These outputs can be written in a more convenient form: with y(i) = Hc m (i) n(i) y(i) = (y(i); ; y K (i)) T ; c m (i) = (c m (i); ; c m K(i)) T and n
|
|
- Albert Stephens
- 5 years ago
- Views:
Transcription
1 Binary Codes for synchronous DS-CDMA Stefan Bruck, Ulrich Sorger Institute for Network- and Signal Theory Darmstadt University of Technology Merckstr. 25, 6428 Darmstadt, Germany Tel.: , Fax: sbruecknesi.tu-darmstadt.de Abstract In this paper synchronous DS-CDMA with binary linear block coding is investigated for AWGN. First the asymptotic error probability for coded transmission is derived for the optimal decoder. Then it is shown how the asymptotic order of the error probability depends on the codes of the users and how the codes have to be designed to be asymptotically good. It is further demonstrated that separated multiuser detection and decoding has an inherent loss in asymptotic order. Finally a detection and decoding algorithm is presented which is able to reduce this disadvantage. Introduction In the area of spread spectrum communications research eorts on Code Division Multiple Access (CDMA) systems have steadily increased in recent years. Originally used in military systems these Spread Spectrum systems are also investigated with regard to dierent commercial applications, e.g. cellular mobile radio systems or wireless indoor communication []. All the users share the same frequency band, so they are not orthogonal, in general. To achieve optimal performance, all the users have to be detected jointly. This Maximum Likelihood (Sequence) detector was found by Verdu [2]. It was further shown that the optimal detector is able to cope with different received energies []. So, the near-far problem is not inherent in CDMA. Rather, it is just the inability of the conventional detector to exploit the interuser interference. A lot of work was done on suboptimal multiuser detectors in the last years. Up to now almost always uncoded systems were considered. Recently, the Maximum Likelihood detector for a system with convolutional codes was presented [4]. It was shown there that the optimal algorithm consists of a trellis with a number of states which is the product of the number of users and the constraint length of the codes. Detection (user separation) and decoding must be done jointly. But for practical applications, the complexity is too large. So, suboptimal detectors have to be constructed. The easiest method is to separate multiuser detection and decoding. The user separation is done bitwise or symbolwise, the decoding is done for K users independently. Asymptotically, an uncoded DS-CDMA system is equivalent to a single user system with reduced energy []. The reduction factor is called asymptotic eciency. In section 2 we derive the asymptotic order of the error probability for a synchronous DS-CDMA system with BPSK modulation and binary block coding for the optimal joint detector and decoder and AWGN. It turns out, that a coded system is also equivalent to a single user system with reduced energy. The energy reduction depends on the code properties and leads to the denition of asymptotic distance. In section upper and lower bounds for the asymptotic distance are derived. It is shown how the codes of the users can be constructed jointly to reduce the energy loss. In section 4 binary linear codes based on these criteria are constructed. It is further shown in section 5, that separated multiuser detection and decoding leads necessarily to a bad asymptotic error probability. A suboptimal low complexity algorithm which does not suffer from this disadvantage is proposed. Simulations are used to illustrate the benets of the methods presented. 2 Joint Maximum Likelihood Detection and Decoding We assume BPSK modulation and perfect bit synchronization. Each user k is assigned a signature waveform s k (t); t 2 [; T ] with energy! k and a binary linear block code C k with length n and Hamming weight d H. We assume for convenience, that the code parameters are the same for all codes. However, the K codes may be dierent. The mapping from binary to real symbols is done in the usual way:! ;!? Modulated codewords are denoted as c m k for user k and the corresponding modulated code as Ck m. If all the users transmit over a white gaussian multiple access channel, the received signal can be written as KX r(t) = c m k (i)s k (t? it ) n(t) k= i= where c m k (i) is the ith modulated code symbol for user k and n(t) is additive white gaussian noise with variance =2. If all codewords of all the users are equally likely a sucient statistic for a Maximum Likelihood decision are the outputs of a bank of matched lters: y k (i) = Z T r(t it )s k (t)dt
2 These outputs can be written in a more convenient form: with y(i) = Hc m (i) n(i) y(i) = (y(i); ; y K (i)) T ; c m (i) = (c m (i); ; c m K(i)) T and n(i) is a K-dimensional vector with zero mean and covariance matrix 2 H. H is a crosscorrelation matrix with components Z T H ij = s i (t)s j (t)dt The joint Maximum Likelihood detector and decoder selects the most likely modulated codeword matrix ^C m opt = (^c m opt() ^c m opt(n)): ^C m opt = arg max p(y(); ; y(n)j^c m (); ;^c m (n)) = arg max (2y(i) T^c m (i)? ^c m (i) T H^c m (i)) i= {z } =: L(^C m ) Each term in the sum corresponds to the ML metric in the uncoded case [5]. Now we want to consider the probabilty that an error is made by the ML approach. It is assumed that the codeword matrix C m was sent by the K users and ^C m = C m?2e was selected to be the most likely one. An error between two codewords c m i und ^c m i is dened as e i = 2 (cm i? ^c m i ) 2 f?; ; g n The probability that an error event E, where E is a matrix consisting of the errors of all users, might happen is Lemma : P (L(^C m ) > L(C m )) = Q vu u t 2 e(i) T He(i) i= e(i) denotes the ith column of the matrix E. The proof is given in [6]. A The term e(i) T He(i) is the necessary noise power to cause the error pattern e(i) for the K users in the ith symbol. If only one user were active then e(i) T He(i) reduces to P e 2 (i) =: w H, where w H is the Hamming weight of the error for user. Then Lemma is equal to the probability that w H errors occur for single user transmission. Let without loss of generality the probability of error for user be of interest. This probability can be easily bounded by applying the union bound and Lemma. Lemma 2: X P (^c m 6= c m ) Q E;e 6= vu u t 2 e(i) T He(i) i= A where e is the rst row of the matrix E and the sum goes over all possible error matrices. For low signal to noise ratios this upper bound for the probability of error is loose, but for large signal to noise ratios the bound is rather tight, because the union bound was applied. In this region that term in the sum with smallest argument determines the behaviour of the probability of error. So far the upper bound is valid for xed energies of all the users. In mobile applications the received energies are not constant, but they vary, because the power control might not be perfect. The capability of the receiver to cope with the worst energy constellation of the interfering users is known as near-far resistance []. Therefore we dene: Denition: Let! be the energy per bit of user and let w = (!2; ;! K ) be the energies of the interfering users. Then d A (CjC2; ; C K ) = min min E;e 6= w! e(i) T He(i) i= is called the asymptotic distance of the code C for the set of codes (C; ; C K ). If minimization is done only over E for xed energies, the term is called coded asymptotic eciency. This denition for coded transmission is equivalent to the denition of near-far resistance for uncoded transmission []. A similar denition for coded CDMA including the code rate was given in [4]. Note, that the coded asymptotic eciency is not upper bounded by as the asymptotic eciency in the uncoded case. We call this term a distance in the worst case, because later on it will turn out that the value of d A (CjC2; ; C K ) depends on the properties of the codes C k. With this denition it follows that the probability of decoding error for user is asymptotically of the same order as Q r 2Rd A E b with d A = d A (CjC2; ; C K ). For coded single user transmission the error probability is asymptotically of the same order as Q r 2Rd H E b R is the code rate and E b is the energy per information bit, i.e.! = R E b. So, for a multiple access situation with AWGN the Hamming distance d H has to be replaced by the asymptotic distance d A (CjC2; ; C K ). Bounds for the Asymptotic Distance Now some bounds for the asymptotic distance are derived. We assume that all the signature waveforms are linearly independent, then the crosscorrelation matrix H is positive denite, i.e. the matrix has only positive eigenvalues. The correlations between the K? interfering users are described by a (K? ) (K? ) dimensional submatrix. So, it is convenient to decompose the normalized!!
