(Luận văn thạc sĩ) design and implementation of a testbed for indoor mimo systems

V IETN A M N A T IO N A L UNIVERSITY HANOI C O L L E G E OF TECHN O LO GY VU XUAN THANG DJEDESIGN AND IMPLEMENTATION OF A TESTBED FOR INDOOR MIMO SYSTEMS M a j o r : E l e c t r o n i c s & T e l e c o m m u n i c a t i o n F n g i n e e r in g S p e c ia lity : C ode: E le c tro n ic s E n g in e e rin g 60 52 70 M A S T E R T H E S I S IN E L E C T R O N I C S E N G I N E E R I N G SUPERVISO R: DR TRINH ANH v u H anoi - 2009 D E C L A R A T IO N B Y C A N D ID A T E I h e re b y d e c la re that th is th e sis is m y o w n w o r k a n d e ffo rt a n d it h as not been s u b m itte d a n y w h e r e fo r an y a w a r d W h e r e o th e r s o u r c e s o f in f o r m a tio n h a v e been u sed , they h a v e b e e n a c k n o w le d g e d A u th o r Vu Xuan Thang ACKNO W LED GEM EN T I w o u ld like to g iv e a w a rm th a n k to Prof N g u y e n D in h T h o n g and Dr T rin h A n h V u, m y s u p e rv is o rs , for th e ir c o n s id e b le help in m y tim e s tu d y in g m y m a ster w o u ld like to th a n k m y c o lle a g u e s , fam ily and friends for th e ir u n b e n d in g s u p p o rt and e n c o u r a g e m e n t CONTENTS A b s tra c t A b b r e v ia t io n s L ist o f F ig u re C h ap ter I n t r o d u c t i o n C h ap ter M I M O m o d e ls a n d c h a r a c t e r is t ic s 2.1 M a th e m a ti c a l M I M O m o d e l 2.1.1 C a p a c ity via S in g le V a lu e D e c o m p o s i t i o n 2 R a n k an d C o n d it io n n u m b e r 2.2 P h y s ic a l M I M O m o d e l 2.2.1 L in e o f sig ht S I M O 2 L in e o f s ig h t M S O 2.2 A n t e n n a a rra y s w ith o n ly L O S p a t h 10 2.3 K e y p a r a m e te r s in M I M O c h a n n e l 1 2.3.1 A n te n n a s e p a r a t i o n 11 R e s o lv a b ility in th e a n g u l a r d o m a i n 15 2.4 A n t e n n a S e le c tio n A l g o r i t h m s 16 C hap ter M I M O T e s t b e d fo r i n d o o r e n v i r o n m e n t 21 3.1 A s u rv e y o f M I M O T e s tb e d d e s i g n 21 3.1.1 T h e M I M O T e s t b e d at V ie n n a U n iv e rs ity .21 T h e M I M O T e s t b e d at B r ig h a m Y o u n g U n iv e r s ity 21 3.1.3 T h e M I M O T e s t b e d at T h e U n iv e r s ity o f B ris to l 22 T h e M I M O T e s t b e d at A lb e r ta U n iv e r s ity .22 3.2 D e s ig n T o o ls 22 3.2.1 X ilin x X t r e m e D S P V irte x - K i t 22 2 S y s te m G e n e r a t o r 27 3.2.3 IS E S o f t w a r e 29 3.3 T e s t b e d D e s c r ip tio n 30 3.3.1 R F M o d u l e 30 3.3.2 D ig ita l T r a n s m i tt e r 32 3.3.3 D ig ital R e c e i v e r 35 3.3.3.1 T i m in g S y n c h r o n iz a tio n 3.3 C o r r e la tio n B lo c k .36 3.3 3 M a x im u m S e l e c t o r 37 3 S ig n al D e te c tio n B lo c k 38 3.3 S y n c h r o n iz a tio n D e te c to r 39 C hap ter I m p l e m e n t in g R e s u lts o f M I M O T e s t b e d .41 4.1 R F I m p l e m e n t in g R e s u l t s 41 B a s e b e n d I m p l e m e n t in g R e s u lts 42 4.3 C o m p l e t e R e c e iv e r for M I M O s y s te m .45 C o n c l u s i o n s 49 R e f e r e n c e s 50 R e la te d P u b lic a tio n s 52 ABSTRACT T h e M u ltip le Inp ut - M u ltip le O u u t ( M I M O ) te c h n iq u e a lo n g w ith o th e r te c h n iq u e s s u c h as S p a c e T i m e B lo c k C o d e ( S T B C ) O r th o g o n a l F r e q u e n c y D iv isio n M u ltip le x in g ( O F D M ) h a s p la y e d an im p o rta n t ro le in w ir e le s s c o m m u n ic a tio n s y ste m s T