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55
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.
Scaling up MIMO: Opportunities and challenges with very large arrays
, 2011
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Complex lattice reduction algorithms for lowcomplexity MIMO detection
 IN IEEE GLOBAL TELECOMMN. CONF. (GLOBECOM
, 2006
"... Recently, latticereductionaided detectors have been proposed for multipleinput multipleoutput (MIMO) systems to give performance with full diversity like maximum likelihood receiver, and yet with complexity similar to linear receivers. However, these latticereductionaided detectors are based ..."
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Cited by 59 (7 self)
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Recently, latticereductionaided detectors have been proposed for multipleinput multipleoutput (MIMO) systems to give performance with full diversity like maximum likelihood receiver, and yet with complexity similar to linear receivers. However, these latticereductionaided detectors are based on the traditional LLL reduction algorithm that was originally introduced for reducing real lattice bases, in spite of the fact that the channel matrices are inherently complexvalued. In this paper, we introduce the complex LLL algorithm for direct application to reduce the basis of a complex lattice which is naturally defined by a complexvalued channel matrix. We prove that complex LLL reductionaided detection can also achieve full diversity. Our analysis reveals that the new complex LLL algorithm can achieve a reduction in complexity of nearly 50 % over the traditional LLL algorithm, and this is confirmed by simulation. It is noteworthy that the complex LLL algorithm aforementioned has nearly the same biterrorrate performance as the traditional LLL algorithm.
From Single user to Multiuser Communications: Shifting the MIMO paradigm
 IEEE Sig. Proc. Magazine
, 2007
"... In multiuser MIMO networks, the spatial degrees of freedom offered by multiple antennas can be advantageously exploited to enhance the system capacity, by scheduling multiple users to simultaneously share the spatial channel. This entails a fundamental paradigm shift from single user communications, ..."
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Cited by 46 (13 self)
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In multiuser MIMO networks, the spatial degrees of freedom offered by multiple antennas can be advantageously exploited to enhance the system capacity, by scheduling multiple users to simultaneously share the spatial channel. This entails a fundamental paradigm shift from single user communications, since multiuser systems can experience substantial benefit from channel state information at the transmitter and, at the same time, require more complex scheduling strategies and transceiver methodologies. This paper reviews multiuser MIMO communication from an algorithmic perspective, discussing performance gains, tradeoffs, and practical considerations. Several approaches including nonlinear and linear channelaware precoding are reviewed, along with more practical limited feedback schemes that require only partial channel state information. The interaction between precoding and scheduling is discussed. Several promising strategies for limited multiuser feedback design are looked at, some of which are inspired from the single user MIMO precoding scenario while others are fully specific to the multiuser setting. 1 DRAFT
Coordinated Beamforming with Limited Feedback in the MIMO Broadcast Channel
, 2008
"... In this paper, we propose a new joint optimization of linear transmit beamforming and receive combining vectors for the multipleinput multipleoutput (MIMO) broadcast channel. We consider the transmission of a single information stream to two users with two or more receive antennas. Unlike past wo ..."
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Cited by 26 (8 self)
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In this paper, we propose a new joint optimization of linear transmit beamforming and receive combining vectors for the multipleinput multipleoutput (MIMO) broadcast channel. We consider the transmission of a single information stream to two users with two or more receive antennas. Unlike past work in which iterative computation is required to design the beamformers, we derive specific formulations for the transmit beamformers for two active users via a power iteration and a generalized eigen analysis. To enable practical implementation, a new limited feedback algorithm is proposed that exploits the structure of the algorithm to avoid full channel quantization. The feedback overhead of the proposed algorithm is independent of the number of receive antennas. Monte Carlo simulations are used to evaluate the bit error rate and the sum rate performances of the proposed algorithm. Simulation results show that the proposed method performs close to the sum capacity of the MIMO broadcast channel even with limited feedback.
Communication Over MIMO Broadcast Channels Using LatticeBasis Reduction
, 2006
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Lattice Reduction  A survey with applications in wireless communications
, 2011
"... Lattice reduction is a powerful concept for solving diverse problems involving point lattices. Signal processing applications where lattice reduction has been successfully used include global positioning system (GPS), frequency estimation, color space estimation in JPEG pictures, and particularly da ..."