3 crosscorrelation matrix R in r T R = r R K? R = W?=2 HW?=2 with W = diag(!; ;! K ). With this decomposition the rst bound can be formulated: Theorem : The asymptotic distance is upper and lower bounded by d H (? r T R? K? r) d A(CjC2; ; C K ) d H Corollary : If C = = C K, then d A (CjC2; ; C K ) = d H (? r T R? K?r) For K = 2 it is then For a proof see [6]. d A (CjC2) = d H (? 2 ) This corollary says, that the worst asymptotic behaviour of the joint Maximum Likelihood detector and decoder is achieved, if all the K codes are the same. Now code properties for a good asymptotic distance are investigated. Let I = f2; ; Kg be the indices of the interfering users and I s a subset of I. We dene U;I s = fc : c 2 C ^ c 2 C j 8 j 2 I s g S;I s (k) = fc : c = c c 2 8 c 2 U;I s ^ c 2 2 C k g 8k =2 I s. is the mod 2 addition in GF(2). Further the matrix D Is = diag(d2; ; d K ) is dened with d k = dh (S;I s (k)) k =2 I s k 2 I s Is is the smallest (positive) eigenvalue of the matrix R if the rst and all rows and columns with indices in I s are cancelled. With these denitions the following theorem is proved in [6]: Theorem 2: For the asymptotic distance holds: with d A (CjC2; ; C K ) min I s dh (U;I s )(? r T A? I s r) A Is = R K? Theorem 2 simplies for K = 2: Is d H (U;I s ) D I s Corollary 2: For the asymptotic distance with K = 2 users holds with d A = d A (CjC2): d A min d 2 d H H? ; d H (U2)(? 2 ) d H d H (S(2)) These lower bounds state which parameters of the codes are important for a large asymptotic distance and therefore, for an at least asymptotically good error rate. If the Hamming distances of the intersection codes are larger than the Hamming distance of the original code than the asymptotic distance will be larger than the lower bound. Also large Hamming distances of the sum codes reduce the energy loss due to interuser interference further. 4 Code Constructions In the last section it has been shown what the important parameters for a large asymptotic distance are. Now we construct some codes for K = 2 and K = based on these distances. All the codes are BCH codes, spectral techniques are used to describe them [7]. Example: The rst code is a (7,4,) Hamming Code. We choose the zeros in the spectral domain as given in the table below. The intersection code must contain the zeros of both codes. The sum code has a zero at those positions where both codes have a zero: A A A2 A A4 A5 A6 C C2 U2 S(2) From this table it follows, that the intersection code U2 consists of the all zero and the all one word and has distance d H (U2) = 7. The sum code ist the complete GF(2) 7 and has just distance d H (S(2)) =. If both d A equal code bound lower bound Figure : lower bound and equal code bound for asymptotic distance of (7,4,) Hamming code codes are the same, then d A (CjC2) = d H (? 2 ) by Corollary. Figure shows the gain of the constructed codes by using the lower bound of Corollary 2 compared to the case, where both user share the same code. Example: The second example for K = 2 is a (5,9,) BCH code. For the two user codes we choose the zeros as shown in the table: A A A2 A A4 A5 A6 C C2 U2 S(2) A7 A8 A9 A A A2 A A4 C C2 U2 S(2)
4 The intersection code is a (5,5,7) BCH code with d H (U2) = 7, the sum code is a (5,,2) BCH code with d H (S(2)) = 2. Here the coding gain is still larger (g. d A equal code bound lower bound Figure 2: lower bound and equal code bound for asymptotic distance of (5,9,) BCH code 2), because the distance of the sum code is 2 compared to in the former example. Example: For more than two users, there should at least be one pair of users, who have dierent codes. Let us choose K = with the following zeros in the spectral domain: A A A2 A A4 A5 A6 C C2 C U2 U U2 S2() S(2) S(2) S() The matrices D Is are D ; = D = ; D 2 = ; D 2 = The intersection codes and the sum codes are either the original Hamming code or the (7,,7) repetition code. The examples show, that on the one side dierent zeros in the spectral domain are needed for large Hamming distances of the intersection codes. On the other side there should be joint zeros, so that the sum codes have a large Hamming distance. 5 Remarks on Suboptimal Receivers In this section we turn to suboptimal receivers. As already mentioned, a suboptimal receiver may consist of a multiuser detector and K independent decoders. A lot of research has been done in the last years on suboptimal multiuser detectors. For an overview on this research area see [8]. We will demonstrate that any separation of detection and decoding is not able to use the benets of the code constructions of the last section, even if the ML detector and ML decoders are used. Instead, some form of multistage decoding could be used. Only the two user case is considered, because this example is sucient to illustrate the eects. The Maximum Likelihood detector and Maximum Likelihood decoders are used. The detector provides hard values to the decoders, but it is also possible to use soft outputs of the detector. For large signal to noise ratios, the worst case bit error probability p after multiuser detection for user is bounded by [5] p constq s 2(? 2 )RE b A conste? (? 2 )RE b After decoding the word error probability p c for large signal to noise ratios is approximately: p c const p dh=2 const?(? 2 )d H RE b e 2 Here the asymptotic behaviour is determined by the factor (? 2 )d H =2. This is even below the equal code bound from section and does not depend on the codes used. Thus the word error rate is at least asymptotically independent on the codes. The equal code bound can be achieved with soft detector outputs [9], but not exceeded, even if the asymptotic distance is larger. The reason, why the asymptotic eciency of this suboptimal receiver is as small as possible is, that the multiuser detector does not take into account the code construction. Due to this construction, it is not possible that after decoding d H errors occur in both codewords at the same places. The errors must dier in at least one place or both codewords must contain more than d H errors. That means, a larger noise power is needed to lead to wrong codewords for both users compared to the equal code case. Thus, the decoded codewords can be used for successive cancellation. This leads to the following algorithm: use matched lter outputs by any suboptimal multiuser receiver for the n codesymbols independently to detect the two users decode the two users with independent decoders. The results are ^c m and ^c m 2, respectively.
