105 research outputs found
On Constant Gaps for the Two-way Gaussian Interference Channel
We introduce the two-way Gaussian interference channel in which there are
four nodes with four independent messages: two-messages to be transmitted over
a Gaussian interference channel in the direction, simultaneously
with two-messages to be transmitted over an interference channel (in-band,
full-duplex) in the direction. In such a two-way network, all
nodes are transmitters and receivers of messages, allowing them to adapt
current channel inputs to previously received channel outputs. We propose two
new outer bounds on the symmetric sum-rate for the two-way Gaussian
interference channel with complex channel gains: one under full adaptation (all
4 nodes are permitted to adapt inputs to previous outputs), and one under
partial adaptation (only 2 nodes are permitted to adapt, the other 2 are
restricted). We show that simple non-adaptive schemes such as the Han and
Kobayashi scheme, where inputs are functions of messages only and not past
outputs, utilized in each direction are sufficient to achieve within a constant
gap of these fully or partially adaptive outer bounds for all channel regimes.Comment: presented at 50th Annual Allerton Conference on Communication,
Control, and Computing, Monticello, IL, October 201
The adaptive zero-error capacity for a class of channels with noisy feedback
The adaptive zero-error capacity of discrete memoryless channels (DMC) with
noiseless feedback has been shown to be positive whenever there exists at least
one channel output "disprover", i.e. a channel output that cannot be reached
from at least one of the inputs. Furthermore, whenever there exists a
disprover, the adaptive zero-error capacity attains the Shannon (small-error)
capacity. Here, we study the zero-error capacity of a DMC when the channel
feedback is noisy rather than perfect. We show that the adaptive zero-error
capacity with noisy feedback is lower bounded by the forward channel's
zero-undetected error capacity, and show that under certain conditions this is
tight
Inner and Outer Bounds for the Gaussian Cognitive Interference Channel and New Capacity Results
The capacity of the Gaussian cognitive interference channel, a variation of
the classical two-user interference channel where one of the transmitters
(referred to as cognitive) has knowledge of both messages, is known in several
parameter regimes but remains unknown in general. In this paper we provide a
comparative overview of this channel model as we proceed through our
contributions: we present a new outer bound based on the idea of a broadcast
channel with degraded message sets, and another series of outer bounds obtained
by transforming the cognitive channel into channels with known capacity. We
specialize the largest known inner bound derived for the discrete memoryless
channel to the Gaussian noise channel and present several simplified schemes
evaluated for Gaussian inputs in closed form which we use to prove a number of
results. These include a new set of capacity results for the a) "primary
decodes cognitive" regime, a subset of the "strong interference" regime that is
not included in the "very strong interference" regime for which capacity was
known, and for the b) "S-channel" in which the primary transmitter does not
interfere with the cognitive receiver. Next, for a general Gaussian cognitive
interference channel, we determine the capacity to within one bit/s/Hz and to
within a factor two regardless of channel parameters, thus establishing rate
performance guarantees at high and low SNR, respectively. We also show how
different simplified transmission schemes achieve a constant gap between inner
and outer bound for specific channels. Finally, we numerically evaluate and
compare the various simplified achievable rate regions and outer bounds in
parameter regimes where capacity is unknown, leading to further insight on the
capacity region of the Gaussian cognitive interference channel.Comment: submitted to IEEE transaction of Information Theor
A New Capacity Result for the Z-Gaussian Cognitive Interference Channel
This work proposes a novel outer bound for the Gaussian cognitive
interference channel in strong interference at the primary receiver based on
the capacity of a multi-antenna broadcast channel with degraded message set. It
then shows that for the Z-channel, i.e., when the secondary receiver
experiences no interference and the primary receiver experiences strong
interference, the proposed outer bound not only is the tightest among known
bounds but is actually achievable for sufficiently strong interference. The
latter is a novel capacity result that from numerical evaluations appears to be
generalizable to a larger (i.e., non-Z) class of Gaussian channels
On Discrete Alphabets for the Two-user Gaussian Interference Channel with One Receiver Lacking Knowledge of the Interfering Codebook
In multi-user information theory it is often assumed that every node in the
network possesses all codebooks used in the network. This assumption is however
impractical in distributed ad-hoc and cognitive networks. This work considers
the two- user Gaussian Interference Channel with one Oblivious Receiver
(G-IC-OR), i.e., one receiver lacks knowledge of the interfering cookbook while
the other receiver knows both codebooks. We ask whether, and if so how much,
the channel capacity of the G-IC- OR is reduced compared to that of the
classical G-IC where both receivers know all codebooks. Intuitively, the
oblivious receiver should not be able to jointly decode its intended message
along with the unintended interfering message whose codebook is unavailable. We
demonstrate that in strong and very strong interference, where joint decoding
is capacity achieving for the classical G-IC, lack of codebook knowledge does
not reduce performance in terms of generalized degrees of freedom (gDoF).
Moreover, we show that the sum-capacity of the symmetric G-IC- OR is to within
O(log(log(SNR))) of that of the classical G-IC. The key novelty of the proposed
achievable scheme is the use of a discrete input alphabet for the non-oblivious
transmitter, whose cardinality is appropriately chosen as a function of SNR
On Identifying a Massive Number of Distributions
Finding the underlying probability distributions of a set of observed
sequences under the constraint that each sequence is generated i.i.d by a
distinct distribution is considered. The number of distributions, and hence the
number of observed sequences, are let to grow with the observation blocklength
. Asymptotically matching upper and lower bounds on the probability of error
are derived.Comment: Under Submissio
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