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91
Opportunistic Beamforming Using Dumb Antennas
 IEEE Transactions on Information Theory
, 2002
"... Multiuser diversity is a form of diversity inherent in a wireless network, provided by independent timevarying channels across the different users. The diversity benefit is exploited by tracking the channel fluctuations of the users and scheduling transmissions to users when their instantaneous cha ..."
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Cited by 802 (1 self)
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Multiuser diversity is a form of diversity inherent in a wireless network, provided by independent timevarying channels across the different users. The diversity benefit is exploited by tracking the channel fluctuations of the users and scheduling transmissions to users when their instantaneous channel quality is near the peak. The diversity gain increases with the dynamic range of the fluctuations and is thus limited in environments with little scattering and/or slow fading. In such environments, we propose the use of multiple transmit antennas to induce large and fast channel fluctuations so that multiuser diversity can still be exploited. The scheme can be interpreted as opportunistic beamforming and we show that true beamforming gains can be achieved when there are sufficient users, even though very limited channel feedback is needed. Furthermore, in a cellular system, the scheme plays an additional role of opportunistic nulling of the interference created on users of adjacent cells. We discuss the design implications of implementing this scheme in a complete wireless system.
Capacity Limits of MIMO Channels
 IEEE J. SELECT. AREAS COMMUN
, 2003
"... We provide an overview of the extensive recent results on the Shannon capacity of singleuser and multiuser multipleinput multipleoutput (MIMO) channels. Although enormous capacity gains have been predicted for such channels, these predictions are based on somewhat unrealistic assumptions about t ..."
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Cited by 411 (17 self)
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We provide an overview of the extensive recent results on the Shannon capacity of singleuser and multiuser multipleinput multipleoutput (MIMO) channels. Although enormous capacity gains have been predicted for such channels, these predictions are based on somewhat unrealistic assumptions about the underlying timevarying channel model and how well it can be tracked at the receiver, as well as at the transmitter. More realistic assumptions can dramatically impact the potential capacity gains of MIMO techniques. For timevarying MIMO channels there are multiple Shannon theoretic capacity definitions and, for each definition, different correlation models and channel information assumptions that we consider. We first provide a comprehensive summary of ergodic and capacity versus outage results for singleuser MIMO channels. These results indicate that the capacity gain obtained from multiple antennas heavily depends
The capacity region of the Gaussian multipleinput multipleoutput broadcast channel
 IEEE TRANS. INF. THEORY
, 2006
"... The Gaussian multipleinput multipleoutput (MIMO) broadcast channel (BC) is considered. The dirtypaper coding (DPC) rate region is shown to coincide with the capacity region. To that end, a new notion of an enhanced broadcast channel is introduced and is used jointly with the entropy power inequa ..."
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Cited by 341 (7 self)
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The Gaussian multipleinput multipleoutput (MIMO) broadcast channel (BC) is considered. The dirtypaper coding (DPC) rate region is shown to coincide with the capacity region. To that end, a new notion of an enhanced broadcast channel is introduced and is used jointly with the entropy power inequality, to show that a superposition of Gaussian codes is optimal for the degraded vector broadcast channel and that DPC is optimal for the nondegraded case. Furthermore, the capacity region is characterized under a wide range of input constraints, accounting, as special cases, for the total power and the perantenna power constraints.
Duality, achievable rates, and sumrate capacity of Gaussian MIMO broadcast channels
 IEEE TRANS. INFORM. THEORY
, 2003
"... We consider a multiuser multipleinput multipleoutput (MIMO) Gaussian broadcast channel (BC), where the transmitter and receivers have multiple antennas. Since the MIMO BC is in general a nondegraded BC, its capacity region remains an unsolved problem. In this paper, we establish a duality between ..."
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Cited by 339 (21 self)
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We consider a multiuser multipleinput multipleoutput (MIMO) Gaussian broadcast channel (BC), where the transmitter and receivers have multiple antennas. Since the MIMO BC is in general a nondegraded BC, its capacity region remains an unsolved problem. In this paper, we establish a duality between what is termed the “dirty paper” achievable region (the Caire–Shamai achievable region) for the MIMO BC and the capacity region of the MIMO multipleaccess channel (MAC), which is easy to compute. Using this duality, we greatly reduce the computational complexity required for obtaining the dirty paper achievable region for the MIMO BC. We also show that the dirty paper achievable region achieves the sumrate capacity of the MIMO BC by establishing that the maximum sum rate of this region equals an upper bound on the sum rate of the MIMO BC.
Sum capacity of the vector Gaussian broadcast channel and uplinkdownlink duality
 IEEE Trans. on Inform. Theory
, 1912
"... We characterize the sum capacity of the vector Gaussian broadcast channel by showing that the existing inner bound of Marton and the existing upper bound of Sato are tight for this channel. We exploit an intimate fourway connection between the vector broadcast channel, the corresponding pointtopo ..."
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Cited by 324 (2 self)
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We characterize the sum capacity of the vector Gaussian broadcast channel by showing that the existing inner bound of Marton and the existing upper bound of Sato are tight for this channel. We exploit an intimate fourway connection between the vector broadcast channel, the corresponding pointtopoint channel (where the receivers can cooperate), the multiple access channel (where the role of transmitters and receivers are reversed), and the corresponding pointtopoint channel (where the transmitters can cooperate). 1
Sum Capacity of a Gaussian Vector Broadcast Channel
 IEEE Trans. Inform. Theory
, 2002
"... This paper characterizes the sum capacity of a class of nondegraded Gaussian vectB broadcast channels where a singletransmitter with multiple transmit terminals sends independent information to multiple receivers. Coordinat+[ is allowed among the transmit teminals, but not among the different recei ..."
