66 Mathematics of Distances and Applications A BRIEF SURVEY OF METRICS IN CODING THEORY Ernst Gabidulin Abstract: The main objects of Coding theory are metric vector or matrix spaces. Subsets of spaces are known as codes. The main problem is constructing codes of given pairwise distance and having maximal cardinality. Most known and most investigated spaces are Hamming spaces. Thousands papers and books are devoted to codes in the Hamming metric. We mention here only two books [1; 2] and will not consider this metric in details. Other metrics are investigated much less. In this paper, we give many examples of useful metrics. It is still non exhaustive review. Keywords: metrics and norms, the uniform and non-uniform Hamming metrics, the Lee and Sharma-Kaushik metrics, the city block (Manhattan) metric, the Varshamov metric, the burst metric, the 2-dimensional burst metric, the term rank metric, the rank metric, combinatorial metrics, projective metrics, graph metrics, the subspace metric. ACM Classification Keywords: A.0 General Literature - Conference proceedings MSC: 94B05, 94B20, 94B25 Introduction Coding theory studies techniques to correct errors arising during communications through noisy channels. Its distinguishing features are using discrete signals and introducing the artificial redundancy. Discrecity allows to describe signals in terms of abstract symbols not connected with any physical realization. The artificial redundancy gives possibilities to correct errors using hard enough combinatorial constructions of signals. One can say that coding theory uses a wide spectrum of mathematical tools from simple binary arithmetic to modern algebraic geometry. The main objects of Coding theory are metric vector spaces. Subsets of spaces are known as codes. Main problem is constructing codes of given cardinality and having maximal pairwise distance as large as possible. Most known and most investigated spaces are Hamming spaces. The distance function between two vectors is defined as the number of non identical their coordinates. Thousands papers and books are devoted to the Hamming metrics. We mention here only two books [1; 2]. Other metrics are investigated much less. In this paper, we describe connections between channels and metrics and give many examples of useful metrics. It is still non exhaustive review. Also it is important to mention that not all metrics allow the good mathematic theory. General properties of codes Let X be an alphabet of q elements. Let X n be the space of all vectors over X of dimension n. A code C⊆X n of size |C| = M and length n is defined as any set of n-vectors over X n: C {x x x } = 1, 2, ..., M . We assume also that the space of vectors X n is considered as a metric space. A metric can be defined either by a distance function, or by a norm function. We shall consider only integer-valued distance functions and norms. Mathematics of Distances and Applications 67 A distance d(x, y) between a n-vectors x, y is a function satisfying conditions d(x, y) ≥ 0, ∀ x, y; (Non-negative). d(x, y) = 0 ⇐⇒ x = y; (Zero value). d(x, y) = d(y, x); (Symmetry). d(x, y) ≤ d(x, z)+ d(z, y), ∀ x, y, z (Triangle inequality). A norm function N (x) should satisfy next axioms: N (x) ≥ 0, ∀ x; (Non-negative). N (x) = 0 ⇐⇒ x = 0; (Zero value). N (x + y) ≤ N (x)+ N (y), ∀ x, y (Triangle inequality). The norm function allows to construct the distance function as follows: d(x, y) := N (x − y). Often distance and norm functions are defined coordinate-wise. A distance d(x,y) between letters of the alphabet X (respectively, N (x), x ∈X ) is defined first. Assume that the distance takes all values 0, 1,...,D, where D is the maximal possible value. Then the distance between n-vectors x, y ∈X n is defined as follows: n d(x, y)= d(xi,yi). Xi=1 This distance takes values 0, 1,...,nD. There exist still distance functions which are not coordinate-wise. For instance, the Varshamov distance and the rank distance (see, below) can not be represented in such a manner. Similarly, for the coordinate-wise norm, we have n Nn(x)= N (xi). Xi=1 It is useful for applications to introduce