MUD for interference cancellation - Aalto · MUD - Limitations • Existence of other-cell MAI •...

transcript

1 © NOKIA F ILENAMs.PPT / DAT E / NN

MUD for interference cancellation

February 26, 2002

Yang.yongzhao@ nokia.com

Contents• Multiple access interference (MAI)

• Conventional detection – S ingle user detection

• Conventional detectors

• Multiuser detection (MUD)

• MUD Detectors

• Interference cancellation (IC)

• Combined MUD for interferene cancellation

• S ubtractive IC detetors

• Reference

Multi access interference (MAI)

• A factor to limit the capacity and performance of DS -CDMAsystems

• MAI comes from the random time offsets between s ignals ,which results in the imposs ibility to des ign comletelyorthogonal code waveforms

• Conventional detector does not take the MAI exis tence intoaccount – s ingle user detection, (not take other users intoaccount)

• Better s trategy is one of multiuser detection to gain moreadditional benefits for DS -CDMA

• Multiuser detection also referred to joint detection orinterference cancellation

• MAI can incur the near-far problem

Conventional detection - Model

• Assume• S ynchronous channel (bits aligned in time)• All carrier phases are equal to zero• S ingle trans imittion path (no multipath) - baseband analys is• BPS K modulation

• Baseband received s ignal (K users)

• where, Ak(t), gk(t), and dk(t) are amplitude, s ignature code waveform andmodulation of the kth user. n(t) is the additive white Gauss ian noise (AWGN)

)()()()()(1

tntdtgtAtr kk

kk += ∑

Conventional detection – Detectors (1)

• A bank of K correlators (Next s lide)

• Each code is regenerated and correlated with the receiveds ignal in a separate detector branch

• Conventional detector can be implemented with matched filter⇒ matched filter detector

• T he output s ignal of correlators can be sampled at bit times –S oft decis ion of transmitted s ignal

• F inal ”+1/-1” can be ”hard” decided according to the s igns ofsoft estimation

• Data of each user can be detected by one branch withoutregard to the exis tence of other users – No sharing of multiuserinformation or joint s ignal process ing

Conventional detection – Detectors (2)

Conventional detection – Detectors (3)• T he success of detection depends on the properties of the

correlation of codes• Require: autocorrelation >> cross-correlation• Correlation value:

• here, if i=k, ρk,k=1, if i≠k, 0≤ρk,k<1.

• T he output of kth user’s correlator for a particular bit interval is

•• where Akdk is the recovered term, MAIk is the cross-correlation term and zk

is the noise term

• MAI can be important if number of users is increased

∫=bT

ki dttgtgT

kkkkk zMAIdAy ++=

Conventional detection – Mitigating MAI• Mitigating MAI with conventional detectors have focused on:

• Code waveform des ign• For obtaining S pread codes with good cross-correlation performance –

orthogonality

• Power control• It is indispensable for a successful DS -CDMA system• All users arrive to BT S at about the same power – fairness• MS transmits power inversely to the received power from BT S (open loop)• PC command from BT S to MS , based on received power from MS (Closed

• FEC codes• Powerful FEC allows the correction the error s ignal with lower S NR level

• S ectored/Adaptive antennas• S ome MAIs can be rejected out of main beam of directed antennas• Adaptive s ignal process ing can be used to direct the antenna beam to a

des ired user

Multiuser detection (MUD)

• Code and timing of information of multiuser are jointly used tobetter detect each individual user

• Important assumpton is codes of multiuser is known by thereceiver

• Multiuser detectors are divided two catagories• Linear MUD detectors

• A linear mapping is used to the conventional detector to produce a new set of output,which hoppfully provide better performance

• S ubtractive interference cancellation detectors• Estimition of interference are generated and subtracted out

MUD - L imitations

• Exis tence of other-cell MAI• In DS -CDMA, same UL/DL frequencies are used in each cell• A s ignal transmitted from a cell can cause interference to other cells• A system without MUD, the total interference (ignore the background

noise) is• I=IMAI+fIMAI , where IMAI is from the same-cell users , and fIMAI is the from

other-cell users , f is the spillover ratio (= other-cell MAI/same-cell MAI)• T he maximum capacity gain factor is (1+f)/f. (IMAI can be eliminated

(ideal system), left fIMAI )

• Difficulty in implementing MUD on the downlink• Due to the limitations of s ize, weight and price of ME , it is not practice to

implemented MUD in mobile.• MUD is required to be implemented in BS in any case• However improving capacity jus t in UL does not improve the overall

capacity of the system

MUD – Potential B enefits• S ignificant improvement in Capacity

• T he improvement is s till important even it is bounded• Bound can be improved by including surrounding-cell s ignals in MUD algorithm• S atellite communiton has much smaller spillover ratio• DS -CDMA is cons idered to be uplink-limited even MUD does not be

implemented in DL.

