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版本更新 · v2arXiv:2610.03335

Randomization Beyond Deterministic Adaptivity in Parallel Sampling

Da Li, Bo Jin, Xiaopeng Wei

原文摘要

Parallel samplers, including masked diffusion models, generate several coordinates per round from conditional marginals, and their accuracy depends on which coordinates are revealed together. A schedule can be improved in two natural ways: adaptively, by choosing later batches from the values already generated, or by randomization, by drawing the schedule at random. We show that randomization is strictly more powerful. For every $r \geq 1$ we construct a full-support distribution on $9r$ binary coordinates such that every deterministic three-round sampler, even with value-dependent batches and arbitrary product kernels, has forward Kullback-Leibler divergence at least $γr - \log 4$ with $γ\approx 0.0966$, whereas drawing one of four fixed schedules with two fair bits drives the divergence below $18 r β^r$ with $β\approx 0.966$. Both samplers query conditionals of the same target. The separation holds for every fixed number of rounds $T \geq 2$, a strict advantage survives perturbations of the target and errors in the conditional predictor, and exact rational arithmetic certifies the gap at nine coordinates, where trained neural predictors preserve it. The mechanism is a hidden mixture: each schedule preserves the dependence pattern of one component, while no deterministic first batch can serve them all.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02470

Finding optimal measurement sequences for broken or constrained measuring devices

Peter N. Loxley

原文摘要

Measuring devices do not always function perfectly due to fatigue, damage, or malfunction. When device repair or replacement is not possible, it becomes necessary to seek optimal measurements from a broken device. We show this is possible via a sequential decision making process where we optimize measurement sequences over the remaining available measurements using information theory. With dynamic programming, we show greedy measurement sequences are always suboptimal for a simple class of measurement constraints. Greedy measurement sequences generally lead to equiprobable measurement outcomes, while the optimal policy is found to select each measurement in such a way as to assign a low probability to any measurement outcome that triggers a constraint condition; leading to measurement outcomes that are not equiprobable. A measurement sequence generated under this policy is shown to decompress its source: that is, the expected number of measurement outcomes is strictly greater than the number of bits it encodes, defining a key property of constrained measurements. Dynamic programming is inefficient for finding optimal measurement sequences, so we develop an alternative algorithm related to optimal binary decision trees. By deriving upper and lower complexity bounds for the task, we propose this algorithm as an efficient means to find optimal measurements for a broken or constrained measuring device.

cs.IT · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02425

Finite attainment and efficient computation of regularized quantum channel relative entropy

Yiwei Xu, Kun Fang

原文摘要

Quantum channel relative entropy can be nonadditive, making its regularization essential for asymptotic analysis. We study when the regularized value is attained at finite blocklength and how efficiently it can be approximated. For the first question, we establish a sharp dichotomy: the regularized value is either attained already at the single-copy level, implying additivity at every blocklength, or is never attained at any finite blocklength. Notably, a simple two-copy additivity test distinguishes between these alternatives for qubit channels and for channel pairs in arbitrary dimensions admitting an interior optimizer. We further show that both the interiority condition and finite nonattainment are generic, holding for almost every channel pair. For the second question, we establish explicit convergence rates for approximating the regularized channel relative entropy. For fixed channel dimensions, $\widetilde O(\varepsilon^{-3/2})$ channel copies suffice to approximate the regularized value to additive accuracy $\varepsilon$. Alternatively, a semidefinite hierarchy achieves the same accuracy at level $\widetilde O(\varepsilon^{-2})$, yielding a certified polynomial-time approximation. Together, these results reveal the intriguing structure and generic behavior of regularized channel relative entropy, sharpening our understanding of both its analytical and computational aspects.