5 subtract p!!2 ^c m from the n matched lter outputs for user 2 and p!!2 ^c m 2 from the n matched lter outputs for user. These are the corrected matched lter outputs. use the signs of the corrected matched lter outputs for a second decoding step by independent decoders. The results are ^c m2 and ^c m2 2, respectively. choose arg max (L(^c m,^c m2 2 ), L(^c m2,^c m 2 )) as nal result. The probability that both decoding results in the rst step are wrong determines the behaviour of the algorithm. This probability is reduced for the constructed codes, thus the algorithm is able to benet from the construction. The extension of this algorithm to K users is straightforward. Soft values can also be used in each step. 6 Simulation Results First we consider a two user system. Each user has a (7,4,) Hamming code. The correlation between the signature waveforms is 5/7 and the received energies are equal. The error probability is not as bad as possible, because the worst case interfering energy is less than that of the considered user [5]. Nevertheless the eective energy of user is reduced, because the crosscorrelation is large. Figure shows the bit error probability if both users share the same code and if the dierent Hamming codes of section 4 are used. It is seen that the joint code construction leads to a gain of approximately db for a bit error rate of?. Although the theory is only asymptotically valid, a gain is achieved for moderate signal to noise ratios... BER.. equal codes di. codes e Figure : joint ML detection and decoding Now we consider a suboptimal receiver with separated multiuser detection and decoding. Because we just want to demonstrate the inability of this approach to benet from the code construction we use the ML principle in both detection and decoding. The decoders use reliability information about the detection results. As predicted in the former section, the loss is the same for equal and dierent codes (g. 4)... BER.. equal codes di. codes e Figure 4: separated ML multiuser detection and ML soft decoding In Figure 5 the error rates for the multistage algorithm are shown. Here, also soft values are used for each stage. It is seen, that the performance for dierent codes is better than that for equal codes. Thus, the proposed algorithm is able to benet from the code construction... BER.. equal codes di. codes e Figure 5: proposed multistage algorithm Finally, the three user example of section 4 is considered. The crosscorrelation matrix R is chosen to be R = 5=7 =7 5=7 5=7 =7 5=7 A The matrices D Is are given in section 4. The eigenvalues are ; = 2 7 ; 2 = ; = 2 does not exist. Applying Theorem 2 gives a lower bound of.597 for the asymptotic distance. Figure 6 shows the error performance for this example. The energies of the interfering users are chosen to be:!2 =
6 25=6! and! = =6!. Although users and share the same code, there is a gain of nearly db to the equal code case (g. 6). If all the codes are the same, the asymptotic distance is exactly.429. Now the distribution of the codes to the users is changed. If users and 2 shared the same code, the lower bound for the asymptotic distance would be.445. Here only a small gain can be achieved. The third possibility is, that users 2 and share the same code. Then the lower bound is.596. The case that users and have the same code is the best regarding the asymptotic distance. The simulation results in gure 6 and 7 show the same behaviour... BER.. equal codes C = C e Figure 6: joint ML detection and decoding for three users.. BER.. C = C 2 C 2 = C e Figure 7: joint ML detection and decoding for three users The lower bounds for the asymptotic distances and the coded asymptotic eciencies for the chosen energies are listed in the table below: codes lower bound asymptotic eciency C = C2 = C C = C C = C C2 = C Summary First the asymptotic order of the error probability of joint Maximum Likelihood detection and decoding was derived. It turned out that this order is dependent on the codes. Therefore, we introduced the denition of asymptotic distance. For this distance upper and lower bounds were given. In the two user case, the exact asymptotic distance can be calculated [6]. It was shown that the asymptotic order of the error probability is minimal if all users share the same code. For dierent codes the asymptotic distance can be larger than the lower bound if the codes are constructed jointly. That means, the error performance is at least asymptotically better than in the equal code case. Further separated multiuser detection and decoding was investigated. We explained, why this approach is not able to achieve the designed distance. We stated an algorithm based on multistage decoding, which achieves a better asymptotic order. Simulations illustrated the theoretical results. References [] A. J. Viterbi, CDMA. Addison-Wesley, 995. [2] S. Verdu, \Minimum probability of error for asynchronous gaussian multiple-access channels," IEEE Trans. Inform. Theory, vol. 2, pp. 85{96, January 986. [] S. Verdu, \Optimum multiuser asymptotic eciency," IEEE Trans. Commun., vol. 4, pp. 89{897, September 986. [4] T. Giallorenzi and S. Wilson, \Multiuser ML sequence estimator for convolutionally coded asynchronous DS- CDMA systems," IEEE Trans. Commun., vol. 44, pp. 997{8, August 996. [5] R. Lupas and S. Verdu, \Linear multiuser detectors for synchronous code-division multiple-access channels," IEEE Trans. Inform. Theory, vol. 5, pp. 2{ 6, January 989. [6] S. Bruck and U. Sorger, \Binary codes with inner spreading for gaussian multiple access channels," in Int. Seminar on Coding Theory, Thahkadzor, Armenia, Oct [7] R. E. Blahut, Theory and Practice of Error Control Codes. Addison-Wesley, 984. [8] A. Klein, Multi-user detection of CDMA signals - algorithms and their application to cellular mobile radio. PhD thesis, University of Kaiserslautern, 996. [9] S. Bruck, \Multiuser detection for coded transmission and AWGN," in Winter School on Coding and Information Theory, Moelle, Sweden, Dec. 996.
