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39
Communication over mimo x channels: Interference alignment, decomposition, and performance analysis
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2008
"... In a multipleantenna system with two transmitters and two receivers, a scenario of data communication, known as the X channel, is studied in which each receiver receives data from both transmitters. In this scenario, it is assumed that each transmitter is unaware of the other transmitter’s data (n ..."
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Cited by 43 (4 self)
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In a multipleantenna system with two transmitters and two receivers, a scenario of data communication, known as the X channel, is studied in which each receiver receives data from both transmitters. In this scenario, it is assumed that each transmitter is unaware of the other transmitter’s data (noncooperative scenario). This system can be considered as a combination of two broadcast channels (from the transmitters ’ points of view) and two multipleaccess channels (from the receivers ’ points of view). Taking advantage of both perspectives, two signaling schemes for such a scenario are developed. In these schemes, some linear filters are employed at the transmitters and at the receivers which decompose the system into either two noninterfering multipleantenna broadcast subchannels or two noninterfering multipleantenna multipleaccess subchannels. The main objective in the design of the filters is to exploit the structure of the channel matrices to achieve the
Competitive design of multiuser MIMO systems based on game theory: A unified view
 IEEE Journal on Selected Areas in Communications
, 2008
"... Abstract—This paper considers the noncooperative maximization of mutual information in the Gaussian interference channel in a fully distributed fashion via game theory. This problem has been studied in a number of papers during the past decade for the case of frequencyselective channels. A variety ..."
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Cited by 27 (2 self)
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Abstract—This paper considers the noncooperative maximization of mutual information in the Gaussian interference channel in a fully distributed fashion via game theory. This problem has been studied in a number of papers during the past decade for the case of frequencyselective channels. A variety of conditions guaranteeing the uniqueness of the Nash Equilibrium (NE) and convergence of many different distributed algorithms have been derived. In this paper we provide a unified view of the stateoftheart results, showing that most of the techniques proposed in the literature to study the game, even though apparently different, can be unified using our recent interpretation of the waterfilling operator as a projection onto a proper polyhedral set. Based on this interpretation, we then provide a mathematical framework, useful to derive a unified set of sufficient conditions guaranteeing the uniqueness of the NE and the global convergence of waterfilling based asynchronous distributed algorithms. The proposed mathematical framework is also instrumental to study the extension of the game to the more general MIMO case, for which only few results are available in the current literature. The resulting algorithm is, similarly to the frequencyselective case, an iterative asynchronous MIMO waterfilling algorithm. The proof of convergence hinges again on the interpretation of the MIMO waterfilling as a matrix projection, which is the natural generalization of our results obtained for the waterfilling mapping in the frequencyselective case. Index Terms—Game Theory, MIMO Gaussian interference channel, Nash equilibrium, totally asynchronous algorithms, waterfilling. I.
Cooperative Spatial Multiplexing with Hybrid Channel Knowledge
 Proc. International Zurich Seminar on Communications
, 2006
"... Abstract — We explore the concept of cooperative spatial multiplexing for use in MIMO multicell networks. One key application of this is the transmission of independent streams jointly by several multipleantenna access points toward multiple single antenna user terminals located in neighboring cells ..."
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Cited by 9 (3 self)
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Abstract — We explore the concept of cooperative spatial multiplexing for use in MIMO multicell networks. One key application of this is the transmission of independent streams jointly by several multipleantenna access points toward multiple single antenna user terminals located in neighboring cells. To augment the realism of this setting, we further introduce a constraint on hybrid channel state information (HCSI) in which any given transmitter know its own CSI perfectly while it only has statistical information about other transmitter’s channels. This yield a game situation in which each cooperating transmitter makes a guess about the behavior of the other transmitter. We show different transmission strategies in under this setting and compare them with fully cooperative (full CSI) and non cooperative schemes. Our results show a substantial cooperation gain despite the lack of instantaneous information. I.
Optimal power schedule for distributed MIMO links
 IEEE Transactions on Wireless Communications
, 2008
"... Abstract—We present an optimal power scheduling scheme to maximize the throughput of a set of distributed multipleinput multipleoutput (MIMO) wireless links. This scheme exploits both spatial and temporal freedoms of the source covariance matrices of all MIMO links. In particular, the source covar ..."
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Cited by 8 (4 self)
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Abstract—We present an optimal power scheduling scheme to maximize the throughput of a set of distributed multipleinput multipleoutput (MIMO) wireless links. This scheme exploits both spatial and temporal freedoms of the source covariance matrices of all MIMO links. In particular, the source covariance matrix of each MIMO link is allowed to vary within a block of time (and/or frequency) slots. This scheme, also referred to as spacetime power scheduling, optimizes an integration of link scheduling and power control for MIMO links. The computational problem involved in this scheme is nonconvex. However, a gradientprojection algorithm developed for this scheme consistently yields a higher capacity than all other existing schemes. Index Terms—Network of MIMO links, medium access control, spacetime power scheduling. I.
Conjugate Gradient Projection Approach for MultiAntenna Gaussian Broadcast Channels
"... It has been shown recently that the dirtypaper coding is the optimal strategy for maximizing the sum rate of multipleinput multipleoutput Gaussian broadcast channels (MIMO BC). Moreover, by the channel duality, the nonconvex MIMO BC sum rate problem can be transformed to the convex dual MIMO mult ..."
