Low Complexity QPP Interleavers of Turbo Coding

Similar documents
PERMUTATION POLYNOMIAL BASED INTERLEAVERS FOR TURBO CODES OVER INTEGER RINGS: THEORY AND APPLICATIONS.

QPP Interleaver Based Turbo-code For DVB-RCS Standard

On Quadratic Inverses for Quadratic Permutation Polynomials over Integer Rings

Interleaver Design for Turbo Codes

Chapter 9 Mathematics of Cryptography Part III: Primes and Related Congruence Equations

Chapter 14: Divisibility and factorization

The New Multi-Edge Metric-Constrained PEG/QC-PEG Algorithms for Designing the Binary LDPC Codes With Better Cycle-Structures

AALTO UNIVERSITY School of Electrical Engineering. Sergio Damian Lembo MODELING BLER PERFORMANCE OF PUNCTURED TURBO CODES

Outline. MSRI-UP 2009 Coding Theory Seminar, Week 2. The definition. Link to polynomials

Low complexity state metric compression technique in turbo decoder

Algebra for error control codes

Parallel Concatenated Chaos Coded Modulations

Factoring Algorithms Pollard s p 1 Method. This method discovers a prime factor p of an integer n whenever p 1 has only small prime factors.

Performance of Multi Binary Turbo-Codes on Nakagami Flat Fading Channels

Lecture Notes. Advanced Discrete Structures COT S

Chapter 6 Reed-Solomon Codes. 6.1 Finite Field Algebra 6.2 Reed-Solomon Codes 6.3 Syndrome Based Decoding 6.4 Curve-Fitting Based Decoding

Algebraic Interleavers in Turbo Codes. California State Polytechnic University, Pomona. and Loyola Marymount University

Lecture 12. Block Diagram

Design of Non-Binary Quasi-Cyclic LDPC Codes by Absorbing Set Removal

Chapter 1. Functions 1.1. Functions and Their Graphs

Notes 6: Polynomials in One Variable

ECEN 5022 Cryptography

Chinese Remainder Theorem

g(x) = 1 1 x = 1 + x + x2 + x 3 + is not a polynomial, since it doesn t have finite degree. g(x) is an example of a power series.

Lecture 7.5: Euclidean domains and algebraic integers

6.S897 Algebra and Computation February 27, Lecture 6

On the Construction and Decoding of Cyclic LDPC Codes

Part II. Number Theory. Year

Definitions. Notations. Injective, Surjective and Bijective. Divides. Cartesian Product. Relations. Equivalence Relations

Experience in Factoring Large Integers Using Quadratic Sieve

CPSC 467: Cryptography and Computer Security

Attempt QUESTIONS 1 and 2, and THREE other questions. penalised if you attempt additional questions.

Performance Analysis and Code Optimization of Low Density Parity-Check Codes on Rayleigh Fading Channels

(January 14, 2009) q n 1 q d 1. D = q n = q + d

Lecture 7: Polynomial rings

On The Weights of Binary Irreducible Cyclic Codes

MacWilliams Extension Theorem for the Lee Weight

Definition For a set F, a polynomial over F with variable x is of the form

Iterative Encoding of Low-Density Parity-Check Codes

Self-dual Repeated Root Cyclic and Negacyclic Codes over Finite Fields

Algebra. Modular arithmetic can be handled mathematically by introducing a congruence relation on the integers described in the above example.

18. Cyclotomic polynomials II

CSIR - Algebra Problems

GALOIS THEORY. Contents

Public-key Cryptography: Theory and Practice

MATH 431 PART 2: POLYNOMIAL RINGS AND FACTORIZATION

Decomposing Bent Functions

Course 2316 Sample Paper 1

Mathematical Foundations of Cryptography

Elliptic Curves I. The first three sections introduce and explain the properties of elliptic curves.