Performance Analysis of Spread Spectrum CDMA systems
1 Performance Analysis of Spread Spectrum CDMA systems 16:33:546 Wireless Communication Technologies Spring 5 Instructor: Dr. Narayan Mandayam Summary by Liang Xiao lxiao@winlab.rutgers.edu WINLAB, Department
More informationANALYSIS OF A PARTIAL DECORRELATOR IN A MULTI-CELL DS/CDMA SYSTEM
ANAYSIS OF A PARTIA DECORREATOR IN A MUTI-CE DS/CDMA SYSTEM Mohammad Saquib ECE Department, SU Baton Rouge, A 70803-590 e-mail: saquib@winlab.rutgers.edu Roy Yates WINAB, Rutgers University Piscataway
More informationMultiuser Detection. Summary for EECS Graduate Seminar in Communications. Benjamin Vigoda
Multiuser Detection Summary for 6.975 EECS Graduate Seminar in Communications Benjamin Vigoda The multiuser detection problem applies when we are sending data on the uplink channel from a handset to a
More informationLinear and Nonlinear Iterative Multiuser Detection
1 Linear and Nonlinear Iterative Multiuser Detection Alex Grant and Lars Rasmussen University of South Australia October 2011 Outline 1 Introduction 2 System Model 3 Multiuser Detection 4 Interference
More informationSIPCom8-1: Information Theory and Coding Linear Binary Codes Ingmar Land
SIPCom8-1: Information Theory and Coding Linear Binary Codes Ingmar Land Ingmar Land, SIPCom8-1: Information Theory and Coding (2005 Spring) p.1 Overview Basic Concepts of Channel Coding Block Codes I:
More informationLecture 12. Block Diagram
Lecture 12 Goals Be able to encode using a linear block code Be able to decode a linear block code received over a binary symmetric channel or an additive white Gaussian channel XII-1 Block Diagram Data
More informationMulti User Detection I
January 12, 2005 Outline Overview Multiple Access Communication Motivation: What is MU Detection? Overview of DS/CDMA systems Concept and Codes used in CDMA CDMA Channels Models Synchronous and Asynchronous
More informationDesign of MMSE Multiuser Detectors using Random Matrix Techniques
Design of MMSE Multiuser Detectors using Random Matrix Techniques Linbo Li and Antonia M Tulino and Sergio Verdú Department of Electrical Engineering Princeton University Princeton, New Jersey 08544 Email:
More informationList Decoding: Geometrical Aspects and Performance Bounds
List Decoding: Geometrical Aspects and Performance Bounds Maja Lončar Department of Information Technology Lund University, Sweden Summer Academy: Progress in Mathematics for Communication Systems Bremen,
More informationChapter 7: Channel coding:convolutional codes
Chapter 7: : Convolutional codes University of Limoges meghdadi@ensil.unilim.fr Reference : Digital communications by John Proakis; Wireless communication by Andreas Goldsmith Encoder representation Communication
More informationPerformance Bounds for Joint Source-Channel Coding of Uniform. Departements *Communications et **Signal
Performance Bounds for Joint Source-Channel Coding of Uniform Memoryless Sources Using a Binary ecomposition Seyed Bahram ZAHIR AZAMI*, Olivier RIOUL* and Pierre UHAMEL** epartements *Communications et
More informationOptimal Sequences, Power Control and User Capacity of Synchronous CDMA Systems with Linear MMSE Multiuser Receivers
Optimal Sequences, Power Control and User Capacity of Synchronous CDMA Systems with Linear MMSE Multiuser Receivers Pramod Viswanath, Venkat Anantharam and David.C. Tse {pvi, ananth, dtse}@eecs.berkeley.edu
More informationDirect-Sequence Spread-Spectrum
Chapter 3 Direct-Sequence Spread-Spectrum In this chapter we consider direct-sequence spread-spectrum systems. Unlike frequency-hopping, a direct-sequence signal occupies the entire bandwidth continuously.
More informationOptimum Signature Sequence Sets for. Asynchronous CDMA Systems. Abstract
Optimum Signature Sequence Sets for Asynchronous CDMA Systems Sennur Ulukus AT&T Labs{Research ulukus@research.att.com Abstract Roy D.Yates WINLAB, Rutgers University ryates@winlab.rutgers.edu In this
More informationCode design: Computer search
Code design: Computer search Low rate codes Represent the code by its generator matrix Find one representative for each equivalence class of codes Permutation equivalences? Do NOT try several generator
More informationApproximate Minimum Bit-Error Rate Multiuser Detection
Approximate Minimum Bit-Error Rate Multiuser Detection Chen-Chu Yeh, Renato R. opes, and John R. Barry School of Electrical and Computer Engineering Georgia Institute of Technology, Atlanta, Georgia 30332-0250
More informationMulti-Branch MMSE Decision Feedback Detection Algorithms. with Error Propagation Mitigation for MIMO Systems
Multi-Branch MMSE Decision Feedback Detection Algorithms with Error Propagation Mitigation for MIMO Systems Rodrigo C. de Lamare Communications Research Group, University of York, UK in collaboration with
More informationECE 564/645 - Digital Communications, Spring 2018 Homework #2 Due: March 19 (In Lecture)
ECE 564/645 - Digital Communications, Spring 018 Homework # Due: March 19 (In Lecture) 1. Consider a binary communication system over a 1-dimensional vector channel where message m 1 is sent by signaling
More informationPerformance Analysis and Code Optimization of Low Density Parity-Check Codes on Rayleigh Fading Channels
Performance Analysis and Code Optimization of Low Density Parity-Check Codes on Rayleigh Fading Channels Jilei Hou, Paul H. Siegel and Laurence B. Milstein Department of Electrical and Computer Engineering
More informationOptimal Sequences and Sum Capacity of Synchronous CDMA Systems
Optimal Sequences and Sum Capacity of Synchronous CDMA Systems Pramod Viswanath and Venkat Anantharam {pvi, ananth}@eecs.berkeley.edu EECS Department, U C Berkeley CA 9470 Abstract The sum capacity of
More informationInteractions of Information Theory and Estimation in Single- and Multi-user Communications
Interactions of Information Theory and Estimation in Single- and Multi-user Communications Dongning Guo Department of Electrical Engineering Princeton University March 8, 2004 p 1 Dongning Guo Communications
More informationAdvanced 3 G and 4 G Wireless Communication Prof. Aditya K Jagannathan Department of Electrical Engineering Indian Institute of Technology, Kanpur
Advanced 3 G and 4 G Wireless Communication Prof. Aditya K Jagannathan Department of Electrical Engineering Indian Institute of Technology, Kanpur Lecture - 19 Multi-User CDMA Uplink and Asynchronous CDMA
More informationIntroduction to Wireless & Mobile Systems. Chapter 4. Channel Coding and Error Control Cengage Learning Engineering. All Rights Reserved.