quant-ph · cs.IT

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新论文 · v1arXiv:2611.02400

Inverse Design of Finite-State Channels by Dynamic Measure Matching

Lav R. Varshney

原文摘要

Inspired by the optimization approach to biology, we formulate an inverse problem for communication with memory: construct a finite-state channel for which a given signaling behavior supports capacity-achieving codes. Dynamic measure matching is introduced to turn a directed-information converse into conditions on the channel dynamics. These conditions lead to a new notion of dynamic surprise: the log ratio of physical to test output probabilities with a continuation correction for future signaling opportunities. The matching inequalities bound its conditional mean. For fixed architecture, test law, and potential, channel row synthesis under convex physical restrictions is convex. For every signaling constraint represented by a finite irreducible constrained graph of positive entropy, we construct a channel law under which undesired inputs remain available and behaviorally distinct while incurring a strict information penalty. A noisy refractory family has an exact capacity plateau, followed by an interval in which Shannon and zero-error feedback capacities differ. Explicit zero-error codes attain capacity in the graph construction and on the plateau. In this family, a Gibbs choice maximizes the penalty and makes dynamic surprise equal its conditional mean on every transition, yielding an exact trajectory-wise likelihood identity, a finite-block bound on successful decoding with premature requests, and an exponential strong converse. An exact gap decomposition into action residuals and output-law divergence constrains every reliable capacity-approaching code. Within the strict plateau, positive discrimination exponents for unused dynamics require a positive rate loss, while sparse probing permits consistent estimation at asymptotic capacity. A chemical channel illustrates how premature stimulation can alter future responsiveness while leaving the immediate output law unchanged.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02392

A Sharp Mutual Information Bound for Binary Pairs

Qianmian Guo

原文摘要

We prove a sharp upper bound on the mutual information between two binary random variables in terms of their distribution-dependent KL contraction coefficient. This resolves the binary-pair conjecture of Anantharam, Gohari, Kamath, and Nair. Equality holds if and only if the variables are independent or form a doubly symmetric binary source. The proof uses supporting tangents of an entropy curve to vary the input distribution and the channel while preserving the contraction coefficient. Together with tensorization and data processing, the result also bounds the mutual information between binary outputs obtained separately from two correlated observation sequences. When the observation pairs are independent copies of a finite-alphabet pair, this bound depends only on the law of one pair and not on the number of observations.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02390

Which Chains to Calibrate for Physical Backpropagation: Per-User Factors Versus Base-Station Chains

Xianzhong Li, Yuzhi Yang, Tianhao Guo

原文摘要

In physical backpropagation, users return loss derivatives over the time-division duplex uplink, giving the base station the downlink gradient without channel reconstruction. Reciprocity calibration conventionally prioritizes base-station chains, as column-normalized zero-forcing reduces a per-user factor to a stream rotation. This priority can reverse. With ideal base-station chains and no noise, per-user amplitude errors make the returned field a weighted sum-rate gradient. With zero-forcing full service at both optima, the loss is independent of array size. Otherwise the selection penalty, serving the wrong users, carries 86 to 94 percent of the amplitude-induced loss in five of six configurations. Per-user phase errors mainly slow the iteration. Base-station chain errors scale the returned field per antenna, and at 3 dB and 25 degrees they cost one user 1.3 dB of signal-to-noise ratio (SNR) that no array size removes. With 80 users, such errors on both sides, and mismatched pilots, oracle per-user and base-station calibration leave 6.2 and 12.3 percent loss at 256 antennas on clustered channels. This ordering holds at 30 dB SNR and reverses for 1 dB, 10 degree user errors. A capped one-echo estimator comes within 2.4 percentage points of oracle per-user calibration but underperforms no calibration at 10 dB SNR.

eess.SP · cs.IT · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02384

Nearest Is Not Safest: Fluid-Antenna Port Selection for Over-the-Air Backpropagation in Urban Multipath

Xianzhong Li, Yuzhi Yang, Tianhao Guo

原文摘要

Over-the-air backpropagation returns a loss gradient through the reciprocal channel, which requires the return port to see the channel of the forward port. If the terminal moves in between, a fluid antenna system can return through the port nearest to the forward position. In ray-traced Chicago streets, with a pedestrian displacement of about half a wavelength and a street-level base station, this rule reverses 20 percent of the gradients at a port spacing of 0.7 wavelengths and 49 percent at 0.9 wavelengths, while one of the four nearest ports stays aligned in all but 2 and 9 percent. We derive a certificate for the sign of the mean correlation from the second moment of a planar angular spectrum. It reduces to a linear program with three variables and infinitely many constraints, is tight, and holds over a region of second moments. In a static Gaussian model, observations collected along a line cannot tell an angular spectrum from its mirror image, so the sign of the cross moment must come from other information. At unsurveyed positions of the same city, a knowledge map that transports surveyed paths through virtual sources and fits them to the causal channel history lowers the reversal rate at 0.9 wavelengths from 49 to 27 percent. Its gain over the nearest port survived every tested error, while its gain over a geometry rule stayed significant at the tested single-error levels of 5 degrees of phase drift per step and 2 mm of position error but not at the next tested levels. Without any survey, across five cities and ten base stations, the nearest port reverses 37 percent of the returns at 0.9 wavelengths and a rule based on the direction and elevation of the base station 20.5 percent. The multilayer perceptrons we tested did not beat these physical rules by the margin fixed in advance in the primary settings.

