Hindawi Publishing CorporationEURASIP Journal on Wireless Communications and NetworkingVolume 2011, Article ID 496763, 13 pagesdoi:10.1155/2011/496763
MU-MIMO in LTE Systems
Jonathan Duplicy,1 Biljana Badic,2 Rajarajan Balraj,2 Rizwan Ghaffar,3
Peter Horvath,4 Florian Kaltenberger,3 Raymond Knopp,3 Istvan Z. Kovacs,5
Hung T. Nguyen,6 Deepaknath Tandur,1 and Guillaume Vivier7
1Agilent Technologies Laboratories, Wingepark 51, 3110 Rotselaar, Belgium2 Intel Mobile Communications (IMC), Dusseldorfer Landstrae 401, 47259 Duisburg, Germany3Eurecom, 2229 route des Cretes, B.P. 193, 06904 Sophia Antipolis Cedex, France4Department of Broadband Infocommunications, Budapest University of Technology and Economics,Goldmann Gyorgy ter 3., 1111 Budapest, Hungary
5Nokia Siemens Networks Denmark A/S, Niels Jernes Vej 10, 9220 Aalborg, Denmark6Department of Electronics Systems, Aalborg University, Niels Jernes Vej 12, 9220 Aalborg, Denmark7 Sequans Communications, Le Parvis de La Defense 19, 92 073 Paris La Defense Cedex, France
Correspondence should be addressed to Jonathan Duplicy, jonathan firstname.lastname@example.org
Received 1 December 2010; Revised 2 February 2011; Accepted 8 March 2011
Academic Editor: Rodrigo C. De Lamare
Copyright 2011 Jonathan Duplicy et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.
A relatively recent idea of extending the benefits of MIMO systems to multiuser scenarios seems promising in the context ofachieving high data rates envisioned for future cellular standards after 3G (3rd Generation). Although substantial research hasbeen done on the theoretical front, recent focus is on making Multiuser Multiple-Input Multiple-Output (MUMIMO) practicallyrealizable. This paper presents an overview of the dierent MU-MIMO schemes included/being studied in 3GPP standardizationfrom LTE (long-term evolution) to LTE Advanced. MU-MIMO system concepts and implementation aspects have been studiedhere. Various low-complexity receiver architectures are investigated, and their performance assessed through link-level simulations.Appealing performance oered by low-complexity interference aware (IA) receivers is notably emphasized. Furthermore, systemlevel simulations for LTE Release 8 are provided. Interestingly, it is shown that MU-MIMO only oers marginal performance gainswith respect to single-user MIMO. This arises from the limited MU-MIMO features included in Release 8 and calls for improvedschemes for the upcoming releases.
Wireless data usage is increasing faster now than ever before.Smartphones and broadband-enabled portables, such aslaptops or tablets, are now seeing high penetration in manymarkets, and the superior user experience oered by suchdevices has lead to exponential growth of mobile data tracas shown in . The demand for mobile data services hasincreased by an average of 160% in the year 2009 alone, andsome mobile carriers have experienced even more aggressivegrowth numbers. According to a recent forecast, the globalmobile data trac is expected to continue to double everyyear through 2014, leading to a global compound annualgrowth rate of 108% .
These large capacity demands can be met only by highlyecient and optimized mobile network infrastructures.Significant improvements are expected with the ongoing roll-out of OFDMA- (Orthogonal Frequency Division MultipleAccess-) based networks: IEEE 802.16x (WiMAX) and3GPP (3rd generation partnership project) LTE. These twostandards, although they do not fulfill the requirements, arethe first steps towards the 4th generation (4G) definitiongiven by the International Telecommunications Union (ITU)and targeting data rates of 100Mbps in high-mobilityapplications and 1Gbps for low-mobility applications suchas nomadic/local wireless access.
To meet these needs, advanced features are investigatedfor inclusion in future releases of these standards (WiMAX
2 EURASIP Journal on Wireless Communications and Networking
evolution and LTE Advanced). Among these various tech-niques, two promising ones are currently being investigatedby EU FP7 project SAMURAI (Spectrum Aggregation andMultiuser MIMO: ReAl-World Impact) , namely, car-rier aggregation and MU-MIMO. The main objective ofSAMURAI project is to investigate innovative techniquesin the area of MU-MIMO and SA with focus on practicalimplementation and deployment aspects.