Introduction to Wireless & Mobile Systems Chapter 4 Channel Coding and Error Control 1 Outline Introduction Block Codes Cyclic Codes CRC (Cyclic Redundancy Check) Convolutional Codes Interleaving Information
More informationLecture 5: Antenna Diversity and MIMO Capacity Theoretical Foundations of Wireless Communications 1. Overview. CommTh/EES/KTH
: Antenna Diversity and Theoretical Foundations of Wireless Communications Wednesday, May 4, 206 9:00-2:00, Conference Room SIP Textbook: D. Tse and P. Viswanath, Fundamentals of Wireless Communication
More informationOn the minimum distance of LDPC codes based on repetition codes and permutation matrices 1
Fifteenth International Workshop on Algebraic and Combinatorial Coding Theory June 18-24, 216, Albena, Bulgaria pp. 168 173 On the minimum distance of LDPC codes based on repetition codes and permutation
More informationThe E8 Lattice and Error Correction in Multi-Level Flash Memory
The E8 Lattice and Error Correction in Multi-Level Flash Memory Brian M Kurkoski University of Electro-Communications Tokyo, Japan kurkoski@iceuecacjp Abstract A construction using the E8 lattice and Reed-Solomon
More informationOptimum Soft Decision Decoding of Linear Block Codes
Optimum Soft Decision Decoding of Linear Block Codes {m i } Channel encoder C=(C n-1,,c 0 ) BPSK S(t) (n,k,d) linear modulator block code Optimal receiver AWGN Assume that [n,k,d] linear block code C is
More informationCoding theory: Applications
INF 244 a) Textbook: Lin and Costello b) Lectures (Tu+Th 12.15-14) covering roughly Chapters 1,9-12, and 14-18 c) Weekly exercises: For your convenience d) Mandatory problem: Programming project (counts
More informationIterative Equalization using Improved Block DFE for Synchronous CDMA Systems
Iterative Equalization using Improved Bloc DFE for Synchronous CDMA Systems Sang-Yic Leong, Kah-ing Lee, and Yahong Rosa Zheng Abstract Iterative equalization using optimal multiuser detector and trellis-based
More informationBinary Convolutional Codes
Binary Convolutional Codes A convolutional code has memory over a short block length. This memory results in encoded output symbols that depend not only on the present input, but also on past inputs. An
More informationEM Channel Estimation and Data Detection for MIMO-CDMA Systems over Slow-Fading Channels
EM Channel Estimation and Data Detection for MIMO-CDMA Systems over Slow-Fading Channels Ayman Assra 1, Walaa Hamouda 1, and Amr Youssef 1 Department of Electrical and Computer Engineering Concordia Institute
More informationTHIS paper is aimed at designing efficient decoding algorithms
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 45, NO. 7, NOVEMBER 1999 2333 Sort-and-Match Algorithm for Soft-Decision Decoding Ilya Dumer, Member, IEEE Abstract Let a q-ary linear (n; k)-code C be used
More informationEE6604 Personal & Mobile Communications. Week 13. Multi-antenna Techniques
EE6604 Personal & Mobile Communications Week 13 Multi-antenna Techniques 1 Diversity Methods Diversity combats fading by providing the receiver with multiple uncorrelated replicas of the same information
More informationOn the Optimum Asymptotic Multiuser Efficiency of Randomly Spread CDMA
On the Optimum Asymptotic Multiuser Efficiency of Randomly Spread CDMA Ralf R. Müller Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Lehrstuhl für Digitale Übertragung 13 December 2014 1. Introduction
More informationLecture 7. Union bound for reducing M-ary to binary hypothesis testing
Lecture 7 Agenda for the lecture M-ary hypothesis testing and the MAP rule Union bound for reducing M-ary to binary hypothesis testing Introduction of the channel coding problem 7.1 M-ary hypothesis testing
More informationVector spaces. EE 387, Notes 8, Handout #12
Vector spaces EE 387, Notes 8, Handout #12 A vector space V of vectors over a field F of scalars is a set with a binary operator + on V and a scalar-vector product satisfying these axioms: 1. (V, +) is
More informationNoncoherent Multiuser Detection for Nonlinear Modulation Over the Rayleigh-Fading Channel
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL 47, NO 1, JANUARY 2001 295 Noncoherent Multiuser Detection for Nonlinear Modulation Over the Rayleigh-Fading Channel Artur Russ and Mahesh K Varanasi, Senior
More informationDecoupling of CDMA Multiuser Detection via the Replica Method
Decoupling of CDMA Multiuser Detection via the Replica Method Dongning Guo and Sergio Verdú Dept. of Electrical Engineering Princeton University Princeton, NJ 08544, USA email: {dguo,verdu}@princeton.edu
More informationNon Orthogonal Multiple Access for 5G and beyond
Non Orthogonal Multiple Access for 5G and beyond DIET- Sapienza University of Rome mai.le.it@ieee.org November 23, 2018 Outline 1 5G Era Concept of NOMA Classification of NOMA CDM-NOMA in 5G-NR Low-density
More informationarxiv:cs/ v1 [cs.it] 11 Sep 2006
0 High Date-Rate Single-Symbol ML Decodable Distributed STBCs for Cooperative Networks arxiv:cs/0609054v1 [cs.it] 11 Sep 2006 Zhihang Yi and Il-Min Kim Department of Electrical and Computer Engineering
More informationLecture 9: Diversity-Multiplexing Tradeoff Theoretical Foundations of Wireless Communications 1. Overview. Ragnar Thobaben CommTh/EES/KTH
: Diversity-Multiplexing Tradeoff Theoretical Foundations of Wireless Communications 1 Rayleigh Wednesday, June 1, 2016 09:15-12:00, SIP 1 Textbook: D. Tse and P. Viswanath, Fundamentals of Wireless Communication
