Combined Collaborative and Precoded MIMO for Uplink of the LTE-Advanced

8/11/2019 Combined Collaborative and Precoded MIMO for Uplink of the LTE-Advanced

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29 th NATIONAL RADIO SCIENCE CONFERENCE(NRSC 2012)

April 10 12, 2012, Faculty of Engineering/Cairo University, Egypt

Combined Collaborative and Precoded MIMO for Uplink of the LTE-AdvancedKarim A. Banawan 1, Essam A. Sourour 2

1Faculty of Engineering, University of Alexandria, Alexandria, Egypt, [email protected] Faculty of Engineering, University of Alexandria, Alexandria, Egypt, [email protected]

ABSTRACTIn this paper, we will focus on the collaborative MIMO in combination with the LTE-advanced system. In this

system, two or more users each using multiple antennas are transmitting their data within the same frequency/timegrid. In this paper, we investigate the MIMO enhancements introduced to the LTE uplink of the LTE-advancedsystem. User equipment here is equipped by 2-4 antennas which can be used along the collaborative MIMOsystem to increase data rate, enhance system performance and match channel conditions according to the channelknowledge at transmitter. We propose a space frequency block codes (SFBC) precoding for the uplink to achievespace diversity in accompany with spatial multiplexing gain achieved before using the collaborative system. Then,we propose a combination between the collaborative system and the precoded MIMO whether ideally (SVDprecoding) or suboptimally using codebook precoding. The results show dramatic enhancement in performanceand achieves the peak spectral efficiency of the uplink mode of the LTE-advanced.

Keywords : LTE-advanced, Collaborative MIMO, SC-FDMA, SFBC, SVD-precoding, Codebook precoding, ML.

I. INTRODUCTION The LTE-advanced defined in release 10 of the third generation partnership project (3GPP) aims to achieve

uplink (UL) peak spectrum efficiency of 15bits/s/Hz [1] . LTE is the evolution of GPPs Universal MobileTelecommunication System (UMTS) towards an all-IP network [2]. The multiple access scheme in LTE downlinkis the Orthogonal Frequency Division Multiple Access (OFDMA) and uplink uses Single Carrier FrequencyDivision Multiple Access (SC-FDMA). SC-FDMA has been adopted for uplink transmission to allow efficientterminal power amplifier design, which is relevant to the terminal battery life [3]. Collaborative MIMO (knownalso as Virtual MIMO or collaborative spatial multiplexing (CSM)) is introduced to the uplink of the LTE-advanced to increase the whole throughput of the uplink and to achieve virtual MIMO without increasing thecomplexity of the user equipment (UE) [4]. CSM works as two or more users having UEs equipped with single ormultiple antennas, each one of them transmits independent data stream from the others. Those users arecollaboratively transmitting on the same resource blocks (RBs), i.e., same frequency/time grid resource block. Theabove transmission technique creates virtual MIMO link in a sense that the collaborative users are just antennasfor a virtual larger UE. The Evolved Node B (eNodeB) receives combined data from all collaborative users.eNodeB will then separate the data of each user using multiuser equalization techniques [5].

In this paper, we discuss the multiple antennas enhancements introduced to the LTE uplink of the LTE-advanced [1]. In LTE-advanced, the LTE uplink mode is equipped by 2-4 antennas which can be used inaccompany with the collaborative MIMO system to increase data rate, enhance system performance and matchchannel conditions to achieve the channel capacity assuming full or partial channel knowledge at the transmitterend. Three precoding schemes are presented in the context of this paper. In the first scheme, we proposecombining the spatial multiplexing advantages of the CSM with the diversity advantages of the space frequencyblock codes (SFBC) [6],[7] , this will enhance the whole performance of collaborative system without any need ofchannel knowledge at the transmitter. In the second scheme, we propose exploiting the multiple antennas at thetransmitter by apriori precoding the spatial substreams using singular value decomposition (SVD) of the channelmatrix [8] and filtering the resultant signals at eNodeB. This will maximize the capacity of the MIMO channel inexpense of full channel state information (CSI). In practice, the feedback messages cannot provide full CSI atUE, also the selection of precoder in LTE-advanced is signaled via eNodeB, so a limited number of codebook-based precoders should be available. So, we propose a suboptimal combination between codebook precodedMIMO and collaborative MIMO system as a third scheme with different selection methods [9]. Varioussimulation results are examined in different channel conditions and different multiuser equalization techniques.

