Hindawi Publishing CorporationEURASIP Journal on Wireless Communications and NetworkingVolume 2010, Article ID 161642, 14 pagesdoi:10.1155/2010/161642

Research Article

ANext GenerationWireless Simulator Based onMIMO-OFDM:LTE Case Study

Gerardo Gómez, DavidMorales-Jiménez, Juan J. Sánchez-Sánchez,and J. Tomás Entrambasaguas

Department of Communications Engineering, University of Malaga, 29071 Malaga, Spain

Correspondence should be addressed to Gerardo Gómez, ggomez@ic.uma.es

Received 1 June 2009; Revised 29 September 2009; Accepted 3 February 2010

Academic Editor: Faouzi Bader

Copyright © 2010 Gerardo Gómez 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.

The complexity of next generation wireless systems is growing exponentially. The combination of Multiple-Input Multiple-Output(MIMO) technology with Orthogonal Frequency Division Multiplexing (OFDM) is considered as the best solution to providehigh data rates under frequency-selective fading channels. The design and evaluation of MIMO-OFDM systems require a detailedanalysis of a number of functionalities such as MIMO transmission modes, channel estimation, MIMO detection, channel coding,or cross-layer scheduling. In this paper we present a MIMO-OFDM-based simulator that includes the main physical and link layerfunctionalities. The simulator has been used to evaluate the performance of the 3GPP Long-Term Evolution (LTE) technology fordifferent MIMO-OFDM techniques under realistic assumptions such as user mobility or bandwidth-limited feedback channel.

1. Introduction

Orthogonal Frequency Division Multiplexing (OFDM) isone of the most popular physical layer technologies forcurrent broadband wireless communications due to its highspectral efficiency and robustness to frequency selectivefading. The use of Multiple-Input Multiple-Output (MIMO)technology in combination with OFDM increases the diver-sity gain and/or the system capacity by exploiting spatialdomain. Hence, MIMO-OFDM is an attractive solution forfuture broadband wireless systems like 3GPP Long-TermEvolution (LTE) [1, 2] and it will surely be a serious candidatefor future 4G technologies.

The evaluation of the physical layer performance under“realistic” varying channel conditions requires extremelytime-consuming simulations. The use of specialized math-ematical tools such as MATLAB/Simulink or Mathematicais widely extended to perform such simulations. These toolsprovide a wide set of built-in libraries that allow a rapiddevelopment of prototypes. There are other simulation toolslike Visual System Simulator (VSS) or Ptolemy II that includestandard building blocks to model and simulate complexcommunication systems.

However, the performance evaluation of LTE technol-ogy requires specific MIMO-OFDM-based simulators tominimize the simulation time. An open-source MATLAB-based LTE physical layer simulator is presented in [3];nevertheless, it is proved that general purpose simulationplatforms like MATLAB lead to very long execution times [4].Other works are only focused on the LTE simulation results[5, 6] under different MIMO-configurations, but no detailson the employed simulator are provided. Besides, realisticassumptions such as user mobility, antenna correlation, orbandwidth limited feedback channel are often simplified oreven omitted in some of previous works.

This paper presents a new simulator for next generationMIMO-OFDM-based wireless systems. The proposed simu-lator runs on top of Wireless Mobile SIMulation (WM-SIM)platform [4], oriented to model and simulate complex wire-less systems. WM-SIM is an optimized data flow orientedplatform, based on C++, which is proved to outperformSimulink, VSS, and Ptolemy II (as discussed later on). Thesimulator includes the main functionalities carried out at thePhysical (PHY) and Medium Access Control (MAC) layersof a wireless MIMO-OFDM system. Besides, the proposed

mailto:ggomez@ic.uma.es

2 EURASIP Journal on Wireless Communications and Networking

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simulator architecture allows having a fully configurableMIMO-OFDM transmission, including the main next gen-eration MIMO-OFDM schemes. The simulator has beenused to evaluate the performance of the LTE technologyaccording to the parameters configuration defined by the3GPP standard [1]. The different MIMO-OFDM techniquesincluded in LTE are evaluated under realistic assumptionssuch as user mobility, antenna correlation, or bandwidth-limited feedback channel.

The rest of this paper is structured as follows. First, adescription of the simulator architecture and functionalitiesis provided in Section 2. Next, Section 3 presents severalsimulation results for different LTE scenarios and MIMO-OFDM configurations. Finally, the main conclusions aresummarized in Section 4.

2. Simulator Architecture

The simulator is composed by a number of User Equipments(UEs) connected to a Base Station (BS) through a frequency-selective Rayleigh fading channel. The corresponding high-level architecture is depicted in Figure 1.

BS and UEs functionalities are split into user andcontrol planes. In general, the control plane includes mostof the BS/UE intelligence, acting as a decision point toconfigure user plane functionalities. In the case of the UEs,control plane is also responsible for performing physicallayer measurements and for periodically reporting quality-related information to the BS through a return channel;such information is used by the BS to perform a cross-layerlink adaptation, as described in next subsections. The userplane includes a group of functionalities (configured fromthe control plane) to facilitate the actual data transmission.