More informationLecture 9: Diversity-Multiplexing Tradeoff Theoretical Foundations of Wireless Communications 1
: Diversity-Multiplexing Tradeoff Theoretical Foundations of Wireless Communications 1 Rayleigh Friday, May 25, 2018 09:00-11:30, Kansliet 1 Textbook: D. Tse and P. Viswanath, Fundamentals of Wireless
More informationNew Puncturing Pattern for Bad Interleavers in Turbo-Codes
SERBIAN JOURNAL OF ELECTRICAL ENGINEERING Vol. 6, No. 2, November 2009, 351-358 UDK: 621.391.7:004.052.4 New Puncturing Pattern for Bad Interleavers in Turbo-Codes Abdelmounaim Moulay Lakhdar 1, Malika
More informationOutput MAI Distributions of Linear MMSE Multiuser Receivers in DS-CDMA Systems
1128 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 47, NO. 3, MARCH 2001 Output MAI Distributions of Linear MMSE Multiuser Receivers in DS-CDMA Systems Junshan Zhang, Member, IEEE, Edwin K. P. Chong, Senior
More informationMultiuser Detection. Ateet Kapur Student Member, IEEE and Mahesh K. Varanasi Senior Member, IEEE
ACCEPTED FOR PUBLICATIONS IEEE TRANS. INFO. THEORY 1 Multiuser Detection for Overloaded CDMA Systems Ateet Kapur Student Member, IEEE and Mahesh K. Varanasi Senior Member, IEEE Abstract Multiuser detection
More informationModulation & Coding for the Gaussian Channel
Modulation & Coding for the Gaussian Channel Trivandrum School on Communication, Coding & Networking January 27 30, 2017 Lakshmi Prasad Natarajan Dept. of Electrical Engineering Indian Institute of Technology
More informationPSK bit mappings with good minimax error probability
PSK bit mappings with good minimax error probability Erik Agrell Department of Signals and Systems Chalmers University of Technology 4196 Göteborg, Sweden Email: agrell@chalmers.se Erik G. Ström Department
More informationChannel Coding and Interleaving
Lecture 6 Channel Coding and Interleaving 1 LORA: Future by Lund www.futurebylund.se The network will be free for those who want to try their products, services and solutions in a precommercial stage.
More informationOnamethodtoimprove correlation properties of orthogonal polyphase spreading sequences
Regular paper Onamethodtoimprove correlation properties of orthogonal polyphase spreading sequences Beata J. Wysocki and Tadeusz A. Wysocki Abstract In this paper, we propose a simple but efficient method
More informationBit-wise Decomposition of M-ary Symbol Metric
Bit-wise Decomposition of M-ary Symbol Metric Prepared by Chia-Wei Chang Advisory by Prof. Po-Ning Chen In Partial Fulfillment of the Requirements For the Degree of Master of Science Department of Communications
More informationConstrained Detection for Multiple-Input Multiple-Output Channels
Constrained Detection for Multiple-Input Multiple-Output Channels Tao Cui, Chintha Tellambura and Yue Wu Department of Electrical and Computer Engineering University of Alberta Edmonton, AB, Canada T6G
More informationA Computationally Efficient Block Transmission Scheme Based on Approximated Cholesky Factors
A Computationally Efficient Block Transmission Scheme Based on Approximated Cholesky Factors C. Vincent Sinn Telecommunications Laboratory University of Sydney, Australia cvsinn@ee.usyd.edu.au Daniel Bielefeld
More informationRandom Matrices and Wireless Communications
Random Matrices and Wireless Communications Jamie Evans Centre for Ultra-Broadband Information Networks (CUBIN) Department of Electrical and Electronic Engineering University of Melbourne 3.5 1 3 0.8 2.5
More informationBASICS OF DETECTION AND ESTIMATION THEORY
BASICS OF DETECTION AND ESTIMATION THEORY 83050E/158 In this chapter we discuss how the transmitted symbols are detected optimally from a noisy received signal (observation). Based on these results, optimal
More informationADVANCES IN MULTIUSER DETECTION
ADVANCES IN MULTIUSER DETECTION Michael Honig Northwestern University A JOHN WILEY & SONS, INC., PUBLICATION CHAPTER 3 ITERATIVE TECHNIQUES ALEX GRANT AND LARS RASMUSSEN 4 ITERATIVE TECHNIQUES 3.1 INTRODUCTION
More informationA Simple Example Binary Hypothesis Testing Optimal Receiver Frontend M-ary Signal Sets Message Sequences. possible signals has been transmitted.
Introduction I We have focused on the problem of deciding which of two possible signals has been transmitted. I Binary Signal Sets I We will generalize the design of optimum (MPE) receivers to signal sets
More informationAn Introduction to Low Density Parity Check (LDPC) Codes
An Introduction to Low Density Parity Check (LDPC) Codes Jian Sun jian@csee.wvu.edu Wireless Communication Research Laboratory Lane Dept. of Comp. Sci. and Elec. Engr. West Virginia University June 3,
More informationLattice Reduction Aided Precoding for Multiuser MIMO using Seysen s Algorithm
Lattice Reduction Aided Precoding for Multiuser MIMO using Seysen s Algorithm HongSun An Student Member IEEE he Graduate School of I & Incheon Korea ahs3179@gmail.com Manar Mohaisen Student Member IEEE
More informationImpact of channel-state information on coded transmission over fading channels with diversity reception
Impact of channel-state information on coded transmission over fading channels with diversity reception Giorgio Taricco Ezio Biglieri Giuseppe Caire September 4, 1998 Abstract We study the synergy between
More informationPerformance of DS-CDMA Systems With Optimal Hard-Decision Parallel Interference Cancellation
2918 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 49, NO. 11, NOVEMBER 2003 Performance of DS-CDMA Systems With Optimal Hard-Decision Parallel Interference Cancellation Remco van der Hofstad Marten J.
