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Downlink training techniques for FDD massive MIMO systems: openloop and closedloop training with memory
 IEEE Journal of Selected Topics in Signal Processing
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Optimal Design of EnergyEfficient MultiUser MIMO Systems: Is Massive MIMO the Answer?
"... Assume that a multiuser multipleinput multipleoutput (MIMO) system is designed from scratch to uniformly cover a given area with maximal energy efficiency (EE). What are the optimal number of antennas, active users, and transmit power? The aim of this paper is to answer this fundamental question ..."
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Cited by 14 (6 self)
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Assume that a multiuser multipleinput multipleoutput (MIMO) system is designed from scratch to uniformly cover a given area with maximal energy efficiency (EE). What are the optimal number of antennas, active users, and transmit power? The aim of this paper is to answer this fundamental question. We consider jointly the uplink and downlink with different processing schemes at the base station and propose a new realistic power consumption model that reveals how the above parameters affect the EE. Closedform expressions for the EEoptimal value of each parameter, when the other two are fixed, are provided for zeroforcing (ZF) processing in singlecell scenarios. These expressions prove how the parameters interact. For example, in sharp contrast to common belief, the transmit power is found to increase (not to decrease) with the number of antennas. This implies that energyefficient systems can operate in high signaltonoise ratio regimes in which interferencesuppressing signal processing is mandatory. Numerical and analytical results show that the maximal EE is achieved by a massive MIMO setup wherein hundreds of antennas are deployed to serve a relatively large number of users using ZF processing. The numerical results show the same behavior under imperfect channel state information and in symmetric multicell scenarios.
Designing multiuser MIMO for energy efficiency: When is massive MIMO the answer
 in Proc. IEEE Wireless Commun. and Networking Conf. (WCNC
, 2014
"... Abstract—Assume that a multiuser multipleinput multipleoutput (MIMO) communication system must be designed to cover a given area with maximal energy efficiency (bits/Joule). What are the optimal values for the number of antennas, active users, and transmit power? By using a new model that describ ..."
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Cited by 13 (7 self)
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Abstract—Assume that a multiuser multipleinput multipleoutput (MIMO) communication system must be designed to cover a given area with maximal energy efficiency (bits/Joule). What are the optimal values for the number of antennas, active users, and transmit power? By using a new model that describes how these three parameters affect the total energy efficiency of the system, this work provides closedform expressions for their optimal values and interactions. In sharp contrast to common belief, the transmit power is found to increase (not decrease) with the number of antennas. This implies that energy efficient systems can operate at high signaltonoise ratio (SNR) regimes in which the use of interferencesuppressing precoding schemes is essential. Numerical results show that the maximal energy efficiency is achieved by a massive MIMO setup wherein hundreds of antennas are deployed to serve relatively many users using interferencesuppressing regularized zeroforcing precoding. I.
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.
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.
Uplink performance of timereversal MRC in massive MIMO systems subject to phase noise,”
 IEEE Trans. Wirel. Commun.,
, 2015
"... AbstractMultiuser multipleinput multipleoutput (MUMIMO) cellular systems with an excess of base station (BS) antennas (Massive MIMO) offer unprecedented multiplexing gains and radiated energy efficiency. Oscillator phase noise is introduced in the transmitter and receiver radio frequency chain ..."
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AbstractMultiuser multipleinput multipleoutput (MUMIMO) cellular systems with an excess of base station (BS) antennas (Massive MIMO) offer unprecedented multiplexing gains and radiated energy efficiency. Oscillator phase noise is introduced in the transmitter and receiver radio frequency chains and severely degrades the performance of communication systems. We study the effect of oscillator phase noise in frequencyselective Massive MIMO systems with imperfect channel state information (CSI). In particular, we consider two distinct operation modes, namely when the phase noise processes at the M BS antennas are identical (synchronous operation) and when they are independent (nonsynchronous operation). We analyze a linear and lowcomplexity timereversal maximumratio combining (TRMRC) reception strategy. For both operation modes we derive a lower bound on the sumcapacity and we compare their performance. Based on the derived achievable sumrates, we show that with the proposed receive processing an O( √ M ) array gain is achievable. Due to the phase noise drift the estimated effective channel becomes progressively outdated. Therefore, phase noise effectively limits the length of the interval used for data transmission and the number of scheduled users. The derived achievable rates provide insights into the optimum choice of the data interval length and the number of scheduled users. Index TermsReceiver algorithns, MUMIMO, phase noise.
Dealing with Interference in Distributed Largescale MIMO Systems: A Statistical Approach
, 2014
"... This paper considers the problem of interference control through the use of secondorder statistics in massive MIMO multicell networks. We consider both the cases of colocated massive arrays and largescale distributed antenna settings. We are interested in characterizing the lowrankness of user ..."
