154 papers
This paper introduces the non-commutative Group Ring Learning with Errors () problem based on semi-direct products of cyclic groups and establishes its cryptographic security by providing polynomial-time quantum reductions from the worst-case Shortest Independent Vectors Problem () to both the search and decision versions of .
This paper proposes a movable-antenna-assisted Rate-Splitting Multiple Access (RSMA) framework integrated with a Reconfigurable Intelligent Surface (RIS) to maximize sum rate by jointly optimizing beamforming, reflection coefficients, rate splitting, and antenna positions, demonstrating significant performance gains over conventional fixed-antenna systems.
This paper establishes explicit formulas for the dimension and Bose distance of narrow-sense and certain non-narrow-sense BCH codes of length over within significantly expanded ranges of designed distance, thereby enabling the construction of several optimal linear codes.
This paper introduces the Alternating Subspace Method (ASM), a novel sparse recovery algorithm that integrates greedy and splitting strategies by enforcing data fidelity within a subspace, thereby guaranteeing global convergence and achieving fast local geometric convergence on LASSO problems while demonstrating high efficiency and flexibility across various applications.
This paper presents a theoretical algebraic framework for the Linear Code Equivalence problem using Plücker coordinates and invariant rational functions to construct polynomials with the underlying permutation matrix as a root, demonstrating the potential of algebraic geometry in cryptanalysis despite the resulting polynomials being computationally infeasible for practical attacks.
This paper establishes a finite-blocklength rate-distortion framework for heterogeneous random fields on finite lattices that explicitly incorporates tile-based processing constraints, providing non-asymptotic bounds and a second-order expansion to quantify the effects of spatial correlation, heterogeneity, and tile size on compression performance.
This paper introduces a flexible transformed minimization model with two adjustable parameters to enhance sparse signal recovery, establishing exact and stable recovery guarantees via the restricted isometry property, proposing an efficient IRLSTLp algorithm with convergence proofs, and demonstrating its superior performance and theoretical bounds through numerical experiments.
This paper proposes three content-aware MIMO-CC strategies (min-G, Grouping, and Phantom) for cache-aided asymmetric MIMO systems that dynamically optimize user selection and stream allocation to significantly enhance achievable degrees of freedom compared to existing approaches.
This paper summarizes a memorial session held at the FFCS conference in Braunschweig honoring Dr. Vladimir Sidorenko (1949–2025), celebrating his profound scientific contributions to coding theory, cryptography, and quantum error correction alongside his cherished personal qualities of mentorship, humor, and generosity.
This paper proposes a computationally efficient channel estimation algorithm for MIMO-AFDM systems that utilizes a Vandermonde-structured tensor train decomposition to accurately handle fractional delay and Doppler effects, while also introducing a global Ziv-Zakai bound that outperforms the Cramér-Rao bound in characterizing low-SNR performance.
This paper proposes an alternating optimization framework that jointly designs precoding vectors and beyond-diagonal RIS phase-shift matrices to effectively balance the trade-off between multiuser communication sum rate and sensing beam gain in integrated sensing and communication systems.
This paper introduces the Randomized Distributed Function Computation (RDFC) framework, a semantic communication approach that achieves local differential privacy and significantly reduces transmission rates compared to lossless methods, even in scenarios without shared randomness, by leveraging strong coordination metrics and randomized function generation.
This paper establishes scaling laws for secrecy rates in MIMO wiretap channels to analyze the interplay between transmit, receive, and eavesdropper antennas, revealing that secure communication may fail when the eavesdropper has more than twice the transmitter's antennas and identifying conditions under which artificial noise remains effective against artificial noise elimination countermeasures.
This paper proposes a new symbol detection framework for ambient backscatter communication receivers equipped with low-noise amplifiers, demonstrating through bit error rate analysis and deflection coefficient evaluation that such amplifiers enhance detection performance at low-to-moderate transmission powers and deriving a near-optimal threshold estimation method using pilot symbols.
This paper introduces D2AJSCC, a novel framework that enables the deployment of high-fidelity analog joint source-channel coding on standard digital transceivers by utilizing orthogonal frequency-division multiplexing as a waveform synthesizer and a differentiable neural surrogate to overcome hardware mismatches and non-differentiable operations, thereby achieving graceful degradation without requiring hardware modifications.
This paper proposes a differential space-time block coding (DSTBC) scheme for the downlink of cell-free massive MIMO systems that effectively mitigates UE antenna calibration errors and phase offsets, enabling reliable communication without requiring explicit calibration or channel phase knowledge.
This paper proposes the distributed multichannel Wiener filter (dMWF), a non-iterative algorithm for wireless acoustic sensor networks that achieves optimal, centralized-level speech estimation performance with reduced communication bandwidth, even when nodes observe different sets of sources, thereby outperforming existing iterative solutions like DANSE.
This paper extends the fundamental privacy-accuracy trade-off analysis for differentially private secure multiplication from two to an arbitrary number of multiplicands by proposing a framework based on encoding polynomials and layered noise injection that achieves improved estimation accuracy through systematic noise cancellation.
This paper empirically demonstrates that site-specific finetuning of neural receivers using real-world 5G NR measurements significantly improves error-rate performance across diverse environments and hardware, validating previous synthetic findings with practical benchmarks.
This paper establishes the equivalence between linear perfect zero-knowledge codes and quantum CSS codes, leveraging this connection to construct explicit asymptotically-good zero-knowledge locally-testable codes.