Results 1  10
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12
Linear precoding based on polynomial expansion: Reducing complexity . . .
 IEEE J. SEL. TOPICS SIGNAL PROCESS
, 2014
"... Massive multipleinput multipleoutput (MIMO) techniques have the potential to bring tremendous improvements in spectral efficiency to future communication systems. Counterintuitively, the practical issues of having uncertain channel knowledge, high propagation losses, and implementing optimal non ..."
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Cited by 10 (5 self)
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Massive multipleinput multipleoutput (MIMO) techniques have the potential to bring tremendous improvements in spectral efficiency to future communication systems. Counterintuitively, the practical issues of having uncertain channel knowledge, high propagation losses, and implementing optimal nonlinear precoding are solved moreorless automatically by enlarging system dimensions. However, the computational precoding complexity grows with the system dimensions. For example, the closetooptimal and relatively “antennaefficient ” regularized zeroforcing (RZF) precoding is very complicated to implement in practice, since it requires fast inversions of large matrices in every coherence period. Motivated by the high performance of RZF, we propose to replace the matrix inversion and multiplication by a truncated polynomial expansion (TPE), thereby obtaining the new TPE precoding scheme which is more suitable for realtime hardware implementation and significantly reduces the delay to the first transmitted symbol. The degree of the matrix polynomial can be adapted to the available hardware resources and enables smooth transition between simple maximum ratio transmission and more advanced RZF. By deriving new random matrix results, we obtain a deterministic expression for the asymptotic signaltointerferenceandnoise ratio (SINR) achieved by TPE precoding in massive MIMO systems. Furthermore, we provide a closedform expression for the polynomial coefficients that maximizes this SINR. To maintain a fixed peruser rate loss as compared to RZF, the polynomial degree does not need to scale with the system, but it should be increased with the quality of the channel knowledge and the signaltonoise ratio.
Random Beamforming over QuasiStatic and Fading Channels: A Deterministic Equivalent Approach
"... In this work, we study the performance of random isometric precoding over quasistatic and correlated fading channels. We derive deterministic approximations of the mutual information and the signaltointerferenceplusnoise ratio (SINR) at the output of the minimummeansquareerror (MMSE) receive ..."
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Cited by 4 (2 self)
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In this work, we study the performance of random isometric precoding over quasistatic and correlated fading channels. We derive deterministic approximations of the mutual information and the signaltointerferenceplusnoise ratio (SINR) at the output of the minimummeansquareerror (MMSE) receiver and provide simple provably converging fixedpoint algorithms for their computation. Although the deterministic approximations are only asymptotically exact, almost surely, we show by simulations that they are very accurate for small system dimensions. The analysis is based on the Stieltjes transform method which enables the derivation of deterministic equivalents of functionals of largedimensional random matrices. In contrast to previous works, our analysis does not rely on arguments from free probability theory which allows us to consider random matrix models for which asymptotic freeness does not hold. Thus, the results of this work are also a novel contribution to the field of random matrix theory and are shown to be applicable to a wide spectrum of practical systems. In this article, we specifically characterize the performance of multicellular communication systems, multipleinput multipleoutput multipleaccess channels (MIMOMAC), and MIMO interference channels.
Lowcomplexity polynomial channel estimation in largescale MIMO with arbitrary statistics
 IEEE J. Select. Topics in Signal Process
, 2014
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Random Beamforming over Correlated Fading Channels
, 2011
"... We study a multipleinput multipleoutput (MIMO) multiple access channel (MAC) from several multiantenna transmitters to a multiantenna receiver. The fading channels between the transmitters and the receiver are modeled by random matrices, composed of independent column vectors with zero mean and ..."
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Cited by 2 (2 self)
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We study a multipleinput multipleoutput (MIMO) multiple access channel (MAC) from several multiantenna transmitters to a multiantenna receiver. The fading channels between the transmitters and the receiver are modeled by random matrices, composed of independent column vectors with zero mean and different covariance matrices. Each transmitter is assumed to send multiple data streams with a random precoding matrix extracted from a Haardistributed matrix. For this general channel model, we derive deterministic approximations of the normalized mutual information, the normalized sumrate with minimummeansquareerror (MMSE) detection and the signaltointerferenceplusnoiseratio (SINR) of the MMSE decoder, which become arbitrarily tight as all system parameters grow infinitely large at the same speed. In addition, we derive the asymptotically optimal power allocation under individual or sumpower constraints. Our results allow us to tackle the problem of optimal stream control in interference channels which would be intractable in any finite setting. Numerical results corroborate our analysis and verify its accuracy for realistic system dimensions. Moreover, the techniques applied in this paper constitute a novel contribution to the field of large random matrix theory and could be used to study even more involved channel models.
