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23
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 351 (14 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
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 288 (20 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.
On the duality of Gaussian multipleaccess and broadcast channels
 IEEE Trans. Inf. Theory
, 2004
"... Abstract—We define a duality between Gaussian multipleaccess channels (MACs) and Gaussian broadcast channels (BCs). The dual channels we consider have the same channel gains and the same noise power at all receivers. We show that the capacity region of the BC (both constant and fading) can be writt ..."
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Cited by 101 (14 self)
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Abstract—We define a duality between Gaussian multipleaccess channels (MACs) and Gaussian broadcast channels (BCs). The dual channels we consider have the same channel gains and the same noise power at all receivers. We show that the capacity region of the BC (both constant and fading) can be written in terms of the capacity region of the dual MAC, and vice versa. We can use this result to find the capacity region of the MAC if the capacity region of only the BC is known, and vice versa. For fading channels we show duality under ergodic capacity, but duality also holds for different capacity definitions for fading channels such as outage capacity and minimumrate capacity. Using duality, many results known for only one of the two channels can be extended to the dual channel as well. Index Terms—Broadcast channel (BC), channel capacity, duality, fading channels, multipleinput multipleoutput (MIMO) systems, multipleaccess channel (MAC). I.
On the Capacity of Multiple Input Multiple Output Broadcast Channels
 In Proceedings of Int. Conf. Commun
, 2002
"... We consider a twouser multiple input multiple output (MIMO) Gaussian broadcast channel (BC), where the transmitter has t transmit antennas and receivers have r1 ; r2 antennas respectively. Since the MIMO broadcast channel is in general a nondegraded broadcast channel, its capacity region remains a ..."
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Cited by 45 (12 self)
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We consider a twouser multiple input multiple output (MIMO) Gaussian broadcast channel (BC), where the transmitter has t transmit antennas and receivers have r1 ; r2 antennas respectively. Since the MIMO broadcast channel is in general a nondegraded broadcast channel, its capacity region remains an unsolved problem. In this paper, we establish a duality between what is termed the \dirty paper" region (or the CostaCaireShamaiYu achievable region) [5, 7] for the MIMO broadcast channel and the capacity region of the the MIMO multipleaccess channel (MAC), which is easy to compute. Using this duality, we greatly reduce the computation complexity required for obtaining the dirty paper achievable region for the MIMO BC. The duality also enables us to translate previously known results for the MIMO MAC (like iterative waterlling [7]) to the MIMO BC. We show that the dirty paper achievable region achieves the sumrate capacity of the MIMO BC by establishing that the sumrate point in this region equals an upperbound on the sum rate of the MIMO BC. I.
Phantomnet: Exploring optimal multicellular multiple antenna systems
 EURASIP Journal on Applied Signal Processing, Special Issue on MIMO Communications and Signal Processing
, 2002
"... Abstract—We address the problem of providing the best possible service to new users joining a multicellular multiple antenna system without affecting existing users. Since, interferencewise, new users are invisible to existing users, the network is dubbed Phantom Net. I. ..."
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Cited by 38 (3 self)
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Abstract—We address the problem of providing the best possible service to new users joining a multicellular multiple antenna system without affecting existing users. Since, interferencewise, new users are invisible to existing users, the network is dubbed Phantom Net. I.
Optimal Power Control in Multiple Access Fading Channels with Multiple Antennas
, 2001
"... This paper characterizes the optimal power control method for maximum sum capacity in a multiple access fading channel with multiple transmitter and receiver antennas when perfect channel side information is available at both the transmitters and the receiver. The profound benefit of multiantenna d ..."
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Cited by 19 (2 self)
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This paper characterizes the optimal power control method for maximum sum capacity in a multiple access fading channel with multiple transmitter and receiver antennas when perfect channel side information is available at both the transmitters and the receiver. The profound benefit of multiantenna diversity is demonstrated by a dimension counting argument. The optimal power allocation strategy in a system with n transmit an tennas for each user and m receive antennas is a combination of successive cancelation and a TDMAlike scheme where in each time slot the rank of the transmit signals rk for all users must satisfy k r(r + 1) _< ra(ra + 1). Thus, the total number of users that are allowed to transmit simultaneously is constrained by the number of receiver antennas. Receiver diversity increases the total number of dimensions thus allowing more users to transmit at the same time. By contrast, transmitter diversity allows a single user to occupy multiple dimensions as to benefit its own transmission, thus having the effect of precluding simultaneous transmission by other users.