a k i n g a d v a n ta g e fro m s c a tte rin g e n v i r o n m e n t a n d s p a tia l d iv e rsity , M I M O c o u ld in c re a s e w ire le s s lin ks s ig n if ic a n tly in b oth d a ta rate a n d reliab ility In th e o p tim a l c o n d i tio n w h e r e rich s c a tte r in g e n v i r o n m e n t a n d s ig n a l u n c o rre la te d a re a v a ila b le , th e c h a n n e l c a p a c ity c a n be i m p r o v e d lin e a rly w ith th e m i n im u m n u m b e r o f tra n s m it a n t e n n a s a n d r e c e iv e a n te n n a s U n fo r tu n a te ly , e v e n th o u g h in rich scatters, th e c h a n n e l m a tr ix c o u ld still b e ill-c o n d itio n e d This is k n o w n as key-h o le o r p in e - hole p h e n o m e n o n T h u s , c h a n n e l state in f o r m a tio n ( C S I ) is v a l u a b le in M I M O ch an n e l In a p c tic a l p o in t o f view', e n g in e e r s s h o u ld k n o w C S I o f a p a rtic u la r ch a n n e l to a p p ly M I M O te c h n iq u e s e f fe c tiv e ly T h is r a is e s r e q u i r e m e n t o f c h a n n e l m e a s u r e m e n t A te stb e d is o n e o f the m o s t c o m fo r t a b le a n d c o st e f f e c tiv e s o lu tio n s T h is th e s is p re s e n ts a d e s ig n an d im p le m e n ta t io n o f b o th R F s id e and B a s e b a n d s id e w h ic h g u a r a n t e e to b u ild a c o m p le te M I M O te s t b e d in in d o o r e n v iro n m e n t T h e c o m p le te d te s tb e d w o u ld s u p p o r t d u a l b a n d o f 2.4 G FIz a n d G H z a n d a n u m b e r o f m o d u l a ti o n ty p e s T h e R F p a rt is b u ilt b a s e d on IC M a x w h ic h is special IC for d io fre q u e n c y tr a n s m is s io n T h e o th e r p a rts o f th e te s tb e d a r e im p le m e n te d in th e X t r e m e D S P X ilin x V irte x -4 Kit A t th e tr a n s m itte r , d a ta s e q u e n c e is m u ltip lie d w ith d iffe re n t W a l s h c o d e s w h ic h are c o r r e s p o n d in g to tr a n s m it a n t e n n a s , b e fo re g o in g to m o d u l a to r a n d fre q u e n c y u p - c o n v e r te r T h e IF s ig n a l th e n g o e s to th e D A C to be c o n v e rte d in to a n a l o g a n d u p - c o n v e r t e d to c a r rie r f r e q u e n c y T h e re c e iv e r uses a c o r re la to r to d e te c t ch a n n e l c o e ffic ie n ts E a c h s ig n a l fro m a r e c e iv e a n te n n a w ill b e p a s s e d th r o u g h c o rre la to rs , ex in x te stb e d T h e s e c o r r e l a to r s h a v e W alsh c o d e s e q u e n c e s th e s a m e as in th e tra n s m itte r T h e re c e iv e d d a ta w ill th e n b e sen t to M a tla b to c o m p u te th e c h a n n e l m a tr ix to e s tim a te th e c h a n n e l c a p a c ity A B B R E V IA T IO N S ADC A n a lo g to D igital C o n v e r te r CSC C o n v e n tio n a l S ele ctio n C o m b in in g CSI C h a n n e l S tate In fo rm atio n DAC D ig ital to A n a lo g C o n v e rte r DSP D igital S ig n a l P ro c e s s in g EGC E q u a l G a in C o m b in in g FFT F ast F o u r ie r T n s f o r m FPGA F ield P r o g r a m m a b le G a te A rray s GSC G e n e liz e d S e le ctio n C o m b in in g IF In te r m e d ia te F re q u e n c y M IM O M u ltip le In p u t M u ltip le O u u t M ISO M u ltip le In p u t S in g le O u u t MRC M a x im u m R atio C o m b in g O F D M /A O r th o g o n a l F re q u e n c y D iv isio n M u ltip le x in g / A c c e ss RF R a d io F r e q u e n c y Rx R e c e iv e r SIM O S in g le Input M u ltip le O u u t SISO S in