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256
How Much Training is Needed in MultipleAntenna Wireless Links?
 IEEE Trans. Inform. Theory
, 2000
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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 ..."
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Cited by 422 (13 self)
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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...
A VectorPerturbation technique for NearCapacity . . .
 IEEE TRANS. COMMUN
, 2005
"... Recent theoretical results describing the sum capacity when using multiple antennas to communicate with multiple users in a known rich scattering environment have not yet been followed with practical transmission schemes that achieve this capacity. We introduce a simple encoding algorithm that achi ..."
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Cited by 323 (10 self)
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Recent theoretical results describing the sum capacity when using multiple antennas to communicate with multiple users in a known rich scattering environment have not yet been followed with practical transmission schemes that achieve this capacity. We introduce a simple encoding algorithm that achieves nearcapacity at sum rates of tens of bits/channel use. The algorithm is a variation on channel inversion that regularizes the inverse and uses a “sphere encoder ” to perturb the data to reduce the power of the transmitted signal. This paper is comprised of two parts. In this first part, we show that while the sum capacity grows linearly with the minimum of the number of antennas and users, the sum rate of channel inversion does not. This poor performance is due to the large spread in the singular values of the channel matrix. We introduce regularization to improve the condition of the inverse and maximize the signaltointerferenceplusnoise ratio at the receivers. Regularization enables linear growth and works especially well at low signaltonoise ratios (SNRs), but as we show in the second part, an additional step is needed to achieve nearcapacity performance at all SNRs.
On MaximumLikelihood Detection and the Search for the Closest Lattice Point
 IEEE TRANS. INFORM. THEORY
, 2003
"... Maximumlikelihood (ML) decoding algorithms for Gaussian multipleinput multipleoutput (MIMO) linear channels are considered. Linearity over the field of real numbers facilitates the design of ML decoders using numbertheoretic tools for searching the closest lattice point. These decoders are colle ..."
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Cited by 273 (9 self)
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Maximumlikelihood (ML) decoding algorithms for Gaussian multipleinput multipleoutput (MIMO) linear channels are considered. Linearity over the field of real numbers facilitates the design of ML decoders using numbertheoretic tools for searching the closest lattice point. These decoders are collectively referred to as sphere decoders in the literature. In this paper, a fresh look at this class of decoding algorithms is taken. In particular, two novel algorithms are developed. The first algorithm is inspired by the Pohst enumeration strategy and is shown to offer a significant reduction in complexity compared to the ViterboBoutros sphere decoder. The connection between the proposed algorithm and the stack sequential decoding algorithm is then established. This connection is utilized to construct the second algorithm which can also be viewed as an application of the SchnorrEuchner strategy to ML decoding. Aided with a detailed study of preprocessing algorithms, a variant of the second algorithm is developed and shown to offer significant reductions in the computational complexity compared to all previously proposed sphere decoders with a nearML detection performance. This claim is supported by intuitive arguments and simulation results in many relevant scenarios.
Diversitymultiplexing tradeoff in multipleaccess channels
 IEEE Trans. Inform. Theory
, 2004
"... In a pointtopoint wireless fading channel, multiple transmit and receive antennas can be used to improve the reliability of reception (diversity gain) or increase the rate of communication for a fixed reliability level (multiplexing gain). In a multiple access situation, multiple receive antennas ..."
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Cited by 187 (4 self)
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In a pointtopoint wireless fading channel, multiple transmit and receive antennas can be used to improve the reliability of reception (diversity gain) or increase the rate of communication for a fixed reliability level (multiplexing gain). In a multiple access situation, multiple receive antennas can also be used to spatially separate signals from different users (multiple access gain). Recent work has characterized the fundamental tradeoff between diversity and multiplexing gains in the pointtopoint scenario. In this paper, we extend the results to a multiple access fading channel. Our results characterize the fundamental tradeoff between the three types of gain and provide insights on the capabilities of multiple antennas in a network context. 1
Capacity of MIMO systems with antenna selection
, 2005
"... We consider the capacity of multipleinput multipleoutput systems with reduced complexity. One linkend uses all available antennas, while the other chooses the L out of N antennas that maximize capacity. We derive an upper bound on the capacity that can be expressed sa sthe sum of the logarithms o ..."
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Cited by 126 (14 self)
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We consider the capacity of multipleinput multipleoutput systems with reduced complexity. One linkend uses all available antennas, while the other chooses the L out of N antennas that maximize capacity. We derive an upper bound on the capacity that can be expressed sa sthe sum of the logarithms of ordered chisquaredistributed variables. This bound is then evaluated analytically and compared to the results obtained by Monte Carlo simulations. Our results show that the achieved capacity is close to the capacity of a fullcomplexity system provided that L is at least as large as the number of antennas at the other linkend. For example, for L=3, N=8 antennas at the receiver and three antennas at the transmitter, the capacity of the reducedcomplexity scheme is 20 bits/s/Hz compared to 23 bits/s/Hz of a fullcomplexity scheme. We also present a suboptimum antenna subset selection algorithm that has a complexity of N2 compared to eht optimum algorithm with a complexity of (N L).
