Results 1 
8 of
8
Markov Approximation for Combinatorial Network Optimization
"... Many important network design problems can be formulated as a combinatorial optimization problem. A large number of such problems, however, cannot readily be tackled by distributed algorithms. The Markov approximation framework studied in this paper is a general technique for synthesizing distribut ..."
Abstract

Cited by 31 (14 self)
 Add to MetaCart
Many important network design problems can be formulated as a combinatorial optimization problem. A large number of such problems, however, cannot readily be tackled by distributed algorithms. The Markov approximation framework studied in this paper is a general technique for synthesizing distributed algorithms. We show that when using the logsumexp function to approximate the optimal value of any combinatorial problem, we end up with a solution that can be interpreted as the stationary probability distribution of a class of timereversible Markov chains. Certain carefully designed Markov chains among this class yield distributed algorithms that solve the logsumexp approximated combinatorial network optimization problem. By three case studies, we illustrate that Markov approximation technique not only can provide fresh perspective to existing distributed solutions, but also can help us generate new distributed algorithms in various domains with provable performance. We believe the Markov approximation techniques will find application in many network optimization problems, and this paper serves as a call for participation of it.
On the optimality of treating interference as noise
 in Proc. 51st Annu. Allerton Conf. Commun., Control, Comput
, 2013
"... Abstract — It is shown that in the Kuser interference channel, if for each user the desired signal strength is no less than the sum of the strengths of the strongest interference from this user and the strongest interference to this user (all values in decibel scale), then the simple scheme of usin ..."
Abstract

Cited by 9 (4 self)
 Add to MetaCart
(Show Context)
Abstract — It is shown that in the Kuser interference channel, if for each user the desired signal strength is no less than the sum of the strengths of the strongest interference from this user and the strongest interference to this user (all values in decibel scale), then the simple scheme of using pointtopoint Gaussian codebooks with appropriate power levels at each transmitter and treating interference as noise (TIN) at every receiver (in short, TIN scheme) achieves all points in the capacity region to within a constant gap. The generalized degrees of freedom (GDoF) region under this condition is a polyhedron, which is shown to be fully achieved by the same scheme, without the need for timesharing. The results are proved by first deriving a polyhedral relaxation of the GDoF region achieved by TIN, and then providing a dual characterization of this polyhedral region via the use of potential functions, and finally proving the optimality of this region in the desired regime. Index Terms — Capacity region, Gaussian interference channel, generalized degrees of freedom (GDoF), treating interference as noise (TIN). I.
On the maximum achievable sumrate with successive decoding in interference channels
 IEEE Trans. Inf. Theory
, 2012
"... Abstract—In this paper, we investigate the maximum achievable sumrate of the twouser Gaussian interference channel with Gaussian superposition coding and successive decoding. We first examine an approximate deterministic formulation of the problem, and introduce the complementarity conditions that ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
Abstract—In this paper, we investigate the maximum achievable sumrate of the twouser Gaussian interference channel with Gaussian superposition coding and successive decoding. We first examine an approximate deterministic formulation of the problem, and introduce the complementarity conditions that capture the use of Gaussian coding and successive decoding. In the deterministic channel problem, we find the constrained sumcapacity and its achievable schemes with the minimum number of messages, first in symmetric channels, and then in general asymmetric channels. We show that the constrained sumcapacity oscillates as a function of the cross link gain parameters between the information theoretic sumcapacity and the sumcapacity with interference treated as noise. Furthermore, we show that if the number of messages of either of the two users is fewer than the minimum number required to achieve the constrained sumcapacity, the maximum
On the sumcapacity with successive decoding in interference channels. submitted to
 IEEE Transactions on Information Theory
"... Abstract—In this paper, we investigate the sumcapacity of the twouser Gaussian interference channel with Gaussian superposition coding and successive decoding. We first examine an approximate deterministic formulation of the problem, and introduce the complementarity conditions that capture the u ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
(Show Context)
Abstract—In this paper, we investigate the sumcapacity of the twouser Gaussian interference channel with Gaussian superposition coding and successive decoding. We first examine an approximate deterministic formulation of the problem, and introduce the complementarity conditions that capture the use of Gaussian coding and successive decoding. In the deterministic channel problem, we show that the constrained sumcapacity oscillates as a function of the cross link gain parameters between the information theoretic sumcapacity and the sumcapacity with interference treated as noise. Furthermore, we show that if the number of messages of either user is fewer than the minimum number required to achieve the constrained sumcapacity, the maximum achievable sumrate drops to that with interference treated as noise. We translate the optimal schemes in the deterministic channel model to the Gaussian channel model, and also derive two upper bounds on the constrained sumcapacity. Numerical evaluations show that the constrained sumcapacity in the Gaussian channels oscillates between the sumcapacity with Gaussian HanKobayashi schemes and that with single message schemes. I.
TCP Reno over Adaptive CSMA
, 2010
"... Any person(s) intending to use a part or the whole of the materials in this thesis in a proposed publication must seek copyright release from the Dean of ..."
Abstract
 Add to MetaCart
(Show Context)
Any person(s) intending to use a part or the whole of the materials in this thesis in a proposed publication must seek copyright release from the Dean of