The paper is organized as follows: section (II) introduces the used system model, section (III) gives a briefliterature review about SVD and codebook precoding, then section (IV) presents the proposed precoding

techniques. Section (V) enumerates the performance of these schemes via computer simulations. Note that boldface letters denote matrices , ( . ) H , . T , . are Hermitian, transpose and Euclidean norm.


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1

1 1 1

Channelcoding

Symbolmapping

(base bandmodulation)

DFTwith size

"M"

Subcarriermappingand DRSinsertion

IFFTwith size

"N"

CPinsertion

Channelcoding

Symbolmapping


DFTwith size

"M"


IFFTwith size

"N"

CPinsertion

Frequencydomain

precoding

1 1

UE 1

1

1 1 1

Channelcoding

Symbolmapping


DFTwith size

"M"


IFFTwith size

"N"

CPinsertion

Channelcoding

Symbolmapping


DFTwith size

"M"


IFFTwith size

"N"

CPinsertion

Frequencydomain

precoding

1 1

UE K

FrequencyDomain

Multiuserequalizer

CPextraction FFT

CPextraction FFT

Subcarrierdemapping

IDFT

Subcarrierdemapping

IDFT

SymbolDemodulation

ChannelDecoding

Symbol Demodulation

ChannelDecoding

1

1 1

II. SYSTEM M ODEL

Fig.1 system model (a) UEs transmitter block diagram (b) eNodeB receiver

The CSM system shown in Fig.(1) has users each has a UE equipped with multiple antennas . The data ofthe user is independently processed, first each user will have independent data streams (called rank oftransmission), , these data streams are symbol mapped to have the modulated symbols in layer . Themodulation symbols are then transformed to the frequency representation via the unitary Discrete Fouriertransform (DFT) of size to have

=1 =1 exp 2 1 1

(1)

where is the subcarrier index and is the symbol index , , 1,2,

, . The output of the DFT is then

mapped using the subcarrier mapping [3], which has two versions: the distributed and the localized, the later isadopted in Release 8, where the user's symbols are mapped into consecutive subcarriers. This will achieve themultiuser diversity and frequency diversity when assigning each user to subcarriers with favorable transmissioncharacteristics. So, localized subcarrier mapping is applied to have . In this work, we assume full RB usage so

( ) = 0 (2)

where is the element mapping set of the user +22 ,, +2 and is the FFT size. The output ofthe subcarrier mapping is then precoded in the frequency domain to map the streams to streams by one of theprecoding schemes that will be presented in section (IV). The output of the precoding stage is fed to aconventional OFDM transmitter which consists of Inverse Fourier transform stage (IFFT) with size ( > ) ,

, 1,2,

,

=1

=1

exp2

1 1 (3)Finally, the cyclic prefix (CP) is inserted with length larger than the maximum delay spread of the multipath

channel, to mitigate intersymbol interference (ISI) and enable simple frequency domain equalization (FDE). Theabove steps will result in a SC-FDMA signal. Now, each user's data streams have been transmitted throughmultipath Rayleigh channel which is modeled as normalized baseband equivalent sample spaced channel impulseresponse hkp (m, l) where m is the time instant , l is the path number of L taps and p is the receiving antenna indexwith uniform power delay profile. Each path is assumed to be Wide Sense Stationary Uncorrelated Scattering(WSSUS) filtered by Doppler PSD modeled in Jakes [10]. In this paper, we assume perfect channel knowledge atthe transmitter. So, the result of multipath filtering of the k th user to p th receiving antenna channel can bemodeled as


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= , ( )=0 (4)At eNodeB, which is equipped by Nr antennas ( Nr K.) , the collaborative users' signals are added together

with contamination of AWGN , which is modeled by IID complex Gaussian noise samples w(m) with zero meanand variance w2 = 1/ N. s , where s is the symbol to noise ratio ( Es /N o ) . Then, the received signal rp at thep th antenna is written as

( ) = +

.