g le In p u t S in g le O u u t SNR S ig n al to N o is e R atio STBC S p a c e - l im e B lo c k C o d in g SVD S in g u la r V a lu e D e c o m p o s itio n Tx T r a n s m itte r LIST OF FIGURES F ig E q u iv a le n t c h a n n e l o f MI M O c h a n n e l th r o u g h S V D F ig A r c h ite c tu r e o f M I M O w ith S V D F ig L in e o f s ig h t S I M O an d L in e o f s ig h t M I S O c h a n n e l s F ig A g e n e l M I M O s y s te m w ith U l A s at both th e T x an d R x 12 F ig E i g e n v a lu e s for 3x3 M I M O s y s te m as a fun ctio n o f d e v ia tio n facto r in dB for p u r e I.O S c h a n n e l .14 F ig T h e c a p a c ity o f M I M O s y s te m 14 F ig T h e f u n c tio n f r ( Q , ) p lo te d as a fu n ctio n o f Q, for fixed L r = and d iffe re n t v a lu e s o f th e n u m b e r o f r e c e iv e a n te n n a n r 16 F ig A n in d o o r M I M O s c e n a r io c o m m u n ic a tin g th ro u g h a sm a ll h o le in the w a ll b e t w e e n tw o r o o m s 17 F ig V a ria tio n o f e ig e n v a lu e s w ith th e w id th o f th e ho le 18 F ig 10 C a p a c ity v e r s u s h o le s iz e d u e to s e le c tio n o f th ree a n d tw o re c e iv e a n te n n a u s in g n o r m - b a s e d in c re m e n ta l a lg o rith m 19 F ig 11 A c tu a l c a p a c ity loss fro m F ig u re 10 c o m p a r e d to th e u p p e r b o u n d L r in e q u a t io n (5 ) 20 F ig 12 T h e p h y s ic a l la y o u t b o a rd 23 F ig 13 A D C to F P G A I n te r fa c e .24 F ig 14 D A C I n t e r f a c e 25 F ig 15 Z B T S R A M In te rfa c e 26 F ig 16 X ilin x D S P B lo c k s e ts 28 F ig 17 H a r d w a r e C o - s im u l a ti o n 28 F ig 18 P r o je c t N a v i g a t o r 29 F ig 19 T h e T e s t b e d D ia g r a m 30 F ig 20: S tru c tu re o f R F IC M a x 31 F ig 21: B lo c k d ia g m o f R F t r a n s c e i v e r 32 F ig 22: D u a l- b a n d R F tr a n s c e iv e r m o d u l e 32 F ig 23: B a s e b a n d T r a n s m i tt e r D ia g m 33 F ig 24: D a ta G e n e r a t o r B lo c k 33 F ig 25: D ata, - le n g th W a lsh c o d e a n d C o d e d s i g n a l 34 F ig 26: B a s e b a n d S ig n a l, IF w a v e a n d IF s ig n a l 34 F ig 27: B a s e b a n d R e c e iv e r D ia g r a m 35 F ig 28: T i m in g s y n c h r o n i z a t i o n 36 F ig 29: C o r re la tio n B lo c k .36 F ig 30: C o r r e la tio n V a lu e .37 F ig 31: A b s o l u to r 37 F ig 32: M a x im u m s e l e c t o r .38 F ig 33 C o r r e la tio n sig n a l A b s o l u te sig n al an d M a x i m u m t i m e 38 F ig 34 S ig n al d e te c to r 38 F ig 35 S y n c h r o n iz a tio n D e te c tio n B lo c k 39 F ig 36: T r a n s m itte d d a ta C o r r e la tio n a n d R e c e iv e d F ig 37: R F c o n tro lle r in te r f a c e D a t a 40 42 F ig 38: S p e c tru m o f tr a n s m itt e d s ig n a l w ith cen tre f r e q u e n c y is at G H z 42 F ig 39: C o rr e la tio n R e c e iv e I m p l e m e n t a ti o n W a lsh c o d e a n d D a t a 43 F ig 40: B a s e b a n d s ig n al an d IF s ig n al 43 F ig 41: B a s e b a n d C o rr e la tio n R e c e iv e R e s u lts 43 F ig 42: Channel coefficients estimated vs S N R 4 F ig 43: BER o f Correlation Receiver for SISO 4 F ig 44: x M I M O M e a s u r e m e n t D ia g m 45 F ig 45: R e c o v e r e d D a ta in R X 46 F ig 46: R e c o v e r e d D a ta in R X 47 F ig 47: R X D a ta at A n te n n a w h e n D iffe re n t T X D a ta a re u s e d .47 F ig 48: C h a n n e l c o e f f ic ie n ts e s tim a te d o v e r S N R 48 CHAPTER INTRODUCTION T h e d e