More informationReceived Signal, Interference and Noise
Optimum Combining Maximum ratio combining (MRC) maximizes the output signal-to-noise ratio (SNR) and is the optimal combining method in a maximum likelihood sense for channels where the additive impairment
More informationCDMA Systems in Fading Channels: Admissibility, Network Capacity, and Power Control
962 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 46, NO. 3, MAY 2000 CDMA Systems in Fading Channels: Admissibility, Network Capacity, and Power Control Junshan Zhang, Student Member, IEEE, and Edwin
More informationConstellation Shaping for Communication Channels with Quantized Outputs
Constellation Shaping for Communication Channels with Quantized Outputs Chandana Nannapaneni, Matthew C. Valenti, and Xingyu Xiang Lane Department of Computer Science and Electrical Engineering West Virginia
More informationBayesian Data Fusion for Asynchronous DS-CDMA Sensor Networks in Rayleigh Fading
Bayesian Data Fusion for Asynchronous DS-CDMA Sensor Networs in Rayleigh Fading Justin S. Dyer 1 Department of EECE Kansas State University Manhattan, KS 66506 E-mail: jdyer@su.edu Balasubramaniam Natarajan
More informationPerformance of small signal sets
42 Chapter 5 Performance of small signal sets In this chapter, we show how to estimate the performance of small-to-moderate-sized signal constellations on the discrete-time AWGN channel. With equiprobable
More informationECE Information theory Final (Fall 2008)
ECE 776 - Information theory Final (Fall 2008) Q.1. (1 point) Consider the following bursty transmission scheme for a Gaussian channel with noise power N and average power constraint P (i.e., 1/n X n i=1
More informationINFORMATION PROCESSING ABILITY OF BINARY DETECTORS AND BLOCK DECODERS. Michael A. Lexa and Don H. Johnson
INFORMATION PROCESSING ABILITY OF BINARY DETECTORS AND BLOCK DECODERS Michael A. Lexa and Don H. Johnson Rice University Department of Electrical and Computer Engineering Houston, TX 775-892 amlexa@rice.edu,
More informationEVALUATION OF PACKET ERROR RATE IN WIRELESS NETWORKS
EVALUATION OF PACKET ERROR RATE IN WIRELESS NETWORKS Ramin Khalili, Kavé Salamatian LIP6-CNRS, Université Pierre et Marie Curie. Paris, France. Ramin.khalili, kave.salamatian@lip6.fr Abstract Bit Error
More informationInterleave Division Multiple Access. Li Ping, Department of Electronic Engineering City University of Hong Kong
Interleave Division Multiple Access Li Ping, Department of Electronic Engineering City University of Hong Kong 1 Outline! Introduction! IDMA! Chip-by-chip multiuser detection! Analysis and optimization!
More informationCooperative Diversity in CDMA over Nakagami m Fading Channels
Cooperative Diversity in CDMA over Nakagami m Fading Channels Ali Moftah Ali Mehemed A Thesis in The Department of Electrical and Computer Engineering Presented in Partial Fulfillment of the Requirements
More informationPractical Polar Code Construction Using Generalised Generator Matrices
Practical Polar Code Construction Using Generalised Generator Matrices Berksan Serbetci and Ali E. Pusane Department of Electrical and Electronics Engineering Bogazici University Istanbul, Turkey E-mail:
More informationLinear Block Codes. Saravanan Vijayakumaran Department of Electrical Engineering Indian Institute of Technology Bombay
1 / 26 Linear Block Codes Saravanan Vijayakumaran sarva@ee.iitb.ac.in Department of Electrical Engineering Indian Institute of Technology Bombay July 28, 2014 Binary Block Codes 3 / 26 Let F 2 be the set
More informationWireless Communication Technologies 16:332:559 (Advanced Topics in Communications) Lecture #17 and #18 (April 1, April 3, 2002)
Wireless Communication echnologies Lecture 7 & 8 Wireless Communication echnologies 6:33:559 (Advanced opics in Communications) Lecture #7 and #8 (April, April 3, ) Instructor rof. arayan Mandayam Summarized
More informationShannon meets Wiener II: On MMSE estimation in successive decoding schemes
Shannon meets Wiener II: On MMSE estimation in successive decoding schemes G. David Forney, Jr. MIT Cambridge, MA 0239 USA forneyd@comcast.net Abstract We continue to discuss why MMSE estimation arises
More informationImproved MUSIC Algorithm for Estimation of Time Delays in Asynchronous DS-CDMA Systems
Improved MUSIC Algorithm for Estimation of Time Delays in Asynchronous DS-CDMA Systems Thomas Ostman, Stefan Parkvall and Bjorn Ottersten Department of Signals, Sensors and Systems, Royal Institute of
More informationMessage-Passing Decoding for Low-Density Parity-Check Codes Harish Jethanandani and R. Aravind, IIT Madras
Message-Passing Decoding for Low-Density Parity-Check Codes Harish Jethanandani and R. Aravind, IIT Madras e-mail: hari_jethanandani@yahoo.com Abstract Low-density parity-check (LDPC) codes are discussed
More informationA SEMI-BLIND TECHNIQUE FOR MIMO CHANNEL MATRIX ESTIMATION. AdityaKiran Jagannatham and Bhaskar D. Rao
A SEMI-BLIND TECHNIQUE FOR MIMO CHANNEL MATRIX ESTIMATION AdityaKiran Jagannatham and Bhaskar D. Rao Department of Electrical and Computer Engineering University of California, San Diego La Jolla, CA 9093-0407
More informationIntroduction to Convolutional Codes, Part 1
Introduction to Convolutional Codes, Part 1 Frans M.J. Willems, Eindhoven University of Technology September 29, 2009 Elias, Father of Coding Theory Textbook Encoder Encoder Properties Systematic Codes
More informationA Thesis for the Degree of Master. An Improved LLR Computation Algorithm for QRM-MLD in Coded MIMO Systems
A Thesis for the Degree of Master An Improved LLR Computation Algorithm for QRM-MLD in Coded MIMO Systems Wonjae Shin School of Engineering Information and Communications University 2007 An Improved LLR
More informationexercise in the previous class (1)
exercise in the previous class () Consider an odd parity check code C whose codewords are (x,, x k, p) with p = x + +x k +. Is C a linear code? No. x =, x 2 =x =...=x k = p =, and... is a codeword x 2