=1

( ) (5)

The eNodeB takes the FFT of each received stream to transform the input streams back into frequency domainand prepares them for the FDE. The received signal in the frequency domain at any subcarrier can be written as

= + ( ) (6)

where : is the subcarrier index and : is the channel matrix upon the subcarrier

=11 1

1

(7)

III. E XISTENT P RECODING SCHEMES F OR O RDINARY SPATIAL M ULTIPLEXING The main motivation of precoding the spatial substreams is to match channel conditions by increasing the

received signal power in case of perfect or partial CSI, also decrease the interstream interference from otherantennas and maximize the channel capacity of the wireless channel [1]. So, optimal communication over

MIMO channel uses channel-dependent precoder, which adopts the roles of both transmit beamformingand power allocation across the transmitted streams, and a matching receive beamforming structure [6].

A. Optimal Precoding Scheme (SVD Precoding)The well-known optimal (capacity-maximizing) precoder in literature for exploiting the channel knowledge at

the transmitter is to send data over the strongest eigenmodes of the channel. A common way to express thechannel matrix in subcarrier is through its SVD [8], [9] as

= ( ) (8)

where is orthonormal matrix , is orthonormal matrix and= ( 1 , 2 ,. . ) is eigenmodes diagonal matrix. The matrix is used to precode the input datastream in the frequency domain to have ( ) as

( ) = ( ) (9)

In the eNodeB frequency domain equalizer, the precoding process leads to orthogonal streams if channel isfurther filtered by UH(l) (channel matched filter) as

( ) = ( ) (10)or using the MMSE linear equalizer for the equivalent channel = ( )

= ( ( ) ( ) + 1 / )1 ( ) (11)The SVD precoding allows decomposing channel matrix into SISO channels, whose gains are 1

2 , 22 ,

to

increase the capacity of the MIMO system, since the interstream interference is apriori removed, however thescheme requires perfect CSI and can enhance peak to average power ratio (PAPR).


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B. Codebook Based Precoding For Uplink of LTE-AdvancedIn practice, the feedback messages cannot provide full CSI at UE, also the selection of precoder in LTE-

advanced is signaled via eNodeB, so a limited number of codebook-based precoders Fi(l) should be available.

A finite set of NT precoders is defined in uplink of LTE-advanced .These precoders are chosen to beconfined to QPSK alphabet {1, j} as in the table 5.3.3A.2-1 through table 5.3.3A.2-5 in release 10 [11] to easethe precoding process implementation, and have constant modulus property for maximizing power efficiency ofthe power amplifier [1].The UL precoders differ from the DL ones of having one nonzero element in each rowwhich ensures that no linear combination between different layers is allowed to decrease the resulting PAPRwhich is considered a major issue in UL transmission. On the other hand, these precoding matrices suffer fromdegradation in precoding gain. The precoder is chosen so that it maximizes the received signal power i.e.

= argmax

2

= argmax

( ) ( ) 2 (12)

This is equivalent to maximizing the post-MMSE channel power matrix metric which corresponds to the sumof the equivalent channel gains for the layers defined in [9]

( ) = ( ( ) ( ) + / ) ( ) ( ) (13) = argmax

. 2

1 ( )=1 (14)where = ( ( )) .IV. P ROPOSED P RECODING SCHEMES F OR C OLLABORATIVE SPATIAL M ULTIPLEXING

A. Combined Collaborative MIMO and Space Frequency Block Codes (SFBC) for theUplink of the LTE-AdvancedTo achieve spatial diversity and spatial multiplexing jointly without a need of any feedback signaling, a novel

combined scheme of SFBC and collaborative spatial multiplexing is proposed. In this scheme, each user equippedby 2 antennas will precode its signal by the well known frequency domain version of the orth ogonal Alamoutismatrix [7],[12] .This will result in encoding each two successive subcarriers for the k th user as 1 2 ( )/ 2 ( + 1)/ 2( + 1)/ 2 ( )/ 2 (15)The result of SFBC precoding of each user will achieve transmit spatial diversity and orthogonal substreamtransmission. The result of SFBC will be transmitted collaboratively across same time and frequency resourceblock, which corresponds to spatial multiplexing. Considering the case of 2 users x 2 receiving antennas, then thereceived signal at the lth subcarrier would be