eess.SP · cs.IT · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02378

A Beam-Switched Sparse Circular Array for Passive Wi-Fi Sensing

Yang Chen, Riccardo Bersan, Daniele Piazza, Stefano Savazzi, Michele D'Amico

原文摘要

Wi-Fi sensing applications require unambiguous angle-of-arrival (AoA) estimation over the full azimuth plane. Uniform circular arrays (UCAs) are shown to remove this ambiguity, but deploying the four RF chains typically available on commercial Wi-Fi chipsets into a sparse layout suffers from spatial undersampling. Such undersampling produces strong grating lobes. To overcome this hardware bottleneck, we first establish a generalized array manifold incorporating element radiation patterns, revealing the suitability and degradation mechanisms of directional UCAs. Then we propose an angularly staggered beam-switched UCA architecture that leverages the spatial complementarity. Numerical evaluations demonstrate that the proposed scheme effectively mitigates grating-lobe ambiguities without increasing the number of RF chains, significantly reducing AoA estimation outliers and improving omnidirectional tracking performance.

eess.SP · cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02339

Achievability of CSS codes performance limits over Pauli channels under separate ML decoding with bounded graphical complexity

Gulnihal Mercanoglu, Achilleas Anastasopoulos, S. Sandeep Pradhan

原文摘要

Capacity-approaching quantum error correction faces a fundamental tension: the required parity-check matrices need to become increasingly dense, while practical decoding and syndrome extraction favor sparse, bounded-degree structures. Reconciling these competing requirements remains an important open problem in quantum code design. We construct Calderbank-Shor-Steane (CSS) code ensembles that approach the CSS rate over memoryless Pauli channels under maximum-likelihood decoding while retaining sparse, bounded-degree graphical representations. The construction separates parity-check density from graphical complexity: although capacity-approaching components may require dense parity-check matrices, they are represented by sparse concatenated factor graphs. We first study a construction combining low-density parity-check (LDPC) and low-density generator-matrix (LDGM) component codes and numerically analyze the average weight distributions of the resulting codes. Motivated by these results, we introduce a second construction based on concatenated LDPC and LDGM codes and generalized irregular repeat-accumulate (GIRA) codes. We derive bounds on the average weight distribution of the resulting sum ensemble and use the Miller-Burshtein framework to prove simultaneous reliability of the two CSS decoding components. Consequently, the proposed ensemble achieves rates arbitrarily close to the CSS hashing bound under separate bit- and phase-error correction, while maintaining bounded graphical complexity, thus providing a foundation for future iterative decoders and indirect syndrome-extraction architectures.

cs.IT · quant-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02192

Concatenation Locality under Successive Adjacent Transpositions

Adelin Ataba, Aryeh Lev Zabokritskiy

原文摘要

We study codes correcting up to $t\geq1$ successive adjacent transpositions, using block concatenation to support blockwise encoding and decoding. A fixed set $P$ of words of length $b$ over a $q$-symbol alphabet generates, for every $m$, the length-$mb$ code of all concatenations of $m$ words from $P$. For our explicit constructions, the prepared encoder and decoder depend only on the block parameters and are reused at every concatenation length. We first prove that correction at every concatenation length is certified by a single finite test at $2t+1$ blocks, and this test is tight over a $q$-symbol alphabet exactly when $\binom q2\geq2t$. For binary words and $b\geq2t+1$, we give an explicit construction with total redundancy at most $mt\log_2 b+O_t(m)$, matching the optimal per-block redundancy $t\log_2 b+O_t(1)$ among block sets correcting at every concatenation length. Among all binary $t$-correcting codes of length $mb$, the optimal total redundancy is $t\log_2(mb)+O_t(1)$. Using all concatenations of one block set makes our total redundancy grow linearly in $m$ and buys independent block encoding, with one encoder and decoder at every length. The construction encodes and decodes in time linear in $mb$ for fixed $b,t$, and recovers the $j$-th source block from the first $jb$ received symbols. We also prove that, whenever the length-$mb$ code corrects $t$ transpositions, the $j$-th source block is determined within $2tb$ received symbols beyond position $jb$, and that explicit binary block sets with rates tending to one as $b$ grows with $t$ fixed attain this bound in the worst case. Finally, we prove that every ordinary correcting code contains a constant fraction of its words whose arbitrary concatenations retain correction, for fixed $q,t$.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02109

