Adaptive Modulation schemes for MIMO HSDPA

Javier R Fonollosa1, Markku Heikkilä2, Xavier Mestre1, Alba Pagès1, Adam Pollard3, Laurent Schumacher4,Lars Torsten Berger4, Ami Wiesel1, Juha Ylitalo5

1Universitat Politècnica de Catalunya,2Nokia Mobile Phones

3Vodafone Group Research & Development4Aalborg University

5Nokia Networkswww.ist-imetra.org

ABSTRACT

There is a growing interest in the standardisation ofMultiple Input Multiple Output (MIMO) schemes forHigh Speed Downlink Packet Access (HSDPA) forfuture releases of UMTS. The I-METRA project,building on the legacy of METRA, has set up as one ofits priorities to contribute to this standardisation effortby developing and evaluating pertinent transmission andreception schemes for this type of systems.

I. INTRODUCTION

The concept of HSDPA has been recently standardisedin 3GPP for UMTS [1]. It considers enhancements thatcan be applied to UTRA to provide very high-speeddownlink packet access by means of a high-speeddownlink shared channel (HS-DSCH). Among theseenhancements, the I-METRA project is focused onMIMO antenna processing techniques which arenecessarily also related to the evolution of otherprocedures, such as Adaptive Modulation and Coding(AMC) or Hybrid Automatic Repeat on Request (H-ARQ), in order to include adaptive MIMO techniques inthe HS-DSCH structure.

Within the HSDPA concept, it is expected thatmodulation and coding are adapted to channel, trafficand user requirements. In this procedure, the userequipment (UE) should be able to estimate the channelstate information and translate it into a metric, which istransmitted to its serving Node B within the DPCCH-HS channel (an uplink Dedicated Physical Controlchannel associated with HS-DSCH). It is required thatthis metric value can be mapped to the downlinkchannel FER. The Node B is continuously selecting theuser to be served and its data rate based on a schedulingalgorithm and on that feedback information receivedfrom all active users. The uplink DPCCH-HS alsocarries H-ARQ acknowledgements, which are used bythe Node B to update the user traffic queues. The use of

AMC and H-ARQ procedures along with Fast CellSelection and Stand-Alone DSCH [2] will provide up to10.7Mbps of data rate. It is envisioned that the use ofMIMO techniques on top of these other procedures willallow doubling this upper bound [3].

In the first stage of I-METRA1, project activitiesconcentrate in two main issues. First of all, the analysisand development of space-time coding andbeamforming techniques suitable for MIMO HSDPAand, secondly, the specification of link- and system-level simulations for evaluating these techniques underrealistic scenarios.

II. LINK AND SYSTEM LEVEL SIMULATIONS

According to 3GPP recommendations in [3], link-levelsimulations alone are not enough to conclude about theperformance of a HSDPA system. Moreover it requiresthe use of system level simulations for algorithmcomparison.

Consequently, the MIMO HSDPA simulations in I-METRA will be performed in two stages: link-levelsimulations and system-level simulations. Several look-up tables obtained in the link-level simulation stageshould feed the system-level simulations. Those look-uptables, which mainly relate FER values to a givenmetric, will be computed for different Modulation andCoding (MC) schemes and different MIMO techniques.As described in this section, the MIMO singularity ontop of the HSDPA adds some specific requirements toboth the link- and system-level simulator.

A. Link-level Simulator

The main objective of the HSDPA link-levelsimulations are to compute the FER for severalscenarios, several MC schemes and different MIMO

1 I-METRA started on the 1st of November 2001.

techniques. The additional complexity that MIMO link-level simulator demand with respect to the conventionalcase can be summarised in three aspects: (1) channelmodelling, (2) definition of the metric and (3) HS-DSCH structure.

A.1. Channel modelling

The I-METRA project will proceed with the MIMOchannel model already developed in METRA, see [4]for more details. This model was filed in 3GPP inFebruary 2001 [5] and after few months, majorcompanies such as Nokia, Lucent, Siemens andEricsson [6] endorsed the stochastic philosophy ofMETRA’s MIMO model.

