Derives a marginal maximum likelihood estimator that uses both pilot and unknown data symbols for improved localization in OFDM passive distributed antenna systems without requiring data decoding.
A short tutorial on Wirtinger Calculus with applications in quantum information
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An analytical Hessian-vector product kernel for arbitrary linear map compositions in tensor networks is derived via recursive tangent-state propagation, enabling scalable Riemannian trust-region optimization with major fidelity gains on spin-chain circuits.
Necessary and sufficient conditions are derived for tubal products to satisfy an Eckart-Young theorem for tubal tensor SVD.
Complex SGD using Wirtinger calculus achieves convergence guarantees paralleling the real-valued setting without analyticity assumptions, and directional bias results extend to complex kernel regression.
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Localization in OFDM Passive Distributed Antenna Systems with Pilots and Unknown Data Payloads: A Marginal Maximum Likelihood Approach
Derives a marginal maximum likelihood estimator that uses both pilot and unknown data symbols for improved localization in OFDM passive distributed antenna systems without requiring data decoding.
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Hessian-vector products for tensor networks via recursive tangent-state propagation
An analytical Hessian-vector product kernel for arbitrary linear map compositions in tensor networks is derived via recursive tangent-state propagation, enabling scalable Riemannian trust-region optimization with major fidelity gains on spin-chain circuits.
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Sufficient and Necessary Conditions for Eckart-Young like Result for Tubal Tensors
Necessary and sufficient conditions are derived for tubal products to satisfy an Eckart-Young theorem for tubal tensor SVD.
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Complex Stochastic Gradient Descent and Directional Bias in Reproducing Kernel Hilbert Spaces
Complex SGD using Wirtinger calculus achieves convergence guarantees paralleling the real-valued setting without analyticity assumptions, and directional bias results extend to complex kernel regression.