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31
HighSNR power offset in multiantenna communication
 IEEE Transactions on Information Theory
, 2005
"... Abstract—The analysis of the multipleantenna capacity in the high regime has hitherto focused on the high slope (or maximum multiplexing gain), which quantifies the multiplicative increase as a function of the number of antennas. This traditional characterization is unable to assess the impact of ..."
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Cited by 91 (18 self)
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Abstract—The analysis of the multipleantenna capacity in the high regime has hitherto focused on the high slope (or maximum multiplexing gain), which quantifies the multiplicative increase as a function of the number of antennas. This traditional characterization is unable to assess the impact of prominent channel features since, for a majority of channels, the slope equals the minimum of the number of transmit and receive antennas. Furthermore, a characterization based solely on the slope captures only the scaling but it has no notion of the power required for a certain capacity. This paper advocates a more refined characterization whereby, as a function of �f, the high capacity is expanded as an affine function where the impact of channel features such as antenna correlation, unfaded components, etc., resides in the zeroorder term or power offset. The power offset, for which we find insightful closedform expressions, is shown to play a chief role for levels of practical interest. Index Terms—Antenna correlation, channel capacity, coherent communication, fading channels, high analysis, multiantenna arrays, Ricean channels.
MIMO wireless linear precoding
 IEEE Signal Processing Magazine
, 2006
"... The benefits of using multiple antennas at both the transmitter and the receiver in a wireless system are well established. Multipleinput multipleoutput (MIMO) systems enable a growth in transmission rate linear in the minimum of the number of antennas at either end [1][2]. MIMO techniques also en ..."
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Cited by 44 (0 self)
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The benefits of using multiple antennas at both the transmitter and the receiver in a wireless system are well established. Multipleinput multipleoutput (MIMO) systems enable a growth in transmission rate linear in the minimum of the number of antennas at either end [1][2]. MIMO techniques also enhance link reliability and
Optimum power allocation for singleuser MIMO and multiuser MIMOMAC with partial CSI
 IEEE Journal on Selected Areas in Communications
, 2007
"... Abstract — We consider both the singleuser and the multiuser power allocation problems in MIMO systems, where the receiver side has the perfect channel state information (CSI), and the transmitter side has partial CSI, which is in the form of covariance feedback. In a singleuser MIMO system, we co ..."
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Cited by 28 (4 self)
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Abstract — We consider both the singleuser and the multiuser power allocation problems in MIMO systems, where the receiver side has the perfect channel state information (CSI), and the transmitter side has partial CSI, which is in the form of covariance feedback. In a singleuser MIMO system, we consider an iterative algorithm that solves for the eigenvalues of the optimum transmit covariance matrix that maximizes the rate. The algorithm is based on enforcing the KarushKuhnTucker (KKT) optimality conditions of the optimization problem at each iteration. We prove that this algorithm converges to the unique global optimum power allocation when initiated at an arbitrary point. We, then, consider the multiuser generalization of the problem, which is to find the eigenvalues of the optimum transmit covariance matrices of all users that maximize the sum rate of the MIMO multiple access channel (MIMOMAC). For this problem, we propose an algorithm that finds the unique optimum power allocation policies of all users. At a given iteration, the multiuser algorithm updates the power allocation of one user, given the power allocations of the rest of the users, and iterates over all users in a roundrobin fashion. Finally, we make several suggestions that significantly improve the convergence rate of the proposed algorithms. Index Terms — Multiuser MIMO, MIMO multiple access channel, partial CSI, covariance feedback, optimum power allocation.
Optimality of beamforming in fading MIMO multiple access channels
 IEEE Transactions on Communications
, 2008
"... Abstract—We consider the sum capacity of a multiinput multioutput (MIMO) multiple access channel (MAC) where the receiver has the perfect channel state information (CSI), while the transmitters have either no or partial CSI. When the transmitters have partial CSI, it is in the form of either the c ..."
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Cited by 16 (4 self)
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Abstract—We consider the sum capacity of a multiinput multioutput (MIMO) multiple access channel (MAC) where the receiver has the perfect channel state information (CSI), while the transmitters have either no or partial CSI. When the transmitters have partial CSI, it is in the form of either the covariance matrix of the channel or the mean matrix of the channel. For the covariance feedback case, we mainly consider physical models that result in singlesided correlation structures. For the mean feedback case, we consider physical models that result in inphase received signals. Under these assumptions, we analyze the MIMOMAC from three different viewpoints. First, we consider a finitesized system. We show that the optimum transmit directions of each user are the eigenvectors of its own channel covariance and mean feedback matrices, in the covariance and mean feedback models, respectively. Also, we find the conditions under which beamforming is optimal for all users. Second, in the covariance feedback case, we prove that the region where beamforming is optimal for all users gets larger with the addition of new users into the system. In the mean feedback case, we show through simulations that this is not necessarily true. Third, we consider the asymptotic case where the number of users is large. We show that in both no and partial CSI cases, beamforming is asymptotically optimal. In particular, in the case of no CSI, we show that a simple form of beamforming, which may be characterized as an arbitrary antenna selection scheme, achieves the sum capacity. In the case of partial CSI, we show that beamforming in the direction of the strongest eigenvector of the channel feedback matrix achieves the sum capacity. Finally, we generalize our covariance feedback results to doublesided correlation structures in the Appendix. Index Terms—Multiuser MIMO, MIMO multiple access channel, partial CSI, covariance feedback, mean feedback, optimality of beamforming, large system analysis. I.