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Cited by 3 (1 self)
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This paper considers the problem of interference control through the use of secondorder statistics in massive MIMO multicell networks. We consider both the cases of colocated massive arrays and largescale distributed antenna settings. We are interested in characterizing the lowrankness of users ’ channel covariance matrices, as such a property can be exploited towards improved channel estimation (socalled pilot decontamination) as well as interference rejection via spatial filtering. In previous work, it was shown that massive MIMO channel covariance matrices exhibit a useful finiterank property that can be modeled via the angular spread of multipath at a MIMO uniform linear array. This paper extends this result to more general settings including certain nonuniform arrays, and more surprisingly, to two dimensional distributed large scale arrays. In particular our model exhibits the dependence of the signal subspace’s richness on the scattering radius around the user terminal, through a closed form expression. The applications of the lowrankness covariance property to channel estimation’s denoising and lowcomplexity interference filtering are highlighted.
Optimizing multicell massive MIMO for spectral efficiency: How many users should be scheduled
 in Proc. IEEE Global Conf. Signal and Inf. Process. (GLOBALSIP
, 2014
"... Abstract—Massive MIMO is a promising technique to increase the spectral efficiency of cellular networks, by deploying antenna arrays with hundreds or thousands of active elements at the base stations and performing coherent beamforming. A common ruleofthumb is that these systems should have an ord ..."
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Abstract—Massive MIMO is a promising technique to increase the spectral efficiency of cellular networks, by deploying antenna arrays with hundreds or thousands of active elements at the base stations and performing coherent beamforming. A common ruleofthumb is that these systems should have an order of magnitude more antennas, N, than scheduled users, K, because the users ’ channels are then likely to be quasiorthogonal. However, it has not been proved that this ruleofthumb actually maximizes the spectral efficiency. In this paper, we analyze how the optimal number of scheduled users, K?, depends on N and other system parameters. The value of K? in the largeN regime is derived in closed form, while simulations are used to show what happens at finite N, in different interference scenarios, and for different beamforming. Index Terms—Massive MIMO, pilot contamination, user scheduling. I.
Massive MIMO with NonIdeal Arbitrary Arrays: Hardware Scaling Laws and CircuitAware Design
 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
, 2015
"... Massive multipleinput multipleoutput (MIMO) systems are cellular networks where the base stations (BSs) are equipped with unconventionally many antennas, deployed on colocated or distributed arrays. Huge spatial degreesoffreedom are achieved by coherent processing over these massive arrays, whi ..."
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Cited by 1 (1 self)
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Massive multipleinput multipleoutput (MIMO) systems are cellular networks where the base stations (BSs) are equipped with unconventionally many antennas, deployed on colocated or distributed arrays. Huge spatial degreesoffreedom are achieved by coherent processing over these massive arrays, which provide strong signal gains, resilience to imperfect channel knowledge, and low interference. This comes at the price of more infrastructure; the hardware cost and circuit power consumption scale linearly/affinely with the number of BS antennas N. Hence, the key to costefficient deployment of large arrays is lowcost antenna branches with low circuit power, in contrast to today’s conventional expensive and powerhungry BS antenna branches. Such lowcost transceivers are prone to hardware imperfections, but it has been conjectured that the huge degreesoffreedom would bring robustness to such imperfections. We prove this claim for a generalized uplink system with multiplicative phasedrifts, additive distortion noise, and noise amplification. Specifically, we derive closedform expressions for the user rates and a scaling law that shows how fast the hardware imperfections can increase with N while maintaining high rates. The connection between this scaling law and the power consumption of different transceiver circuits is rigorously exemplified. This reveals that one can make the circuit power increase as N, instead of linearly, by careful circuitaware system design.
Impact of Residual Transmit RF Impairments on TrainingBased MIMO Systems
"... Abstract—Radiofrequency (RF) impairments, that exist intimately in wireless communications systems, can severely degrade the performance of traditional multipleinput multipleoutput (MIMO) systems. Although compensation schemes can cancel out part of these RF impairments, there still remains a ce ..."
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Abstract—Radiofrequency (RF) impairments, that exist intimately in wireless communications systems, can severely degrade the performance of traditional multipleinput multipleoutput (MIMO) systems. Although compensation schemes can cancel out part of these RF impairments, there still remains a certain amount of impairments. These residual impairments have fundamental impact on the MIMO system performance. However, most of the previous works have neglected this factor. In this paper, a trainingbased MIMO system with residual transmit RF impairments (RTRI) is considered. In particular, we derive a new channel estimator for the proposed model, and find that RTRI can create an irreducible estimation error floor. Moreover, we show that, in the presence of RTRI, the optimal training sequence length can be larger than the number of transmit antennas, especially in the low and high signaltonoise ratio (SNR) regimes. An increase in the proposed approximated achievable rate is also observed by adopting the optimal training sequence length. When the training and data symbol powers are required to be equal, we demonstrate that, at high SNRs, systems with RTRI demand more training, whereas at low SNRs, such demands are nearly the same for all practical levels of RTRI. I.