= 11 1221 22

13 1423 24

1 ( )1 ( + 1)

2 ( )

2 ( + 1)

+ ( ) (16)

where is 2x4 MIMO channel which is assumed to be quasi static over every two successive subcarriers.Similarly the received signal at the ( + 1) would be

+ 1 =1 ( + 1)

1 ( )2 ( + 1)2 ( )

+ ( + 1) (17)

Then, the receiver utilizing the inherent orthogonality of the SFBC encoding, the receiver will do thefollowing multiplication, conjugate and Hermitian processes to have X l


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=

11 14 121 24 2

11

14 1 + 1

21

24 2 + 1

(18)

where 1 , 2 are the received signals along the subcarrier on antenna 1 and antenna 2 respectively.Apparently we can exploit the full diversity of the collaborative system per user and also exclude the interstreaminterference as we make a specific linear combination of the rows of . To maximize the diversity of 1 , therows are added in the following order

1 = 1 + 5 + 10 + 14 = 11

2+ 12

2+ 21

2+ 22

21 + 11 13 + 21 23 + 12 14 + 22 24 2

+ 11 14 + 21 24

12 13

22 23 2 + 1

= 1 1 + 2 + 2 + 1 (19)

Equation (19) shows the exclusion of interstream interference 1 ( + 1) and exploiting all the spatial channelsexperienced by 1 .Similarly, to maximize 1 + 1 , 2 , 2 ( + 1) to have 2 , 3 , 4 respectively as

2 = 2 + 6 9 13 3 = 3 + 7 + 12 + 16 4 = 4 + 8 11 15 (20)

Then the equivalent transmission scheme can be written as quasi-orthogonal system of equations as

1 00 1

2 00 2( )

1 ( )

1 ( + 1)

2 ( )

2 ( + 1)

=

1

2

3

4( )

(21)

where 2 = 132

+ 142+ 23

2+ 24

2

The estimate of the received subcarriers can be readily obtained as= ( ) ( ) (22)

The scheme makes use of inherent spatial diversity shown in 1 , 2 , doubles the spectral efficiency(4 symbols x 2 subcarriers) with no CSI at the transmitting end. Note that the selective nature of the channel is amain issue in this scheme because the channel gains are assumed to be quasi static over two successivesubcarriers, so highly selective channel will degrade the whole performance of the scheme. It is worth saying thatextending the scheme for 2 users x 4 receiving antennas at eNodeB to achieve higher diversity order is quitesimple with only extensions of X l , l , 1 , 2 , , to acquire the extra channels.

B. Combined Collaborative and SVD-Precoded MIMO for the Uplink of the LTE-AdvancedTo optimally exploit the extra antennas introduced to the uplink of the LTE-advanced in the context of

collaborative MIMO system, the precoding of all streams of the collaborative users seems to be attractive.However, the optimal SVD- precoding requires the knowledge of other users data to use the precoding matrixresulted from SVD decomposition of channel matrix H l as equations (8), (9). A suboptimal SVD-precodingwhich dont require knowing other users data is introduced in this section. Consider the channel matrix at the lt h subcarrier assuming perfect CSI at UE


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=

11 12

21 22 1 1 12 1 2

1 1 1 21 2 1 1 11 (23)

where is channel matrix. To tackle the problem of joint precoding, we will suboptimally precodethe data streams of each user individually i.e., we will precode each submatrix independently as

1 =

11 1221 22

1 1 1 21 2

1

2

1 (24)This submatrix will be SVD decomposed as (8),(9) as

= ( ) (25)The process will continue to the remaining K

1 users to have K precoding matrices.Then the transmitted

signals of all users will be

=

( )

(26)

and the equivalent channel matrix will be= [ ] (27)Then the equalization methods discussed in equations (11),(12) can be used. Note that this scheme is

suboptimal in a sense that it ensures removal of all interstream interference for each user alone, while themultiuser interference still exists, however this comes in expense for not needing the knowledge of other usersdata. The performance of this scheme will be the middle way between perfectly precoded scheme whichcorresponds to KNT orthogonal streams at the receiver and unprecoded one which suffers from interstream andmultiuser interferences.

