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313
Sum power iterative waterfilling for multiantenna Gaussian broadcast channels
 IEEE Trans. Inform. Theory
, 2005
"... In this paper we consider the problem of maximizing sum rate of a multipleantenna Gaussian broadcast channel. It was recently found that dirty paper coding is capacity achieving for this channel. In order to achieve capacity, the optimal transmission policy (i.e. the optimal transmit covariance str ..."
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Cited by 136 (14 self)
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In this paper we consider the problem of maximizing sum rate of a multipleantenna Gaussian broadcast channel. It was recently found that dirty paper coding is capacity achieving for this channel. In order to achieve capacity, the optimal transmission policy (i.e. the optimal transmit covariance structure) given the channel conditions and power constraint must be found. However, obtaining the optimal transmission policy when employing dirty paper coding is a computationally complex nonconvex problem. We use duality to transform this problem into a wellstructured convex multipleaccess channel problem. We exploit the structure of this problem and derive simple and fast iterative algorithms that provide the optimum transmission policies for the multipleaccess channel, which can easily be mapped to the optimal broadcast channel policies.
Optimized Signaling for MIMO Interference Systems with Feedback
"... The system mutual information of a multipleinput multipleoutput (MIMO) system with multiple users which mutually interfere is considered. Perfect channel state information is assumed to be known to both transmitters and receivers. Asymptotic performance analysis shows that the system mutual info ..."
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Cited by 106 (0 self)
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The system mutual information of a multipleinput multipleoutput (MIMO) system with multiple users which mutually interfere is considered. Perfect channel state information is assumed to be known to both transmitters and receivers. Asymptotic performance analysis shows that the system mutual information changes behavior as the interference becomes sufficiently strong. In particular, beamforming is the optimum signaling for all users when the interference is large. We propose several numerical approaches to decide the covariance matrices of the transmitted signals and compare their performance in terms of the system mutual information. We model the system as a noncooperative game, and perform iterative waterfilling to find the Nash equilibrium distributively. A centralized global approach and a distributed iterative approach based on the gradient projection method are also proposed. Numerical results show that all proposed approaches give better performance than the standard signaling which is optimum for the case without interference. Both the global and the iterative gradient projection methods are shown to outperform the Nash equilibrium significantly.
Wireless systems and interference avoidance
 IEEE Trans. Wireless Commun
, 2002
"... Abstract—Motivated by the emergence of programmable radios, we seek to understand a new class of communication system where pairs of transmitters and receivers can adapt their modulation/demodulation method in the presence of interference to achieve better performance. Using signal to interference r ..."
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Cited by 77 (12 self)
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Abstract—Motivated by the emergence of programmable radios, we seek to understand a new class of communication system where pairs of transmitters and receivers can adapt their modulation/demodulation method in the presence of interference to achieve better performance. Using signal to interference ratio as a metric and a general signal space approach, we present a class of iterative distributed algorithms for synchronous systems which results in an ensemble of optimal waveforms for multiple users connected to a common receiver (or colocated independent receivers). That is, the waveform ensemble meets the Welch Bound with equality and, therefore, achieves minimum average interference over the ensemble of signature waveforms. We derive fixed points for a number of scenarios, provide examples, look briefly at ensemble stability under user addition and deletion as well as provide a simplistic comparison to synchronous codedivision multipleaccess. We close with suggestions for future work. Index Terms—Adaptive modulation, codedivision multipleaccess systems, codeword optimization, interference avoidance, multiuser
Transceiver optimization for multiuser MIMO systems
 IEEE Tran. on Signal Processing, 52(1):214 – 226
, 2004
"... Abstract—We consider the uplink of a multiuser system where the transmitters as well as the receiver are equipped with multiple antennas. Each user multiplexes its symbols by a linear precoder through its transmit antennas. We work with the systemwide mean squared error as the performance measure a ..."
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Cited by 76 (10 self)
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Abstract—We consider the uplink of a multiuser system where the transmitters as well as the receiver are equipped with multiple antennas. Each user multiplexes its symbols by a linear precoder through its transmit antennas. We work with the systemwide mean squared error as the performance measure and propose algorithms to find the jointly optimum linear precoders at each transmitter and linear decoders at the receiver. We first work with the case where the number of symbols to be transmitted by each user is given. We then investigate how the symbol rate should be chosen for each user with optimum transmitters and receivers. The convergence analysis of the algorithms is given, and numerical evidence that supports the analysis is presented. Index Terms—MMSE receivers, multiuser MIMO system, receiver beamforming, transmitter beamforming.
MIMO Transceiver Design via Majorization Theory
, 2007
"... and unified representation of different physical communication systems, ranging from multiantenna wireless channels to wireless digital subscriber line systems. They have the key property that several data streams can be simultaneously established. In general, the design of communication systems f ..."
