Iterative multiuser detection for ultra wideband systems

ITERATIVE MULTIUSER DETECTION FOR ULTRA-WIDEBAND SYSTEMS WANG XIAOLI NATIONAL UNIVERSITY OF SINGAPORE 2004 ITERATIVE MULTIUSER DETECTION FOR ULTRA-WIDEBAND SYSTEMS WANG XIAOLI (B.Eng University of Electronic Science & Technology of China) A THESIS SUBMITTED FOR THE DEGREE OF MASTER OF ENGINEERING DEPARTMENT OF ELECTRICAL & COMPUTER ENGINEERING NATIONAL UNIVERSITY OF SINGAPORE 2004 Acknowledgements I would like to express my sincere appreciate to my supervisors, Prof Ko Chi Chung and Dr Huang Lei, for their invaluable guidance, advice, encouragement, and patience throughout my research work and this thesis Special thanks to my parents and my boyfriend, who always love and care for me It’s their encouragement and support made me pull through all the difficulties I also want to thanks all the students and staffs from Communications Lab in Department of Electrical & Computer Engineering Their friendship made my life colorful and meaningful Last but not least, I’m grateful for National University of Singapore for giving me the opportunity to pursue my postgraduate study iii Contents Acknowledgements i Contents ii List of Figures iv List of Tables vi Abbreviations vii Summary ix Chapter Introduction 1.1 Introduction to UWB 1.2 UWB Technology 1.2.1 Technology Considerations 1.2.2 Advantages and Disadvantages 1.3 UWB Signal Model 1.3.1 Monocycle 1.3.2 Time-Hopping 1.3.3 Modulation 10 1.4 UWB Channel Modeling 11 1.5 Organization of the Thesis 13 Chapter 2.1 Multiuser Detection for UWB Systems 15 Advanced Rake Receivers 15 2.1.1 ARake, SRake and PRake 16 2.1.2 Rake MMSE 17 2.2 Optimum Multiuser Detection 20 2.3 Adaptive MMSE Multiuser Detection 22 iv 2.4 Iterative Interference Cancellation & Decoding 24 2.5 Summary 27 Chapter Iterative Multiuser Detection for UWB Systems 28 3.1 System Model 28 3.2 Iterative Multiuser Detection 33 3.3 Simulation Results and Discussions 36 3.4 Summary 40 Chapter Low-Complexity Iterative Multiuser Detection for Space-Time Coded UWB Systems 41 4.1 Introduction 41 4.2 System Model 43 4.3 Iterative Multiuser Detection 47 4.4 Simulation Results and Discussions 50 4.5 Summary 52 Chapter Conclusions and Future Work 53 5.1 Conclusions 53 5.2 Future Work 54 References … 56 Published papers by the Author 60 v List of Figures 1.1 Coverage range of wireless communications networks 1.2 UWB spectrum allocation 1.3 Spatial capacity comparisons of IEEE802.11, Bluetooth, and UWB 1.4 Application and protocal layers for UWB 1.5 The Scholtz’s monocycle waveform and spectrum 1.6 Typical channel response of CM1 12 1.7 Typical channel response of CM2 12 1.8 Typical channel response of CM3 12 1.9 Typical channel response of CM4 13 2.1 The BEP for the ARake, SRake and PRake (taken from [11]) 17 2.2 Receiver structure comparison: a) Rake MRC; b) Rake MMSE 18 2.3 BER performance comparison (taken from [13]) 19 2.4 BER performance comparison with SIR=-30 dB (taken from [13]) 19 2.5 SER comparison: optimum MUD vs SUD (taken from [14]) 21 2.6 BER in the presence of 15 interfering users (taken from [16]) 22 2.7 BER in the presence of 15 users and one interferer (taken from [16]) 23 2.8 Block diagram of the iterative interference cancellation receiver 25 2.9 BER versus SNR with active users (taken from [17]) 26 2.10 BER versus SNR with active users (taken from [17]) 27 3.1 The general receiver structure 31 3.2 The received sequence model in the detection window 31 3.3 The