C. Combined Collaborative and Codebook-Precoded MIMO for the Uplink of the LTE-AdvancedTo make use of the new spatial dimension introduced in LTE-advanced with limited feedback signaling in

case of collaborative MIMO system, codebook-based precoding should be taken into consideration. Threedifferent precoder selection techniques are presented in comparison

1) Submatrix Precoding : This selection method is the same as the previous scheme, the channel matrix isdivided into K submatrices and each one of them is precoded independently by metric identified in

equations (12),(13),(14) thus precoding matrix of k th

user is= argmax

( ) ( ) 2 (28)

where ( ) is the channel columns corresponds to the user. The equivalent channel would be

= [ ] (29)2) Joint Maximization Precoding: In this selection method, the MIMO channel is divided into KxK submatrices , For example for rank 1 , 2 users equipped by 2 transmitting antennas and 4 receivingantennas , we will have 4 submatrices 2x2 each

= 112 ( ) 212 ( )

134 ( ) 234 ( )

(30)

where 112

( ) is 2x2 MIMO channel between user 1 and receiving antennas 1,2. Here we will choosethe precoder so as to jointly maximize the norm of these subchannels as


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= argmax

( 112 2 + 134 2 ) (31)

and the equivalent channel would be as equation (29)

3) Linear Combination Precoding: In this selection criteria, the precoder is chosen as a linearcombination of the precoders which maximize the metric of Huser 112 (l) , Huser 134 (l) independently as

= argmax

112 2 + argmax

1

34 2 (32)

The chosen precoder must be normalized. The drawback of this selection method is that the resultant precoderdoesnt have a constant modulus and can probably enhance PAPR.

V. SIMULATION R ESULTS For link level simulations, the simulation parameters of most of simulation cases are shown in table 1, other

simulation parameters are mentioned explicitly

Fig.2 comparison between presented precoding techniques

Table 1: common simulation parameters

SimulationParameters

Values

Channel coding None (default)Modulation schemes 16QAMFrame duration 10msFrame structure Type 1 FDDFFT size 256 subcarriersNumber of RBs 15 , fully utilizedBandwidth 2.7 MHz

SC-FDMA symbols 6Cyclic prefix choice extendedSubcarrier separation 15KHzCarrier frequency 2GHzSampling frequency 3.84MHzDelay spread 5 s (default)Delay- power profile Uniform/0 km/hrChannel estimation Perfect CSI at

transmitter

Fig.4 selectivity effects on the presented precoding schemes Fig.3 Codebook and SVD precoding Vs. ordinary CSM

-10 -5 0 5 10 15 20 25 3010

-8

10-7

10-6

10-5

10-4

10-3

10-2

10-1

100

Eb /No

B E R

Comparison between presented precoding schemes

reference curve flat fading without precoding 2x4 antennasSFBC precoding 2 users x 4 receive antennasCodebook precoding 2 user x 4 ant. rank 1SVD precoding 2 users x 4 ant. rank 2unprecoded rank 2 and codebook precoded rank 2