The simulator also includes a module responsible forcollecting Quality of Service (QoS) statistics. A detaileddescription of the whole architecture is given in nextsubsections.

2.1. Base Station. The BS is responsible for schedulingthe transmission turns to different UEs, for maintaininga reliable radio link between each UE and the BS (bydynamically adapting the transmission parameters) as well

as for transmitting data packets to the UEs. A detailed blockdiagram of the BS architecture (downlink) is depicted inFigure 2.

Incoming data packets arrive at the MAC layer followinga “full-buffer” model; that are, data is always available at thetransmission queues. Thus, typical performance indicatorslike spectral efficiency or Bit Error Rate (BER) are notservice-dependent. User data at the MAC layer are extractedfrom the queues according to the decisions taken in the cross-layer (PHY-MAC) scheduling, as explained later.

Physical layer functionalities are grouped into threesubsystems.

(i) Modulation and Coding: this subsystem is responsiblefor channel coding, scrambling, and modulationmapping of each transport block. The requirednumber of instances of this subsystem is N , beingN the number of code words. Throughout thispaper, the term code word will be employed to referan independently coded and modulated transportblock. These functions are managed from the controlplane, where the Modulation and Coding Scheme(MCS) adaptation is performed and the HybridAutomatic Repeat reQuest (H-ARQ) block processesthe requested retransmissions of corrupted codewords.

(ii) MIMO Processing: the use of MIMO technologyrequires additional processing in order to adequatethe data streams to be transmitted over the multi-ple antennas. Hence, this subsystem is responsiblefor mapping and precoding the modulation sym-bols according to a particular configuration, whichdepends on the MIMO transmission mode (e.g.,transmit diversity, beamforming, or spatial multi-plexing). This processing is strongly related to therank/precoding selection carried out at the controlplane (as described later).

(iii) OFDM Processing: modulation symbols for eachantenna are mapped to specific resource elementsin the time-frequency resource grid. Afterwards, thegeneration of the OFDM time-domain signal for eachof the M antennas is fulfilled.

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2.1.1. Cross-Layer Scheduling. The time variability of wirelesschannels leads to rapid changes in the short-term channelcapacity over time. In an OFDM-based technology, thechannel response associated to each OFDM subcarrier alsovaries along the frequency domain. Consequently, chan-nel response can be seen as a time-frequency randomprocess. In a multiuser environment, channel quality alsovaries independently for different users. Such variationsin time-frequency channel conditions for each user canbe exploited by a cross-layer scheduler to increase systemthroughput.

Figure 3 shows an example of how the instantaneousChannel State Information (CSI) is used to perform cross-layer scheduling. Upper 3D graph depicts the instantaneousSignal-to-Noise Ratio (SNR) received by two users. SNRvalues are periodically reported to the BS via ChannelQuality Indicators (CQIs), which are used by the schedulerto decide the time-frequency resource allocation for each UE(depicted in the lower graph).

When OFDM is jointly used with multiple antennas,a fourth dimension is added to the scenario. Hence, the

multiplexing algorithm has a high degree of flexibility as itworks on a time-frequency-space-user basis.

The time-variant radio channel and user specific condi-tions (e.g., speed) imply the need for adapting or switch-ing the MIMO configuration to be applied to each user.The adaptation of the MIMO scheme is accomplished bythe cross-layer scheduler, which selects dynamically theappropriate scheme for each user. The scheduler decisionsare based on the per-user reported PHY measurements asCQI, Rank Indicator (RI), and Precoding Matrix Indicator(PMI). Other considerations such as the terminal speedor Quality of Service (QoS) requirements (e.g., priorityhandling) can be also taken into account in the schedulingprocess.

In order to achieve higher system performance, schedul-ing must be tightly integrated with MCS adaptation, H-ARQprocesses, and rank/precoding selection. The schedulingdecisions are based on the reported CQI, PMI, and RI values,as well as pending HARQ retransmissions, QoS parameters,and UE capabilities. As a result, the cross-layer schedulerselects the modulation and coding scheme, the MIMO

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configuration to be applied, and the PHY resources for datatransmission.

2.1.2. Modulation and Coding. Adaptive Modulation andCoding (AMC) techniques [2] are used to improve datathroughput whereas BER is kept below a predefined targetvalue (BERT). Modulation and coding schemes are jointlychanged by the BS to adapt the transmitted signal to thevarying channel conditions both in time and, in frequencydomains.

The control plane is responsible for making decisionson MCS adaptation, whereas the user plane enforces suchdecisions. MCS adaptation is based on the instantaneousCQI reported from the terminals. For the sake of simplicity,only the processing chain for transport channels is describedbelow as the equivalent one for control channel is usuallysimilar except for the channel coding scheme applied.