MSE corresponding to the number of iterations 37 vi 3.4 BER performance of the iterative MUD with 10 active users 38 3.5 BER performance of the iterative MUD with 30 active users 39 4.1 The general structure of the ST-coded UWB system 45 4.2 BER comparison for a 10-user ST coded UWB system 51 4.3 BER comparison for a 20-user ST coded UWB system 51 4.4 BER comparison for a 30-user ST coded UWB system 52 vii List of Tables 1.1 Advantages, disadvantages, and applications of UWB properties 1.2 UWB modulation options .11 viii Abbreviations 2G second-generation 3G third-generation ARake all-Rake AWGN additive white Gaussian noise BER bit error rate BPAM binary phase amplitude modulation BPPM binary pulse position modulation CDMA code division multiple access CM channel model DS direct-sequence FCC Federal Communications Commission GSM global system for mobile communications IFI inter-frame interference IR impulse radio ISI inter-symbol interference LMS least mean square LOS line of sight MAC medium access control MAI multiple access interference MAP maximum a posteriori MF matched filter ML maximum-likelihood MMSE minimum mean squared error MOE minimum output energy MRC maximum ratio combining MSE mean squared error ix MUD multiuser detection NLOS none line of sight OFDM orthogonal frequency division multiplexing OOK on-off keying PAM pulse amplitude modulation PDP power density profile PHY physical layer PPM pulse position modulation PRake partial Rake RF radio frequency RLS recursive least square SER symbol error rate SIC soft interference canceller SIR signal-to-interference ratio SISO soft-input soft-output SNR signal-to-noise ratio SRake selective Rake SS spread-spectrum ST space-time TH time-hopping UWB ultra-wideband WLAN wireless local area network WMAN wireless metropolitan area network WPAN wireless personal area networks WWAN wireless wide area network viii τ ⎛ ⎞ (δ − δ k ' ) + 0, k ⎟ ( k ', j '; k , j ) = γ 0, k ' ⎜ (2 j − j ') Nc + c(k , j ) − c(k ', j ') + k T T c c ⎝ ⎠ τ ⎛ ⎞ (δ − δ k ' ) +γ 1, k ' ⎜ (2 j − j ') Nc + c(k , j ) − c(k ', j ') + k + 1, k ⎟ T T c c⎠ ⎝ , (4.6) and τ ⎛ ⎞ (δ − δ k ' ) + 0, k ⎟ hb ( k ', j '; k , j ) = γ 0, k ' ⎜ (2 j − j '− 1) Nc + c(k , j ) − c(k ', j ') + k Tc Tc ⎠ ⎝ ⎛ (δ − δ k ' ) +γ 1, k ' ⎜ (2 j − j '− 1) Nc + c(k , j ) − c(k ', j ') + k + Tc ⎝ τ1, k ⎞ Tc ⎟⎠ (4.7) As can be seen from (4.4) and (4.5), we’ve considered possible pulse collisions due to multipath effects within the same symbol across all the users The equations here may look quite complicated, while actually they are straightforward, and similar to the analysis of channel impact in Chapter The actual MAI should be much less than what we have specified here, basically one pulse being affected by one or two neighboring pulses from every other user, but we just write everything down in order to be rigorous Since both ϕk (2 j ) and ϕk (2 j + 1) contain the information of these two symbols, a scheme similar to the MRC is used to combine them, and the resultant rk ,1 ( j ) and rk ,2 ( j ) , which mainly represent the information of symbol and symbol 2, respectively, are fed to the iterative MUD Notice that γi,k stands for the pulse-sampled value of the strongest path of the pulses related to the kth user and the ith transmit antenna 46 rk ,1 ( j ) = γ 0, kϕk (2 j ) + γ 1, kϕk (2 j + 1) ( Nu ) N f −1 = γ 0,2 k + γ 1,2k b1 (k ) + γ 0, kς k (2 j ) + γ 1, kς k (2 j + 1) + ε m ∑ ∑   k ' =1 j ' = 0, j ' ≠ j (4.8) ηk ,1 ( j ) ⎡⎣( b1 (k ') + b2 (k ') ) H (k ', j '; k , j ) + ( b1 (k ') − b2 (k ') ) H1 (k ', j '; k , j ) ⎤⎦ In the equation above, H (k ', j '; k , j ) = γ 0, k ( k ', j '; k , j ) + γ 1, k hb ( k ', j '; k , j ) and H1 (k ', j '; k , j ) = γ 0, k hb ( k ', j '; k , j ) − γ 1, k hb ( k ', j '; k , j ) are assumed to make it more concise Similarly we can deduce: rk ,2 ( j ) = γ 1, kϕ k (2 j ) − γ 0, kϕ k (2 j + 1) ( Nu ) N f −1 = γ 0,2 k + γ 1,2k b2 (k ) + γ 1, k ς k (2 j ) − γ 0, kς k (2 j + 1) + ε m ∑ ∑   k ' =1 j ' = 0, j ' ≠ j (4.9) η k ,2 ( j ) ⎡⎣( b1 (k ') − b2 (k ') ) H ( k ', j '; k , j ) − ( b1 ( k ') + b2 ( k ') ) H1 ( k ', j '; k , j ) ⎤⎦ Similar to Chapter 3, the noise components ηk ,1 ( j ) and ηk ,2 ( j ) can still be assumed to be white, and their variances can be calculated as: ση 4.3 k ,1 ( ) = γ 0,2 k + γ 1,2k ε mσ = σ ηk ,2 (4.10) Iterative Multiuser Detection This iterative MUD algorithm is based on the r’s we’ve already got As specified before, the sequence we actually use is not the whole but within an interval of one symbol’s duration, where two consecutive pairs of symbols from a single user not coexist Without loss of generality, these r’s used for detection can be sorted as to ⎢⎣ N f ⎥⎦ in this interval for each user 47 Let Φk,g represents the likelihood ratio of the MAP probabilities for bg(k) equaling to either or -1 The following shows how to deduce Φk,g(n+1) from Φk,g(n), where n indexes the cycle of iterations Define Φ k , g (n + 1) = ln ( ( )) , ( ⎢⎣ N 2⎥⎦ ) ) Pr bg (k ) = 1| rk , g (1) , rk , g ( ) , , rk , g ⎢⎣ N f 2⎥⎦ ( Pr bg (k ) = −1| rk , g (1) , rk , g ( ) , , rk , g (4.11) f for g=0 or Similar to Chapter 3, we treat all the rk,g’s as independent random variables, and based on the Bayes’ rule we can further get: Φ k , g (n + 1) = ⎢⎣ N f 2⎥⎦ Φ k , g (n) + ⎢⎣ N f ⎥⎦ ∑ j =1 ln f ( rk , g ( j ) | bg (k ) = 1) f ( rk , g ( j ) | bg (k ) = −1) (4.12) Since Φ k ', g (n) , k’ taken from to Nu, has already been obtained in the last iteration, from its definition we can easily deduce a soft estimation of bg ( k ') : E {bg ( k ')} = ( ) Φ k ', g ( n) (4.13) The substitution of (4.8) into (4.12) yields: Φ k ,1 (n + 1) = ⎢⎣ N f ⎥⎦ Φ k ,1 (n) + N f −1 Nu ⎛ ⎞ γ γ η ε ( j ) + + + ⎜ 0, k ⎟ ∑ 1, k k ,1 m∑ k ' =1 j ' = 0, j ' ≠ j f⎜ ⎟ ⎜ ⎟ ⎜ ⎡( b1 (k ') + b2 ( k ') ) H (k ', j '; k , j ) + ( b1 (k ') − b2 (k ') ) H1 ( k ', j '; k , j ) ⎤ ⎟ ⎣⎢ N f ⎦⎥ ⎣ ⎦⎠ ⎝ ln ∑ N f −1 Nu ⎛ ⎞ j =1 γ γ η ε ( ) j − − + + ⎜ 0, k ⎟ ∑ 1, k k ,1 m∑ k ' =1 j ' = 0, j ' ≠ j f⎜ ⎟ ⎜ ⎟ ⎜ ⎡( b1 (k ') + b2 ( k ') ) H (k ', j '; k , j ) + ( b1 (k ') − b2 (k ') ) H1 ( k ', j '; k , j ) ⎤ ⎟ ⎦⎠ ⎝ ⎣ (4.14) Similarly, the substitution of (4.9) into (4.12) yields: 48 Φ k ,2 (n + 1) = ⎢⎣ N f ⎥⎦ Φ k ,2 (n) + N f −1 Nu ⎛ ⎞ ⎜ γ 0, k + γ 1, k + ηk ,2 ( j ) + ε m ∑ ∑ ⎟ k ' =1 j ' = 0, j ' ≠ j f⎜ ⎟ ⎜ ⎟ ⎢⎣ N f ⎥⎦ ⎜ ⎡( b1 (k ') − b2 ( k ') ) H (k ', j '; k , j ) − ( b1 ( k ') + b2 (k ') ) H1 ( k ', j '; k , j ) ⎤ ⎟ ⎣ ⎦ ⎠ ln ⎝ ∑ N f −1 Nu ⎛ ⎞ j =1 2 ⎜ −γ 0, k − γ 1, k + η k ,2 ( j ) + ε m ∑ ∑ ⎟ k ' =1 j ' = 0, j ' ≠ j f⎜ ⎟ ⎜ ⎟ ⎜ ⎡( b1 (k ') − b2 ( k ') ) H (k ', j '; k , j ) − ( b1 ( k ') + b2 (k ') ) H1 ( k ', j '; k , j ) ⎤ ⎟ ⎦⎠ ⎝ ⎣ (4.15) Based on the Central Limit Theorem, it can be well assumed that the part of MAI in (4.14) is Gaussian, with mean and variance as follows: Nu µI = ∑ k ,1 N f −1 ∑ ⎡⎣( H k ' =1 j ' = 0, j ' ≠ j (k ', j '; k , j ) + H1 (k ', j '; k , j ) ) ( ( + ( H (k ', j '; k , j ) − H1 (k ', j '; k , j ) ) Nu σ ( n) = ∑ I k ,1 N f −1 ∑ ⎡⎣( H