Wideband Microwave Linear Analog Computer (MiLAC) for Frequency-Flat and Frequency-Selective Processing

Juan Vidal Alegría, Matteo Nerini, Bruno Clerckx

原文摘要

Microwave linear analog computer (MiLAC) has been proposed as an analog processing architecture to perform efficient computation in the microwave domain. Consisting of reconfigurable impedance networks, a MiLAC can extract many of the gains of large multi-antenna systems without costly digital processing, e.g., achieving capacity in narrowband multiple-input single-output (MISO). However, the behavior of wideband MiLAC, required to support high data-rate transmissions, is widely unknown. The impedance components of MiLACs have non-trivial frequency-dependent response, requiring specific implementations to characterize it. In this work, we consider MiLAC implementations based on LC resonators with tunable capacitance, which allow synthesizing near-arbitrary passive lossless impedance matrices at individual frequencies using inductors and varactors. We characterize the mapping between optimal MISO precoding at a reference frequency, and the vector of capacitances configuring the MiLAC. We derive the degrees of freedom (DoFs) remaining after fixing such optimum precoding at the reference frequency, and show that these DoFs can be exploited to tune the frequency response of the MiLAC. We propose two techniques to exploit these DoFs: a closed-form approach for frequency-flat MiLAC processing, and a numerical approach for multi-frequency MiLAC processing, optimizing processing at frequencies other than the reference one. The frequency-flat approach shows satisfactory performance in a simulated frequency-flat millimeter-wave scenario, approaching the performance of digital processing. The multi-frequency approach can adapt to the frequency-selectivity of a simulated sub-6GHz scenario, attaining gains over frequency-flat digital processing. This constitutes the first demonstration that realistic MiLAC implementations can be configured to perform independent computations at different frequencies.

cs.IT · eess.SP

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2403.10232

Over-fitting Control in Deep Nonlinear Matrix Completion via Gradual Nonsmooth Regularization

Sajad Faramarzi, Farzan Haddadi, Sajjad Amini, Masoud Ahookhosh, Symeon Chatzinotas

原文摘要

Conventional matrix completion methods exploit the underlying low-rank structure of a target matrix to recover its missing entries, but may be inadequate for capturing complex nonlinear dependencies. Deep neural networks have therefore been increasingly adopted for nonlinear matrix completion (NLMC). However, their high representational capacity, particularly in deep fully connected neural networks (FCNNs), can lead to over-fitting when training is restricted to incomplete observations. In this paper, we propose a Deep Neural Network with Nonsmooth Regularization (DNN-NSR) framework to address this issue by incorporating two complementary structural priors: sparsity of intermediate representations and low-rankness of layer-wise weight matrices. Specifically, the $\ell_1$-norm of hidden representations promotes sparse latent features, while the nuclear norm of weight matrices encourages low-rank layer-wise transformations. To optimize the resulting nonsmooth and nonconvex objective, we develop an extrapolated proximal gradient algorithm that employs soft-thresholding and singular-value thresholding to handle the two regularization terms, respectively. We further introduce a gradual regularization strategy that progressively increases the influence of these structural penalties during training. Under suitable assumptions, we establish convergence properties of the proposed algorithm toward a critical point of the training objective. The effectiveness of DNN-NSR is evaluated through extensive experiments on synthetic matrix recovery, single-image inpainting, and recommender systems using the MovieLens 100K and MovieLens 1M datasets. Comparisons with representative linear and nonlinear matrix completion methods demonstrate the potential of the proposed framework to improve recovery performance by jointly exploiting sparse latent representations and low-rank network transformations.

cs.IT · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03733

A new class of optimal binary linear complementary pairs of codes

Yansheng Wu, Jing Chen

原文摘要

The construction of optimal binary linear complementary pair (LCP) has recently attracted considerable attention in coding theory. Based on the Belov codes, this paper proposes a novel construction of optimal binary LCPs with $θ=4$, extending our previous work on $θ=3$. Our results not only yield abundant new optimal binary LCPs with flexible parameters, but also enable more optimal binary LCPs with smaller minimum distances.