User plane processing includes the following functional-ities:

(i) Channel Coding: the channel coding procedure isbased on LTE specifications [6]. In the first place, aCyclic Redundancy Check (CRC) is appended to eachtransport block coming from the MAC layer. Theresulting bit sequence is segmented, if needed, and anadditional CRC is added to each resulting segment.Then, a turbo coding with mother coding rate 1/3is applied to each segment for transport channels.On the other hand, a tail biting convolutionalcoding with mother rate 1/3 is applied to controlchannels. Finally, a rate matching process is appliedto each coded segment to match the final coding rateindicated by the MCS information. Rate-matchedcoded sequences are concatenated to form a codeword.

(ii) Scrambling: the purpose of this block is to ensurethat the receiver-side decoding can fully utilize

the processing gain provided by the channel coding.Scrambling operation consists of an exclusive-oroperation between the code word and a bit-levelscrambling sequence.

(iii) Modulation Mapping: scrambled code words aremapped onto the modulation scheme selected fromthe control plane, thus generating a sequence ofcomplex modulated symbols.

Hybrid-ARQ (Automatic Repeat-reQuest) has become anessential physical layer feature in mobile communicationsystems like WiMAX or LTE [7, 8]. H-ARQ allows retrans-missions of a packet at the MAC/PHY level, with significantadvantages like delay reduction and/or increased capacity.H-ARQ may be jointly used with Chase Combining (CC)and Incremental Redundancy (IR) features [9]. When ChaseCombining is applied, an erroneous code word is stored at thereceiver in order to be combined with other retransmittedcode words. Incremental Redundancy is based on the trans-mission of a punctured version of the original code word.If the decoding fails, additional redundancy information istransmitted thus having a different puncturing scheme andhence, facilitating the decoding. The process may be repeateduntil either a successful decoding or a maximum numberof retransmissions is reached. H-ARQ procedures are tightlyintegrated with the cross-layer scheduler, which takes intoaccount pending retransmissions (from ACK/NACK reports)and associated redundancy versions.

2.1.3. TransmitMIMOProcessing. The MIMO-OFDM trans-mission schemes currently being incorporated to next gener-ation wireless standards have been included in the simulatorin order to provide a fully configurable MIMO-OFDMtransmission. The transmit MIMO processing frameworkmay be configured by the control plane in order to applydifferent MIMO schemes, including both transmit diversityand spatial multiplexing.

(I) Transmit Diversity: it is used to overcome the effectsof fading by transmitting redundant informationfrom different antennas. In particular, the followingschemes are supported:

(i) space-frequency block codes (SFBC),

(ii) transmit beamforming (BF).

(II) Spatial Multiplexing: it is used to increase the spectralefficiency by transmitting simultaneously several datastreams from the multiple transmit antennas. Thefollowing schemes are supported:

(i) open-loop spatial multiplexing (MUX withoutprecoding),

(ii) precoded spatial multiplexing (PrecodedMUX).

The transmit MIMO processing block takes as input thecomplex modulation symbols of each code word and gives asa result the complex values to be transmitted on each antenna

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Figure 4: Transmit MIMO processing with different configurations for (a) precoded spatial multiplexing and (b) Transmit beamforming.

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Figure 5: User Equipment Architecture (downlink).

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port. As shown in Figure 2, the complete transmit MIMOprocessing is separated into two functionalities, layer mappingand MIMO precoding, which are defined in a different waydepending on the MIMO scheme to be applied.

(1) Layer Mapping: its purpose is to map the modulationsymbols of different code words (independentlycoded transport blocks) onto the spatially multi-plexed streams (layers) that will be transmitted. Thenumber of code words must be less than or equal tothe number of layers, which is indicated by the RI.Symbols from the same code word can be mappedto one or several layers, as shown in Figure 4. Thenumber of layers may be dynamically adapted to therank of the channel matrix in spatial multiplexingschemes, thus making possible to perform rank adap-tation. The cross-layer scheduler selects the numberof layers of the transmission, which is forwardedto the layer mapping functionality by means of theRI. Although the concept of layer does not applyto transmit diversity schemes, the same mapping isassumed with some distinctions: BF is considered asa special case of spatial multiplexing with one-layertransmission, whereas in SFBC the number of layersis always equal to the number of antennas.

(2) MIMO Precoding: one modulation symbol isextracted from each layer, being the resulting vectorprocessed according to the applied MIMO scheme.In case of closed-loop schemes (i.e., BF and precodedSM) the precoding matrix indicated by PMI isapplied. In the open-loop SM scheme, the PMIis set to the identity matrix (i.e., no precoding isapplied). If SFBC is selected (with a reserved valuein PMI), the coding and mapping procedure definedby SFBC [10] is performed consequently. The outputof MIMO precoding is an M-size vector of complexvalues to be mapped onto the M antennas.

An example of MIMO processing is depicted in Figure 4 withtwo different configurations for three-layer spatial multiplex-ing and beamforming. The particular configuration of spatialmultiplexing depicted in Figure 4(a) corresponds to 2 codewords that are transmitted over 3 spatially multiplexed layersand 4 transmit antennas. The precoding stage performsthe conversion from 3 layers to the 4 antennas of thebase station by means of the precoding matrix, which isselected from the corresponding indexed position (PMI)within a codebook. On the other hand, Figure 4(b) showsthe transmit beamforming configuration corresponding to4 transmit antennas. In this case, only one code wordand one layer are allowed since no spatial multiplexingwill be performed (i.e., the layer mapping functionalityis transparent). Therefore, the precoding matrix becomesa precoding vector, also called beamformer, which is alsodetermined by the corresponding PMI value.