This model has the structure of a tapped delay line andits taps are matrices whose size depends on the numberof active elements at the transmitting and receivingends. As such, this model appears as a natural extensionof well-accepted ITU profiles. This model is ofstochastic nature. It manages to embed the fullcorrelation information of the channel into twocorrelation matrices defined independently at both ends.A simple Kronecker product is performed to combinethese matrices so as to achieve the full characterisationof the correlation properties of a given MIMO channel.The model in itself is able to address a wide variety ofsimulation environments. Indeed, the proposed modelmanages to account for the time dispersion, the fadingand the spatial properties of MIMO channels, using areduced set of parameters, namely the Power DelayProfile (PDP), the Power Angular Spectrum (PAS),including Angle of Arrival (AoA), Angle of Departure(AoD) and Azimuth Spread (AS) and the PowerDoppler Spectrum (PDoS).

I-METRA workpackage 2 is devoted to thedevelopment and maintenance of a platform suited forlink-level simulations. Up to now, a package enabling tosimulate MIMO channels as described by METRA'smodel has been developed in Matlab. The choice forMatlab has been motivated by the wish to perform link-level simulations relying on the matrix formalismdescribed in [7]. The well-known performancelimitations of Matlab when it comes to intensive Monte-Carlo simulations are expected to be addressed in a lateroptimisation phase.

This Matlab package uses as input a high-leveldescription of the simulation environment, namely PDP,PAS, AoD, AoA, direction of movement, Dopplerspectrum, etc. In an initialisation stage, it derives thecorrelation properties of ULAs placed at thetransmitting and receiving ends of the communicationlinks. This description of the correlation properties isthen used to spatially correlate M x N x L vectors offading samples which are later used, during the actualsimulations, to populate the L matrix taps of the MIMOtapped delay line model. Figure 1 to Figure 3 showrespectively the PDP, the Doppler spectrum and the

correlation coefficient of a 4x2 MIMO channelsimulating 3GPP Case 4 of [8].

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Figure 3. 1st row of the full 8x8 correlation matrix of a4x2 MIMO model simulated according to 3GPP Case 4.

In Figure 1 and Figure 3, the red curve is the targetdefinition, and the blue one is the achievedcharacteristic. One can see on Figure 3 the incidence ofalternating AoA at the UE. In addition, the different

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AoD at Node B for each tap generate slightly differentcorrelation patterns between even taps on the one hand,odd taps on the other.

Thanks to the flexibility of the proposed stochasticmodel, its update with respect to the progress of 3GPPdiscussions is usually quite easy, as it is just a matter ofsome high-level parameter changes, like the AS or theAoA. However, the applicability of this model is notrestricted to the 3GPP test cases. Whatever environmentof interest might be simulated provided thecorresponding description is fed to the package.

A.2. Channel metric and HS-DSCH structure

For a given data rate, the user performance in a SingleInput Single Output (SISO) HSDPA channel, usuallystated in terms of FER, is reliably predicted using anestimate of the C/I. Conversely, it is not clear whichparameter is the C/I counterpart in a MIMO HSDPAchannel. In order to deal with an HSDPA system able toadapt to channel propagation conditions, it is mandatoryto define a metric function that maps the channelestimation to the FER on that channel. It is expectedthat this metric will be different for different MIMOtechniques. Additionally, this metric should be simpleenough to be transmitted to Node B within the uplinkDPCCH-HS using up to 6 bits.

Another important aspect to take into account in theMIMO HSDPA simulator is the HS-DSCH channelstructure defined in [1]. Concerning the complexity ofthe system, the study of MIMO techniques for HSDPAshould evaluate up to what degree the HS-DSCHstructure, which includes CRC, H-ARQ, interleavingand channel coding procedures, is affected.

B. System-level Simulations

System-level simulations are mandatory in order toinclude system attributes such as Fast Cell Selection, H-ARQ or Node B scheduling. System level simulationswill take a similar format to that proposed in 3GPP forthe evaluation of MIMO techniques for HSDPA [3].These will take as input a range of link-level results thatwill be used in a look-up table – relevant factors mayinclude modulation scheme, coding, redundancy, ratioof in-cell/out-of-cell interference (G-parameter). Thereferencing into the look-up table will depend on thechosen MIMO technique and the associated metric. TheG-parameter is important as the interference situationwill determine the modulation and coding (for example)chosen by the Node B and the distribution of thisparameter will determine the availability of the higherdata rates.