the generator norm function D W (z)= N(i)zi, Xi=0 where N(i)= |x ∈X : N (x)= i| , i = 0, 1,...,D, is the number of elements X with norm i. D means the maximal norm. The generator norm function of the extended norm Nn(·) is clearly nD D n W (z)= N (i)zi = W (z)n = N(i)zi . n n ! Xi=0 Xi=0 We shall consider metrics defined by a coordinate-wise norm Nn(·). The main problem of coding theory is n constructing codes Cn ⊆ X of maximal cardinality M if minimal distance d is given. For a metric on X we define the average norm N and the average pair-wise distance D by D P N (x) P iN(i) x∈X i=0 N = q = q ; P P N (x−y) P N(x) x∈X y∈X x∈X D = q(q−1) = q , 68 Mathematics of Distances and Applications where N(x) denotes the average distance from X \x to x. X q If is an additive group, then N(x) does not depend on x. Moreover N(x) = q−1 N. Hence in this case q D = q−1 N. N x qn For the extended metric n( ) we have N n = Nn, Dn = qn−1 N n. If Cn is a code of cardinality M and of distance d> N n, then the Plotkin-style bound is valid: d M ≤ . d − N n Let Sd−1(0)= y : NN (y) ≤ d − 1 be the ball of radius d − 1 with the center at the all zero vector 0. Then asymptotically, when n → ∞ and d−1 x = Dn < N, the volume of the ball is equal to n(1−R) Vd−1 = |Sd−1(0)|≍ cq , where D − 1 R = H(α0, α1,...,αD)+ αi logq Ni; iP=1 i Niγ αi = W (γ) , i = 0, 1,...,D; γW ′(γ) γ is the positive root of W (γ) = xD, D − H(α0, α1,...,αD) = αi logq αi. iP=0 log q M − It follows the upper Gilbert-style bound for a code with rate R = n and distance d 1= xDn: D − − R = 1 H(α0, α1,...,αD) αi logq Ni. Xi=1 One can show that this equation can be reduced to the next simple form: − R = 1 logq W (γ)+ xD log γ; γW ′(γ) xD = W (γ) . Examples of metrics Hamming metric The most known is the Hamming metric. The Hamming norm wH (x) of a vector x is defined as the number of its non zero coordinates. The Hamming distance between x and y is the norm of its difference: d(x, y)= wH (x − y). The Hamming metric is matched strongly with all full symmetrical memoryless channels. Full symmetrical channels are channels such that all non diagonal elements of the transfer probability matrix are identical. Huge amount of papers are devoted to this metric. Mathematics of Distances and Applications 69 Norms and metrics for Zq Let the alphabets X be the integer ring Zq = {0, 1,...,q − 1}. Integer-valued norms and metrics can be defined in many ways. It is clear that for any norm N (i)= N (q − i), i ∈ Zq. All elements of Zq can be divided into subsets of equal weight elements Bj = {a : a ∈ Zq, N (a)= j} , j = ≤ q ∈ − ∈ 0, 1,...,D, where D 2 is the maximal norm. If a Bj, then also q a Bj. q The maximal norm D can take values between 1 and 2 . Open problem: find all possible values of D for Zq. Open problem: describe all non-equivalent norms for Zq. q Two extreme cases are the Hamming metric (D = 1) and the Lee metric (D = 2 ). The Hamming metric is defined by 0, if i = 0; N (i)= , 1, if i = 1,...,q − 1; so D = 1. The subsets of equal weight elements are B0 = {0} , B1 = {1, 2,...,q − 1} . The Lee metric is defined by 0, if i = 0; N ≤ ≤ q (i)= i, if 1 i 2 ; , N − q ≤ − (q i), if 2 < i q 1; q so D = 2 . The subsets of equal weight elements are q q B0 = {0} , B1 = {1,q − 1} ,B2 = {2,q − 2} ,...,B⌊ q ⌋ = ,q − . 2 nj2k j2ko Main results for codes with the Lee metric were obtained by Berlekamp [7]: • The weight generator function 2 q−1 1 + 2z + 2z + · · · + 2z 2 , if q odd; W z −2 ( )= ( 2 q q 1 + 2z + 2z + · · · + 2z 2 + z 2 , if q even. • The average vector weight 2 n q −1 , if q odd; N 4q n = ( q n 4 , if q even. • The Plotkin-style bound for cardinality M of a code with Lee distance d: d M ≤ , if d> N n. d − N n • The asymptotic Gilbert-style bound: a code with cardinality M and Lee distance d exists, if log q M − R = n = 1 logq W (γ)+ xD log γ; γW ′(γ) xD = W (γ) , q d−1 where D = 2 and xD = n . 70 Mathematics of Distances and Applications All non-equivalent norms for Z4 1. The Hamming metrics with the subsets of equal weight elements B0 = {0}, B1 = {1, 2, 3}, and with the distance matrix x\y 0 12 3 0 0 11 1 D = 1 1 01 1 . 2 1 10 1 3 1 11 0 2.