k ' =1 j ' = 0, j ' ≠ j Φ k ',1 (n) ) ) , (4.16) Φ k ',2 (n) ⎤⎦ ( (k ', j '; k , j ) + H1 (k ', j '; k , j ) ) − ( 12 Φ k ',1 (n) ) 2 ) ( ) + ( H (k ', j '; k , j ) − H1 (k ', j '; k , j ) ) − ( 12 Φ k ',2 (n) ) ⎤ ⎦ (4.17) Similarly, we can derive the mean and variance for the MAI in (4.15) as: Nu µI = ∑ k ,2 σ I k ,2 N f −1 (k ', j '; k , j ) − H1 (k ', j '; k , j ) ) ( Φ k ',1 (n) − ( H (k ', j '; k , j ) + H1 (k ', j '; k , j ) ) ( Φ k ',2 (n) ⎤⎦ ∑ ⎡⎣( H k ' =1 j ' = 0, j ' ≠ j Nu ( n) = ∑ N f −1 ∑ ⎡⎣( H k ' =1 j ' = 0, j ' ≠ j ( ) , (k ', j '; k , j ) − H1 (k ', j '; k , j ) ) − ( 12 Φ k ',1 (n) ) ) ( (4.18) ) ) + ( H (k ', j '; k , j ) + H1 (k ', j '; k , j ) ) − ( 12 Φ k ',2 (n) ) ⎤ ⎦ (4.19) 49 In accordance with (4.10) and (4.16)-(4.19), (4.12) can be further expressed as follows: Φ k , g (n + 1) = ⎢⎣ N f 2⎥⎦ Φ k , g (n) + ⎢⎣ N f 2⎥⎦ ∑ j =1 ( )( ⎡ γ + γ r ( j) − µ 0, k 1, k k,g Ik ,g ⎢ 2 ⎢ σ I k , g ( n) + σ η k , g ⎣ ) ⎤⎥ ⎥ ⎦ (4.20) Notice that this joint detection produces 2Nu symbols at the same time over each duration of two consecutive symbols 2NfTf And the computational complexity for each time of iteration is O(Nu2) 4.4 Simulation Results and Discussions In the computer based simulations, we consider a BPAM TH-UWB system with two transmit antennas and one receiver antenna The channels are generated according to Foerster’s database of impulse radio channels [10], CM1 also The chip (pulse) duration Tc=1ns, chip number Nc =50, frame number Nf =10, thus each user transmits at a data rate of Mbps Fig 4.2 to Fig 4.4 are the BER performances of the iterative MUD algorithm with respectively 10, 20 and 30 active users The results are quite satisfactory in terms of the asynchronous scenario and the low-complexity The performance of the proposed iterative MUD in the case of single-antenna UWB system (without ST-coding) is also plotted Obviously, a BER performance improvement of around 3dB can be achieved 50 Fig 4.2 BER comparison for a 10-user ST coded UWB system Fig 4.3 BER comparison for a 20-user ST coded UWB system 51 Fig 4.4 4.5 BER comparison for a 30-user ST coded UWB system Summary We have extended the already proposed iterative MUD algorithm to ST coded multi-antenna UWB systems in this chapter We aim to combine the advantages of both UWB technology and ST coding After using an analog ST coding scheme, we also find a way to further reduce the complexity Computer based simulations have demonstrated its satisfactory BER performance and low complexity 52 Chapter Conclusions and Future Work 5.1 Conclusions Impulse radio is a technique with a long history going back to the 1960’s, and has recently been revitalized in indoor wireless applications The rising popularity of this technique, commonly referred to as UWB technology, mainly comes from the recently released spectrum bandwidth specified for it, as well as its promising features over normal narrowband communication systems, such as low power, low cost, high data rate and well stability As for the MUD for multiple access UWB systems, most of the popular receivers seem to be simple applications of standard MUD methods to UWB systems, which may not be quite satisfactory in terms of both the computational complexity and the BER performance In this thesis, a novel iterative MUD algorithm specifically designed for UWB systems is proposed, which features low-complexity and good BER performance This algorithm is based on the MAP criterion by iteratively 53 