New Puncturing Pattern for Bad Interleavers in Turbo-Codes

Lecture 7.4: Divisibility and factorization

CHAPTER 3. Congruences. Congruence: definitions and properties

Prime Fields 04/05/2007. Hybrid system simulator for ODE 1. Galois field. The issue. Prime fields: naïve implementation

Chapter 7: Channel coding:convolutional codes

Finite Fields. Mike Reiter

Mathematics for Cryptography

Turbo Codes for Deep-Space Communications

An Efficient Algorithm for Finding Dominant Trapping Sets of LDPC Codes

Practice problems for first midterm, Spring 98

Chapter 0: Preliminaries

Linear Cyclic Codes. Polynomial Word 1 + x + x x 4 + x 5 + x x + x

Reed-Solomon codes. Chapter Linear codes over finite fields

x 3 2x = (x 2) (x 2 2x + 1) + (x 2) x 2 2x + 1 = (x 4) (x + 2) + 9 (x + 2) = ( 1 9 x ) (9) + 0

Math 314 Course Notes: Brief description

Codes and Rings: Theory and Practice

Part V. Chapter 19. Congruence of integers

FUNCTIONS OVER THE RESIDUE FIELD MODULO A PRIME. Introduction

MA257: INTRODUCTION TO NUMBER THEORY LECTURE NOTES

Efficient Bounded Distance Decoders for Barnes-Wall Lattices

School of Mathematics

New algebraic decoding method for the (41, 21,9) quadratic residue code

LDPC Codes. Intracom Telecom, Peania

MATH 361: NUMBER THEORY FOURTH LECTURE

Introduction to Number Theory

1. Group Theory Permutations.

Self-Dual Cyclic Codes

CONTENTS. Preface Preliminaries 1

VLSI Architecture of Euclideanized BM Algorithm for Reed-Solomon Code

Mathematical Olympiad Training Polynomials

Efficient Decoding of Permutation Codes Obtained from Distance Preserving Maps

Commutative Rings and Fields

Basic Algorithms in Number Theory

RINGS: SUMMARY OF MATERIAL

Analysis of coding on non-ergodic block-fading channels

Homework 4 Solutions

Relations. Binary Relation. Let A and B be sets. A (binary) relation from A to B is a subset of A B. Notation. Let R A B be a relation from A to B.

Lecture 11 - Basic Number Theory.

NOTES ON FINITE FIELDS

Know the meaning of the basic concepts: ring, field, characteristic of a ring, the ring of polynomials R[x].

CYCLOTOMIC POLYNOMIALS

Metacommutation of Hurwitz primes

Turbo Codes. Manjunatha. P. Professor Dept. of ECE. June 29, J.N.N. College of Engineering, Shimoga.

Gauss s Theorem. Theorem: Suppose R is a U.F.D.. Then R[x] is a U.F.D. To show this we need to constuct some discrete valuations of R.

May 6, Be sure to write your name on your bluebook. Use a separate page (or pages) for each problem. Show all of your work.

Section IV.23. Factorizations of Polynomials over a Field

M3P11/M4P11/M5P11. Galois Theory

RSA Cryptosystem and Factorization

Course 311: Michaelmas Term 2005 Part III: Topics in Commutative Algebra

Lagrange s polynomial

Transcription:

Low Complexity QPP Interleavers of Turbo Coding Guan-Jhe Wei, Ching-Lung Chi Dept. of Computer and Communication SHU-TE University Kaohsiung City, Taiwan Chien-Lung Kuo, Chun-Chieh Li Dept. of Computer and Communication SHU-TE University Kaohsiung City, Taiwan ABSTRACT Interleaver is an important component of turbo code and has a strong impact on its error correcting performance. Quadratic permutation polynomial (QPP) interleaver is a contention-free interleaver which is suitable for parallel turbo decoder implementation. To improve the performance of LTE QPP interleaver, largest-spread and maximum-spread QPP interleavers are analyzed for Long Term Evolution (LTE) turbo codes in this paper. Compared with LTE QPP interleaver, those algorithms have low computing complexity and better performances from simulation results. Keywords: Quadratic permutation polynomial (QPP) interleaver, Long Term Evolution (LTE), largest-spread QPP interleaver, maximum-spread QPP interleaver. I. INTRODUCTION The selection of turbo coding was considered during the study phase of Long Term Evolution (LTE) standard to meet the stringent requirements. Following deliberations within the working group, the quadratic permutation polynomials (QPP) were selected as interleavers for turbo codes, emerging as the most promising solutions to the LTE requirements. The QPP interleavers for the LTE standard [1] involve 1 different lengths. In this paper we take two kinds of methods to improve the LTE QPP interleaver. The polynomial interleavers offer the following benefits []: special performance, complete algebraic structure, and efficient implementation (high speed and low memory requirements). A QPP interleaver of length L is defined as: π(x) = (q 0 + q 1 x + q x )mod L, x = 0 L 1 (1) where q 1 and q are chosen so that the quadratic polynomialin (1) is a permutation polynomial (i.e. the set{π(0), π(1),, π(l 1)} is a permutation of the set {0,1,, L 1} and q 0 determines a shift of the permutation elements. In the following we only consider quadratic polynomials with free term q 0 = 0, as for the QPP interleavers in the LTEstandard. If Z L = {0,1,, L 1}, then the permutation function isπ: Z L Z L. The spread factor D is defined as D = min i j i,j Z L δ L p i, p j () δ L p i, p j is the Lee metric between pointsp i = i, π(i) and p j = j, π(j) δ L p i, p j = i j L + π(i) π(j) L, (3) Where i j L = mmm{(i j)(mod L), (j i)(mod L)}. () The quadratic polynomials which lead to the largest spreading factor Dfor some interleaver lengths are given in []. An algorithm for faster computation of D is also presented. It is based on the representatives of orbits in the representation of interleavercode. Section II gives a brief review of QPP interleaver. Largest-Spread and Maximum-Spread QPP interleavers are the topics of Section III. Section IV presents the bit error rates (BER) resulted from simulations for LTE standard, LS and MS QPP interleavers length 51. Section V concludes the paper. II. QPP INTERLEAVERS A. Permutation Polynomial(PP) over Integer N Given an integern, a polynomialf(x) = f 0 + f 1 x + f x + + f m x m, where f 0, f 1, f,,f m and m are nonnegative integers, is said to be a permutation polynomial over Z N when F(x) permutes {0, 1,,, N 1}. In this paper, all the summations and multiplications are modulo N unless explicitly stated. We further define the formal derivative of the polynomial F(x) to be a polynomial F (x) such that F (x) = f 1 + f x + 3f 3 x + + mf m x m 1. For any integer N, whether polynomial F(x) is a permutation polynomial can be determined by the following theorem [3]. Theorem 1: Let F(x) = f 1 + f x + 3f 3 x + + mf m x m be a polynomial with integer coefficients. F(x) is a permutation polynomial over the integer ring N = n if and only if 1) f 1 is odd, ) f + f + f 6 + is even, and 3) f 3 + f 5 + f 7 + is even. Theorem : F(x) is a permutation polynomial over the integer ringn = n if and only iff(x)is a permutation polynomial over Z(p) and F (x) 0modulopfor all integersxp n. Theorem 3: For anyn = p n pp, wherep i=1 i sare distinctprime numbers, F(x) is a permutation polynomial modulo Nif and only iff(x)is also a n permutation polynomial modulop pp i, i. n pp Because the function of interleaver is to permute