MIMO Systems with Antenna Selection
, 2004
"... Multipleinput–multipleoutput (MIMO) wireless systems are those that have multiple antenna elements at both the transmitter and receiver [1]. They were first investigated by computer simulations in the 1980s [2], and later papers explored them analytically [3], [4]. Since that time, interest in MIM ..."
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Cited by 124 (19 self)
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Multipleinput–multipleoutput (MIMO) wireless systems are those that have multiple antenna elements at both the transmitter and receiver [1]. They were first investigated by computer simulations in the 1980s [2], and later papers explored them analytically [3], [4]. Since that time, interest in MIMO systems has exploded. They are now being used for thirdgeneration cellular systems (WCDMA) and are discussed for future highperformance modes of the highly successful IEEE 802.11 standard for wireless local area networks. MIMOrelated topics also occupy a considerable part of today’s academic communications research. The multiple antennas in MIMO systems can be exploited in two different ways. One is the creation of a highly effective antenna diversity system; the other is the use of the multiple antennas for the transmission of several parallel data streams to increase the capacity of the system. Antenna diversity is used in wireless systems to combat the effects of fading. If multiple, independent copies of the same signal are available, we can combine them into a total signal with high quality—even if some of the copies exhibit low quality. Antenna diversity at the receiver is well known and has been studied for more than 50 years. The different signal copies are linearly combined, i.e., weighted and added. The resulting signal at the combiner output can then be demodulated and decoded in the usual way. The optimum weights for this combining are matched to the wireless channel [maximum ratio combining (MRC)]. If we have N receive antenna elements, the diversity order, which describes the effectiveness of diversity in avoiding
Approximately universal codes over slow fading channels
 IEEE Trans. Inform. Theory
, 2006
"... Performance of reliable communication over a coherent slow fading channel at high SNR is succinctly captured as a fundamental tradeoff between diversity and multiplexing gains. We study the problem of designing codes that optimally tradeoff the diversity and multiplexing gains. Our main contribution ..."
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Cited by 107 (1 self)
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Performance of reliable communication over a coherent slow fading channel at high SNR is succinctly captured as a fundamental tradeoff between diversity and multiplexing gains. We study the problem of designing codes that optimally tradeoff the diversity and multiplexing gains. Our main contribution is a precise characterization of codes that are universally tradeoffoptimal, i.e., they optimally tradeoff the diversity and multiplexing gains for every statistical characterization of the fading channel. We denote this characterization as one of approximate universality where the approximation is in the connection between error probability and outage capacity with diversity and multiplexing gains, respectively. The characterization of approximate universality is then used to construct new coding schemes as well as to show optimality of several schemes proposed in the spacetime coding literature. 1
Precoding in MultiAntenna and MultiUser Communications
"... In this paper, TomlinsonHarashima precoding for multipleinput/multipleoutput systems including multipleantenna and multiuser systems is studied. It is shown that nonlinear preequalization offers significant advantages over linear preequalization which increases average transmit power. Moreover ..."
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Cited by 102 (2 self)
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In this paper, TomlinsonHarashima precoding for multipleinput/multipleoutput systems including multipleantenna and multiuser systems is studied. It is shown that nonlinear preequalization offers significant advantages over linear preequalization which increases average transmit power. Moreover, it outperforms decisionfeedback equalization at the receiver side which is applicable if joint processing at the receiver side is possible, and which suffers from error propagation. A number of aspects of practical importance are studied. Loading, i.e., the optimum distribution of transmit power and rate is discussed in detail. It is shown that the capacity of the underlying MIMO channel can be utilized asymptotically by means of nonlinear precoding.
LowComplexity NearMaximumLikelihood Detection and Precoding for MIMO Systems using Lattice Reduction
 IEEE Information Theory Workshop 2003
, 2003
"... Abstract — We consider the latticereductionaided detection scheme for 2×2 channels recently proposed by Yao and Wornell [11]. Using an equivalent realvalued substitute MIMO channel model their lattice reduction algorithm can be replaced by the wellknown LLL algorithm, which enables the applicati ..."
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Cited by 82 (12 self)
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Abstract — We consider the latticereductionaided detection scheme for 2×2 channels recently proposed by Yao and Wornell [11]. Using an equivalent realvalued substitute MIMO channel model their lattice reduction algorithm can be replaced by the wellknown LLL algorithm, which enables the application to MIMO systems with arbitrary numbers of dimensions. We show how lattice reduction can also be favourably applied in systems that use precoding and give simulation results that underline the usefulness of this approach. I.