13 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
Two-Way Interference Channel Capacity: How to Have the Cake and Eat it Too
Two-way communication is prevalent and its fundamental limits are first
studied in the point-to-point setting by Shannon [1]. One natural extension is
a two-way interference channel (IC) with four independent messages: two
associated with each direction of communication. In this work, we explore a
deterministic two-way IC which captures key properties of the wireless Gaussian
channel. Our main contribution lies in the complete capacity region
characterization of the two-way IC (w.r.t. the forward and backward sum-rate
pair) via a new achievable scheme and a new converse. One surprising
consequence of this result is that not only we can get an interaction gain over
the one-way non-feedback capacities, we can sometimes get all the way to
perfect feedback capacities in both directions simultaneously. In addition, our
novel outer bound characterizes channel regimes in which interaction has no
bearing on capacity.Comment: Presented in part in the IEEE International Symposium on Information
Theory 201
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
Feedback through Overhearing
In this paper we examine the value of feedback that comes from overhearing,
without dedicated feedback resources. We focus on a simple model for this
purpose: a deterministic two-hop interference channel, where feedback comes
from overhearing the forward-links. A new aspect brought by this setup is the
dual-role of the relay signal. While the relay signal needs to convey the
source message to its corresponding destination, it can also provide a feedback
signal which can potentially increase the capacity of the first hop. We derive
inner and outer bounds on the sum capacity which match for a large range of the
parameter values. Our results identify the parameter ranges where overhearing
can provide non-negative capacity gain and can even achieve the performance
with dedicated-feedback resources. The results also provide insights into which
transmissions are most useful to overhear
Computation in Multicast Networks: Function Alignment and Converse Theorems
The classical problem in network coding theory considers communication over
multicast networks. Multiple transmitters send independent messages to multiple
receivers which decode the same set of messages. In this work, computation over
multicast networks is considered: each receiver decodes an identical function
of the original messages. For a countably infinite class of two-transmitter
two-receiver single-hop linear deterministic networks, the computing capacity
is characterized for a linear function (modulo-2 sum) of Bernoulli sources.
Inspired by the geometric concept of interference alignment in networks, a new
achievable coding scheme called function alignment is introduced. A new
converse theorem is established that is tighter than cut-set based and
genie-aided bounds. Computation (vs. communication) over multicast networks
requires additional analysis to account for multiple receivers sharing a
network's computational resources. We also develop a network decomposition
theorem which identifies elementary parallel subnetworks that can constitute an
original network without loss of optimality. The decomposition theorem provides
a conceptually-simpler algebraic proof of achievability that generalizes to
-transmitter -receiver networks.Comment: to appear in the IEEE Transactions on Information Theor