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47
MIMO broadcast channels with finite rate feedback
 IEEE Trans. on Inform. Theory
, 2006
"... Multiple transmit antennas in a downlink channel can provide tremendous capacity (i.e. multiplexing) gains, even when receivers have only single antennas. However, receiver and transmitter channel state information is generally required. In this paper, a system where each receiver has perfect channe ..."
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Cited by 94 (10 self)
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Multiple transmit antennas in a downlink channel can provide tremendous capacity (i.e. multiplexing) gains, even when receivers have only single antennas. However, receiver and transmitter channel state information is generally required. In this paper, a system where each receiver has perfect channel knowledge, but the transmitter only receives quantized information regarding the channel instantiation is analyzed. The well known zero forcing transmission technique is considered, and simple expressions for the throughput degradation due to finite rate feedback are derived. A key finding is that the feedback rate per mobile must be increased linearly with the SNR (in dB) in order to achieve the full multiplexing gain, which is in sharp contrast to pointtopoint MIMO systems in which it is not necessary to increase the feedback rate as a function of the SNR. I.
Impact of antenna correlation on the capacity of multiantenna channels
 IEEE TRANS. INFORM. THEORY
, 2005
"... This paper applies random matrix theory to obtain analytical characterizations of the capacity of correlated multiantenna channels. The analysis is not restricted to the popular separable correlation model, but rather it embraces a more general representation that subsumes most of the channel model ..."
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Cited by 54 (2 self)
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This paper applies random matrix theory to obtain analytical characterizations of the capacity of correlated multiantenna channels. The analysis is not restricted to the popular separable correlation model, but rather it embraces a more general representation that subsumes most of the channel models that have been treated in the literature. For arbitrary signaltonoise ratios @ A, the characterization is conducted in the regime of large numbers of antennas. For the low and high regions, in turn, we uncover compact capacity expansions that are valid for arbitrary numbers of antennas and that shed insight on how antenna correlation impacts the tradeoffs among power, bandwidth, and rate.
MIMO Broadcast Channels With FiniteRate Feedback
, 2006
"... Multiple transmit antennas in a downlink channel can provide tremendous capacity (i.e., multiplexing) gains, even when receivers have only single antennas. However, receiver and transmitter channel state information is generally required. In this correspondence, a system where each receiver has per ..."
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Cited by 45 (1 self)
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Multiple transmit antennas in a downlink channel can provide tremendous capacity (i.e., multiplexing) gains, even when receivers have only single antennas. However, receiver and transmitter channel state information is generally required. In this correspondence, a system where each receiver has perfect channel knowledge, but the transmitter only receives quantized information regarding the channel instantiation is analyzed. The wellknown zeroforcing transmission technique is considered, and simple expressions for the throughput degradation due to finiterate feedback are derived. A key finding is that the feedback rate per mobile must be increased linearly with the signaltonoise ratio (SNR) (in decibels) in order to achieve the full multiplexing gain. This is in sharp contrast to pointtopoint multipleinput multipleoutput (MIMO) systems, in which it is not necessary to increase the feedback rate as a function of the SNR.
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
MultiCell MIMO Cooperative Networks: A New Look at Interference
 J. Selec. Areas in Commun. (JSAC
, 2010
"... Abstract—This paper presents an overview of the theory and currently known techniques for multicell MIMO (multiple input multiple output) cooperation in wireless networks. In dense networks where interference emerges as the key capacitylimiting factor, multicell cooperation can dramatically improv ..."
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Cited by 35 (14 self)
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Abstract—This paper presents an overview of the theory and currently known techniques for multicell MIMO (multiple input multiple output) cooperation in wireless networks. In dense networks where interference emerges as the key capacitylimiting factor, multicell cooperation can dramatically improve the system performance. Remarkably, such techniques literally exploit intercell interference by allowing the user data to be jointly processed by several interfering base stations, thus mimicking the benefits of a large virtual MIMO array. Multicell MIMO cooperation concepts are examined from different perspectives, including an examination of the fundamental informationtheoretic limits, a review of the coding and signal processing algorithmic developments, and, going beyond that, consideration of very practical issues related to scalability and systemlevel integration. A few promising and quite fundamental research avenues are also suggested. Index Terms—Cooperation, MIMO, cellular networks, relays, interference, beamforming, coordination, multicell, distributed.
High SNR Analysis of MIMO Broadcast Channels
"... The behavior of the multiple antenna broadcast channel at high SNR is investigated. The multiple antenna broadcast channel achieves the same multiplexing gain as the system in which all receivers are allowed to perfectly cooperate (i.e. transforming the system into a pointtopoint MIMO system). H ..."
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Cited by 28 (8 self)
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The behavior of the multiple antenna broadcast channel at high SNR is investigated. The multiple antenna broadcast channel achieves the same multiplexing gain as the system in which all receivers are allowed to perfectly cooperate (i.e. transforming the system into a pointtopoint MIMO system). However, the multiplexing gain alone is not sufficient to accurately characterize the behavior of sum rate capacity at high SNR. An affine approximation to capacity which incorporates the multiplexing gain as well as a power offset (i.e. a zeroorder term) is a more accurate representation of high SNR behavior. The power offset of the sum rate capacity is shown to equal the power offset of the cooperative MIMO system when there are less receivers than transmit antennas. In addition, the power offset of using the suboptimal strategy of beamforming is calculated. These calculations show that beamforming can perform quite well when the number of antennas is sufficiently larger than the number of receivers, but performs very poorly when there are nearly as many receivers as transmit antennas.