The configurable MIMO processing framework allowsfor selecting dynamically the MIMO scheme to be applied todifferent physical channels. A MIMO scheme is completelydetermined (including precoding details if necessary) bythe pair of values {RI, PMI}. Thus, the complete MIMOprocessing is configured by the control plane with properRI and PMI values for each of the physical channels beingprocessed.

The adaptation of the MIMO scheme is accomplishedby the cross-layer scheduler, which selects dynamically theappropriate scheme for each user. Once the schedulingfunctionality has made the decision, the different physicaldata channels are processed by the MIMO framework. Asdetailed above, this framework allows for changing thesettings of a certain MIMO configuration (e.g., number oflayers or precoding matrix), as well as switching to a differentMIMO scheme by simply updating the RI and PMI values.

2.1.4. Transmit OFDM Processing. The generation of theOFDM signal to be transmitted towards the radio interfacerequires the following processing blocks.

(i) Resource Element Mapping: resource allocation infor-mation from the control plane is used to mapmodulation symbols to specific resource elements inthe time-frequency resource grid for each antenna.

(ii) OFDM Transmission Modem: once a subframe iscompleted, OFDM symbols must be sent orderly tothe receiver. In the first place, OFDM symbols areconverted into the time domain by means of anM-point Inverse Fast Fourier Transform (M-IFFT).Afterwards, a cyclic prefix (CP) is added to eachOFDM symbol. Finally, the power of the transmittedsignal is normalized.

2.2. User Equipment. At the receiver side, UEs receive dataflows through their multiple antennas. A complex physicallayer processing is then required until data packets reach theMAC layer. Figure 5 shows the downlink UE architecture,which is divided into a control plane and a user plane.

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Control plane in the UE plays two roles.

(i) Physical Layer Measurements and Reporting: a set ofindicators are periodically reported to the BS so thatlink adaptation can be applied at the transmitterside. In particular, the UE shall estimate the Signalto Interference-plus-Noise Ratio (SINR), suggest theBS to use a certain rank (RI) and precoding matrix(PMI) for a given transmit MIMO configuration,and request potential retransmissions via H-ARQACK/NACK messages.

(ii) Physical Layer Configuration: control channels (CCH)sent from the BS carry essential control informa-tion for user plane configuration. Therefore, CCHdecoding (whose details are omitted for the sakeof simplicity) is the first task to be fulfilled beforeprocessing user data. Concretely, control channelsinclude scheduling assignments (i.e., resource allo-cation, modulation and coding scheme) as well asMIMO-related information like the RI and PMIapplied by the BS. All this control information isused to configure the different user plane functions,as depicted in Figure 5.

User plane subsystems are analogous to the ones listed forthe base station, although specific processing is required atthe receiver side.

(i) OFDM Processing: received time-domain symbolsthrough each of the M antennas must be syn-chronized and processed to form a complete time-frequency resource grid.

(ii) MIMO Processing: this subsystem is in charge ofdetecting the different data streams (or layers) thathave been transmitted from the BS. This task requirescertain information from the control plane, likethe estimated CSI and the RI/PMI used by theBS (decoded from control channels). In MIMO-OFDM systems, CSI is essential at the receiver in

order to coherently detect the received signal and toperform diversity combining or spatial interferencesuppression.

(iii) Demodulation and Decoding: this subsystem isresponsible for the channel decoding, descramblingand modulation demapping of each of the N datastreams. The configuration of these functions is per-formed from the MCS, which is previously decodedfrom control channels.

2.2.1. Receive OFDM Processing. Each UE must processOFDM signals from the different receive antennas. This pro-cessing is differentiated between control plane and user planefunctionalities. Processing at the user plane is performed bythe two following blocks.

(i) OFDM Modem: this block is dual to the OFDMtransmission modem at the BS; that is, cyclic prefixis removed from the received OFDM symbol in thetime domain; then, an N-FFT is applied to convertOFDM symbols into the frequency domain. It alsorecovers the reference pilots required by the channelestimation block.

(ii) Resource Element Demapping: once all the OFDMsymbols corresponding to a subframe are received,the complete subframe is processed in a dual manneras done at the transmitter side. That is, informationfrom the control plane is used to extract the mod-ulated symbols from their corresponding resourceelements.

At the control plane, several estimates and measurements arederived upon reception of the OFDM signal. Specifically, thefollowing functionalities are included.