• More efficient UL spectrum utilization• UL improvement allows MS to operate at a lower process ing gain• A smaller bandwidth is required for UL• Extra band can be used for UL high data rate or for use by DL

• Reduced precis ion requirements for Power Control• MAI reduced, and near-far effect is reduced, S o, PC requirement of exactly

same power at BS is reliefed• Additonal complexity of BS may allow reduced complexity in mobiles

• More efficient power utilization• Interference reduction in UL can be trans lated to some reduction of mobile

transmitted power• S ame mobile power can be used to extend the s ize of coverage

MUD – S ys tem model• One bit output of one user

• where, A is a diagonal matrix conatining the received amplitudes of K users ,d is ”+1” or ”-1”, R is a KxK symmtric correlation matrix, which can be breakup into two matrices R=I+Q, I is the identity matrix (autocorrelation), Qcontains the off-diagonal elements (cross-correlation), z is the backgroundnoise

• Asynchronous channel• T he bits are overlaped between different intervals• T he detection must optimally be framed over the whole message• R contains partial correlations of NK code words (partial overlapping)• If N (bits ) is much greater tha K (users), then the correlation matrix ia

sparse because some of bits are not overlapping at all• T he received s ignal in a asynchronous channel is

• where τk is the delay of kth user

zQAdAdzRAdy ++=+=

)()()()()(1

tntdtgtAtr k

kkkkk +−−= ∑

Maximum likelihood sequence detection

• T his method is NOT suitable for MUD

• Complexity will be increased in the number of users

• MLS detector requires knowledge of the phase andamplitude of received s ignals

• S uboptimal detectors are s impler to implement

MUD – L inear Detectors

• A linear mapping L is applied to the soft decis ion of theconventional detector to reduce the MAI seen by eachuser

• Decorrelating detectors

• Minimum mean-squred error (MMS E) detectors

• Polynomial expans ion (PE ) detectors

L inear Detectors - Decorrelatingdetectors

• Use the inverse of correlation matrix to the output ofconventional detector output to decouple the data, L=R -1

• Received most attention of any MUD detectors in the literature• Advantages

• Provides performance/capacity gains over conventional detector• Does not need to estimate received ampltitudes• Computaional complexity is much lower than that of MLS detector

• Other des irable features of decorrelation detectors• Joint MLS estimation of transmitted bits and received amplitudes• Has a probability of error independent of the s ignal energies• Yields the optimal value of the near-far res is tance performance matrix• Can decorrelate one bit at a time

• Disadvantages• Cause noise enhancement compared to conventional detector• Difficult to compute the inverse matrix of R in real time• Computational burden s till can be transferred to implementation

difficulties

Minimum mean-squred error (MMS E )detectors

• MMS E detector takes the background noise into account,and utilizes the knowledge of the received s ignals ,L=[R+(No/2)A-2]-1

• Implements a partial or modified inverse of correlationmatrix: which is directly proportional to the backgroundnoise (the bigger noise, the less complete an invers ion of R nca bedone without noise enhancement)

• When noise goes to zero, the MMS E detector converges inperformance to a decorrelating detector

• Disadvantages• Requires estimation of s ignal amplitudes• Performance depends on the powers of the interfering users• Loss of res is tance to near-far problem compared to decorrelating

detector• Implementing matrix invers ion

Polynomial expans ion (PE ) detector (1)• Apply a polynomial epans ion in R to the matched filter bank output y

• For a given R and Ns (number of s tages), weight wi, i∈ [0, Ns] can be chosento optimize the performance

• F igure 3: implementation of R

• F igure 4: full detector with two s tage

• S oft decis ion is given by

• Be used to approximate the decorrelating detector:• S elect a suitable w set, so that, p(R)=R -i, the approxaite good

performance (=decorrlating detector) can be finished with small numberof s tages

ii RwL

PE detector

S ingle s tage T wo stage

Polynomial expans ion (PE ) detector (2)

• Also can be used to approximate the MMS Edetector

• A number of attractive features :• Can approximate the decorrelating and MMS E detectors• Has low computational complexity• No need to estimate the received amplitude• Can be implemented just as eas ily us ing long code as

short code• Can use wight that work well over a large variation of

system parameters• Has a relatively s imple structure

S ubtractive interference cancellation - 1

• Another important group of detectors can be class ifies assubtractive interference cancellation detectors .

