pith:R3YVECNI
Vectorized Generalized Nearest Neighbor Decoding for In-block Memory Channel
For in-block memory channels the optimal vectorized generalized nearest neighbor decoder admits an analytical characterization when Gaussian codebooks are employed.
arxiv:2605.15950 v1 · 2026-05-15 · cs.IT · math.IT
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Claims
Leveraging the generalized mutual information (GMI) as an operational lower bound on the mismatch capacity, an analytical characterization of the optimal Vec-GNND is obtained for general IBM channels with Gaussian codebooks. The formalism further provides closed-form optimality conditions and achievable GMIs for restricted variants of the receiver architecture.
That Gaussian codebooks are employed and that the GMI lower bound remains sufficiently tight to characterize optimality for the vectorized receiver on general IBM channels (abstract, paragraph 2).
Vec-GNND yields closed-form optimality conditions and achievable GMIs for IBM channels with Gaussian codebooks, plus a GMI-based joint covariance-metric design that reduces to covariance optimization.
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Receipt and verification
| First computed | 2026-05-20T00:01:46.298684Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
8ef15209a83d7579f836d87136896c319ab573f4e0372889da7d25af2dd144a3
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/R3YVECNIHV2XT6BW3BYTNCLMGG \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 8ef15209a83d7579f836d87136896c319ab573f4e0372889da7d25af2dd144a3
Canonical record JSON
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