A Deterministic Equivalent for the Analysis of NonGaussian Correlated MIMO Multiple Access Channels
, 2011
"... Large dimensional random matrix theory (RMT) has provided an efficient analytical tool to understand multipleinput multipleoutput (MIMO) channels and to aid the design of MIMO wireless communication systems. However, previous studies based on large dimensional RMT rely on the assumption that the ..."
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Cited by 1 (0 self)
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Large dimensional random matrix theory (RMT) has provided an efficient analytical tool to understand multipleinput multipleoutput (MIMO) channels and to aid the design of MIMO wireless communication systems. However, previous studies based on large dimensional RMT rely on the assumption that the transmit correlation matrix is diagonal or the propagation channel matrix is Gaussian. There is an increasing interest in the channels where the transmit correlation matrices are generally nonnegative definite and the channel entries are nonGaussian. This class of channel models appears in several applications in MIMO multiple access systems, such as small cell networks (SCNs). To address these problems, we use the generalized Lindeberg principle to show that the Stieltjes transforms of this class of random matrices with Gaussian or nonGaussian independent entries coincide in the large dimensional regime. This result permits to derive the deterministic equivalents (e.g., the Stieltjes transform and the ergodic mutual information) for nonGaussian MIMO channels from the known results developed for Gaussian MIMO channels, and is of great importance in characterizing the spectral efficiency of SCNs.
Author manuscript, published in "International Symposium on Personal, Indoor and Mobile Radio Communications, London: United Kingdom (2013)" LowComplexity Channel Estimation in LargeScale MIMO using Polynomial Expansion
, 1304
"... Abstract—This paper considers pilotbased channel estimation in largescale multipleinput multipleoutput (MIMO) communication systems, also known as “massive MIMO”. Unlike previous works on this topic, which mainly considered the impact of intercell disturbance due to pilot reuse (socalled pilot ..."
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Abstract—This paper considers pilotbased channel estimation in largescale multipleinput multipleoutput (MIMO) communication systems, also known as “massive MIMO”. Unlike previous works on this topic, which mainly considered the impact of intercell disturbance due to pilot reuse (socalled pilot contamination), we are concerned with the computational complexity. The conventional minimum mean square error (MMSE) and minimum variance unbiased (MVU) channel estimators rely on inverting covariance matrices, which has cubic complexity in the multiplication of number of antennas at each side. Since this is extremely expensive when there are hundreds of antennas, we propose to approximate the inversion by an Lorder matrix polynomial. A set of lowcomplexity Bayesian channel estimators, coined Polynomial ExpAnsion CHannel (PEACH) estimators, are introduced. The coefficients of the polynomials are optimized to yield small mean square error (MSE). We show numerically that nearoptimal performance is achieved with low polynomial orders. In practice, the order L can be selected to balance between complexity and MSE. Interestingly, pilot contamination is beneficial to the PEACH estimators in the sense that smaller L can be used to achieve nearoptimal MSEs. I.
0M×1, 1KΦ
"... •Singlecell downlink system. •M base station antennas. •K singleantenna users. •Massive MIMO regime: M K 0. •Received signal yk = h H kx+nk, nk ∼ CN (0, σ2). •Flatfading, Rayleigh blockfading, Gaussian signaling. •Channel vectors hk hk ∼ CN ..."
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•Singlecell downlink system. •M base station antennas. •K singleantenna users. •Massive MIMO regime: M K 0. •Received signal yk = h H kx+nk, nk ∼ CN (0, σ2). •Flatfading, Rayleigh blockfading, Gaussian signaling. •Channel vectors hk hk ∼ CN
LOWCOMPLEXITY LINEAR PRECODING FOR MULTICELL MASSIVE MIMO SYSTEMS
"... Massive MIMO (multipleinput multipleoutput) has been recognized as an efficient solution to improve the spectral efficiency of future communication systems. However, increasing the number of antennas and users goes handinhand with increasing computational complexity. In particular, the precodin ..."
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Massive MIMO (multipleinput multipleoutput) has been recognized as an efficient solution to improve the spectral efficiency of future communication systems. However, increasing the number of antennas and users goes handinhand with increasing computational complexity. In particular, the precoding design becomes involved since nearoptimal precoding, such as regularizedzero forcing (RZF), requires the inversion of a large matrix. In our previous work [1] we proposed to solve this issue in the singlecell case by approximating the matrix inverse by a truncated polynomial expansion (TPE), where the polynomial coefficients are selected for optimal system performance. In this paper, we generalize this technique to multicell scenarios. While the optimization of the RZF precoding has, thus far, not been feasible in multicell systems, we show that the proposed TPE precoding can be optimized to maximize the weighted maxmin fairness. Using simulations, we compare the proposed TPE precoding with RZF and show that our scheme can achieve higher throughput using a TPE order of only 3. Index Terms—Massive MIMO, linear precoding, low complexity, multicell systems, random matrix theory. 1.