{0,1,, L 1}, we can apply the construction of permutation polynomial over integer N to Turbo interleavers by properly choosing N and polynomial coefficients f. The first-degree polynomial F(x) = f 0 + f 1 x (linear interleaver) is the simplest one among all permutation polynomials. However, the linear interleaver has very bad input weight error event characteristics, causing a higher error floor for medium or long frame size cases. And quadratic permutation polynomial can overcome these defects, this leaves it the next in the chain. When m =, F(x) becomes F(x) = f 0 + f 1 x + f x. Notice that the coefficient f 0 just corresponds to a cyclic rotation in the interleaved sequence. It does not appear in the conditions for F(x) to be a permutation polynomial and it does not affect the performance. We ignore f 0 for simplification, so F(x) becomes quadratic polynomial F(x) = f 1 x + f x.. B. QPP Interleavers QPP Interleaver completes interleaving by making QP (Quadratic Polynomial) satisfy some conditions. In order to make F(x) be a QPP, coefficients f 1 and f have to meet some special conditions. Then the following corollaries deduced from theorem 1 and are given. Corollary 1 [3]: A quadratic polynomial of the formf(x) = f 1 x + f x is a permutation polynomial overz p n if and only iff 1 0andf = 0 modp. Corollary []: Let N = p n N,p p P, denote y divides z by y z and denote y can not divides z byy z. The necessary and sufficient condition for a quadratic polynomial F(x) = f 1 x + f x (mmm N) to be a permutation polynomial can be divided into two cases. 1) Nand N (i.e., n N, = 1) f 1 + f is odd, gcd (f 1, N ) = 1, and f = p n F,p p P, n F,p 1, psuch thatp andn N,p 1. ) Either N or N (i.e., n N, 1 ) gcd (f 1, N) = 1 and f = p n F,p p P, n F,p 1, psuch thatn N,p 1. Apply above corollaries, we can find some available coefficientsf 1 and f in QPP interleavers by computer searching. However, the interleavers with different coefficients have different performances, so we have to choose better based on these coefficients. Then we discuss search metric on choosing better coefficients, before this we present some definitions firstly: 1) δ N Distance δ N (p x1, p x ) = x 1 x N + f(x 1 ) f(x ) N (5) Where i j N = min{(i j)(mod N), (i j)(mod N)}. ) Spread Factor D(f ) D(f ) = min δ N p i, p j p i, p j F (6) Wherei, j N and i j. 3) Nonlinearity Metric of QPP Interleaversζ (F) ζ (F) = N gcd(f, N) (7) Obviously, QPP interleaver can be viewed as a linear interleaver f 1 x that is disturbed by quadratic interleaverf x, the periodicity of the disturbance is at mostζ.this disturbance can overcome a higher error floor of linear interleaver at a certain extent. Since the minimum distance of a turbo code is now known to grow at most logarithmically, therefore, the spread factor that controls the effective free distance should be rewarded at most logarithmically. The nonlinearityζis expected to have a proportional reduction in the multiplicities of low-weight code words so it is reasonable to leave it. So we can get a simple metric of searching coefficients of QPP interleaver []. max Ω(f) = ln D(f) ζ(f) () Further choosing coefficients f 1 and f by maximizationω(f), we can get some coefficients with good performances. Sinceζwill grow fast as N grows, it is not good for improving decoding performance, so it must be modified. We define a new modified nonlinearity metric ζ which can avoid above defect. So we have a corresponding refined metric max Ω (f) = ln D(f) ζ (f) (9) where ζ = {f x (mod N) x = 0,1,, ζ 1} Through this refined metric, we search coefficients more than one couple, since the up-bound of interleavers spread factor D(f ) is N, in order to avoid D(f ) not becoming so small when frame sizenis small that we limit the search process under the condition of D(f ) β N, where β = 0.5. While N > 000, must decrease to 0.30 properly. III. LARGEST-SPREAD AND MAXIMUM-SPREAD QPP INTERLEAVERS A. Largest-Spread QPP Interleaver In this section we present a theorem which states sufficient conditions to be satisfied by the coefficients of two quadratic polynomials [], so that the resulting interleavers are identical. For the LTE standard, the interleaver s length is always an even number. Theorem : Consider two QPP interleavers described by the following polynomials (the free term is considered zero): π 1 (x) = (p 1 x + p x )mod L,x = 0,1,, L 1 (10) π (x) = (q 1 x + q x )mod L,x = 0,1,, L 1 (11) If L is even, then p 1 > q 1, and the following relation is fulfilled: p 1 q 1 = ±(p q ) = L (1) the two quadratic polynomials lead to identical permutations. Proof:

For the two QPP to lead to identical permutations, it is required that π 1 (x) = π (x), x = 0,1,, L 1 (13) we denote p 1 x + p x = k 1 L + π 1 (x) (1) q 1 x + p x = k L + π (x) (15) Where k 1, k N.Under the conditions above we have to Show that there arek 1, k N, x = 0,1,, L 1, which verify relationship (13). Subtracting (1) from (15) and considering (13), we have: (p q )x + (p 1 q 1 ) = (k 1 k ) L (16) The solution of this quadratic equation is: x = (p 1 q 1 )+ (p 1 q 1 ) +(p q )(k 1 k )L (p q ) Using (1) from the theorem statement, we have L + L ± L (k 1 k )L x = = ±L 1 + 1 ± (k 1 k ) ± where 1 ( x + 1) k 1 k = x(x 1) = or (17) (1) (19) k 1 k = ( x + 1) 1 x(x 1) = (0) Relationships (19) and (0) could also be obtained as follows. From (1) we have p 1 = q 1 + L (1) p = q + L () Then, from (1) we get k 1 L = p 1 x + p x π 1 (x) (3) or, taking into account (1), () and (13),k 1 L = q 1 x + q x π (x) + (L ) (x ± x ) () or, from (15), k 1 L = (L ) (x ± x ) + k L (5) From here, (19) and (0) result immediately. Because x = 0,1,, L 1, x(x 1) and x(x + 1) are divisible by, i.e. k 1 k Z then, there arek 1, k N which verify relation (13). The theorem shows that two QPPs generate identical permutation functions, if the coefficients are at the same distance (L ). Therefore, we can only consider coefficients q 1 = 0, 1,, (L ) 1 in polynomial searching, because the interleaverπ 1 (x) = (pp + p x )mod L is the same as theinterleaver π(x) = (q 1 + L )x + (q + L )x mod L, if q < L, or as the interleaver described by the permutationπ(x) = (q 1 + L )x + (q L )x mod L, if q L. As the number of searched QPPs is halved, so is the search time, therefore speeding up the search process. TABLE I EXAMPLES OF MAXIMUM-SPREAD QPP INTERLEAVERS k N f(x) f 1 (x) D = ub D (N) ζ ζ 5 6 7 9 1 51 0 19 376 13107 15x + 3x 31x + 6x 63x + 1x 17x + 56x 55x + 51x 511x + 10x 17x + 3x 33x + 6x 65x + 1x 19x + 56x 57x + 51x 513x + 10x B. Maximum-Spread QPP Interleaver Theorem 5: The following is an infinite sequence of QPPs that generate maximum-spread interleavers: f(x) = ( k 1)x + k+1 x (mod k 1 ) k = 1,,3, (6) Strictly, we have QPP interleavers only when k > 3. The first observation is that fork = 1 and k =, the 16 35 6 1 56 51 1 3 6 3 7 1 3 6 1 56 51 10 0 corresponding QPPsf(x) are immediately reduced to first-degree polynomials because the second-degree coefficientf 0 (modn). We now show after some preliminaries that fork = 3, the QPP f(x) is also reducible to a first-degree polynomial although f = k+1 = 16 (modn = 3). Definition: A polynomial z(x)(modn) that