On the Duality of MultipleAccess and Broadcast Channels
 Channels”, Allerton Conference on Commun., Control, and Computing
, 2001
"... We show that the Gaussian multipleaccess channel and the Gaussian broadcast channel are fundamentally related and are essentially duals of each other. The dual channels we consider have the same channel gains and the same noise power at all receivers. We show that the capacity region of a broadc ..."
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Cited by 9 (6 self)
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We show that the Gaussian multipleaccess channel and the Gaussian broadcast channel are fundamentally related and are essentially duals of each other. The dual channels we consider have the same channel gains and the same noise power at all receivers. We show that the capacity region of a broadcast channel (both constant and fading) can be written in terms of the capacity region of the dual multipleaccess channel, and vice versa. We can use this result to find the capacity region of the MAC if the capacity region of only the broadcast channel is known, and vice versa. For fading channels we show duality under ergodic capacity, but duality also holds for different notions of Shannon capacity for fading channels such as outage capacity and minimum rate capacity. Using duality, many results known for only one of the two channels are now known for the dual channel as well.
Fundamental Capacity of MIMO Channels
 IEEE Journal on Selected Areas in Communications, Special Issue on MIMO systems
, 2002
"... 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 th ..."
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Cited by 5 (0 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 Shannontheoretic capacity definitions and, for each definition, different correlation models and channel side information assumptions that we consider. We first provide a comprehensive summary of ergodic and outage capacity results for singleuser MIMO channels. These results indicate that the capacity gain obtained from multiple antennas heavily depends on the amount of channel knowledge at either the receiver or transmitter, the channel SNR, and the correlation between the channel gains on each antenna element. We then focus attention on the capacity regions for MIMO broadcast and multiple access channels. In contrast to singleuser MIMO channels, capacity results for these multiuser MIMO channels are quite difficult to obtain, even for constant channels. We summarize capacity results for the MIMO broadcast and multiple access channel for channels that are either constant or fading with perfect instantaneous knowledge of the antenna gains at both transmitter(s) and receiver(s). We also show that the MIMO multiple access and broadcast capacity regions are intimately related via a duality transformation. This transformation is not only useful for proving capacity theorems; it also facilitates finding the optimal...
Capacity Limits of MIMO Channels
 IEEE J. Select. Areas Commun
, 2003
"... Abstract—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 ..."
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Cited by 2 (0 self)
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Abstract—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
Adaptive Resource Allocation in Composite Fading Environments
 Global Telecommunications Conference, 2001. GLOBECOM '01. IEEE
"... We obtain optimal resource allocation policies for a single user singlecarrier system and a multipleaccess multicarrierCDMA system when the transmitter adapts to the variations in the shortterm mean (slow fade) in a combined slow and fast fading (composite fading) environment. For the single use ..."
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Cited by 2 (0 self)
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We obtain optimal resource allocation policies for a single user singlecarrier system and a multipleaccess multicarrierCDMA system when the transmitter adapts to the variations in the shortterm mean (slow fade) in a combined slow and fast fading (composite fading) environment. For the single user system, we maximize the average throughput achieved by the user, while in the uplink MCCDMA system, we maximize the sum of average rates of the users in the system. For each system, we nd the optimal resource allocation policies for two scenarios. The rst is when is system is designed for voice transmission, where the bit error rate (BER) of each user, averaged over the fast fade, is maintained at a desired value. The second is when the system is designed for data transmission, where the BER of each user is maintained below a desired value for a given percentage of time. We nd that, for the singleuser system, the solution for both the voice and data transmission cases is waterlling, and that waterlling is the asymptotically optimal solution to multiuser problems in both scenarios, i.e is nearly optimal for a large number of users. We also nd that, when dealing with a voice system, the solution is rstly, independent of the distributions of the slow and fast fades, and secondly, similar to the solutions obtained for single fade environments (fast or slow fading). I.