Results 1  10
of
499
Diversity and Multiplexing: A Fundamental Tradeoff in Multiple Antenna Channels
 IEEE Trans. Inform. Theory
, 2002
"... Multiple antennas can be used for increasing the amount of diversity or the number of degrees of freedom in wireless communication systems. In this paper, we propose the point of view that both types of gains can be simultaneously obtained for a given multiple antenna channel, but there is a fund ..."
Abstract

Cited by 1159 (20 self)
 Add to MetaCart
(Show Context)
Multiple antennas can be used for increasing the amount of diversity or the number of degrees of freedom in wireless communication systems. In this paper, we propose the point of view that both types of gains can be simultaneously obtained for a given multiple antenna channel, but there is a fundamental tradeo# between how much of each any coding scheme can get. For the richly scattered Rayleigh fading channel, we give a simple characterization of the optimal tradeo# curve and use it to evaluate the performance of existing multiple antenna schemes.
HighRate Codes that are Linear in Space and Time
 IEEE Trans. Inform. Theory
, 2000
"... Multipleantenna systems that operate at high rates require simple yet effective spacetime transmission schemes to handle the large traffic volume in real time. At rates of tens of bits/sec/Hz, VBLAST, where every antenna transmits its own independent substream of data, has been shown to have good ..."
Abstract

Cited by 420 (12 self)
 Add to MetaCart
(Show Context)
Multipleantenna systems that operate at high rates require simple yet effective spacetime transmission schemes to handle the large traffic volume in real time. At rates of tens of bits/sec/Hz, VBLAST, where every antenna transmits its own independent substream of data, has been shown to have good performance and simple encoding and decoding. Yet VBLAST suffers from its inability to work with fewer receive antennas than transmit antennasthis deficiency is especially important for modern cellular systems where a basestation typically has more antennas than the mobile handsets. Furthermore, because VBLAST transmits independent data streams on its antennas there is no builtin spatial coding to guard against deep fades from any given transmit antenna. On the other hand, there are many previouslyproposed spacetime codes that have good fading resistance and simple decoding, but these codes generally have poor performance at high data rates or with many antennas. We propose a highrate coding scheme that can handle any...
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 ..."
Abstract

Cited by 420 (17 self)
 Add to MetaCart
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
Communication on the Grassmann Manifold: A Geometric Approach to the Noncoherent MultipleAntenna Channel
 IEEE TRANS. INFORM. THEORY
, 2002
"... In this paper, we study the capacity of multipleantenna fading channels. We focus on the scenario where the fading coefficients vary quickly; thus an accurate estimation of the coefficients is generally not available to either the transmitter or the receiver. We use a noncoherent block fading model ..."
Abstract

Cited by 273 (7 self)
 Add to MetaCart
In this paper, we study the capacity of multipleantenna fading channels. We focus on the scenario where the fading coefficients vary quickly; thus an accurate estimation of the coefficients is generally not available to either the transmitter or the receiver. We use a noncoherent block fading model proposed by Marzetta and Hochwald. The model does not assume any channel side information at the receiver or at the transmitter, but assumes that the coefficients remain constant for a coherence interval of length symbol periods. We compute the asymptotic capacity of this channel at high signaltonoise ratio (SNR) in terms of the coherence time , the number of transmit antennas , and the number of receive antennas . While the capacity gain of the coherent multiple antenna channel is min bits per second per hertz for every 3dB increase in SNR, the corresponding gain for the noncoherent channel turns out to be (1 ) bits per second per herz, where = min 2 . The capacity expression has a geometric interpretation as sphere packing in the Grassmann manifold.
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 ..."
Abstract

Cited by 256 (40 self)
 Add to MetaCart
(Show Context)
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.
Multiuser MIMO Achievable Rates with Downlink Training and Channel State Feedback
"... We consider a MIMO fading broadcast channel and compute achievable ergodic rates when channel state information is acquired at the receivers via downlink training and it is provided to the transmitter by channel state feedback. Unquantized (analog) and quantized (digital) channel state feedback sche ..."
Abstract

Cited by 114 (8 self)
 Add to MetaCart
We consider a MIMO fading broadcast channel and compute achievable ergodic rates when channel state information is acquired at the receivers via downlink training and it is provided to the transmitter by channel state feedback. Unquantized (analog) and quantized (digital) channel state feedback schemes are analyzed and compared under various assumptions. Digital feedback is shown to be potentially superior when the feedback channel uses per channel state coefficient is larger than 1. Also, we show that by proper design of the digital feedback link, errors in the feedback have a minor effect even if simple uncoded modulation is used on the feedback channel. We discuss first the case of an unfaded AWGN feedback channel with orthogonal access and then the case of fading MIMO multiaccess (MIMOMAC). We show that by exploiting the MIMOMAC nature of the uplink channel, a much better scaling of the feedback channel resource with the number of base station antennas can be achieved. Finally, for the case of delayed feedback, we show that in the realistic case where the fading process has (normalized) maximum Doppler frequency shift 0 ≤ F < 1/2, a fraction 1 − 2F of the optimal multiplexing gain is achievable. The general conclusion of this work is that very significant downlink throughput is achievable with simple and efficient channel state feedback, provided that the feedback link is properly designed.
Massive MIMO in the UL/DL of cellular networks: How many antennas do we need?
 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
, 2013
"... We consider the uplink (UL) and downlink (DL) of noncooperative multicellular timedivision duplexing (TDD) systems, assuming that the number N of antennas per base station (BS) and the number K of user terminals (UTs) per cell are large. Our system model accounts for channel estimation, pilot con ..."
Abstract