This paper aims at giving an insight into MU-MIMOschemes included/being studied in 3GPP releases. MU-MIMO concepts, fundamentals, and an overview of alreadypublished research results and current outcomes fromSAMURAI project are shown in this paper. Specifically,Section 2 provides a detailed overview of the dierent MU-MIMO schemes from LTE Release 8 to Release 10 (known asLTE Advanced). In addition, a novel scheduling algorithmbased on the geometrical alignment of interference at thebase station which minimizes the eective interference seenby each user equipment (UE) is shown. In Section 3, receiverdesign for MU-MIMO is addressed. The performance ofboth interference unaware and interference aware (IA) typesof receiver algorithms has been studied in an LTE downlinksystem. Performance/complexity tradeo is summarized.System level simulations are provided in Section 4, andgains oered by MU-MIMO schemes with respect to single-user MIMO (SU-MIMO) schemes in LTE Release 8 areemphasized. Finally, conclusions are given in Section 5.
Regarding notations, we will use lowercase or uppercaseletters for scalars, lowercase boldface letters for vectors, anduppercase boldface letters for matrices. Furthermore, || and indicate the norm of scalar and vector while ()T , (),and () stand for the transpose, conjugate, and conjugatetranspose, respectively.
2. Overview of MU-MIMO in 3GPP Standards
2.1. Theoretical Foundations of MU-MIMO. Spatial dimen-sion surfacing from the usage of multiple antennas promisesimproved reliability, higher spectral eciency, and spatialseparation of users. This spatial dimension is particularlybeneficial for precoding in the downlink of MU cellularsystem, where spatial resources can be used to transmit datato multiple users simultaneously. The MIMO transmissiontechniques are integral parts of the LTE and WiMAXstandards. A good overview of the MIMO techniques andconfiguration supported in these radio access technologiescan be found in .
In MU-MIMO mode, the transmissions to several termi-nals are overlapped in the same time-frequency resources byexploiting the spatial diversity of the propagation channel. Inorder to fully exploit MU-MIMO transmission modes, thespatial streams intended to the targeted terminals need tobe well separated, ideally orthogonal at both transmit andreceive sides. As a consequence, the theoretical performancegain of the MU-MIMO over SU-MIMO is expected tosignificantly increase in spatially correlated channels andwith increasing number of transmit antenna at the enhancedNode B (eNB). Various linear and nonlinear precodingtechniques and the corresponding receiver structures have
been proposed in the literature in order to achieve promisingMU-MIMO gains, see, for example, .
Optimal precoding in MU-MIMO Gaussian broadcastchannel involves a theoretical preinterference subtractiontechnique known as dirty paper coding (DPC)  com-bined with an implicit user scheduling and power loadingalgorithm. Linear precoding techniques such as channelinversion (CI)  and regularized channel inversion (RCI) cancel the interference in the former case whileattenuating it in the latter case. These precoding strategiesstrive to transform the cross-coupled channels into parallelnoninteracting channels therefore transforming MU down-link into parallel SU systems. They are assuming Gaussianityof the interference. However, in the real world, inputs mustbe drawn from discrete constellations which have (non-Gaussian) structures that can be exploited in the detectionprocess.
For practical purposes, the derived theoretical solutionshave to be further adapted to the requirements and restric-tions of standardized air interfaces. The following sectionssummarize some of the critical physical layer design aspects.
2.2. Overview of 3GPP LTE PHY MIMO
2.2.1. Reference Signals. The downlink transmission schemesare supported at physical layer by a set of downlink referencesignals. These reference signals can be either UE specific orcell specific. The latter are referred to as common referencesignals (CRSs) while the former are referred to as dedicated(or demodulation) reference signals (DRSs or DM-RSs). TheCRSs are not precoded signals and are used by the UE forchannel estimation, while the DM-RSs are precoded andused for demodulation purposes on the scheduled physicalresource blocks (PRBs). The 3GPP standard defines thetransmission of one time-frequency pattern for CRS andDM-RS assigned to one real or virtual antenna port.
2.2.2. Transmission Modes. The defined SIMO (Singe-InputMultiple-Output) and MIMO transmission schemes arecategorized in several transmission modes. The definition ofeach transmission mode includes the required configurationinformation in the common downlink signaling channel andinformation on how the user terminal should search forthis configuration message . This mechanism is part ofthe general downlink signaling framework designed to allowa flexible time-frequency resource allocation separately toeach UE based on the available system resources and thereported or measured channel conditions. The transmissionmode for each UE is configured semistatically via higher layersignaling, in order to avoid excessive downlink signaling.