cs.IT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2411.00721

New classes of reversible cellular automata

Jan Kristian Haugland, Tron Omland

原文摘要

Cellular automata find applications in cryptography, error correction, theoretical computer science, and the simulation of physical and biological processes. We say that a Boolean function is a proper lifting if its induced cellular automaton is reversible. This paper introduces new classes of proper liftings, constructed through compositions of landscape functions, and explores their cryptographic potential by computing indicators relevant to differential and linear cryptanalysis. We combine theoretical and computational methods to obtain a complete classification of proper liftings for diameters up to $6$. Several open problems and conjectures are posed along the way.

math.CO · cs.CR · cs.DM · cs.IT · math.DS

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2203.00778

Affine and cyclic affine equivalence of rotation-symmetric S-boxes

Tron Omland, Pantelimon Stanica

原文摘要

A rotation-symmetric S-box on $n$ bits is a vectorial Boolean function that commutes with the right cyclic shift and is therefore determined by a single Boolean function. The map $χ$ used in Keccak is a well-known example. Ordinary affine equivalence allows arbitrary invertible affine transformations, which need not preserve rotation symmetry. We introduce \emph{cyclic affine equivalence}, in which the implementing transformations each have an invertible $k$-circulant linear part with $\gcd(k,n)=1$ and a translation vector fixed by the shift. For $n\ge3$, the two relations give the same classes of rotation-symmetric permutations exactly when $n$ is an odd prime for which $2$ is a primitive root. In every other dimension $n\ge3$, explicit constructions exhibit affine classes containing several cyclic affine classes. We derive exact class-size formulas, count cyclic affine classes for $3\le n\le9$, and classify a four-bit family of $χ$-siblings under both relations. Finally, we characterize when a permutation is affinely equivalent to a rotation-symmetric one and apply the criterion to the Ascon S-box.

math.CO · cs.CR · cs.IT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.22418

Optimal Condition Numbers in Low-Rank Positive Semidefinite Matrix Sensing

Mingxuan Sun, Zhiqiang Xu

原文摘要

In this paper we focus on the stability of positive semidefinite matrix sensing maps $Φ_{\mathcal{A}}=(\langle A_i,X\rangle)_{i=1}^m$ where $A_i\succeq 0$, $X\succeq0$ and $\operatorname{rank}(X)\le r$. We introduce the bi-Lipschitz constants of $Φ_{\mathcal{A}}$ and define the global condition numbers as the ratio of upper and lower Lipschitz constants. We give deterministic universal lower bounds for these condition numbers that depend only on the rank $r$ and on the underlying field. We then show that rank-one Gaussian measurements asymptotically attain these bounds, except for the operator-norm condition number over $\mathbb R$ when $r=1$. For this exceptional case, we establish a sharper universal lower bound and prove its asymptotic sharpness. As an application, we derive stability guarantees for an $\ell_1$-residual PhaseLift-type estimator at the optimal sampling scale. These results identify the intrinsic limits of conditioning in positive semidefinite matrix sensing and connect these limits to stable low-rank recovery.

cs.IT · math.FA · math.NA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01906

Improved lower bounds for the kissing number in dimensions 49 to 61

Dmitry Kamenetsky

原文摘要

We give explicit kissing configurations in R^d for every d = 49, ..., 61. The lower bound is improved over the best previously published value in all thirteen dimensions, by margins from 232 to 159,220 points and by 506,461 points in total. The configurations are obtained from the antipodal block lift of Yang et al. applied to the 52,416,000-point minimal shell of the extremal even unimodular lattice P_48p, using a class of 7,193 minimal lines and, in each dimension, a kissing configuration of R^k (k <= 15) with the largest available number of antipodal pairs. Two ingredients are new. The published class of 7,077 lines is grown to 7,193 by local search in three stages, as a sequence of exchanges between candidate lines and class lines; and the point count, which equals 52,416,000 + 2S + 2t with t the number of antipodal pairs and S the size of the union of t isometric images of the class, is maximised as a maximum-coverage problem by greedy search with iterated local search over pools of 4,000 automorphic images, reaching 92-100% of the trivial bound 7,193t. All results are verified in exact integer arithmetic, and the certificates are distributed with the code. Code and data: https://github.com/dimkadimon/KissingNumbers

math.MG · cs.IT · math.CO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01903

Federated Estimation of Loading Spaces in Tensor Factor Models under Differential Privacy

