for publication to the 2012 IEEE International Symposium on
Information Theory, 5 pages, 1 figur
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