Cited by 108 (13 self)
 Add to MetaCart
(Show Context)
We consider the uplink (UL) and downlink (DL) of noncooperative multicellular timedivision duplexing (TDD) systems, assuming that the number N of antennas per base station (BS) and the number K of user terminals (UTs) per cell are large. Our system model accounts for channel estimation, pilot contamination, and an arbitrary path loss and antenna correlation for each link. We derive approximations of achievable rates with several linear precoders and detectors which are proven to be asymptotically tight, but accurate for realistic system dimensions, as shown by simulations. It is known from previous work assuming uncorrelated channels, that as N →∞while K is fixed, the system performance is limited by pilot contamination, the simplest precoders/detectors, i.e., eigenbeamforming (BF) and matched filter (MF), are optimal, and the transmit power can be made arbitrarily small. We analyze to which extent these conclusions hold in the more realistic setting where N is not extremely large compared to K. In particular, we derive how many antennas per UT are needed to achieve η % of the ultimate performance limit with infinitely many antennas and how many more antennas are needed with MF and BF to achieve the performance of minimum meansquare error (MMSE) detection and regularized zeroforcing (RZF), respectively.
Capacity and power allocation for fading MIMO channels with channel estimation error
 IEEE Transactions on Information Theory
, 2006
"... Abstract—In this correspondence, we investigate the effect of channel estimation error on the capacity of multipleinput–multipleoutput (MIMO) fading channels. We study lower and upper bounds of mutual information under channel estimation error, and show that the two bounds are tight for Gaussian i ..."
Abstract

Cited by 105 (0 self)
 Add to MetaCart
(Show Context)
Abstract—In this correspondence, we investigate the effect of channel estimation error on the capacity of multipleinput–multipleoutput (MIMO) fading channels. We study lower and upper bounds of mutual information under channel estimation error, and show that the two bounds are tight for Gaussian inputs. Assuming Gaussian inputs we also derive tight lower bounds of ergodic and outage capacities and optimal transmitter power allocation strategies that achieve the bounds under perfect feedback. For the ergodic capacity, the optimal strategy is a modified waterfilling over the spatial (antenna) and temporal (fading) domains. This strategy is close to optimum under small feedback delays, but when the delay is large, equal powers should be allocated across spatial dimensions. For the outage capacity, the optimal scheme is a spatial waterfilling and temporal truncated channel inversion. Numerical results show that some capacity gain is obtained by spatial power allocation. Temporal power adaptation, on the other hand, gives negligible gain in terms of ergodic capacity, but greatly enhances outage performance. Index Terms—Capacity, channel estimation error, feedback delay, multipleinput–multipleoutput (MIMO), mutual information, outage capacity, power allocation, waterfilling. I.
Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels
"... Highrate data communication over a multipath wireless channel often requires that the channel response be known at the receiver. Trainingbased methods, which probe the channel in time, frequency, and space with known signals and reconstruct the channel response from the output signals, are most co ..."
Abstract

Cited by 87 (9 self)
 Add to MetaCart
(Show Context)
Highrate data communication over a multipath wireless channel often requires that the channel response be known at the receiver. Trainingbased methods, which probe the channel in time, frequency, and space with known signals and reconstruct the channel response from the output signals, are most commonly used to accomplish this task. Traditional trainingbased channel estimation methods, typically comprising of linear reconstruction techniques, are known to be optimal for rich multipath channels. However, physical arguments and growing experimental evidence suggest that many wireless channels encountered in practice tend to exhibit a sparse multipath structure that gets pronounced as the signal space dimension gets large (e.g., due to large bandwidth or large number of antennas). In this paper, we formalize the notion of multipath sparsity and present a new approach to estimating sparse (or effectively sparse) multipath channels that is based on some of the recent advances in the theory of compressed sensing. In particular, it is shown in the paper that the proposed approach, which is termed as compressed channel sensing, can potentially achieve a target reconstruction error using far less energy and, in many instances, latency and bandwidth than that dictated by the traditional leastsquaresbased training methods.
Multipleantenna capacity in the lowpower regime
 IEEE TRANS. INFORM. THEORY
, 2003
"... This paper provides analytical characterizations of the impact on the multipleantenna capacity of several important features that fall outside the standard multipleantenna model, namely: i) antenna correlation, ii) Ricean factors, iii) polarization diversity, and iv) outofcell interference; all ..."
Abstract

Cited by 85 (11 self)
 Add to MetaCart
This paper provides analytical characterizations of the impact on the multipleantenna capacity of several important features that fall outside the standard multipleantenna model, namely: i) antenna correlation, ii) Ricean factors, iii) polarization diversity, and iv) outofcell interference; all in the regime of low signaltonoise ratio. The interplay of rate, bandwidth, and power is analyzed in the region of energy per bit close to its minimum value. The analysis yields practical design lessons for arbitrary number of antennas in the transmit and receive arrays.