More informationPerformance Analysis of BPSK over Joint Fading and Two-Path Shadowing Channels
IEEE VTC-Fall 2014, Vancouver, Sept. 14-17, 2014 IqIq Performance of BPSK over Joint Fading and Two-Path Shadowing Channels I. Dey and G. G. Messier Electrical and Computer Engineering University of Calgary,
More informationChapter 7. Error Control Coding. 7.1 Historical background. Mikael Olofsson 2005
Chapter 7 Error Control Coding Mikael Olofsson 2005 We have seen in Chapters 4 through 6 how digital modulation can be used to control error probabilities. This gives us a digital channel that in each
More informationLECTURE 18. Lecture outline Gaussian channels: parallel colored noise inter-symbol interference general case: multiple inputs and outputs
LECTURE 18 Last time: White Gaussian noise Bandlimited WGN Additive White Gaussian Noise (AWGN) channel Capacity of AWGN channel Application: DS-CDMA systems Spreading Coding theorem Lecture outline Gaussian
More informationLecture 8: MIMO Architectures (II) Theoretical Foundations of Wireless Communications 1. Overview. Ragnar Thobaben CommTh/EES/KTH
MIMO : MIMO Theoretical Foundations of Wireless Communications 1 Wednesday, May 25, 2016 09:15-12:00, SIP 1 Textbook: D. Tse and P. Viswanath, Fundamentals of Wireless Communication 1 / 20 Overview MIMO
More informationError Correction and Trellis Coding
Advanced Signal Processing Winter Term 2001/2002 Digital Subscriber Lines (xdsl): Broadband Communication over Twisted Wire Pairs Error Correction and Trellis Coding Thomas Brandtner brandt@sbox.tugraz.at
More informationELE539A: Optimization of Communication Systems Lecture 15: Semidefinite Programming, Detection and Estimation Applications
ELE539A: Optimization of Communication Systems Lecture 15: Semidefinite Programming, Detection and Estimation Applications Professor M. Chiang Electrical Engineering Department, Princeton University March
More informationOn Random Rotations Diversity. Motorola Semiconductors Toulouse, France. ENST, 46 rue Barrault Paris, France. November 19, 1999.
On Random Rotations Diversity and Minimum MSE Decoding of Lattices Catherine LAMY y - Joseph BOUTROS z y Motorola Semiconductors Toulouse, France z ENST, 46 rue Barrault 75013 Paris, France lamyc@enst.fr,
More informationSNR i = 2E b 6N 3. Where
Correlation Properties Of Binary Sequences Generated By The Logistic Map-Application To DS-CDMA *ZOUHAIR BEN JEMAA, **SAFYA BELGHITH *EPFL DSC LANOS ELE 5, 05 LAUSANNE **LABORATOIRE SYSCOM ENIT, 002 TUNIS
More informationThe Performance of Quaternary Amplitude Modulation with Quaternary Spreading in the Presence of Interfering Signals
Clemson University TigerPrints All Theses Theses 1-015 The Performance of Quaternary Amplitude Modulation with Quaternary Spreading in the Presence of Interfering Signals Allison Manhard Clemson University,
More informationState-of-the-Art Channel Coding
Institut für State-of-the-Art Channel Coding Prof. Dr.-Ing. Volker Kühn Institute of Communications Engineering University of Rostock, Germany Email: volker.kuehn@uni-rostock.de http://www.int.uni-rostock.de/
More informationExpected Error Based MMSE Detection Ordering for Iterative Detection-Decoding MIMO Systems
Expected Error Based MMSE Detection Ordering for Iterative Detection-Decoding MIMO Systems Lei Zhang, Chunhui Zhou, Shidong Zhou, Xibin Xu National Laboratory for Information Science and Technology, Tsinghua
More informationOn Network Interference Management
On Network Interference Management Aleksandar Jovičić, Hua Wang and Pramod Viswanath March 3, 2008 Abstract We study two building-block models of interference-limited wireless networks, motivated by the
More informationCoding Techniques for Data Storage Systems
Coding Techniques for Data Storage Systems Thomas Mittelholzer IBM Zurich Research Laboratory /8 Göttingen Agenda. Channel Coding and Practical Coding Constraints. Linear Codes 3. Weight Enumerators and
More informationGraph-based codes for flash memory
1/28 Graph-based codes for flash memory Discrete Mathematics Seminar September 3, 2013 Katie Haymaker Joint work with Professor Christine Kelley University of Nebraska-Lincoln 2/28 Outline 1 Background
More informationOn the Performance of. Golden Space-Time Trellis Coded Modulation over MIMO Block Fading Channels
On the Performance of 1 Golden Space-Time Trellis Coded Modulation over MIMO Block Fading Channels arxiv:0711.1295v1 [cs.it] 8 Nov 2007 Emanuele Viterbo and Yi Hong Abstract The Golden space-time trellis
More informationImproved Multiple Feedback Successive Interference Cancellation Algorithm for Near-Optimal MIMO Detection
Improved Multiple Feedback Successive Interference Cancellation Algorithm for Near-Optimal MIMO Detection Manish Mandloi, Mohammed Azahar Hussain and Vimal Bhatia Discipline of Electrical Engineering,
More informationDecision Feedback Multiuser Detection: A Systematic Approach
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 45, NO. 1, JANUARY 1999 219 Decision Feedback Multiuser Detection: A Systematic Approach Mahesh K. Varanasi, Senior Member, IEEE Abstract A systematic approach
More informationChannel Coding I. Exercises SS 2017
Channel Coding I Exercises SS 2017 Lecturer: Dirk Wübben Tutor: Shayan Hassanpour NW1, Room N 2420, Tel.: 0421/218-62387 E-mail: {wuebben, hassanpour}@ant.uni-bremen.de Universität Bremen, FB1 Institut
More information4 An Introduction to Channel Coding and Decoding over BSC
4 An Introduction to Channel Coding and Decoding over BSC 4.1. Recall that channel coding introduces, in a controlled manner, some redundancy in the (binary information sequence that can be used at the
More informationBit Error Rate Estimation for a Joint Detection Receiver in the Downlink of UMTS/TDD
in Proc. IST obile & Wireless Comm. Summit 003, Aveiro (Portugal), June. 003, pp. 56 60. Bit Error Rate Estimation for a Joint Detection Receiver in the Downlink of UTS/TDD K. Kopsa, G. atz, H. Artés,
More information