subtracting the MAI Moreover, a truncated detection window is used in face of the asynchronous transmission of multiple users Simulation results have demonstrated our theoretical analysis Another contribution is the even lower complexity extension of this algorithm to ST coded multi-antenna UWB systems By using the analog ST coding scheme, we can further deduce the complexity and improve the system performance Also, the detection problem caused by asynchronous transmission can be intentionally avoided 5.2 Future Work The proposed low-complexity iterative MUD algorithm seems to be the first effort made to implement iterative MUD algorithms to asynchronous UWB systems, as well as ST-coded multi-antenna UWB systems Further examinations can be carried out to evaluate the performance of this algorithm, for instance its capability under various interferences, or the study of the BER performance vs number of multiple users In this algorithm we just sampled the strongest path of each transmitted pulse as the desired information, which can be called as one-path based iterative MUD Obviously things should be better if we can combine several paths’ information This work may relay on favorable and novel combination schemes, either prior, in between, or posterior to the detection process, otherwise the complexity will increase remarkably 54 It is also recommended to develop new and related iterative MUD algorithms for other coding schemes, for example Turbo coding Noticeably, this proposed algorithm requires perfect channel information, and synchronization to the desired symbols Thus the problem of channel estimation and synchronization become especially important Actually we think these two problems are quite essential in all the detection issues related to UWB, which is really sensitive to channel estimation and timing errors The investigation of these two problems could be tough while significant 55 References [1] “Wireless networking overview,” http://www.microsoft.com/resources/documentation/windows/xp/all/prodd ocs/en-us/wireless_networking_overview.mspx [2] 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Conf ICCCAS04, vol 1, pp 89-92, June, 2004 [2] XiaoLi Wang, Lei Huang and ChiChung Ko, “Low-complexity iterative MUD for asynchronous UWB systems,” IEEE Conf ICCS2004, Sept., 2004 60 [...]... performance for multiple access UWB systems has already been carried out Among which iterative MUD methods seem especially interesting for their ingenious design In this thesis a low-complexity iterative MUD algorithm for UWB systems is proposed, together with the extension of this algorithm to Space-Time (ST) coded multi-antenna UWB systems, where the complexity is further reduced The proposed iterative. .. possibilities for future work 14 Chapter 2 Multiuser Detection for UWB Systems Along with the increasing interest in UWB communications, motivation for pertinent MUD is induced for multiple access UWB systems Typical existing MUD algorithms for UWB communications will be described in this chapter 2.1 Advanced Rake Receivers Actually a large number of Rake-related receivers may not be classified as multiuser. .. squared error) multiuser receiver, and the iterative interference cancellation multiuser receiver A novel low-complexity iterative MUD algorithm specifically designed for UWB systems is proposed in Chapter 3 The maximum a posteriori (MAP) 13 criterion is applied in the detection process and the MAI is subtracted in an iterative