• T he bas ic principle of these detectors is the each user’scontribution is separate estimated at the receiver, in orderto subtract out some or all of the MAI seen by each user.

• With multiple s tage implementation, the decis ion of outputof each stage is improved.

• T hese detector feed back the previous ly detected symbolsin order to cancel part of MAI, also referred as decis ionfeedback detectors .

S ubtractive interference cancellation - 2

• Bit decisons used to estimate the MAI can be hard or soft.

• S oft decis ion: soft data estimates for joint estimation of thedata and amplitude, eásy to implement.

• Hard decis ion: feed back a bit decis ion and is nonlinear. needamplitude estimtion is a s ignaificant liability. if amplitudeestimate is reliable, HD performs better than S D.

• S uccess ive interference cancellation• Parallel interference cancellation• Zero-Forcing decis ion-feedback detectors

IC – S uccess ive Interference Cancellation(S IC)

• S uccess ive interference cancellation detetor takes a serialapproach to canceling interference.

• Each stage of this detector decisons, regenerates, and cancelsout one additional direct-sequence user from the receiveds ignal, so that the remaining users see less MAI in the nextstage.

• With minimal amount of additional hardware will has potentialto provide s ignificant improvement over the conventionaldetector.

• Important disadvantage:• One additional bit delay is required per s tage of cancellation. More users

cancellation will need more delay.• When the power profile is changed, the s ignal need to be reordered. too

complex.• If the initial data estimation is not reliable, then the interference

amplitude can be double (power quadrupled). S o, it is crucial that thedata estimation of the s trongest user that are cancelaed firs t at least be

S IC detector s tructure

Precedes byan operationwhich ranksthe s ignalsindecendingoreder ofreceivedpowers .

S IC detector function of the firs t s tage

• A s implified diagraph of the firs t s tage of S IC detector isshown in last s lide. Hard-decis ion approach is assumed.

• F irs t s tage implements the following steps:• Detect with the conventional detector the strongest s ignal, s1• Make a hard data decis ion on s1• Regenerate an estimate of the received s ignal for user one, s1(t),

us ing• Data decis ion from step 2• Knowledge of its PN sequence• Estimates of its timing and amplitude (and phase)

• Cancel (subtract out) s1(t) from total receiveed s ignal, r(t), yielding apartially cleaned vers ion of the received s ignal r(1)(t)

• If the estimation is accurate in s tep 3, then the outputs of the firs ts tage are• A data decis ion on the s trongest user• A modified received s ignal without the MAI caused by the strongest user

• T his process can be repeated in a multis tage s tructure.

IC – Parallel Interference Cancellation(PIC)

• Namely, PIC detector estimates and subtracts out all of theMAI for all users in parallel.

• A numbe rof s tudies have investigated PIC detection whichutilized soft decis ions .

• It is found that soft-decis ion PIC is superior in a well-power-controlled channel.

• On the other hand, soft-decis ion S IC is superior in a non-power-controlled channel (in a fading environment). (S ICexploits power variants for IC)

Parallel Interference Cancellation (PIC)

• PIC is a s imple and effective method of parameter estimationin the presence of MAI (Multiple Access Interference)

• PIC is suitable for estimating the time variant channelparameters

• PIC algorithm depends heavily on the quality of MAI estimation

• MAI is formed by us ing channel coefficients , data and delayestimates of all users .

• PIC algorithm may be a recurs ive process

• PIC and MAI are interacted.

• PIC is used for• Data detection• Complex channel coefficients estimation• Delay tracking

PIC detector s tructure

S tage 0:

Dataestimatesaredevidedfrommatchedfilterdetector

PIC detector – firs t s tage

• S ee last s lide.

• A hard-decis ion is asumed.

• Initial bit estimates are scaled by the amplitude estimates andrepread by the codes, which produces a delayed estimate ofthe received s ignal for each user, s k(t-tb)

• T he partial summer sums up all but one input s ignal at each ofthe outputs , which creats the complete MAI estimate for eachuser.

• T his process an be repeated for multiple s tages.

PIC detector improvements

• Us ing the decorrelating detector as the firs t s tage

• Us ing the already detected bits at the output of thecurrent s tage to improve detection of the remainingbits in the same stage

• Linearly combining the soft-decis ion outputs ofdifferent s tages of the PIC detector

• Doing a partial MAI cancellation at each stage, withthe amount of cancellation inreas ing for eachsuccess ive s tage.

