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IEEE Transactions on Signal Processing. 01/2012; 60:834-847.
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ABSTRACT: The algorithms in this paper exploit optimal input structure in interference networks and is a major advance from the state-of-the-art. Optimization under multiple linear constraints is important for interference networks with individual power constraints, per-antenna power constraints, and/or interference constraints as in cognitive radios. While for single-user MIMO channel transmitter optimization, no one uses general purpose optimization algorithms such as steepest ascent because water-filling is optimal and much simpler, this is not true for MIMO multiaccess channels (MAC), broadcast channels (BC), and the non-convex optimization of interference networks because the traditional water-filling is far from optimal for networks. We recently found the right form of water-filling, polite water-filling, for some capacity/achievable regions of the general MIMO interference networks, named B-MAC networks, which include BC, MAC, interference channels, X networks, and most practical wireless networks as special cases. In this paper, we use weighted sum-rate maximization under multiple linear constraints in interference tree networks, a natural extension of MAC and BC, as an example to show how to design highly efficiency and low complexity algorithms. Several times faster convergence speed and orders of magnitude higher accuracy than the state-of-the-art are demonstrated by numerical examples.
Information Theory Proceedings (ISIT), 2011 IEEE International Symposium on; 09/2011
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ABSTRACT: We take two new approaches to design efficient algorithms for transmitter optimization under rate constraints in order to guarantee the Quality of Service for MIMO B-MAC interference networks. A B-MAC network is a generalized interference network that is a combination of multiple interfering broadcast channels (BC) and multiaccess channels (MAC). Two related optimization problems, maximizing the minimum of weighted rates under a sum-power constraint and minimizing the sum-power under rate constraints, are considered. The first approach takes advantage of existing algorithms for SINR problems by building a bridge between rate and SINR through the design of optimal mappings between them. The second approach exploits the polite water-filling structure, which is the network version of water-filling satisfied by all the Pareto optimal input of a large class of achievable regions of B-MAC networks. It replaces most generic optimization algorithms currently used for such networks and reduces the complexity while demonstrating superior performance even in non-convex cases. Both centralized and distributed algorithms are designed and the performance is analyzed in addition to numeric examples.
IEEE Transactions on Signal Processing 02/2011; · 2.63 Impact Factor
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ABSTRACT: It is well known that in general, the traditional water-filling is far from optimal in networks. We recently found the long-sought network version of water-filling named polite water-filling that is optimal for a large class of MIMO networks called B-MAC networks, of which interference Tree (iTree) networks is a subset whose interference graphs have no directional loop. iTree networks is a natural extension of both broadcast channel (BC) and multiaccess channel (MAC) and possesses many desirable properties for further information theoretic study. Given the optimality of the polite water-filling, general purpose optimization algorithms for networks are no longer needed because they do not exploit the structure of the problems. Here, we demonstrate it through the weighted sum-rate maximization. The significance of the results is that the algorithm can be easily modified for general B-MAC networks with interference loops. It illustrates the properties of iTree networks and for the special cases of MAC and BC, replaces the current steepest ascent algorithms for finding the capacity regions. The fast convergence and high accuracy of the proposed algorithms are verified by simulation.
Global Telecommunications Conference (GLOBECOM 2010), 2010 IEEE; 01/2011
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IEEE Transactions on Signal Processing. 01/2011; 59:263-276.
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CoRR. 01/2010; abs/1007.0982.
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Wireless Communications and Mobile Computing. 01/2010; 10:1238-1252.
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ABSTRACT: In practice, a node in a network learns the channel through local message passing and obtains a local view of the network. Pure wireless message passing as well as mixed wireless and wireline message passing are considered in this paper. We study the distributed optimization of sum-rate for a class of deterministic interference networks with local view. A connection based utility function is designed for each user to exploit the local knowledge. This utility design turns out to be a potential game with sum-rate as the potential function. For the one-to-many channel with 1.5 wireless rounds of message passing, we show that there is a unique Nash equilibrium and using this strategy, the sum capacity can be achieved. We provide a sufficient condition for which a topology does not have unique Nash equilibrium. Then we consider the scenario that the network size and the users IDs are provided to each user. For various mixed wireless and wireline message passing patterns, including wireline at transmitter/receiver side and sequential/concurrent message passing scheduling, we identify whether a three-user interference network can achieve the sum capacity in a distributed fashion. Compared with the 1.5 pure wireless rounds of message passing, the results show that 2.5 mixed wireless and wireline rounds of message passing can significantly improve the system performance of three-user interference networks. We also derive some sufficient conditions for general K-user interference networks such that the sum capacity can not be achieved based on each user's local view.
Communication, Control, and Computing, 2009. Allerton 2009. 47th Annual Allerton Conference on; 11/2009
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ABSTRACT: In a communication network, it is often impractical for each node to learn the global channel knowledge (network connectivity and channel state information of each link). In this paper, we address distributed rate optimization for Time-Division Duplex (TDD) Multiple-Input Multiple-Output (MIMO) networks when part of the local channel knowledge is learned via message passing between each transmitter and its intended receivers. The distributed optimization algorithm is based on a rate duality and the corresponding input covariance matrix transformation between the forward and reverse links of TDD MIMO networks under the assumption of global channel knowledge. Noting that the key information required by the proposed transformation is the interference-plus-noise covariance matrix, we propose a local covariance matrix transformation such that each node can distributedly optimize its input covariance matrix by only exchanging interference-plus-noise covariance matrix locally. It is observed from the simulation that the proposed algorithm achieves a performance close to the one with global channel knowledge and outperforms the existing distributed algorithms.
Communication, Control, and Computing, 2009. Allerton 2009. 47th Annual Allerton Conference on; 11/2009
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43rd Annual Conference on Information Sciences and Systems, CISS 2009, The John Hopkins University, Baltimore, MD, USA, 18-20 March 2009; 01/2009
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IEEE Communications Letters. 01/2009; 13:564-566.
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43rd Annual Conference on Information Sciences and Systems, CISS 2009, The John Hopkins University, Baltimore, MD, USA, 18-20 March 2009; 01/2009
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Proceedings of the Global Communications Conference, 2009. GLOBECOM 2009, Honolulu, Hawaii, USA, 30 November - 4 December 2009; 01/2009
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Proceedings of IEEE International Conference on Communications, ICC 2008, Beijing, China, 19-23 May 2008; 01/2008
