8 research outputs found
On the Corner Points of the Capacity Region of a Two-User Gaussian Interference Channel
This work considers the corner points of the capacity region of a two-user
Gaussian interference channel (GIC). In a two-user GIC, the rate pairs where
one user transmits its data at the single-user capacity (without interference),
and the other at the largest rate for which reliable communication is still
possible are called corner points. This paper relies on existing outer bounds
on the capacity region of a two-user GIC that are used to derive informative
bounds on the corner points of the capacity region. The new bounds refer to a
weak two-user GIC (i.e., when both cross-link gains in standard form are
positive and below 1), and a refinement of these bounds is obtained for the
case where the transmission rate of one user is within of the
single-user capacity. The bounds on the corner points are asymptotically tight
as the transmitted powers tend to infinity, and they are also useful for the
case of moderate SNR and INR. Upper and lower bounds on the gap (denoted by
) between the sum-rate and the maximal achievable total rate at the two
corner points are derived. This is followed by an asymptotic analysis analogous
to the study of the generalized degrees of freedom (where the SNR and INR
scalings are coupled such that ), leading to an asymptotic characterization of this gap which is
exact for the whole range of . The upper and lower bounds on
are asymptotically tight in the sense that they achieve the exact asymptotic
characterization. Improved bounds on are derived for finite SNR and
INR, and their improved tightness is exemplified numerically.Comment: Submitted to the IEEE Trans. on Information Theory in July 17, 2014,
and revised in April 5, 2015. Presented in part at Allerton 2013, and also
presented in part with improved results at ISIT 201
At Every Corner: Determining Corner Points of Two-User Gaussian Interference Channels
The corner points of the capacity region of the two-user Gaussian
interference channel under strong or weak interference are determined using the
notions of almost Gaussian random vectors, almost lossless addition of random
vectors, and almost linearly dependent random vectors. In particular, the
"missing" corner point problem is solved in a manner that differs from previous
works in that it avoids the use of integration over a continuum of SNR values
or of Monge-Kantorovitch transportation problems
Practical interference management strategies in Gaussian networks
Increasing demand for bandwidth intensive activities on high-penetration wireless hand-held
personal devices, combined with their processing power and advanced radio features, has
necessitated a new look at the problems of resource provisioning and distributed management
of coexistence in wireless networks. Information theory, as the science of studying
the ultimate limits of communication e ciency, plays an important role in outlining guiding
principles in the design and analysis of such communication schemes. Network information
theory, the branch of information theory that investigates problems of multiuser and
distributed nature in information transmission is ideally poised to answer questions about
the design and analysis of multiuser communication systems. In the past few years, there
have been major advances in network information theory, in particular in the generalized
degrees of freedom framework for asymptotic analysis and interference alignment which have
led to constant gap to capacity results for Gaussian interference channels. Unfortunately,
practical adoption of these results has been slowed by their reliance on unrealistic assumptions
like perfect channel state information at the transmitter and intricate constructions
based on alignment over transcendental dimensions of real numbers. It is therefore necessary
to devise transmission methods and coexistence schemes that fall under the umbrella of
existing interference management and cognitive radio toolbox and deliver close to optimal
performance.
In this thesis we work on the theme of designing and characterizing the performance of
conceptually simple transmission schemes that are robust and achieve performance that is
close to optimal. In particular, our work is broadly divided into two parts. In the rst part,
looking at cognitive radio networks, we seek to relax the assumption of non-causal knowledge
of primary user's message at the secondary user's transmitter. We study a cognitive channel
model based on Gaussian interference channel that does not assume anything about users
other than primary user's priority over secondary user in reaching its desired quality of
service. We characterize this quality of service requirement as a minimum rate that the
primary user should be able to achieve. Studying the achievable performance of simple
encoding and decoding schemes in this scenario, we propose a few di erent simple encoding
schemes and explore di erent decoder designs. We show that surprisingly, all these schemes
achieve the same rate region. Next, we study the problem of rate maximization faced by
the secondary user subject to primary's QoS constraint. We show that this problem is not
convex or smooth in general. We then use the symmetry properties of the problem to reduce
its solution to a feasibly implementable line search. We also provide numerical results to
demonstrate the performance of the scheme.
Continuing on the theme of simple yet well-performing schemes for wireless networks, in
the second part of the thesis, we direct our attention from two-user cognitive networks to
the problem of smart interference management in large wireless networks. Here, we study
the problem of interference-aware wireless link scheduling. Link scheduling is the problem of
allocating a set of transmission requests into as small a set of time slots as possible such that
all transmissions satisfy some condition of feasibility. The feasibility criterion has traditionally
been lack of pair of links that interfere too much. This makes the problem amenable to
solution using graph theoretical tools. Inspired by the recent results that the simple approach
of treating interference as noise achieves maximal Generalized Degrees of Freedom (which is
a measure that roughly captures how many equivalent single-user channels are contained in
a given multi-user channel) and the generalization that it can attain rates within a constant
gap of the capacity for a large class of Gaussian interference networks, we study the problem
of scheduling links under a set Signal to Interference plus Noise Ratio (SINR) constraint.
We show that for nodes distributed in a metric space and obeying path loss channel model, a
re ned framework based on combining geometric and graph theoretic results can be devised
to analyze the problem of nding the feasible sets of transmissions for a given level of desired
SINR. We use this general framework to give a link scheduling algorithm that is provably
within a logarithmic factor of the best possible schedule. Numerical simulations con rm
that this approach outperforms other recently proposed SINR-based approaches. Finally, we
conclude by identifying open problems and possible directions for extending these results