Multiuser Detection in CDMA A. Chockalingam Assistant Professor Indian Institute of Science, Bangalore-12

Dr. A. ChockalingamDept of ECE, IISc, Bangalore2 Outline u Near-Far Effect in CDMA u CDMA System Model u Conventional MF Detector u Optimum Multiuser Detector u Sub-optimum Multiuser Detectors –Linear Detectors »MMSE, Decorrelator –Nonlinear Detectors »Subtractive Interference cancellers (SIC, PIC) »Decision Feedback Detectors

Dr. A. ChockalingamDept of ECE, IISc, Bangalore3 DS-CDMA u Efficient means of sharing a given RF spectrum by different users u User data is spread by a PN code before transmission u Base station Rx distinguishes different users based on different PN codes assigned to them u All CDMA users simultaneously can occupy the entire spectrum »So system is Interference limited

Dr. A. ChockalingamDept of ECE, IISc, Bangalore4 DS-SS u DS-SS signal is obtained by multiplying the information bits with a wideband PN signal Information Bits PN Signal Carrier Modulation Information Bits PN Signal t t TbTb TcTc T b = N T c N : Processing Gain

Dr. A. ChockalingamDept of ECE, IISc, Bangalore5 Processing Gain u Ratio of RF BW (W) to information rate (R) ( e.g., In IS-95A, W = 1.25 MHz, R = 9.6 Kbps i.e., ) u System Capacity (K) proportional to (voice activity gain) (sectorization gain) (other cell interference loss ) (typically required)

Dr. A. ChockalingamDept of ECE, IISc, Bangalore6 Near-Far Effect in DS-CDMA u Assume users in the system. u Let be the average Rx power of each signal. u Model interference from users as AWGN. u SNR at the desired user is u Let one user is near to BS establishes a stronger Rx signal equal to Rx signal equal to u SNR then becomes u When is large, SNR degrades drastically. u To maintain same SNR, has to be reduced u i.e., loss in capacity.

Dr. A. ChockalingamDept of ECE, IISc, Bangalore7 Near-Far Effect u Factors causing near-far effect (unequal Rx Signal powers from different users) in cellular CDMA –Distance loss –Shadow loss –Multipath fading (Most detrimental. Dynamic range of fade power variations: about 60 dB) u Two common approaches to combat near-far effect –Transmit Power Control –Near-far Resistant Multiuser Detectors

Dr. A. ChockalingamDept of ECE, IISc, Bangalore8 CDMA System Model Data of User 1 Spreading Sequence of user 1 Chip shaping filter Data of User 1 Spreading Sequence of user 2 Chip shaping filter Data of User 1 Spreading Sequence of user K Chip shaping filter AWGN ToDemod/Detector

Dr. A. ChockalingamDept of ECE, IISc, Bangalore9 Matched Filter Detector (MFD) MF User 1 MF User 2 MF User K Correlation Matrix

Dr. A. ChockalingamDept of ECE, IISc, Bangalore10 MFD Performance: Near-Far Scenario E/b/No (dB) Bit Error Rate NFR = 0 dB NFR = 5 dB NFR = 10 dB NFR = 20 dB 2-User system: Problem with MF Detector: Treats other user interference Problem with MF Detector: Treats other user interference (MAI) as merely noise (MAI) as merely noise But MAI has a structure which can be exploited in the But MAI has a structure which can be exploited in the detection process detection process

Dr. A. ChockalingamDept of ECE, IISc, Bangalore11 Optimum Multiuser Detector u Jointly detect all users data bits u Optimum Multiuser Detector –Maximum Likelihood Sequence Detector u Selects the mostly likely sequences of data bits given the observations u Needs knowledge of side information such as –received powers of all users –relative delays of all users –spreading sequences of all users

Dr. A. ChockalingamDept of ECE, IISc, Bangalore12 Optimum Multiuser Detector u Optimum ML detector computes the likelihood fn and selects the sequence that minimizes u The above function can be expressed in the form where and is the correlation matrix with elements where

Dr. A. ChockalingamDept of ECE, IISc, Bangalore13 Optimum Multiuser Detector u results in choices of the bits of the users u Thus Optimum Multiuser Detector is highly complex –complexity grows exponentially with number of users –Impractical even for moderate number of users u Need to know the received signal energies of all the users