Optimization of the MIMO compound capacity
 IEEE Transactions on Wireless Communications
, 2007
"... Abstract — In this paper, we consider the optimization of the compound capacity in a rank one Ricean multiple input multiple output channel using partial channel state information at the transmitter side. We model the channel as a deterministic matrix within a known ellipsoid, and address the compou ..."
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Cited by 14 (1 self)
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Abstract — In this paper, we consider the optimization of the compound capacity in a rank one Ricean multiple input multiple output channel using partial channel state information at the transmitter side. We model the channel as a deterministic matrix within a known ellipsoid, and address the compound capacity defined as the maximum worst case mutual information in the set. We find that the optimal transmit strategy is always beamforming, and can be found using a simple one dimensional search. Similar results are derived for the worst case sumrate of a multiple access channel with individual power constraints and a total power constraint. In this multiuser setting we assume equal array response at the receiver for all users. These results motivate the growing use of systems using simple beamforming transmit strategies. Index Terms — MIMO, compound capacity, beamforming. I.
Effective capacity maximization in multiantenna channels with covariance feedback
 In Proceedings of IEEE International Conference on Communications (ICC
, 2009
"... Abstract—The optimal transmit strategies of singleuser multiantenna systems with respect to average capacity maximization are well understood. However, the performance measure does neglect delay aspects which are important for higher layer design. Therefore, we consider the maximization of the effe ..."
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Cited by 12 (0 self)
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Abstract—The optimal transmit strategies of singleuser multiantenna systems with respect to average capacity maximization are well understood. However, the performance measure does neglect delay aspects which are important for higher layer design. Therefore, we consider the maximization of the effective capacity in a singleuser multiantenna system with covariance knowledge. The optimal transmit strategy is derived and the properties as a function of the decayrate requirement of the buffer occupancy are analyzed. In particular, we show that the larger the decayrate requirement, the smaller the beamforming optimality range, i.e., the more spatial eigenmodes are activated. This behavior is illustrated by numerical simulations and explained by the channel hardening effect.
On the Performance of TDMA and SDMA based Opportunistic Beamforming
"... 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 ..."
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Cited by 5 (2 self)
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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.
SubLinear Capacity Scaling Laws for Sparse MIMO Channels
, 2010
"... Recent attention on performance analysis of singleuser multipleinput multipleoutput (MIMO) systems has been on understanding the impact of the spatial correlation model on ergodic capacity. In most of these works, it is assumed that the statistical degrees of freedom (DoF) in the channel can be ..."
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Cited by 4 (2 self)
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Recent attention on performance analysis of singleuser multipleinput multipleoutput (MIMO) systems has been on understanding the impact of the spatial correlation model on ergodic capacity. In most of these works, it is assumed that the statistical degrees of freedom (DoF) in the channel can be captured by decomposing it along a suitable eigenbasis and that the transmitter has perfect knowledge of the statistical DoF. With an increased interest in largeantenna systems in stateoftheart technologies, these implicit channel modeling assumptions in the literature have to be revisited. In particular, multiantenna measurements have showed that largeantenna systems are sparse where only a few DoF are dominant enough to contribute towards capacity. Thus, in this work, it is assumed that the transmitter can only afford to learn the dominant statistical DoF in the channel. The focus is on understanding ergodic capacity scaling laws in sparse channels. Unlike classical results, where linear capacity scaling is implicit, sparsity of MIMO channels coupled with a knowledge of only the dominant DoF is shown to result in a new paradigm of sublinear capacity scaling that is consistent with experimental results and physical arguments. It is also shown that uniformpower signaling over all the antenna dimensions is wasteful and could result in a significant penalty over optimally adapting the antenna spacings in response to the sparsity level of the channel and transmit SNR.
Beamforming maximizes the rank one Ricean MIMO compound capacity
 in Proc. IEEE Signal Processing Advances in Wireless Communications (SPAWC’05
, 2005
"... In this paper, we consider the optimization of the compound capacity in a rank one Ricean multiple input multiple output channels using partial channel state information at the transmitter side. We model the channel as a deterministic matrix within a known ellipsoid, and maximize the compound capaci ..."
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Cited by 3 (1 self)
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In this paper, we consider the optimization of the compound capacity in a rank one Ricean multiple input multiple output channels using partial channel state information at the transmitter side. We model the channel as a deterministic matrix within a known ellipsoid, and maximize the compound capacity defined as the worst case capacity within this set. We find that the optimal transmit strategy is always beamforming, and can be found using a simple one dimensional search. These results motivate the growing use of systems using simple beamforming transmit strategies. 1.
MIMO capacity in correlated interferencelimited channels
 in Proc. IEEE Symp. Inf. Theory
, 2007
"... Abstract — This paper analyzes the capacity of MIMO channels in the presence of both antenna correlation and cochannel interference. We investigate the optimization of the input covariance, characterize the optimality of beamforming, and study the behavior of the input covariance in the low and hi ..."
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Cited by 3 (0 self)
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Abstract — This paper analyzes the capacity of MIMO channels in the presence of both antenna correlation and cochannel interference. We investigate the optimization of the input covariance, characterize the optimality of beamforming, and study the behavior of the input covariance in the low and highpower regimes. For the special case of separable correlations, we also derive analytical expressions for the key statistical properties of the spectral efficiency achievable with an arbitrary input covariance. Altogether, our analysis enables assessing the joint impact of correlation and interference on the capacity of multiantenna architectures in a cellular system. I.