-10 -5 0 5 10 15 20 25 30 3510

-8

10-7

10-6

10-5

10-4

10-3

10-2

10-1

100

Eb /No

B E R

Effect of selectivity along presentedprecoding schemes

codebookflat fadingchannel

codebookdel ay spread=2us



SFBCflat fading

SFBCdelay spread1uS

SFBCdelay spread=3.5us

SFBCdelay spread=2uS

SFBCdelay spread=5us

-10 -5 0 5 10 15 20 25 30 35 4010

-7

10-6

10-5

10-4

10-3

10-2

10

-1

100

Eb /No

B E R

codebook precoding Vs. ordinary CSM

precoding 2x4 rank1unprecoded 2x4rank1precoding 2x4rank2 (asunprecoding) precoding 2x8 rank1

unprecoded 2 usersx8 ant.precoding 2x 8 rank2unprecoded 2 usersx8ant. rank2precoding 2 usersx8 ant. rank3unprecoded 2 users x 8 ant. rank3precoding 2x8 rank4 (asunprecoded)SVDprecoding rank4


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Fig.(2) shows a comparison between the presented precoding schemes. Simulation results reveal that for rank1 transmission in flat fading channel , SFBC precoding and codebook precoding are almost identical , andoutperforms the unprecoded system by more than 3dB at a target BER=10 -5, so SFBC in this case is preferred dueto the no CSI need at the transmitter. For rank 2, the SVD precoding outperforms the codebook precoding by2.5dB. Fig (3) shows codebook precoding versus ordinary collaborative spatial multiplexing for different ranks asincreasing the rank of the transmission, increases the spectral efficiency in expense of enhanced interstreaminterferences and hence higher BER. Fig.(4) shows the selectivity effect on the precoding schemes , SFBCprecoding fails for high selectivity due to the assumption of flat channel gains over each 2 successive subcarriers.So, increasing the delay spread to 2 will enhance the performance of SFBC. An error floor will appear if thedelay spread 2 . On the contrary, codebook precoding exploits the high selectivity of the channel as inherentfrequency diversity. Fig.(5) shows a comparison between the codebook selection methods. The figure reveals thatthe submatrix precoding exploits the full channel knowledge and hence better interstream and interuserinterference cancellation. For the joint maximization method, the performance is degraded by 1dB because thescheme searches for the precoder which jointly maximizes the channel metric of the sub MIMO channels, so moreinterstream interference will exist. Choosing to maximize the best sub MIMO channel only will degrade theperformance by another 0.5dB. Fig.(6) and Fig.(7) show the spectral efficiency of the resultant CSM system for 2

and 4 transmitting antennas in case of 16QAM modulation. The results show that the thresholds of using theadaptive rank codebook precoding for the CSM system. These thresholds can be summarized in table 2. Note thatthe achieved spectral efficiency exceeds the target spectral efficiency of the LTE-advanced .

Fig.(8) shows the effect of precoding the collaborative streams on other equalization techniques. Simplifiedinitial guess (IGML) solutions presented in [5] will be in the order of ( ) . Simplified IGML is a user byuser separation by means of -decomposition of the equivalent channel matrix obtained in (27). SICbased system will be the same as presented in [5], but the interference will be regenerated assuming the channelmatrix is the equivalent channel matrix presented also in (27). Fig.(8) shows that SIC-based equalizer coincideswith the MMSE base equalizer. Because the SIC performance is dependent on the SINR, and for precoded systemmost of the interference components are excluded and hence slight enhancement in performance can be expected.The QR based IGML receiver outperforms the MMSE equalizer by 1dB in expense of the increased complexity ofchecking the error metric of all possible constellation points.

Table 2: SINR thresholds of the adaptive rank precoding for CSM system of 2 users x 8 receiving ant.

CSM Configuration Rank 1 Rank 2 Rank 3 Rank 4Transmitting antennas=2


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Fig.7 Spectral efficiency of 2 users CSM system eachUE having 4 transmitting antennas , 16QAM modulation

Fig.8 Different multiuser equalization techniquesaccompanying the codebook precoding