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Cited by 19 (0 self)
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Lattice reduction is a powerful concept for solving diverse problems involving point lattices. Signal processing applications where lattice reduction has been successfully used include global positioning system (GPS), frequency estimation, color space estimation in JPEG pictures, and particularly data detection and precoding in wireless communication systems. In this article, we first provide some background on point lattices and then give a tutorialstyle introduction to the theoretical and practical aspects of lattice reduction. We describe the most important lattice reduction algorithms and comment on their performance and computational complexity. Finally, we discuss the application of lattice reduction in wireless communications and statistical signal processing. Throughout the article, we point out open problems and interesting questions for future research.
Statistical pruning for nearmaximum likelihood decoding
 IEEE Transactions on Signal Processing
, 2007
"... In many communications problems, maximumlikelihood (ML) decoding reduces to nding the closest (skewed) lattice point in Ndimensions to a given point x 2 CN. In its full generality, this problem is known to be NPcomplete and requires complexity exponential in N. Recently, the expected complexity o ..."
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Cited by 15 (1 self)
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In many communications problems, maximumlikelihood (ML) decoding reduces to nding the closest (skewed) lattice point in Ndimensions to a given point x 2 CN. In its full generality, this problem is known to be NPcomplete and requires complexity exponential in N. Recently, the expected complexity of the sphere decoder, a particular algorithm that solves the ML problem exactly, has been computed where it is shown that over a wide range of rates, SNRs and dimensions the expected computation involves no more than N 3 computations. In this paper, we propose an algorithm that, for large N, offers substantial computational savings over the sphere decoder, while maintaining performance arbitrarily close to ML. We statistically prune the search space to a subset that, with high probability, contains the optimal solution, thereby reducing the complexity of the search. Bounds on the error performance of the new method are proposed. The complexity of the new algorithm is analysed in three ways. One is an asymptotic analysis and holds for very large dimensions and the other two are an upper bound and approximation that are of interest in small to moderately large dimensions. Simulations are presented to compare the algorithm with the original sphere decoder. 1
An overview of massive MIMO: Benefits and challenges
 IEEE J. SEL. TOPICS SIGNAL PROCESS
, 2014
"... Massive multipleinput multipleoutput (MIMO) wireless communications refers to the idea equipping cellular base stations (BSs) with a very large number of antennas, and has been shown to potentially allow for orders of magnitude improvement in spectral and energy efficiency using relatively simple ..."
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Cited by 12 (4 self)
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Massive multipleinput multipleoutput (MIMO) wireless communications refers to the idea equipping cellular base stations (BSs) with a very large number of antennas, and has been shown to potentially allow for orders of magnitude improvement in spectral and energy efficiency using relatively simple (linear) processing. In this paper, we present a comprehensive overview of stateoftheart research on the topic, which has recently attracted considerable attention. We begin with an information theoretic analysis to illustrate the conjectured advantages of massive MIMO, and then we address implementation issues related to channel estimation, detection and precoding schemes. We particularly focus on the potential impact of pilot contamination caused by the use of nonorthogonal pilot sequences by users in adjacent cells. We also analyze the energy efficiency achieved by massive MIMO systems, and demonstrate how the degrees of freedom provided by massive MIMO systems enable efficient singlecarrier transmission. Finally, the challenges and opportunities associated with implementing massive MIMO in future wireless communications systems are discussed.
LLL latticebasis reduction achieves the maximum diversity in MIMO systems
 IN INTERNATIONAL SYMPOSIUM ON INFO. THEORY. IEEE
, 2005
"... Diversity order is an important measure for the performance of different communication systems over MIMO fading channels. In this paper, we define the precoding diversity for the fixedrate MIMO broadcast systems and we prove that in these systems, latticereductionaided precoding achieves the pre ..."
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Cited by 11 (1 self)
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Diversity order is an important measure for the performance of different communication systems over MIMO fading channels. In this paper, we define the precoding diversity for the fixedrate MIMO broadcast systems and we prove that in these systems, latticereductionaided precoding achieves the precoding diversity. Also, we prove that latticereductionaided decoding achieves the receive diversity in MIMO pointtopoint and multipleaccess systems.