Figure 4 shows the distribution of the G-parameter andhow it varies between macro-cell and micro-cellscenarios. These results have been obtained fromsimulations of realistic site deployments in a major citywith traffic distributions derived from actual traffic

statistics. Full details of the simulation model used canbe found in [9].

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It can clearly be seen that higher G-parameters areachieved in micro-cells, which is due to the greaterisolation of the coverage areas of the cells. These resultscan be used as a guide for the range of G-parametersthat should be simulated at link-level to enable goodinterpolation when doing the mapping of link-levelresults in the system-level simulations.

III. SPACE-TIME BLOCK CODING ANDBEAMFORMING

The design of space-time transmit architectures haslately received much attention and a Space-Time blockcoding Transmit Diversity scheme (STTD) wasincluded into the 3GPP UMTS FDD downlinkspecifications [13].

Using the I-METRA MIMO channel model, initialsimulation results show that STTD is able to achieve thepromised diversity order of 2 for uncorrelated frequencyflat fading radio links (Figure 5, graph 1).

Figure 5: STTD Symbol Error Performance

The performance of STTD degrades significantlyhowever in frequency selective environments (Figure 5,graph 2). This is due to the fact that the orthogonalitybetween users, which in UMTS are servedsimultaneously on the same frequency but separated inthe code domain, and the orthogonality between theemployed space-time block codes is lost if the receiveruses standard RAKE reception.

Equalisation might be used to restore orthogonality.Under realistic channel conditions this will significantlyenhance the performance of STTD. Nevertheless,equalisation is not straightforward when only a singlereceive antenna is employed [14] and a modification ofthe transmission scheme [15] was used to achieve thesymbol error rate performance as shown in Figure 5,graph 3. Simulation parameters are given in Table 1:

Table 1: Simulation Parameters.

Besides a combination of space-time block coding withequalisation, recent publications also proposed tocombine space-time block coding with spatial filteringmethodologies.

The so-called linear dispersion codes (LD), see [10],provide a new unifying framework to the actual space-time transmission process. They are defined as linearspace-time codes that transmit Q complex symbols

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TmmMmM BA γϕτττ == +−+− )1()1(

for m=1..M and τ =1..T and mγϕτ , are T-dimensionaland M-dimensional column vectors with ones in the τand m positions, respectively, and zeros elsewhere. Assuming that channel state information is available atthe receiver only, the code matrices are designed tomaximise the MIMO capacity averaged over all thepossible channel realisations. The overall transmit ratewill depend on the number of symbols sent in parallel,the block length and the size of the symbolsconstellation, and the receiver can be implemented veryeffectively (linear receivers are viable). Therefore, thearchitecture allows for easy rate re-configurability in aMIMO architecture and, given the possibility of linearreceivers, the channel performance metric seems easilycomputable. On the other hand, the approach is notoptimal in the sense that no channel state information isused at the transmit side and furthermore the codes areoptimised only for a particular channel statistics. If the available channel state information is to be used atthe transmit side, strategies combining space-timecoding with spatial filtering have to be considered. Aninteresting example of this type of approach is the onepresented in [11]. Assuming that Sk is a generic word ofan orthogonal space-time block code (OSTBC), theauthors propose to transmit Sk=WSk, where W is aspace-time filter matrix. Figure 6 illustrates thecombination of OSTBC and Beamforming. The filter isdesigned to minimise an upper bound to the pairwisecodeword error probability at the receiver side. In orderto consider the most general scenario, it is assumed thatthe transmitter has some information about thereliability of the channel estimation. Thus, thetransmitter uses two different sources of information:the channel estimation itself, denoted by h’, and ameasure of goodness of the estimation, given by themean and correlation of the true channel h conditionedon the estimation h’: mh|h’, Rhh|h’.