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Cited by 280 (21 self)
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This paper characterizes the sum capacity of a class of nondegraded Gaussian vectB broadcast channels where a singletransmitter with multiple transmit terminals sends independent information to multiple receivers. Coordinat+[ is allowed among the transmit teminals, but not among the different receivers. The sum capacity is shown t be a saddlepoint of a Gaussian mu al informat]R game, where a signal player chooses a tansmit covariance matrix to maximize the mutual information, and a noise player chooses a fictitious noise correlation to minimize the mutual information. This result holds fort he class of Gaussian channels whose saddlepoint satisfies a full rank condition. Furt her,t he sum capacity is achieved using a precoding method for Gaussian channels with additive side information noncausally known at the transmitter. The optimal precoding structure is shown t correspond to a decisionfeedback equalizer that decomposes t e broadcast channel into a series of singleuser channels with intk ference presubtract] at the transmiter.
Capacity bounds for the Gaussian interference channel
 IEEE TRANS. INFORM. THEORY
"... The capacity region of the twouser Gaussian Interference Channel (IC) is studied. Three classes of channels are considered: weak, onesided, and mixed Gaussian ICs. For the weak Gaussian IC, a new outer bound on the capacity region is obtained that outperforms previously known outer bounds. The cha ..."
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Cited by 159 (6 self)
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The capacity region of the twouser Gaussian Interference Channel (IC) is studied. Three classes of channels are considered: weak, onesided, and mixed Gaussian ICs. For the weak Gaussian IC, a new outer bound on the capacity region is obtained that outperforms previously known outer bounds. The channel sum capacity for some certain range of the channel parameters is derived. It is shown that when Gaussian codebooks are used, the full HanKobayashi achievable rate region can be obtained by using the naive HanKobayashi achievable scheme over three frequency bands (equivalently, three subspaces). For the onesided Gaussian IC, a new proof for Sato’s outer bound is presented. We derive the full HanKobayashi achievable rate region when Gaussian code books are utilized. For the mixed Gaussian IC, a new outer bound is obtained that again outperforms previously known outer bounds. For this case, the channel sum capacity for all ranges of parameters is derived. It is proved that the full HanKobayashi achievable rate region using Gaussian codebooks is equivalent to that of the onesided Gaussian IC for a particular range of the channel gains.
Gamal, “An outer bound to the capacity region of the broadcast channel
 IEEE Trans. Info. Theory
, 2007
"... Abstract — An outer bound to the capacity region of the tworeceiver discrete memoryless broadcast channel is given. The outer bound is tight for all cases where the capacity region is known. When specialized to the case of no common information, this outer bound is shown to be contained in the Körn ..."
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Cited by 75 (14 self)
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Abstract — An outer bound to the capacity region of the tworeceiver discrete memoryless broadcast channel is given. The outer bound is tight for all cases where the capacity region is known. When specialized to the case of no common information, this outer bound is shown to be contained in the KörnerMarton outer bound. This containment is shown to be strict for the binary skewsymmetric broadcast channel. Thus, this outer bound is in general tighter than all other known outer bounds on the discrete memoryless broadcast channel. 1.
A note on the secrecy capacity of the multiantenna wiretap channel,” Arxiv preprint arXiv:0710.4105
, 2007
"... Recently, the secrecy capacity of the multiantenna wiretap channel was characterized by Khisti and Wornell [1] using a Satolike argument. This note presents an alternative characterization using a channel enhancement argument. This characterization relies on an extremal entropy inequality recently ..."
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Cited by 62 (4 self)
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Recently, the secrecy capacity of the multiantenna wiretap channel was characterized by Khisti and Wornell [1] using a Satolike argument. This note presents an alternative characterization using a channel enhancement argument. This characterization relies on an extremal entropy inequality recently proved in the context of multiantenna broadcast channels, and is directly built on the physical intuition regarding to the optimal transmission strategy in this communication scenario. 1
Uplinkdownlink duality via minimax duality
 in Canadian Workshop on Info. Theory
, 2003
"... Abstract—The sum capacity of a Gaussian vector broadcast channel is the saddle point of a minimax Gaussian mutual information expression where the maximization is over the set of transmit covariance matrices subject to a power constraint and the minimization is over the set of noise covariance matri ..."
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Cited by 62 (6 self)
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Abstract—The sum capacity of a Gaussian vector broadcast channel is the saddle point of a minimax Gaussian mutual information expression where the maximization is over the set of transmit covariance matrices subject to a power constraint and the minimization is over the set of noise covariance matrices subject to a diagonal constraint. This sum capacity result has been proved using two different methods, one based on decisionfeedback equalization and the other based on a duality between uplink and downlink channels. This paper illustrates the connection between the two approaches by establishing that uplink–downlink duality is equivalent to Lagrangian duality in minimax optimization. This minimax Lagrangian duality relation allows the optimal transmit covariance and the leastfavorablenoise covariance matrices in a Gaussian vector broadcast channel to be characterized in terms of the dual variables. In particular, it reveals that the least favorable noise is not unique. Further, the new Lagrangian interpretation of uplink–downlink duality allows the duality relation to be generalized to Gaussian vector broadcast channels with arbitrary linear constraints. However, duality depends critically on the linearity of input constraints. Duality breaks down when the input constraint is an arbitrary convex constraint. This shows that the minimax representation of the broadcast channel sum capacity is more general than the uplink–downlink duality representation. Index Terms—Broadcast channel, Lagrangian duality, minimax optimization, multipleinput multipleoutput (MIMO), multipleaccess