Sum Rate Characterization of Joint Multiple CellSite Processing
, 2005
"... The sumrate capacity of a cellular system model is analyzed, considering the uplink and downlink channels, while addressing both nonfading and flatfading channels. The focus is on a simple Wynerlike multicell model, where the system cells are arranged on a circle, assuming the cellsites are lo ..."
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Cited by 25 (9 self)
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The sumrate capacity of a cellular system model is analyzed, considering the uplink and downlink channels, while addressing both nonfading and flatfading channels. The focus is on a simple Wynerlike multicell model, where the system cells are arranged on a circle, assuming the cellsites are located at the boundaries of the cells. For the uplink channel, analytical expressions of the sumrate capacities are derived for intracell TDMA scheduling, and a “WideBand ” (WB) scheme (where all users are active simultaneously utilizing all bandwidth for coding). Assuming individual percell power constraints, and using the Lagrangian uplinkdownlink duality principle, an analytical expression for the sumrate capacity of the downlink channel is derived for nonfading channels, and shown to coincide with the corresponding uplink result. Introducing flatfading, lower and upper bounds on the average percell sumrate capacity are derived. The bounds exhibit an O(loge K) multiuser diversity factor for a number of users percell K ≫ 1, in addition to the array diversity gain. Joint multicell processing is shown to eliminate outofcell interference, which is traditionally considered to be a limiting factor in highrate reliable communications. This paper was presented in part at the 9
Capacityachieving input covariance for singleuser multiantenna channels
 IEEE Trans. Wireless Commun
, 2006
"... Abstract — We characterize the capacityachieving input covariance for multiantenna channels known instantaneously at the receiver and in distribution at the transmitter. Our characterization, valid for arbitrary numbers of antennas, encompasses both the eigenvectors and the eigenvalues. The eigenv ..."
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Cited by 24 (9 self)
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Abstract — We characterize the capacityachieving input covariance for multiantenna channels known instantaneously at the receiver and in distribution at the transmitter. Our characterization, valid for arbitrary numbers of antennas, encompasses both the eigenvectors and the eigenvalues. The eigenvectors are found for zeromean channels with arbitrary fading profiles and a wide range of correlation and keyhole structures. For the eigenvalues, in turn, we present necessary and sufficient conditions as well as an iterative algorithm that exhibits remarkable properties: universal applicability, robustness and rapid convergence. In addition, we identify channel structures for which an isotropic input achieves capacity. Index Terms — Capacity, MIMO, input optimization, fading, antenna correlation, Ricean fading, keyhole channel.
Spectral efficiency of multicarrier CDMA
 IEEE Trans. Inf. Theory
, 2005
"... Abstract—We analyze the spectral efficiency (sumrate per subcarrier) of randomly spread synchronous multicarrier codedivision multiple access (MCCDMA) subject to frequencyselective fading in the asymptotic regime of number of users and bandwidth going to infinity with a constant ratio. Both upli ..."
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Cited by 15 (1 self)
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Abstract—We analyze the spectral efficiency (sumrate per subcarrier) of randomly spread synchronous multicarrier codedivision multiple access (MCCDMA) subject to frequencyselective fading in the asymptotic regime of number of users and bandwidth going to infinity with a constant ratio. Both uplink and downlink are considered, either conditioned on the subcarrier fading coefficients (for nonergodic channels) or unconditioned thereon (for ergodic channels). The following receivers are analyzed: a) jointly optimum receiver, b) linear minimum meansquare error (MMSE) receiver, c) decorrelator, and d) singleuser matched filter. Index Terms—Channel capacity, multicarrier codedivision multiple access (MCCDMA), random matrix theory, multiuser
High SNR Analysis for MIMO Broadcast Channels: Dirty Paper Coding versus Linear Precoding
 IEEE TRANS. INFORM. THEORY
, 2007
"... In this correspondence, we compare the achievable throughput for the optimal strategy of dirty paper coding (DPC) to that achieved with suboptimal and lower complexity linear precoding techniques (zeroforcing and block diagonalization). Both strategies utilize all available spatial dimensions and ..."
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Cited by 14 (4 self)
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In this correspondence, we compare the achievable throughput for the optimal strategy of dirty paper coding (DPC) to that achieved with suboptimal and lower complexity linear precoding techniques (zeroforcing and block diagonalization). Both strategies utilize all available spatial dimensions and therefore have the same multiplexing gain, but an absolute difference in terms of throughput does exist. The sum rate difference between the two strategies is analytically computed at asymptotically high SNR. Furthermore, the difference is not affected by asymmetric channel behavior when each user has a different average SNR. Weighted sum rate maximization is also considered. In the process, it is shown that allocating user powers in direct proportion to user weights asymptotically maximizes weighted sum rate.