(i) Channel Estimation: Channel estimation is based onpilot symbol arrangement within the 2D OFDMresource grid, where pilots are spread along both timeand frequency domains (see Figure 6). With this pilotarrangement, an efficient channel estimation methodmay apply a 2D time-frequency interpolation inorder to estimate the channel frequency response atall subcarriers within the complete subframe timeinterval (i.e., for the entire OFDM resource grid).Channel estimate (Ĥ) is employed with differentpurposes both in user and control planes at thereceiver. On the one hand, the channel estimate isprovided to the MIMO detection block in the userplane to recover the transmitted complex symbolsfrom the received signals. On the other hand, thechannel estimate is used to derive several channelmeasurements that will be reported back to thebase station for link adaptation purposes. Thesemeasurements include the channel quality, rank, andprecoding indicators (namely CQI, RI, and PMI).

(ii) SINR Estimation: effective (or postdetected) SINRis estimated for each frequency subband, given acertain MIMO transmission mode. The postdetectedSINR depends strongly on the MIMO scheme being

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Figure 9: Average BER for different MIMO-OFDM techniques witha fixed spectral efficiency of 4 bits/s/Hz and low spatial correlation(v = 3 km/h, ρ = 0.3).

applied and it is derived from the channel estimateĤ. Note that the MIMO link can be seen as anequivalent end-to-end SISO channel with an effectiveSNR which depends on the applied MIMO scheme.The effective SINR is then converted into a quantizedCQI value for reporting the channel quality back tothe base station through the uplink control channels.In case of spatial multiplexing, a different CQI valueis derived for each layer of the transmission.

(iii) Rank/Precoding Estimation: the suitable rank andpreferred precoding matrix are derived from thechannel estimate Ĥ for each frequency subband, sothat RI and PMI values can be reported back to thebase station for adapting the MIMO transmission.

2.2.2. Receive MIMO Processing. The receive MIMO process-ing block is in charge of recovering the complex modula-tion symbols that were transmitted through the multipleantennas. The per-antenna received complex symbols aretaken as inputs to provide, as a result, a detected version ofthe complex modulation symbols that were transmitted foreach code word. Following a similar approach to that of thetransmitter, the receive MIMO processing is separated intotwo functionalities: MIMO detection and layer demapping.These functionalities are defined according to the MIMOscheme applied at the transmitter, provided that severalschemes are supported in the simulator (see Section 2.1.3).

The applied MIMO scheme is decided at the base station,which takes into account the information reported by theUE (i.e., suitable rank and preferred precoding matrix).However, the final scheduling decision may differ withrespect to the reported values and, therefore, the UE mustbe informed about the applied MIMO configuration. Hence,the pair of values RI and PMI, which explicitly determinethe applied MIMO scheme, are sent to the UE via thecorresponding downlink control channel. At the receiverside, RI and PMI are extracted from the control channel,which is decoded and conveniently processed by the controlplane. Finally, RI and PMI are used to configure the MIMOdetection and layer demapping functionalities, so that userdata can be properly recovered.

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(i) MIMO Detection: the objective of the detectionprocess is to provide the complex modulation sym-bols that were transmitted onto the different layers.Different detection strategies are implemented sincethe detection problem is associated to the MIMOtechnique applied at the transmitter. Linear detectionand low-complexity algorithms based on successiveinterference cancellation [11] are supported by thesimulator. In case of SFBC transmission, the linearprocessing defined in [10] is carried out in orderto obtain the transmitted complex modulation sym-bols. When precoding-based schemes are applied,the effective channel matrix is needed in order toperform the detection. The effective channel Heff =Ĥ∗V is derived from the channel estimate Ĥ and theprecoding matrix V indicated by PMI.

(ii) Layer Demapping: it performs exactly the inversefunction of the layer mapping, described inSection 2.1.3. In this case, the complex symbolstransmitted over the different layers of thetransmission are de-mapped into the correspondingcode words to be subsequently decoded (as describedin the next section).

2.2.3. Demodulation and Decoding. At the receiver side,incoming modulated symbols are converted to bit sequencesand processed in order to recover the original transportblocks. The UE is aware of the instantaneous MCS appliedto the data from the signalling information contained in thecorresponding control channels. The following processingblocks are included.

(i) Modulation Demapping: the sequence of receivedcomplex symbols is demapped according to the MCSinformation provided by the control plane.

(ii) Descrambling: the scrambling process carried out atthe receiver is undone to recover the descrambledcode word.

(iii) Channel Decoding: the received code word has tobe processed to reverse the rate-matching processbefore being decoded. Once the decoding is complete,the transport block CRC integrity is checked. Ifsegmentation took place at the transmitter, the CRCof each individual segment is checked before.

If data integrity is preserved, an ACK message is sent tothe transmitter through uplink control channels. Otherwise,a NACK message is sent to request a retransmission. IfCC and/or IR features are applied, the erroneous codeword is stored at the receiver to be combined (CC) oradded (IR) to forthcoming code words, as described inSection 2.1.2.