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Cited by 7 (2 self)
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It has been shown recently that the dirtypaper coding is the optimal strategy for maximizing the sum rate of multipleinput multipleoutput Gaussian broadcast channels (MIMO BC). Moreover, by the channel duality, the nonconvex MIMO BC sum rate problem can be transformed to the convex dual MIMO multipleaccess channel (MIMO MAC) problem with a sum power constraint. In this paper, we design an efficient algorithm based on conjugate gradient projection (CGP) to solve the MIMO BC maximum sum rate problem. Our proposed CGP algorithm solves the dual sum power MAC problem by utilizing the powerful concept of Hessian conjugacy. We also develop a rigorous algorithm to solve the projection problem. We show that CGP enjoys provable convergence, nice scalability, and great efficiency for large MIMO BC systems. 1
Monotonic Convergence of Distributed Interference Pricing in Wireless Networks
"... Abstract—We study distributed algorithms for allocating powers and/or adjusting beamforming vectors in a peertopeer wireless network which may have multipleinputsingleoutput (MISO) links. The objective is to maximize the total utility summed over all users, where each user’s utility is a functi ..."
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Cited by 5 (2 self)
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Abstract—We study distributed algorithms for allocating powers and/or adjusting beamforming vectors in a peertopeer wireless network which may have multipleinputsingleoutput (MISO) links. The objective is to maximize the total utility summed over all users, where each user’s utility is a function of the received signaltointerferenceplusnoise ratio (SINR). Each user (receiver) announces an interference price, representing the marginal cost of interference from other users. A particular user (transmitter) then updates its power and beamforming vector to maximize its utility minus the interference cost to other users, which is determined from their announced interference prices. We show that if each transmitter update is based on a current set of interference prices and the utility functions satisfy certain concavity conditions, then the total utility is nondecreasing with each update. The proof is based on the convexity of the utility functions with respect to received interference, and applies to rate utility functions, and an arbitrary number of interfering MISO links. The extension to multicarrier links is discussed as well as algorithmic variations in which the prices are not immediately updated after power or beam updates. I.
Crosslayer optimization of MIMObased mesh networks under orthogonal channels
 in: Proceedings of IEEE WCNC
"... Abstract — MIMO technology is one of the most significant advances in the past decade to increase channel capacity and has a great potential to improve network capacity for mesh networks. In a MIMObased mesh network, the links outgoing from each node sharing the common communication spectrum can be ..."
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Cited by 4 (1 self)
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Abstract — MIMO technology is one of the most significant advances in the past decade to increase channel capacity and has a great potential to improve network capacity for mesh networks. In a MIMObased mesh network, the links outgoing from each node sharing the common communication spectrum can be modeled as a Gaussian vector broadcast channel. Recently, researchers showed that “dirty paper coding ” (DPC) is the optimal transmission strategy for Gaussian vector broadcast channels. So far, there has been little study on how this fundamental result will impact the crosslayer design for MIMObased mesh networks. To fill this gap, we consider the problem of jointly optimizing DPC power allocation in the link layer at each node and multihop/multipath routing in a MIMObased mesh networks. It turns out that this optimization problem is a very challenging nonconvex problem. To address this difficulty, we transform the original problem to an equivalent problem by exploiting the channel duality. For the transformed problem, we develop an efficient solution procedure that integrates Lagrangian dual decomposition method, conjugate gradient projection method based on matrix differential calculus, cuttingplane method, and subgradient method. In our numerical example, it is shown that we can achieve a network performance gain of 34.4 % by using DPC. I.
Distributed interference pricing with MISO channels
 IN PROC. ALLERTON CONFERENCE ON COMMUNICATION, CONTROL, AND COMPUTING
, 2008
"... Abstract—We study a distributed algorithm for adapting transmit beamforming vectors in a multiantenna peertopeer wireless network. The algorithm attempts to maximize a sum of peruser utility functions, where each user’s utility is a function of his transmission rate, or equivalently the received ..."
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Cited by 3 (3 self)
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Abstract—We study a distributed algorithm for adapting transmit beamforming vectors in a multiantenna peertopeer wireless network. The algorithm attempts to maximize a sum of peruser utility functions, where each user’s utility is a function of his transmission rate, or equivalently the received signaltointerference plus noise ratio (SINR). This is accomplished by exchanging interference prices, each of which represents the marginal cost of interference to a particular user. Given the interference prices, users update their beamforming vectors to maximize their utility minus the cost of interference. For a twouser system, we show that this algorithm converges for a suitable class of utility functions. Convergence of the algorithm with more than two users is illustrated numerically. I.
Power Games in MIMO Interference Systems
"... Abstract — We consider a multilink and multiinputmultioutput (MIMO) interference system in which each link wishes to minimize its own power by choosing its own signal vector subject to an information theoretic QualityofService (QoS) requirement. Our setup leads to a multilink game, referred to ..."
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Cited by 3 (2 self)
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Abstract — We consider a multilink and multiinputmultioutput (MIMO) interference system in which each link wishes to minimize its own power by choosing its own signal vector subject to an information theoretic QualityofService (QoS) requirement. Our setup leads to a multilink game, referred to as a “power game”, in which the feasible strategy set of an individual link depends on the strategies of the other links. We characterize the rates for which an equilibrium solution exists in a power game in terms of the equilibria of “capacity games” introduced in our earlier work [1]. We provide an example where the set of equilibrium rates is properly contained in the set of achievable rates. We provide a conservative estimate of the region of equilibrium rates using a minmax approach. We discuss the uniqueness of equilibrium as well as the convergence of best response dynamics (a.k.a. iterative waterfilling) for all rates when the interference is sufficiently small and some other mild conditions are met. Finally, we extend our results to the case where the QoS requirements are softened.