Classification of Boolean cubic forms of nine variables

Reed-Muller codes are error-correcting codes used in many areas related to coding theory, such as electrical engineering and computer science. The binary rth-order Reed-Muller code RM(r, n) can be viewed as the set of all n-variable Boolean functions of algebraic degree at most r. Despite the intense work on these codes, many problems are known to be hard (notably, determining their covering radius) and remain open to this day. Fourteen years ago, Carlet and Mesnager improved in [IEEE Transactions on Information Theory, “Improving the Upper Bounds on the Covering Radii of Binary Reed-Muller Codes”, 53(1), 2007] the upper bound on the covering radius of the Reed-Muller code of order 2, and they deduced improved upper bounds on the covering radii of the Reed-Muller codes of higher orders. Until 2021, these upper bounds remain the best ones in the literature. The Reed-Muller code RM(n -3, n), which corresponds to the dual of the Reed-Muller code RM(2, n), has attracted much attention. One of the main reasons is that it is precisely the code that has been considered to get the upper bounds derived by Carlet and Mesnager. Those upper bounds have been obtained thanks to the characterization of the codewords of the Reed-Muller code, whose Hamming weights are strictly less than 2.5 times the minimum distance 2n-r due to Kasami, Tokura, and Azumi. Despite their impressive work in the seventieth, a more refined study and profound description of those codewords of RM(n -3, n) whose Hamming weight equals 16, and especially 18, seem necessary, as it could help us significantly in improving the covering radius of Reed-Muller codes. In this paper, we push further the known results on the Reed-Muller codes by focusing on the Reed-Muller code RM(n -3, n). We provide a classification of the codewords of weight 16 and 18 of the Reed-Muller code RM(n -3, n). Our algebraic descriptions allow us to count the number of such codewords and to enumerate all of them explicitly.

Linear tail-biting trellises for block codes are considered. By introducing the notions of subtrellis, merging interval, and sub-tail-biting trellis, some structural properties of linear tail-biting trellises are proved. It is shown that a linear tail-biting trellis always has a certain simple structure, the parallel-merged-cosets structure. A necessary condition required from a linear code in order to have a linear tail-biting trellis representation that achieves the square root bound is presented. Finally, the above condition is used to show that for r/spl ges/2 and m/spl ges/4r-1 or r/spl ges/4 and r+3/spl les/m/spl les/[(4r+5)/3] the Reed-Muller code RM(r, m) under any bit order cannot be represented by a linear tail-biting trellis whose state complexity is half of that of the minimal (conventional) trellis for the code under the standard bit order.

Let \(\mathcal R_{t,n}\) denote the set of t-resilient Boolean functions of n variables. First, we prove that the covering radius of the binary Reed-Muller code RM(2,6) in the sets \(\mathcal R_{t,6}\), t=0,1,2 is 16. Second, we show that the covering radius of the binary Reed-Muller code RM(2,7) in the set \(\mathcal R_{3,7}\) is 32. We derive a new lower bound for the covering radius of the Reed-Muller code RM(2,n) in the set \(\mathcal R_{n-1,4}\). Finally, we present new lower bounds in the sets \(\mathcal R_{t,7}\), t=0,1,2.

Consider a binary Reed-Muller code RM(r, m) defined on the full set of binary m-tuples and let this code be punctured to the spherical layer S(b) that includes only m-tuples of a given Hamming weight b. More generally, we can consider punctured RM codes RM(r, m, B) restricted to some set B of several spherical layers S(b), b ∊ B. In this paper we specify this construction for the biorthogonal codes RM(1, m) and the Hadamard codes H(m). It is shown that the overall weight of any code vector in a punctured code H(m, B) is determined by the weight w of its information block. More specifically, this weight depends only on the values of the Krawtchouk polynomials K b m(w) for all b ∊ B. We further refine our codes by limiting the possible weights w of the input information blocks. As a result, we obtain sequences of codes that meet or closely approach the Griesmer bound.

Consider a binary Reed-Muller code RM(r,m) defined on the hypercube \BB F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> and let all code positions be restricted to the m-tuples of a given Hamming weight b. In this paper, we specify this single-layer construction obtained from the biorthogonal codes RM(1,m) and the Hadamard codes H(m). Both punctured codes inherit some recursive properties of the original RM codes; however, they cannot be formed by the recursive Plotkin construction. We first observe that any code vector in these codes has Hamming weight defined by the weight w of its information block. More specifically, this weight depends on the absolute values of the Krawtchouk polynomials K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> (w). We then study the properties of the Krawtchouk polynomials and show that the minimum code weight of a single-layer code RM(1,m,b) is achieved at the minimum input weight w = 1 for any . We further refine our codes by limiting the possible weights w of the input information blocks. As a result, some of the designed code sequences meet or closely approach the Griesmer bound. Finally, we consider more general punctured codes whose positions form several spherical layers.

Consider a binary Reed–Muller code RM $(r,m)$ defined on the $m$ -dimensional hypercube $\mathbb {F}_{2}^{m}$ . In this paper, we study punctured Reed–Muller codes $P_{r}(m,b)$ , whose positions are restricted to the $m$ -tuples of a given Hamming weight $b$ . In combinatorial terms, this paper concerns $m$ -variate Boolean polynomials of any degree $r$ , which are evaluated on a Hamming sphere of some radius $b$ in $\mathbb {F}_{2}^{m}$ . Codes $P_{r}(m,b)$ inherit some recursive properties of RM codes. In particular, they can be built from the shorter codes, by decomposing a spherical $b$ -layer into sub-layers of smaller dimensions. However, these sub-layers have different sizes and do not form the classical Plotkin construction. We analyze recursive properties of the spherically punctured codes $P_{r}(m,b)$ and find their distances for the arbitrary values of parameters $r,m$ , and $b$ . Finally, we describe recursive (successive cancellation) decoding of these codes.