manner Considering the asynchronous scenario, a truncated detection window... interesting for its ingenious design and low-complexity In this thesis, we mainly consider the MUD issues in TH-UWB systems, and focus on a proposal of a low-complexity iterative MUD algorithm as well as its even lower-complexity extension to ST coded multiantenna UWB systems In Chapter 2, several popular multiuser receivers for UWB systems are addressed, namely the advanced Rake receivers, the optimum multiuser. .. to the MAI; the Rake MMSE performs better but not very well; and the MUD MMSE achieves a quite satisfactory BER performance 2.2 Optimum Multiuser Detection It is well known that optimum multiuser detectors are double-edged for both good BER performance and high complexity Though optimum MUD may not be easily applied in practice, theoretically it still acts as the benchmark for other methods The following... extended this algorithm to ST coded multi-antenna UWB systems After using an analog ST coding scheme, we also find a way to counteract the problem caused by asynchronous transmission, and the structure of a detection window lasting several symbols is simplified into a two-symbol by two-symbol detection model x Chapter 1 Ultra- Wideband Overview Ultra- Wideband (UWB) technology, a rising and promising technology...Summary Ultra- Wideband (UWB) technology has drawn considerable attention among both researchers and practitioners over the past few years It offers a solution for the bandwidth, cost, power consumption and physical size issues in wireless personal area networks (WPAN), and enables wireless connectivity with consistent high data rate across multiple devices Research on multiuser detection (MUD) for achieving... channel responses for CM1 to CM4 Fig 1.6 Typical channel response of CM1 Fig 1.7 Typical channel response of CM2 Fig 1.8 Typical channel response of CM3 12 Fig 1.9 Typical channel response of CM4 1.5 Organization of the Thesis Accurate and effective multiuser detection (MUD) algorithms are quite important and attractive issues for multiple-access UWB communication systems Among which, iterative MUD seems... be unavailable for reuse anywhere else in the office or home Fig 1.4 Application and protocol layers for UWB 6 Fig 1.4, taken from [2], reveals the full solution stack required to make UWB a viable radio alternative in the marketplace 1.2.2 Advantages and Disadvantages The uniqueness of UWB technology would offer many advantages over normal narrowband systems However, the main challenge for UWB system... means the transmitted symbols from different users (transmitters) are not synchronized, a truncated detection window is introduced, and the computational ix complexity for this block decoding is reduced in an iterative manner The key features of this proposed algorithm is its low complexity and good BER performance, which approaches to that of the single-user system Aiming to combine the advantages of ... Adaptive MMSE Multiuser Detection 22 iv 2.4 Iterative Interference Cancellation & Decoding 24 2.5 Summary 27 Chapter Iterative Multiuser Detection for UWB Systems 28.. .ITERATIVE MULTIUSER DETECTION FOR ULTRA-WIDEBAND SYSTEMS WANG XIAOLI (B.Eng University of Electronic Science & Technology of China) A THESIS SUBMITTED FOR THE DEGREE OF MASTER... 28 3.2 Iterative Multiuser Detection 33 3.3 Simulation Results and Discussions 36 3.4 Summary 40 Chapter Low-Complexity Iterative Multiuser Detection for Space-Time