v e lo p m e n t o f services in c o m m u n ic a tio n s puts h eav y pressu re on w ireless co m m u n ic a tio n s , n ot only to e n h a n c e the quality o f service but also to increase the sp ectru m efficien cy o f c o m m u n ic a tio n links T h ere h ave been several solutions p ro p o se d an d d evelo ped T h e m u ltip le input- m ultiple o u u t (M IM O ) technique is one o f the m o st p ro m isin g solution s for the next generation w irele ss com m u nicatio ns w h ich ben efits from m u lti-p a th p rop ag ation By splitting a general data stream into several sm all, uncorrelated parallel ones, a M I M O system can achieve significant en h a n c e m e n t in cap acity as w ell as reliability T h e perfo rm a n ce o f a M IM O system dep en d s greatly on h o w m a n y s u b -stre a m s it has and ho w c o rrelated the sub-stream s are In general, the M I M O channel is d e te rm in e d by m a n y p aram eters such as reflection, scattering, sh a d o w in g , an te n n a sep aration, and angle o f arrival w aves U n fortu nately, a given M I M O sy stem is best suited only to the set o f propagation p aram eters it is d esig n ed for This stro n g ly requires us to k n o w th ese p aram eters well before d esig n in g an individual M IM O system , as well as a p p ly in g algorithm s There have been a n u m b e r o f m o d els for sim u latin g M IM O channels H ow ev er, those M IM O m o d els c a n n o t app ly to all situations H e n c e the b est w ay to k n o w accurately about the M I M O chann el is to m e a su re it in real co n d itio n s by using a M I M O testbed That is w h y the a u th o r c h o o se s the design o f a M I M O testbed as the topic for his M asters thesis In general, m u lti-p ath is hostile to w ireless p rop ag ation that results in fading in the received signal In co ntrast, M IM O m ak es u se o f m ultip ath pro pagatio n to im prove its data rate In addition, the use o f m u ltip le an ten n as at both tran sm itter and receiver d ep lo y s c o n sid era b le spatial diversity R ecently , M IM O co m b in e d w ith O F D M te ch n iq u e p ro m ise s a p oten tial solution for 3G and the next generation w ireless co m m u n ica tio n s M I M O ch annel c ap a city d ep en d s m ainly on the statistical properties o f the ch ann el and on the a n ten n as correlation A n te n n a correlation varies significantly as a fun ctio n o f the scatterin g co n d itio n , the tran sm issio n distance, the antenna structures an d the D o p p le r spread A s w e shall see, th e effect o f an te n n a correlation on capacity d e p e n d s on the c h a n n e l’s ch aracteristics at the transm itter a n d receiver A dditionally, c h an n e ls w ith very low co rrelatio n b etw ee n antennas can still exhibit a “k ey h o le” effect w h e re the ch an n e l m atrix 's rank is deficient, leading to loss o f capacity gains 3S Figure 32: Maximum selector Figure 33 Correlation signal (a) Absolute signal (b) and Maximum time (c) 3.3.3.4 Signal Detection Block Figure 34 Signal detector To en su re the correlatio n block and the follow ing blocks w ork correctly in the receiver, a signal d etection blo ck is added before so m e im p o rtan t m odules Figure 34 sh o w s the prin cip le o f h o w it w orks R eceived sam ples and their reversed values are subtracted If there is not signal on the line, this subtraction is equal to zero, hence p u tting ou tpu t to zero O n the other hand, output o f subtraction block