evaluates to zero for all x, i.e., z(x) 0 (modn) x is called a zero-polynomial. Proposition : Let Nbe an integer factorable as N = pp.the following is a zero-polynomial of degreep: p 1 z(x) = mm (x + k + i)(mod N) k, m Z N. i=0 Proof: Exactly one of the numbers in the sequence x + k + i, 0 i p is congruent to0modulo p.therefore, z(x) mod N. Thens(x) p(x) + z(x) (mod N), i.e., s(x) and p(x) + z(x) are equivalent functions modulo N. Proof: This follows directly from the definition of a zero polynomial. From Proposition 1, the following is a zero polynomial of second degree for N = 3: z(x) = 16x(x + 1) = 16x + 16x (mod 3) Therefore, for k = 3, by adding f(x) to z(x) we obtain the equivalent first-degree polynomial s(x) f(x) + z(x) = (16x + 7x) + (16x + 16x) 3x (mod 3) Fork > 3, the polynomials are not reducible to first-degree polynomials because they have a degree of nonlinearity ζ, larger than 1 as explained in Section III (In fact, for k=1,,3, we have ζ = 1). The first six terms of maximum-spread QPPs that are not reducible to first-degree polynomials are shown in Table I. The last three columns of the table I are the degrees of nonlinearity ζ, the refined degree of nonlinearity ζ, and the degree of shift-invariance. The inverse functions f 1 (x) are also provided in Table I. The closed-form expression forf 1 (x) is f 1 (x) = ( k 1)x + k+1 x (mod k 1 ). One easily verifies that f f 1 (x) f 1 f(x) x (mod N). For general QPPs, we are not aware of a closed-form expression for the inverse functions. Further, not all QPPs have an inverse polynomial that is a QPP. However, if it exists, it is efficiently computed algebraically using the extended Euclidean algorithm []. It is easily verified that the necessary and sufficient condition for the existence of a QPP inverse [] for the polynomials in Theorem is satisfied. IV. SIMULATION PARAMETERS The simulations were performed for a turbo code having the global coding rate of 1/3, without puncturing, over an AWGN (Additive White Gaussian Noise) channel, with a BPSK (Binary Phase Shift Keying) modulation. The decoding algorithm is Max Log- MAP with iteration stopping criterion named LLR (Logarithm Likelihood Ratio) module. The maximum number of iteration is 1. The interleavers we have used are QPP interleaver, MS QPP interleaver and LS QPP interleaver for comparison. The data length is10 6, 0 for a block data cutting. Fig. 1 presents BER curves for the without coding and LTE QPP interleavers length 0. Fig. presents BER curves for the LTE, MS and LS QPP interleavers for length 51. From Fig. we note that BER for the MS interleaver is lower than for LS and LTE QPP interleavers. Figure 1.BERcurves for the interleaver length 0 Figure. BER curves for the interleaver length 51 V. CONCLUSION To improve the performance and reduce the complexity, largest-spread and maximum-spread QPP interleavers are analyzed and the Max-Log MAP algorithm is used for Long Term Evolution (LTE) turbo codes in this paper. The simulated BER curves outlined in Figure 3 show that the performance of the MS-QPP interleaver is better compared to the LTE and LS QPP interleavers. Because of the simplicity in structure, MS-QPP interleaver is suitable for LTE standard. REFERENCES [1] 3GPP TS 36.1 V10.3.0, 3rd Generation

Partnership Project, Multiplexing and channel coding, 011-09, (Release 10). [] O.Y. Takeshita, Permutation Polynomial Interleavers: An Algebraic-Geometric Perspective, Information Theory, IEEE Transactions on, vol.53, no.6, pp.116-13, June 007. [3] J. Sun and O.Y. Takeshita, Interleavers for turbo codes using permutation polynomials over integer rings, Information Theory, IEEE Transactions on, vol.51, no.1, pp.101-119, Jan. 005. [] J. Ryu and O.Y. Takeshita, On quadratic inverses for quadratic permutation polynomials over integer rings, Information Theory, IEEE Transactions on, vol.5, no.3, pp.15-160, March 006.