Let a binary Reed-Muller code RM(s;m) of length n be used on a memoryless channel with an input alphabet ±1 and a real-valued output ℝ. Given a received vector y in ℝ n ; we define its generalized distance T to any codeword c as the sum ∑|y j| taken over all positions j, in which vectors y, c have opposite signs. We then consider the list ℒ T of codewords located within distance T from the received vector y and estimate the size L T of this list using the generalized Johnson bound. For any RM code RM(s;m) of fixed order s, the algorithm is proposed that performs list decoding beyond the error-correcting radius with linear complexity in length n and retrieves the code list ℒ T with complexity of order nsL T for any decoding radius T within the generalized Johnson bound.

Let a biorthogonal Reed-Muller code RM (1,m) of length n = 2m be used on a memoryless channel with an input alphabet plusmn1 and a real-valued output R. Given any nonzero received vector y in the Euclidean space Rn and some parameter epsiisin(0,1), our goal is to perform list decoding of the code RM (1, m) and retrieve all codewords located within the angle arccos e from y. For an arbitrarily small epsi, we design an algorithm that outputs this list of codewords with the linear complexity order of n [ln2 isin] bit operations. Without loss of generality, let vector y be also scaled to the Euclidean length radic(n) of the transmitted vectors. Then an equivalent task is to retrieve all coefficients of the Hadamard transform of vector y whose absolute values exceed nisin. Thus, this decoding algorithm retrieves all ne-significant coefficients of the Hadamard transform with the linear complexity n [ln2 isin] instead of the complexity n In2n of the full Hadamard transform.

Consider a binary Reed-Muller code RM(r, m) defined on the m-dimensional hypercube F <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> . In this paper, we study punctured Reed-Muller codes P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> (m, b) whose positions form a spherical b-layer and include all m-tuples of a given Hamming weight b. These punctured codes inherit some recursive properties of the original RM codes and can be built from the shorter codes, by decomposing a spherical b-layer into sub-layers of smaller dimensions. However, codes P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> (m, b) cannot be formed by the recursive Plotkin construction. We analyze recursive properties of these codes and find their code distances for arbitrary values of parameters r, m, and b.

In 1981, Schatz proved that the covering radius of the binary Reed-Muller code RM(2, 6) is 18. It was previously shown that the covering radius of RM(2, 7) is between 40 and 44. In this paper, we prove that the covering radius of RM(2, 7) is at most 42. As a corollary, we also find new upper bounds for RM(2, n), n = 8, 9, 10. Moreover, we give a sufficient and necessary condition for the covering radius of RM(2, 7) to be equal to 42. Using this condition, we prove that the covering radius of RM(2, 7) in RM(4, 7) is exactly 40, and as a by-product, we conclude that the covering radius of RM(2, 7) in the set of 2-resilient Boolean functions is at most 40, which improves the bound given by Borissov et al. (IEEE Trans. Inf. Theory 51(3):1182–1189, 2005).

Let R/sub t,n/ be the set of t-resilient Boolean functions in n variables, and let /spl rho//spl circ/(t,r,n) be the maximum distance between t-resilient functions and the rth-order Reed-Muller code RM(r,n). We prove that /spl rho//spl circ/(t,2,6)=16 for t=0,1,2 and /spl rho//spl circ/(3,2,7)=32, from which we derive the lower bound /spl rho//spl circ/(t,2,n) /spl ges/ 2/sup n-2/ with t /spl les/ n-4. Using a result from coding theory on the covering radius of (n-3)th- and (n-4)th-order Reed-Muller codes, we establish exact values of the covering radius of RM(n-3,n) in the set of 1-resilient Boolean functions in n variables, when /spl lfloor/n/2/spl rfloor/=1 mod 2 and lower bounds of RM(n-4,n) in the set of 2-resilient Boolean functions in n variables. This result leads again to different lower bounds for general dimensions n and r=0 or 3 mod 4.

The covering radius of the Reed–Muller code [formula omitted] is 40

Numerical Results and Asymptotic Lower Bound on the Covering Radius of Reed-Muller Codes RM (2, 11) and RM (3, &lt;i&gt;n&lt;/i&gt;)

On the enumeration of self-dual codes

We consider the problem of checking the generalized affine equivalence of two given Boolean functions. This problem arises in various computer-aided design (CAD) and cryptographic applications, such as circuit synthesis and Reed-Muller codes. Based on the theory of affine group acting on the Boolean functions, we define the coefficient spaces and transition relations, and transform the checking problem into reachability analysis of finite state machines. Two methods are proposed to check the affine equivalence of Boolean functions using Binary Decision Diagrams (BDDs) and Property Directed Reachability (PDR), respectively. Both methods can check affine equivalence of bent and semi-bent functions, which state-of-the-art methods can hardly handle. Furthermore, existing methods only consider the case of affine equivalence, while our methods can handle the generalized affine equivalence of subspaces of Boolean functions. In the application of circuit synthesis, our methods can significantly reduce the size of the library compared to Boolean matching. To classify Boolean functions up to the generalized affine equivalence, we propose a method to obtain a complete classification based on BDDs. In the experiments, we have successfully applied our methods to some examples that can hardly be solved by using the previous methods, thus validating the effectiveness of our methods.