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Swaraj Sharma Durgi

Publications and source records attributed to Swaraj Sharma Durgi.

2 recordsLinked to original sources

Function-Correction with Optimal Data Protection for the General Hamming Code Membership

This paper investigates single-error-correcting function-correcting codes~(SEFCCs) for the $[2^n-1,\,2^n-1-n,\,3]$-Hamming code membership function~(HCMF) for general $n\geq 2$. Necessary and sufficient conditions for valid parity assignments are established, and the distance-$3$ codeword graph is shown to induce a connected bipartite structure for all $n\geq 2$, which is exploited to develop a systematic SEFCC construction achieving the largest possible minimum distance of~$2$. A novel framework is then developed that reduces the minimization of distance-$2$ codeword pairs to a max-cut problem on the distance-$4$ graphs of the two partite sets. Eigenvectors corresponding to the minimum eigenvalue of these graphs are shown to directly yield optimal parity assignments. We reduce the problem of finding these eigenvectors to an optimization problem involving moments of the Walsh coefficients of a related function, which we solve for even~$n$ by deriving a tight lower bound shown to be attained by bent functions, establishing a precise connection between optimal SEFCC design and bent Boolean functions.

cs.IT↗

Function-Correcting Codes with Optimal Data Protection for Hamming Code Membership

This paper investigates single-error-correcting function-correcting codes (SEFCCs) for the Hamming code membership function (HCMF), which indicates whether a vector in $\mathbb{F}_2^7$ belongs to the [7,4,3]-Hamming code. Necessary and sufficient conditions for valid parity assignments are established in terms of distance constraints between codewords and their nearest non-codewords. It is shown that the Hamming-distance-3 relations among Hamming codewords induce a bipartite graph, a fundamental geometric property that is exploited to develop a systematic SEFCC construction. By deriving a tight upper bound on the sum of pairwise distances, we prove that the proposed bipartite construction uniquely achieves the maximum sum-distance, the largest possible minimum distance of 2, and the minimum number of distance-2 codeword pairs. Consequently, for the HCMF SEFCC problem, sum-distance maximisation is not merely heuristic-it exactly enforces the optimal distance-spectrum properties relevant to error probability. Simulation results over AWGN channels with soft-decision decoding confirm that the resulting max-sum SEFCCs provide significantly improved data protection and Bit Error Rate (BER) performance compared to arbitrary valid assignments.

cs.IT↗