Figure 6. Combination of OSTBC and Beamformingproposed in [11]

The spatio-temporal filter is obtained solving a convexoptimisation problem with a unique solution. It is shownin [11] that the architecture includes space-time blockcoding and beamforming as special cases. Classicalbeamforming is obtained when the transmitter has

CK WSymbols

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perfect channel state information (||Rhh|h’||→0), whereasa pure OSTBC scheme is obtained when assuming thatno channel state information is available at thetransmitter. Thus, the main advantage of this approachis that it always outperforms both conventional spatialfilter and OSTBC as standalone techniques.Furthermore, it allows for very simple architectures atthe receiver side, because decoding can be performedwith simple linear receivers followed by hard decisions.The main drawbacks of the technique are the need forfeedback information and the relatively highcomputational demands at Node B. Note that a newbeamformer must be calculated at each channel update,and a relatively complex optimisation procedure mustbe carried out every time. Moreover, the architecture isnot very suitable from the rate re-configurability pointof view, because a new beamformer matrix must becalculated every time the transmission rate is changed.

Figure 7. Performance of OSTBC combined withbeamforming for a 4x4 case with ρ=0.7

Figure 7 compares the performance in terms of BER ofthe conventional space time block coding andbeamforming approaches versus the technique proposedin [11] for a 4x4 antenna configuration. The idealbeamformer is also plotted as a benchmark. Channelestimates at the receive side are assumed unbiased andcharacterised by correlation coefficient ρ of value 0.7with the actual channel.

IV. CONCLUSIONS

This paper has outlined one of the main activities startedwithin the I-METRA project related to the developmentand performance evaluation of MIMO techniques forHSDPA in future releases of UMTS. The MIMO

channel model inherited from METRA has beenupdated and is now fully compliant with the latest 3GPPspecifications. In addition the evaluation methodologyfor link and system level simulations has been definedand several MIMO schemes introduced.

REFERENCES

[1] 3GPP TR 25.858 V1.0.4, January 2002.[2] 3GPP TR 25.848 V4.0.0 (2001-03).[3] RAN WG1 #23, R1-02-0142. Lucent. MIMO

system simulation methodology.[4] Schumacher, L., Pedersen K.I., Kermoal J.P. and

Mogensen P.E., "A Link-Level MIMO RadioChannel Simulator for Evaluation of CombinedTransmit/Receive Diversity Concepts within theMETRA Project", IST Mobile Summit, Galway,Ireland, October 2000.

[5] Nokia: 3GPP TSG R1-01-0260, "MIMO channelmodel for link-level simulations using correlatedantennas".

[6] Lucent, Nokia, Siemens, Ericsson: 3GPP TSG R1-01-1179, "A standardized set of MIMO radiopropagation channels".

[7] Heikkilä, M.J.et al., METRA Deliverable D3.2,"Review and Selection of Relevant Algorithms".Available at www.ist-metra.org

[8] MIMO Rapporteur: 3GPP TSG R1-02-0141,"MIMO Conference Call Summary".

[9] Tiirola E., et al., METRA Deliverable D4,“Performance Evaluation”. Available at www.ist-metra.org

[10] B. Hassibi and B.M. Hochwald, “High-rate codesthat are linear in space and time”, Submitted toIEEE Trans. Info. Theory, August 2000.

[11] G. Jöngren, M. Skoglund and B. Ottersten,“Combining beamforming and orthogonal space-time block coding”, to appear in IEEETransactions on Information Theory.

[12] Lucent: 3GPP TSG R1-01-0879, "IncreasingMIMO throughput with per-antenna rate control".

[13] 3GPP TS 25.211 V 3.3.0, "Physical channels andmapping of transport channels onto physicalchannels (FDD)", June 2000

[14] Ling Li; Yu-Dong Yao; Hongbin Li,"Intersymbol/cochannel interference cancellationfor transmit diversity systems in frequencyselective fading channels", Vehicular TechnologyConference, VTC 2001 Fall, IEEE VTS 54th,Volume: 2 , Page(s): 678-682

[15] L. T. Berger, L. Schumacher, "Modified Space-Time Transmission in DS-CDMA Downlinkfacilitating MISO Channel Equalisation", to appearin VTC 2002 Fall.