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Cited by 66 (1 self)
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and unified representation of different physical communication systems, ranging from multiantenna wireless channels to wireless digital subscriber line systems. They have the key property that several data streams can be simultaneously established. In general, the design of communication systems for MIMO channels is quite involved (if one can assume the use of sufficiently long and good codes, then the problem formulation simplifies drastically). The first difficulty lies on how to measure the global performance of such systems given the tradeoff on the performance among the different data streams. Once the problem formulation is defined, the resulting mathematical problem is typically too complicated to be optimally solved as it is a matrixvalued nonconvex optimization problem. This design problem has been studied for the past three decades (the first papers
Weighted SumRate Maximization using Weighted MMSE for MIMOBC Beamforming Design
 IEEE Trans. on Wireless Comm
, 2008
"... Abstract—This paper studies linear transmit filter design for Weighted SumRate (WSR) maximization in the Multiple Input Multiple Output Broadcast Channel (MIMOBC). The problem of finding the optimal transmit filter is nonconvex and intractable to solve using low complexity methods. Motivated by r ..."
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Cited by 59 (2 self)
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Abstract—This paper studies linear transmit filter design for Weighted SumRate (WSR) maximization in the Multiple Input Multiple Output Broadcast Channel (MIMOBC). The problem of finding the optimal transmit filter is nonconvex and intractable to solve using low complexity methods. Motivated by recent results highlighting the relationship between mutual information and Minimum Mean Square Error (MMSE), this paper establishes a relationship between weighted sumrate and weighted MMSE in the MIMOBC. The relationship is used to propose two low complexity algorithms for finding a local weighted sumrate optimum based on alternating optimization. Numerical results studying sumrate show that the proposed algorithms achieve high performance with few iterations. Index Terms—MIMO systems, transceiver design, smart antennas, antennas and propagation. I.
Uniform Power Allocation in MIMO Channels: a GameTheoretic Approach
 IEEE Trans. Inf. Theory
, 2003
"... This publication has been included here just to facilitate downloads to those people asking for personal use copies. This material may be published at copyrighted journals or conference proceedings, so personal use of the download is required. In particular, publications from IEEE have to be downloa ..."
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Cited by 56 (4 self)
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This publication has been included here just to facilitate downloads to those people asking for personal use copies. This material may be published at copyrighted journals or conference proceedings, so personal use of the download is required. In particular, publications from IEEE have to be downloaded according to the following IEEE note: c○2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
SpaceTime Multiple Access: Linear Growth in the Sum Rate
 in Proc. 40th Annual Allerton Conf. Communications, Control and Computing
, 2002
"... It is known that some of the spectacular capacity gains of using multiple antennas on a pointtopoint rich scattering channel, namely linear growth with the number of antennas, can also be obtained in a multiuser environment. We give the constant of proportionality of linear growth in the sumcapa ..."
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Cited by 54 (8 self)
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It is known that some of the spectacular capacity gains of using multiple antennas on a pointtopoint rich scattering channel, namely linear growth with the number of antennas, can also be obtained in a multiuser environment. We give the constant of proportionality of linear growth in the sumcapacity when the number of users and antennas grow simultaneously, but with fewer users than antennas. We assume that the transmitter and receivers know the channel. Because of the linear growth in sumcapacity, we can accommodate more users simply by adding more antennas, without increasing total transmitted power or bandwidth or lowering the rate to existing users. We dub any scheme that can achieve linear growth in this fashion a spacetime multiple access scheme. Channelhardening arguments show that a "channelinversion" technique used in pointtopoint multipleantenna links achieves a large fraction of this linear growth in a multiuser environment without excessive transmitter power. Thus, multipleantennas offer a tremendous advantage in designing scheduling, networking, and multipleaccess protocols in rich scattering environments.
The waterfilling game in fading multipleaccess channels
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2008
"... A gametheoretic framework is developed to design and analyze the resource allocation algorithms in fading multipleaccess channels (MACs), where the users are assumed to be selfish, rational, and limited by average power constraints. The maximum sumrate point on the boundary of the MAC capacity ..."
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Cited by 47 (0 self)
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A gametheoretic framework is developed to design and analyze the resource allocation algorithms in fading multipleaccess channels (MACs), where the users are assumed to be selfish, rational, and limited by average power constraints. The maximum sumrate point on the boundary of the MAC capacity region is shown to be the unique Nash equilibrium of the corresponding waterfilling game. This result sheds a new light on the opportunistic communication principle. The base station is then introduced as a player interested in maximizing a weighted sum of the individual rates. A Stackelberg formulation is proposed in which the base station is the designated game leader. In this setup, the base station announces first its strategy defined as the decoding order of the different users, in the successive cancellation receiver, as a function of the channel state. In the second stage, the users compete conditioned on this particular decoding strategy. This formulation is shown to be able to achieve all the corner points of the capacity region, in addition to the maximum sumrate point. On the negative side, it is shown that there does not exist a base station strategy in this formulation that achieves the rest of the boundary points. To overcome this limitation, a repeated game approach, which achieves the capacity region of the fading MAC, is presented. Finally, the study is extended to vector channels highlighting interesting differences between this scenario and the scalar channel case.