VI. C ONCLUSION In this paper, we exploit the multiple transmitting antennas in the uplink of the LTE-advanced. We have

introduced a blind SFBC- based precoding which doesnt need any channel knowledge at the transmitter andderived a suitable receiver for it. We concluded that SFBC precoding performance almost coincides withcodebook precoding for flat fading channel, but the main difference that the performance of the SFBC precodingdegrades in case of moderate and high selective channels because of the assumption of flat channel gains overeach two successive subcarriers. On the contrary, we compared between three selection methods for suitableprecoders and we found that the submatrix precoding technique outperforms the others because it exploits the fulldiversity of the MIMO channel. The performance of the codebook precoding is enhanced in case of high selectivechannel because an extra diversity source (frequency) exists. Also we introduce suboptimal SVD precoding foreach of the collaborative users. Also the resultant spectral efficiencies are shown to exceed the target spectralefficiency of the LTE-advanced (15bits/s/Hz). The results instruct using adaptive precoding scheme that usesSFBC based precoding for low and medium selective channel. As selectivity increases, use the adaptive rankcodebook transmission using the submatrix selection method. Finally as SNR is sufficiently high to use the fullrank of the MIMO channel you can use the SVD-based precoding.

R EFERENCES[1] C.S. Park, Y.-P.E.Wang,G. J ngren,D.Hammarwall, Evolution of uplink MIMO for LTE -advanced IEEE

Communications Magazine, Volume: 49 Issue:2, pp. 112 - 121 , Feb. 2011[2] David Astly, Erik Dahlman, Anders Furuskr, Ylva Jading, Magnus Lindstrm, and Stefan Parkvall,

Ericsson Research LTE: The evolution of mobile broadband," IEEE Communications Magazine, vol. 47,no. 4, pp. 44-51, April 2009.

[3] H. G. Myung, J. Lim, and D. J. Goodman, Single Carrier FDMA for Uplink Wireless Transmission, IEEEVehicular Technology Mag., vol. 1, no. 3, pp. 30 38, Sep. 2006.

[4] V. Jungnickel, M. Schellmann, A. Forck, H. Gbler, S. Wahls, A. Ibing, K. Manolakis, T. Haustein, W.Zirwas, J. Eichinger, E. Schulz, C. Juchems, F. Luhn, and R. Zavrtak, Demonstration of Virtual MIMO inthe Uplink, in IET Smart Antennas and Cooperative Communications Seminar, London, UK, Oct. 007,invited.

[5] Karim A. Banawan, Essam Sourour Enhanced SIC and Initial guess ML receivers for collaborative MIMOof the LTE Uplink , Vehicular Technology Conference (VTC2011-Fall), Sept. 2011.

[6] S. Sesia, I. Toufik, and M. Baker, Eds., LTE: The UMTS Long Term Evolution. John Wiley and Sons, 2009[7] J. Lee, J.- K. Han, and J. Zhang, MIMO Technologies in GPP LTE and LTE -Advanced, EURASIP

Journal on Wireless Communications and Networking, vol. 2009, May 2009.[8] A. Goldsmith, Wireless Communications. Cambridge, U.K.: Cambridge Univ. Press, 2004.[9] G.Berardinelli, T.B.Srensen, P.Mogensen, K.Pajukoski SVD -based vs. Release 8 codebooks for Single

User MIMO LTE- A Uplink Vehicular Technology Conference (VTC 2010-Spring),May 2010.[10] W. Jakes and D. Cox, Microwave Mobile Communications. Wiley-IEEE Press, 1994.[11] 3GPP TS 36.211 V10.1.0 (2011-04).[12] Alamouti, S.M. (1998) A simple transmit diversity technique for wireless communications. IEEE Journal on

Selected Areas in Communications, 16(8), 1451 1458.

-10 0 10 20 30 40 500

5

10

15

20

25

30

35

E b /N o

s p e c t r a l e f f i c i e n c y b / s / H z

Codebook precoded collaborative spatial multiplexingfor 4 transmitting antennas

rank 1 2usersx8ant.rank 2 2usersx8ant.rank 3 2users x8ant.rank 4 2userx8ant.

-8 -6 -4 -2 0 2 4 610

-7

10-6

10-5

10-4

10-3

10-2

10-1

E b /N o

B E R

Effect of different equalization techniques accompanyingcodebook precoding 2users , 4Tx ant. , rank 1 , 8 receiving antennas

SIC equalizationsimplified IGML equalizationMMSE equalization
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Combined Collaborative and Precoded MIMO for Uplink of the LTE-Advanced