2.3. Mobile Channel. The mobile channel subsystem allowssimulating a MIMO transmission through the radio link. Theeffects of antenna spatial correlation and frequency-selectiveRayleigh fading are included. Specifically, the standardspatially correlated Rayleigh-faded multiantenna channelmodel [12] is considered. Channel gain is modeled by a 2×2complex matrix H, so that the entries H = (hi j) denote thechannel gain between the jth transmit and the ith receiveantenna. The multitap channel delay profile is fully con-figurable. Assuming the well-known Kronecker correlationstructure [12], the channel matrix can be decomposed asH = R1/2rx · G · R1/2tx , where the entries of G are independentand identically distributed (i.i.d.) Gaussian random variables(RVs) with zero mean and unit variance. We assume the sameantenna correlation factor (ρ) for transmit and for receive

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0

0.02

0.04

0.06

0.08

0.1

BFPrecoded MUX

MUX w/o precodingSFBC

Mean SNR = 10 dB

(a)

70 80 90 100 110 120 130 140

Mea

nde

lay

(s)

Load (Mbps)

0

0.02

0.04

0.06

0.08

0.1

BFPrecoded MUX

MUX w/o precodingSFBC

Mean SNR = 25 dB

(b)

Figure 11: Mean transmission delay for different MIMO schemes when using adaptive modulation subject to restriction BERT = 10−3 in apedestrian channel (v = 3 km/h, ρ = 0.3) with different average SNRs.

antennas, thus being the correlation matrices Rtx and Rrxidentical and given by

Rtx = Rrx =⎛⎝ 1 ρρ∗ 1

⎞⎠. (1)

The effect of Additive White Gaussian Noise (AWGN) at thereceive antennas is also included. Assuming the frequencydomain baseband model, the received signal vector can beexpressed as y = H · x + n, where x is the transmittedsignal vector and n is the channel noise vector, whose entriesare i.i.d. Gaussian RVs with zero mean and variance σ2n . Asthe transmitted signal is power normalized, the average SNRdetermines the average noise power. Thus, we define theaverage SNR γ in terms of the transmit power constraint andthe noise power as γ = 1/σ2n [13].

The simulator includes the ability to run over a timedomain channel model (with higher accuracy) or over anequivalent frequency domain channel model (to minimizecomputational costs). If the later is selected, OFDM modemsare disabled, being OFDM symbols merely affected by chan-nel frequency response. However, it should be noted thatperformance results over an equivalent frequency domainchannel are only valid under the premise that transmissionis free of Intersymbol Interference (ISI) and IntercarrierInterference (ICI).

2.4. QoS Statistics. A QoS statistics block allows assessingthe impact of PHY layer impairments on upper layers’QoS by collecting performance statistics from both sides ofthe communication (as shown in Figure 1). This block isaware of the status of the BS and all UEs in the simulationscenario (queue occupancy at BS, transmitted/received bits

sequences, etc.). This global knowledge allows obtaining QoSindicators like BER, BLock Error Rate (BLER), transmissiondelay, throughput, as well as occupancy and loss rate in thetransmission queues.

3. Simulation Results

The presented simulator allows for evaluating the mainphysical and MAC layer functionalities of a MIMO-OFDMA-based system. The performance of different adaptive trans-mission techniques and MIMO schemes may be evaluated ina complete downlink system that takes into account most ofthe impairments of a realistic scenario.

As aforementioned, the proposed simulator is proved tooutperform Simulink, VSS and Ptolemy II. Figure 7 showsa performance comparison among these platforms for asimplified OFDM scenario (see [4] for further details).

This simulator has been used to support several researchworks in the context of adaptive OFDMA, and MIMO basedsystems. In [11], different low-complexity MIMO detectionalgorithms are evaluated in a realistic LTE downlink scenarioand a performance-complexity tradeoff is established. Anadaptive MIMO transmission is proposed in [14] for anOFDMA-based cellular system under practical limitationssuch as imperfect CSI at the transmitter due to user mobility.

Along this section, simulation results are shown for atypical configuration of the 3GPP LTE technology [1], whichis summarized in the next subsection.

3.1. LTE Configuration. LTE radio resources are structuredin slots of length Tslot = 0.5 millisecond. The TransmissionTime Interval (TTI) is set to 1 millisecond, thus containing 2

EURASIP Journal on Wireless Communications and Networking 11

10−5

10−4

10−3

10−2B

ER

0 5 10 15 20 25 30 35

SNR (dB)

BF (10 kmh)BF (20 kmh)BF (30 kmh)

BF (40 kmh)BF (60 kmh)BF (80 kmh)

BERT

10−5

10−4

10−3

10−2

BE

R

0 5 10 15 20 25 30

SNR (dB)

SFBC (60 kmh)SFBC (70 kmh)SFBC (80 kmh)

Precoded MUX (10 kmh)Precoded MUX (20 kmh)Precoded MUX (30 kmh)

BERT

Figure 12: Evaluation of the maximum admissible user speed for different MIMO schemes under adaptive modulation.