Zetai Cen, Fan Wang, Haeran Cho

原文摘要

Tensor factor models provide low-dimensional representations of tensor-valued observations while preserving their multiway structure. When these observations are held by different clients, privacy constraints may prevent them from being pooled for centralized analysis. We study estimation of common loading spaces in Tucker tensor factor models under federated differential privacy, allowing heterogeneous sample sizes, privacy budgets, and factor strengths. The proposed method combines private stability testing, Huber aggregation, and Gaussian perturbation at each client with weighted aggregation at the server. We establish non-asymptotic upper bounds governed by a scaled harmonic mean of the squared local error rates, and prove minimax optimality up to logarithmic factors under strong factors, fixed factor ranks, and fixed noise variance. We further develop a private calibration procedure that selects the local tuning parameters and aggregation weights without knowledge of the noise level or client-specific factor strengths. The resulting adaptive estimator preserves the privacy guarantee and attains the error rate obtained with oracle tuning. We illustrate the finite-sample performance of the proposed estimators through simulation experiments and an application to a Meditation Traits EEG dataset.

math.ST · cs.IT · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01881

Green Learning-Based Signal-Level Detection of RIS Spoofing Attacks in Vehicular ISAC Systems

Po-Heng Chou, Tingyu Shui, Pin-Qi Fu, Lien-Sheng Chang, Walid Saad, Li-Chun Wang

原文摘要

Integrated sensing and communication (ISAC) enables wireless infrastructure to jointly support communication and environmental sensing, but also introduces new physical-layer security threats. A malicious reconfigurable intelligent surface (RIS) can manipulate its time-varying reflection phases to generate spoofing echoes and distort the sensing results of a roadside unit (RSU). Existing trajectory-level detectors rely on reconstructed sensing parameters and vehicle trajectories, making their performance dependent on upstream estimation accuracy. In this paper, we propose a signal-level Green Learning (GL) framework that directly detects RIS spoofing from matched-filtered RSU echoes. The proposed method combines two-stage Subspace Approximation with Adjusted Bias (SAAB), Discriminant Feature Test (DFT), and eXtreme Gradient Boosting (XGBoost) for spatial-temporal representation learning, feature selection, and classification, respectively. Simulation results show 99.335\% detection accuracy for a 32-element malicious RIS at $-80$ dBm receiver noise power. GL remains competitive with Long Short-Term Memory (LSTM) and bidirectional LSTM (BiLSTM) baselines while requiring only 0.1119 million floating-point operations (FLOPs), and also generalizes to previously unseen RIS sizes.

eess.SP · cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01880

Ray-Tracing-Parameter-Based Lightweight Hybrid Precoding via Cascade XGBoost for Wideband Massive MIMO-OFDM Systems

Po-Heng Chou, Wei-Chen Lin, Wan-Jen Huang, Walid Saad, Ronald Y. Chang

原文摘要

Hybrid precoding reduces the radio-frequency (RF) hardware burden in wideband massive MIMO-OFDM systems, but conventional optimization and deep neural network (DNN)-based approaches can incur high online complexity. This paper proposes XGB-SVD, a lightweight hybrid precoding framework that maps compact ray-tracing path parameters directly to analog beamforming codeword indices using cascade eXtreme Gradient Boosting (XGBoost). The cascade captures sequential beam-selection dependencies and transmit--receive propagation correlation, while the digital precoder and combiner are obtained from the reduced-dimensional equivalent channel using singular value decomposition (SVD). Evaluations over five DeepMIMO scenarios spanning sub-6-GHz and millimeter-wave bands show that XGB-SVD consistently outperforms the considered DNN baselines and approaches simultaneous orthogonal matching pursuit (SOMP) in several environments. Cross-scenario results further indicate improved transfer from propagation-diverse training environments, while XGB-SVD provides lower online computational and training costs than the DNN baselines.

eess.SP · cs.IT

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.02416

Capacity-Aware Multi-UE Handover Optimization for LEO Satellite Networks Using Dueling DDQN