Dr. A. ChockalingamDept of ECE, IISc, Bangalore14 Suboptimum Detectors u Prefer –Better near-far resistance than Matched Filter Detector –Lesser complexity (linear complexity) than Optimum Detector Detector u Linear suboptimum detectors –Decorrelating detector –MMSE detector

Dr. A. ChockalingamDept of ECE, IISc, Bangalore15 Decorrelating Detector Linear Transformation and Detector Decision For the case of 2 users and

Dr. A. ChockalingamDept of ECE, IISc, Bangalore16 Decorrelating Detector u For the case of 2 users and – operation has completely eliminated MAI components at the output (.e., no NF effect) – Noise got enhanced (variance increased by a factor of )

Dr. A. ChockalingamDept of ECE, IISc, Bangalore17 Decorrelating Detector u Alternate representation of Decorrelating detector – By correlating the received signal with the modified signature waveforms, the MAI is tuned out (decorrelated) –Hence the name decorrelating detector

Dr. A. ChockalingamDept of ECE, IISc, Bangalore18 MMSE Detector Linear Transformation and Detector Decision Choose the linear transformation that minimizes the mean square error between the MF outputs and the transmitted data vector

Dr. A. ChockalingamDept of ECE, IISc, Bangalore19 MMSE Detector Choose the linear transformation where is determined so as to minimize the mean square error (MSE) Optimum choice of that minimizes is

Dr. A. ChockalingamDept of ECE, IISc, Bangalore20 MMSE Detector Linear Transformation and Detector Decision When is small compared to the diagonal elements of MMSE performance approaches Decorrelating detector performance When is large becomes (i.e., AWGN becomes dominant)

Dr. A. ChockalingamDept of ECE, IISc, Bangalore21 Adaptive MMSE u Several adaptation algorithms –LMS –RLS u Blind techniques LinearTransversalFilter AdaptiveAlgorithm Re() Estimate of the data bits Training bits

Dr. A. ChockalingamDept of ECE, IISc, Bangalore22 Performance Measures u Bit Error Rate u Asymptotic efficiency: Ratio of SNRs with and without interference represents loss due to multiuser interference u Asymptotic efficiency easy to compute than BER

Dr. A. ChockalingamDept of ECE, IISc, Bangalore23 Performance Measures MMSE Optimum Detector DC MF Detector

Dr. A. ChockalingamDept of ECE, IISc, Bangalore24 Subtractive Interference Cancellation u Multistage interference Cancellation approaches –Serial (or successive) Interference Canceller (SIC) »sequentially recovers users (recover one user per stage) »data estimate in each stage is used to regenerate the interfering signal which is then subtracted from the original received signal »Detects and removes the strongest user first –Parallel Interference Canceller (PIC) »Similar to SIC except that cancellations are done in parallel

Dr. A. ChockalingamDept of ECE, IISc, Bangalore25 SIC MatchedFilterRemodulate & Cancel MFDetector Remodulate Stage-1 Stage-m MFDetector

Dr. A. ChockalingamDept of ECE, IISc, Bangalore26 m-th Stage in SIC MF User m MF User K SelectStrongestUser MF Detector Remodulate & Cancel

Dr. A. ChockalingamDept of ECE, IISc, Bangalore27 Performance of SIC u Good near-far resistance u Most performance gain in achieved using just 2 to 3 stages u High NFR can result in good performance! –Provided accurate estimates of amptitude and timing are available u Sensitive to amplitude and timing estimation errors –increased loss in performance for amplitude estimation errors > 20 % u Some amount of power control may be required to compensate for the near-far resistance loss due to imperfect estimates and low NFR

Dr. A. ChockalingamDept of ECE, IISc, Bangalore28 PIC MF User 1 MF User K Stage 1 Stage j

Dr. A. ChockalingamDept of ECE, IISc, Bangalore29 Performance of PIC u Good near-far resistance u Similar performance observations as in SIC u Performance of PIC depends more heavily on the relative amplitude levels than on the cross-correlations of the user spreading codes u Hybrid SIC/PIC architectures

Dr. A. ChockalingamDept of ECE, IISc, Bangalore30 DFE Detector MF User 1 MF User K FFF FFF CentralizedDecisionFeedback Feedback current data decisions of the stronger users as well Feedback current data decisions of the stronger users as well DFE multiuser detectors outperform linear adaptive receivers Complexity, error propagation issues Complexity, error propagation issues