10−5

10−4

10−3

10−2

BE

R

0 5 10 15 20 25 30 35

SNR (dB)

BF-idealPrecoded MUX-ideal

BF-codebookPrecoded MUX-codebook

BERT

(a)

ASE

(bit

s/s/

Hz)

0 5 10 15 20 25 30 35

SNR (dB)

BF-idealPrecoded MUX-ideal

BF-codebookPrecoded MUX-codebook

0

2

4

6

8

10

12

(b)

Figure 13: Performance degradation due to codebook based precoding.

slots. Each slot can be seen as a time-frequency resource gridcomposed by several OFDM symbols. Each slot is dividedinto a number of Physical Resource Blocks (PRBs), eachof them consisting of 12 consecutive subcarriers along 7consecutive OFDM symbols. Assuming a system bandwidthBW = 20 MHz, a total of 1200 data subcarriers (i.e., 100PRBs) is available for transmission. The minimum allocableunit to a user is formed by two consecutive PRBs along thetime. Although LTE specifications state that the first OFDMsymbols (from 1 to 3) in a TTI are reserved for controlchannels (signaling), our simulations only assume referencesignals overhead, as shown in Figure 8.

A typical value of antenna correlation ρ = 0.3 has beenconsidered from previous works on the antenna correlationfor a typical suburban scenario and linear antenna arrays[15]. A 9-tap typical urban channel delay profile hasbeen considered [16]. The rest of simulation parameters issummarized in Table 1.

3.2. Performance Results. In this section, several simulationresults are shown in order to provide a comparative analysisof the different MIMO-OFDM techniques in the LTEdownlink scenario. MIMO-OFDM techniques (SFBC, BF,MUX without precoding, and precoded MUX) are evaluated

12 EURASIP Journal on Wireless Communications and Networking

50

60

70

80

90

100

110

120

130

140

Cel

lTh

rou

ghpu

t(M

bps)

0 5 10 15 20 25 30 35 40 45 50

Number of users

Precoded MUXMUX w/o precoding

BFSFBC

Figure 14: Multiuser diversity gain with Proportional Fair schedul-ing (average SNR 20 dB).

Table 1: Simulation parameter values.

Parameter Description Value

Nant Number of Antennas 2× 2ρ Antenna Correlation 0.3

— MIMO TransmissionMode

MUX, PrecodedMUX, SFBC, BF

v Terminal Speed 3–80 km/h

γ Average SNR 0–30 dB

— ModulationQPSK, 16QAM,64QAM

TTI Transmission TimeInterval

1 ms

fc Carrier Frequency 2.5 GHz

BW Bandwidth 20 MHz

fs Sampling Frequency 30.72 MHz

Δ f Subcarrier Spacing 15 kHz

FFTsize FFT Size 2048

Nsub Data subcarriers 1200

rsReference signalsoverhead

2/21

CP Cyclic Prefix Length 144 samples

— Source Model Full buffer

t Simulation Length 20 seconds

in terms of average BER, average spectral efficiency (ASE),and mean transmission delay.

The presented results are organized as follows. First, theaverage BER is evaluated without any adaptive modulationmechanism, that is, under a fixed given spectral efficiency.Afterwards, the performance analysis is focused on theadaptive modulation mechanism and, thus, the average BER,spectral efficiency, and mean transmission delay are provided

for the different MIMO-OFDM schemes. Next, differentimpairments associated to a realistic scenario are tackled.Specifically, the maximum admissible user speed under agiven target BER (BERT) requirement is evaluated for thedifferent MIMO techniques. Finally, the performance degra-dation due to nonideal (i.e., codebook based) precoding isaddressed.

Figure 9 shows the average BER results associated to thefour MIMO-OFDM techniques. In order to provide a faircomparative analysis, a fixed spectral efficiency of 4 bits/s/Hzis achieved by employing different modulation order for thedifferent MIMO techniques; that is, QPSK is employed forspatial multiplexing schemes whereas 16-QAM is used in caseof transmit diversity (i.e., SFBC and BF schemes). Simulationresults show that the transmit diversity schemes outperform(for the entire range of SNR) the multiplexing ones in termsof BER. Besides, precoding allows reducing significantly theinterference among transmitted signals at the receiver and,therefore, it improves the performance when applied to theMUX technique.

Figures 10 and 11 show different performance metrics ofthe different MIMO-OFDM schemes with adaptive modula-tion. An instantaneous BER restriction is assumed for all thesimulations so that BER is maintained below a predefinedtarget (BERT = 10−3). Possible modulation schemes areQPSK, 16-QAM, and 64-QAM, as well as no transmission(outage). An error-free feedback channel is assumed andideal precoding based on singular value decomposition(SVD) of the channel matrix is employed for the BF andprecoded MUX techniques.

In Figure 10, the average BER and corresponding ASE isdepicted as a function of the average SNR for the differentMIMO-OFDM schemes. It can be seen that the average BERremains under the BERT within the entire SNR range forall the schemes. Regarding the ASE, different results for thelow and high SNR regime are obtained (see Figure 10). Inthe low SNR regime, BF provides the highest ASE comparedto other schemes; however, the achieved ASE for transmitdiversity techniques (both BF and SFBC) reaches a satura-tion level (6 bits/s/Hz) as the average SNR increases. Thissaturation level corresponds to the maximum achievable ASEby transmitting only one data stream with the maximummodulation order (64-QAM). On the other hand, the spatialmultiplexing techniques provide the highest ASE (close to12 bits/s/Hz) in the high SNR regime due to the simultaneoustransmission of two data streams. Moreover, a significantASE improvement is shown for the MUX technique whenprecoding is applied (ASE increases 2 bits/s/Hz when averageSNR is 20 dB).