IC – Zero-Forcing Decis ion-Feedback (ZF-DF)Detectors

• T he zero-forcing decis ion-feedback detector performs twooperation: linear pre-process ing followed by a form of S ICdetection.• T he linear operation partially decorralteds the users without noise

enhancing, (enables powerful S IC operation), and• T he S IC operation decis ions and subtracts out the interference from

one additional user at a time, in decending oreder of s ignal s trength.

• ZF -DF detector is based on a white noise channel model.

• where, F ia lower triangualr matrix, and zw is the noise term.

• T he data bits are partially decorraleted due to the fact that Fis lower triangular.

• T he output for bit one of the firs t user cantains no MAI; theoutput for bit one of the second user contains MAI only frombit one of firs t user, and is completely decorrelated from allother users .

ww zFAdy +=

ZF-DF detector

• ZF -Df detector uses S IC to exloit the partial decorrelation ofthe bits in the white-noise model.

• T he soft output of bit one of the firs t user, which is completelyfree of MAI, is used to regenerate and cancel out the MAI itcauses.

• T hereby leaving the soft output of bit one of the second useralso free of MAI (decorrelated)

• T his process continues, for each iteration, the MAI contribuedby an additional bit (the previous ly decorrelated bit) isregenerated and canceled, yielding one additional decorrelatedbit.

• Prior to create the white noise model, users are orederedaccroding to their s ignal s trength in order that IC takes place indescending order of s ignal s trength. -> maximize the gain ofS IC detection

ZF-DF detector s tructure

ZF-DF detector

• S ee last s lide – the ZF -DF s tructure.

• Under the assumption that all past decis ions are correct, theZF-DF detector eliminates all MAI and maximizes the S NR.

• An important difficulty whith ZF -DF is the need to compute theCholesky decompos ition and the whitening filter.

• Disadvantage: need to estimate the s ignal amplitude like othernonlinear detectors . If the amplitude estimate is not reliable,ZF -DF detector performs worse than the decorrelatingdetector.

Comparison

Inverseofcorrelation matrix

Matchedfilter bankoutputs

Decorrelating detector

MMS E detectormodified

PEdetector

polynomial expans ion in correlation matrix +

S IC: decis ion, regenerate, cancel a user at a time

PIC: decis ion, regenerate, cancel all user at the same time

ZF-DF :

Better in Fading environmentFew hardwarePower reorderLarge delay

Better in PC channelMore hardware needed

Cholesky facterization and matrixinvers ion

L inear MUD detectors

S ubtractive IC detectors

R eference[1] Multiuser detection for DS -CDMA communications, S himon Moshavi, Bellcore, IEEE

Communications Magzine, October 1996, pp124-136

[2] Parallel interference cancellation receiver for DS -CDMA systems in Fading channels , MattiLatva-aho, etc, IEEE 1997, pp559-564

[3] Parallel interference cancellation in multiuser detection, Matti Latva-aho, Jorma Lilleberg, IEEE1996, pp1151-1155

[4] Evaluation of a DS -CDMA multiuser receiver employing a hybrid form of interferencecanellation in Rayleigh-Fading channels , Dimitris Koulakiotis and A. Hamid Aghvami, IEEECommunication Letters , Vol.2, No.3, March 1998, pp61-63

[5] Overview of multiuser detection/interference cancellation for DS-CDMA, Tero Ojanperä, IEEEICPWC’97, pp115-119

[6] Comparative s tudy of interference cancellation schemes in multiuer detection, DimitrisKoulakiotis and A. Hamid Aghvami, IEE , S avoy Place, London, WC2R 0BL, UK, 1997, pp10/1-10/7

[7] L inear oarallel interfewrence cancellation in long-code CDMA multiuser detection, DongNingGuo, etc. IEEE 1999 Journal on selecte areas in Communications , Vol.17, No.12, December1999, pp2074-2081

[8] Performance analys is of a DS S S CDMA communication sys tem with multiuser interferencecancellation in a R icing fading channel, Ramjee Prasad, and etc, IEEE 1996, pp604,-608

[9] Blind multiuser detection and interference cancellation in DS -CDMA mobile radio sys tems,S amir Kapoor, etc, IEEE 1997, pp269-272

MUD for interference cancellation - Aalto · MUD - Limitations • Existence of other-cell MAI •...

Documents