155 research outputs found
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
Rate-Distortion Function for a Heegard-Berger Problem with Two Sources and Degraded Reconstruction sets
In this work, we investigate an instance of the Heegard-Berger problem with
two sources and arbitrarily correlated side information sequences at two
decoders, in which the reconstruction sets at the decoders are degraded.
Specifically, two sources are to be encoded in a manner that one of the two is
reproduced losslessly by both decoders, and the other is reproduced to within
some prescribed distortion level at one of the two decoders. We establish a
single-letter characterization of the rate-distortion function for this model.
The investigation of this result in some special cases also sheds light on the
utility of joint compression of the two sources. Furthermore, we also
generalize our result to the setting in which the source component that is to
be recovered by both users is reconstructed in a lossy fashion, under the
requirement that all terminals (i.e., the encoder and both decoders) can share
an exact copy of the compressed version of this source component, i.e., a
common encoder-decoders reconstruction constraint. For this model as well, we
establish a single-letter characterization of the associated rate-distortion
function.Comment: Submitted to IEEE Trans. on Information Theor
On Cooperative Multiple Access Channels with Delayed CSI at Transmitters
We consider a cooperative two-user multiaccess channel in which the
transmission is controlled by a random state. Both encoders transmit a common
message and, one of the encoders also transmits an individual message. We study
the capacity region of this communication model for different degrees of
availability of the states at the encoders, causally or strictly causally. In
the case in which the states are revealed causally to both encoders but not to
the decoder we find an explicit characterization of the capacity region in the
discrete memoryless case. In the case in which the states are revealed only
strictly causally to both encoders, we establish inner and outer bounds on the
capacity region. The outer bound is non-trivial, and has a relatively simple
form. It has the advantage of incorporating only one auxiliary random variable.
We then introduce a class of cooperative multiaccess channels with states known
strictly causally at both encoders for which the inner and outer bounds agree;
and so we characterize the capacity region for this class. In this class of
channels, the state can be obtained as a deterministic function of the channel
inputs and output. We also study the model in which the states are revealed,
strictly causally, in an asymmetric manner, to only one encoder. Throughout the
paper, we discuss a number of examples; and compute the capacity region of some
of these examples. The results shed more light on the utility of delayed
channel state information for increasing the capacity region of state-dependent
cooperative multiaccess channels; and tie with recent progress in this
framework.Comment: 54 pages. To appear in IEEE Transactions on Information Theory. arXiv
admin note: substantial text overlap with arXiv:1201.327
Rate-Exponent Region for a Class of Distributed Hypothesis Testing Against Conditional Independence Problems
We study a class of -encoder hypothesis testing against conditional
independence problems. Under the criterion that stipulates minimization of the
Type II error subject to a (constant) upper bound on the Type I
error, we characterize the set of encoding rates and exponent for both discrete
memoryless and memoryless vector Gaussian settings. For the DM setting, we
provide a converse proof and show that it is achieved using the
Quantize-Bin-Test scheme of Rahman and Wagner. For the memoryless vector
Gaussian setting, we develop a tight outer bound by means of a technique that
relies on the de Bruijn identity and the properties of Fisher information. In
particular, the result shows that for memoryless vector Gaussian sources the
rate-exponent region is exhausted using the Quantize-Bin-Test scheme with
\textit{Gaussian} test channels; and there is \textit{no} loss in performance
caused by restricting the sensors' encoders not to employ time sharing.
Furthermore, we also study a variant of the problem in which the source, not
necessarily Gaussian, has finite differential entropy and the sensors'
observations noises under the null hypothesis are Gaussian. For this model, our
main result is an upper bound on the exponent-rate function. The bound is shown
to mirror a corresponding explicit lower bound, except that the lower bound
involves the source power (variance) whereas the upper bound has the source
entropy power. Part of the utility of the established bound is for
investigating asymptotic exponent/rates and losses incurred by distributed
detection as function of the number of sensors.Comment: Submitted for publication to the IEEE Transactions of Information
Theory. arXiv admin note: substantial text overlap with arXiv:1904.03028,
arXiv:1811.0393
Multiple Access Channel with States Known Noncausally at One Encoder and Only Strictly Causally at the Other Encoder
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 study the capacity region of this communication model.
In the discrete memoryless case, we establish inner and outer bounds on the
capacity region. Although the encoder that sends both messages knows the states
fully, we show that the strictly causal knowledge of these states at the other
encoder can be beneficial for this encoder, and in general enlarges the
capacity region. Furthermore, we find an explicit characterization of the
capacity in the case in which the two encoders transmit only the common
message. In the Gaussian case, we characterize the capacity region for the
model with individual message as well. Our converse proof in this case shows
that, for this model, strictly causal knowledge of the state at one of the
encoders does not increase capacity if the other is informed non-causally, a
result which sheds more light on the utility of conveying a compressed version
of the state to the decoder in recent results by Lapidoth and Steinberg on a
multiacess model with only strictly causal state at both encoders and
independent messages.Comment: 5 pages, to appear in the 2011 IEEE International Symposium on
Information Theor
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
Compute-and-Forward on a Multi-User Multi-Relay Channel
In this paper, we consider a system in which multiple users communicate with
a destination with the help of multiple half-duplex relays. Based on the
compute-and-forward scheme, each relay, instead of decoding the users'
messages, decodes an integer-valued linear combination that relates the
transmitted messages. Then, it forwards the linear combination towards the
destination. Given these linear combinations, the destination may or may not
recover the transmitted messages since the linear combinations are not always
full rank. Therefore, we propose an algorithm where we optimize the precoding
factor at the users such that the probability that the equations are full rank
is increased and that the transmission rate is maximized. We show, through some
numerical examples, the effectiveness of our algorithm and the advantage of
performing precoding allocation at the users. Also, we show that this scheme
can outperform standard relaying techniques in certain regimes
On Secure Transmission over Parallel Relay Eavesdropper Channel
We study a four terminal parallel relay-eavesdropper channel which consists
of multiple independent relay-eavesdropper channels as subchannels. For the
discrete memoryless case, we establish inner and outer bounds on the
rate-equivocation region. For each subchannel, secure transmission is obtained
through one of the two coding schemes at the relay: decoding-and-forwarding the
source message or confusing the eavesdropper through noise injection. The inner
bound allows relay mode selection. For the Gaussian model we establish lower
and upper bounds on the perfect secrecy rate. We show that the bounds meet in
some special cases, including when the relay does not hear the source. We
illustrate the analytical results through some numerical examples.Comment: 8 pages, Presented at the Forty-Eighth Annual Allerton Conference on
Communication, Control, and Computing, September 29 - October 1, 2010,
Monticello, IL, US
- …