Figure 11 illustrates the mean transmission delay asa function of the system load for the different MIMO-OFDM schemes being compared. The load point at whichmean delay starts increasing determines the maximumadmissible system load. Two different scenarios are con-sidered in Figure 11 with average SNR of 10 dB (a) and25 dB (b), respectively. The ASE differences between theMIMO schemes in the low and high SNR regime describedabove are also observed in Figure 11 in terms of the meantransmission delay. In the low SNR regime (Figure 11(a)) the

EURASIP Journal on Wireless Communications and Networking 13

limit for the admissible load with BF is about 30–35 Mbps,significantly higher than the one achieved with the otherschemes. In case of high SNR regime (Figure 11(b)), it isshown that MUX schemes (specially the precoded MUX)clearly outperform the transmit diversity schemes.

Figure 12 shows how average BER is degraded as userspeed increases due to the outdated CQI and PMI reports.It is observed that the maximum user speed that fulfills theBER requirement for BF is around 60–70 km/h, which issignificantly higher that the one for precoded MUX scheme(about 20 km/h). However, SFBC is shown to be the mostrobust technique in high mobility scenarios as the maximumadmissible user speed is around 80–90 km/h. This is due tothe fact that, unlike the BF scheme, SFBC is not affected bythe outdated PMI reports.

Another impairment associated to a realistic sce-nario is the bandwidth limited feedback channel, whichmakes unfeasible to report the exact (ideal) precodingmatrix to the base station. Therefore, codebook-basedprecoding is employed and only an index which indi-cates a predefined precoding matrix (within the code-book) is reported. Figure 13 shows the performancedegradation in terms of average BER (a) and ASE (b)when a codebook-based precoding is applied insteadof ideal (SVD based) precoding. The two-bit codebookdefined in LTE [17] is assumed for the simulations. Itis observed that the performance degradation (both inaverage BER and ASE) associated to BF is negligible,while the precoded MUX performance is degraded signifi-cantly.

As described in Section 2.1.1, the scheduling policyapplied at the MAC layer plays an important role inthe overall system performance. Figure 14 shows the totalcell throughput for a different number of users in thecell when applying a Proportional Fair (PF) schedulingpolicy. Simulation results show the multiuser diversity gainachieved by PF for different MIMO schemes, which canbe seen as an effective SNR gain in the radio link. ForSFBC and BF, the maximum achievable cell throughput isRcell ≈ 91.5 Mbps, which corresponds to the maximumASE of 6 bits/s/Hz shown in Figure 10, taking into accountthe physical resources structure defined in Table 1, that is,Rcell ≈ ASE · Nsub · (1 − rs) · fs/(CP + FFTsize). AlthoughMUX without precoding is the worst scheme for a singleuser in the cell, both MUX-based schemes achieve thehighest multiuser diversity gain as the transmission of twosimultaneous streams provides a higher degree of freedom.Anyhow, precoded MUX outperforms the previous schemeboth in throughput and BER due to the reduction of thecochannel interference achieved by means of precoding.

4. Conclusions

In this paper, we present a simulator for next genera-tion MIMO-OFDM-based wireless systems. The simulatorincludes the main functionalities carried out at the physicaland MAC layers of a wireless MIMO-OFDM system. Furtheron, a detailed description of the simulator architecture andmain functionalities is provided. With this architecture, it is

shown that a fully configurable MIMO-OFDM transmissionis supported, including all the MIMO-OFDM schemes beingincorporated to next generation wireless standards.

This simulator has been used to evaluate the perfor-mance of the 3GPP LTE technology, whose simulationresults are here presented and analyzed. Performance resultsare provided for the different MIMO-OFDM techniquesincluded in LTE under realistic assumptions such as usermobility or bandwidth limited feedback channel. It has beenproved that spatial multiplexing techniques provide the bestspectral efficiency for high SNR although very low terminalspeeds (up to 20 km/h) are supported to fulfill the reliabilityrequirements. On the other hand, SFBC is shown to be themost robust technique in high mobility scenarios as themaximum admissible user speed is around 80–90 km/h.

Acknowledgments

This work has been partially supported by the Spanish Gov-ernment (projects TIC2003-07819 and TEC2007-67289),Junta de Andalucı́a (Proyecto de Excelencia TIC 03226), andAT4wireless.

References

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[16] 3GPP TS 36.521-1, “User Equipment (UE) conformancespecification Radio transmission and reception, Part 1: Con-formance Testing,” Release 8, V8.1.0, March 2009.

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IntroductionSimulator ArchitectureBase StationCross-Layer SchedulingModulation and CodingTransmit MIMO ProcessingTransmit OFDM Processing

User EquipmentReceive OFDM ProcessingReceive MIMO ProcessingDemodulation and Decoding

Mobile ChannelQoS Statistics

Simulation ResultsLTE ConfigurationPerformance Results

ConclusionsAcknowledgmentsReferences