is com p ared to a th resh o ld to m a k e sure there is signal rather than noise T he outpu t is set to high w hich enables the d e te c to r ’s fo llo w in g block 39 3.3.3.5 Synchronization Detector d> In ►cast]-► Wb a>b D4 r* C2 CD— a en ► In w b b A R1 a>b z - C3 R2 not Detect signaM Inv e r te r D5 sel dO en out cast ,- Lr* d1 a>b Ct2 Mux 30 ■ R3 C4 Figure 35 Synchronization Detection Block This is the m o st im portant block in this desig n (figure sy n chron ization T h ere are two signals entering into this block: 35) It senses one from the correlation calcu la to r and the other from the m a x im u m block T h ere usually are two s y n c h ro n iza tio n m e c h a n ism s w hich are im m ediately s y n c h ro n iza tio n and pilot-insert sy n ch ro n iza tio n In this schem e, we use the later m ethod In its operating, the block h o lds the m a x im u m valu es and the indexes o f signal and cou nts them I f the s u m m a tio n reaches a certain num ber, say synchronization num ber , w hich is d e term in e d p ractically, the block will fix the tim e sa m p lin g and control sam p ler to get data T h e data will b e stored in a buffer before going to be used in M atlab to calculate the channel m atrix w h ic h will be used to co m pute the channel capacity 40 Figure 36: Transmitted data (a) Correlation (b) and Received Data (c) Figure 36 sh o w s the sim u lation results for the receiv e correlation T h ere are 20 pilots inserted in a 1000 data frame W e see that after a short tim e, o u r design is settled into stability T h e rece iv ed data is exactly the sam e as the tra n sm itte d one The length o f pilot can be ad ju sted a c c o rd in g to the real operating co n d itio n to get the m axim um efficiency in chan nel estim ation 41 C H A PTER IM PLEM ENTIO N RESULTS OF THE MIMO TESTBED 4.1 R F I m p le m e n tio n R esults A fter d e s ig n in g a n d functional testing in Protel softw are, we outline and im p lem en t the RF m o d u le as in figure 21 The m od ule can w ork in both Tx m o de and Rx m o d e in dual b an d 2.45 G H z and 5G H z In Tx m o de, w e n eed to set T X E N A bit to high, R X E N A bit to low B aseb an d signal is passed into IC M ax 4447 w hich converts n o n -s y m m e tric into s y m m e tric signal M ax 4447 can supp ort small signal up to 50 m V T h e sign al is then p a s s e d into 0.1 uF capacity to avoid D C before m odulated to G H z carrier In the R x m o d e , T X E N A is set to low w hile R X E N A is set to high IC M ax 4 4 is e m p lo y e d to convert sym m etric b aseband signal into no n-sy m m etric signal W e can also control other param eters such as: transm it pow er, bandw idth, referen ce fre q u e n c y , etc, by M a x softw are (figure 37) through 3-serial lines This softw are is c o n n e c te d to RF m odule through LPT cable O n ce all param eters are satisfactorily set, p ress sen d a ll button to figure the kit F ig u re 38 sh o w s the sp ectru m o f transm itted signal on the n etw o rk analyzer w hen 1M H z b a s e b a n d signal is used The signal level is ab o u t d B m in com p arison to noise, o r a b s o lu te level is at -20dB m T he m a x im u m p o w e r can be -I ld B m if w e set the m o d u le at the h ig h e st p o w e r mode 42 Exit Options [ Entry Program Help Reg Control Pins fT SHDNB j ff RXHP RX LNA Gain 802 11G (Max RF Frequency ! F B7 | F B6 [l B5 F 03 F B2 F B1 ! F _!_I J Standby Mode Ertables RX VGA Gain MHz |3 ~ jl! T ra_nsmitterMode ; Normal