Po-Heng Chou, Chiapin Wang, Chung-Chi Huang, Kuan-Hao Chen, Chien-Yu Chen

原文摘要

Frequent handovers and finite satellite capacity make multi-user mobility management challenging in low Earth orbit (LEO) satellite networks. This paper develops a capacity-aware multi-user handover framework based on a double deep Q-network (DDQN) with a dueling architecture. Both training and evaluation employ sequential user equipment (UE) processing with shared satellite occupancy, while action masking excludes invisible or capacity-infeasible satellites. The state incorporates satellite visibility, achievable link quality, remaining capacity, previous association, short-term visibility, and time information. A fixed multi-criteria reward balances throughput, blocking, handover cost, connection persistence, and short-term visibility, while expert trajectories are used only for replay-buffer initialization. Simulations under different UE densities and satellite capacities show that the proposed framework maintains high average throughput per UE and low blocking rates with moderate handover frequency. Ablation results show that removing the remaining-capacity vector has only a limited effect on performance when sequential occupancy tracking and feasible-action masking are retained.

cs.IT · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01828

Instance-Specific Sharp Convergence Rate of the Add-$λ$ Estimator via Feature Space Decomposition

Zong Shang

原文摘要

Using the Feature Space Decomposition framework, we establish non-asymptotic matching upper and lower bounds for the expected Kullback-Leibler estimation error of the Add-$λ$ estimator of a discrete probability distribution, for any signal, any dimension, any sample size, and any $λ>0$.

cs.IT · math.ST

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01685

Assessing Temporal Dispersion of Time-Varying Channel at 38 GHz for Different Reference Signals

Jarosław Wojtuń, Bartosz Czaja, Tomasz Grasza, Rafał Przesmycki, Jan M. Kelner, Cezary Ziółkowski, Radek Zavorka, Josef Vychodil 等 12 位作者

原文摘要

This paper investigates the influence of reference waveform selection on temporal dispersion estimation in a time-varying radio channel at 38 GHz. Three reference waveforms-numerically generated in MATLAB, measured through a conducted connection, and recorded in an anechoic chamber -are compared using the same wideband channel measurements conducted in a dynamic road environment. Their effects on the estimated power delay profiles (PDPs), mean delay, and root mean square (RMS) delay spread are evaluated. Despite noticeable differences in the time-domain and spectral characteristics of the reference waveforms, the resulting PDPs exhibit similar multipath structures, while the estimated temporal dispersion parameters show relatively small differences. The results indicate limited sensitivity to reference waveform selection under the investigated measurement conditions.

eess.SP · cs.IT

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新论文 · v1arXiv:2611.01678

Communication with Adversarial Users: Byzantine Models in Multi-User Information Theory

Minglai Cai, Christian Deppe, Neha Sangwan

原文摘要

We consider communication systems operating in environments where some participating users cannot be trusted. This motivates the study of Byzantine communication models, in which users may behave adversarially. In this survey, we provide a unified information-theoretic perspective on communication with adversarial users in multi-user settings. We review recent advances in Byzantine multiple access channels, including reliable communication, adversary identification, and distributed function computation. Furthermore, we discuss extensions to classical-quantum channels and highlight fundamental differences between classical and quantum models. Finally, we outline key open problems and future research directions at the intersection of communication, computation, and security.

cs.IT

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新论文 · v1arXiv:2611.01669

How Much Does Random Rotation Help GPTQ? Sharp Worst-Case Rate Gaps in the High-Rate Regime

Runguo Li

原文摘要

GPTQ is Babai's nearest-plane algorithm on the lattice of the Cholesky factor of the layer's input second moment $Σ$. In the high-rate, entropy-coded regime it needs $\tfrac12\log_2(\AM/\GM)$ of its Cholesky pivots more bits than WaterSIC, which is within $0.255$ bit of the rate-distortion limit for every $Σ$; random rotations close most of this gap in practice, and Ordentlich and Polyanskiy asked for the worst-case gap between rotated GPTQ and WaterSIC. We show that after a Haar rotation the $k$-th pivot converges almost surely, uniformly in $k\geδn$ and over spectra $ν$ of bounded condition number, to $1/S_ν(-k/n)$, a classical formula from the theory of Lyapunov exponents, and use it to answer the question. Over spectra with condition number $κ$ the worst rotated gap is $\tfrac12\log_2κ-\log_2\lnκ+\tfrac12-\tfrac1{\ln2}+o(1)$ bits, attained only by two-point spectra, just $\tfrac12\log_2\lnκ+O(1)$ bits below the worst case without rotation, so rotation cannot replace waterfilling in general. It nearly can on real layers: rotation cuts the cost of an $η$-fraction of outlier eigenvalues of size $μ$ from about $ημ$ to $η^2μ/2$ to leading order, but cannot remove spectral spread on a logarithmic scale; for non-Gaussian weights it also adds a weight-side rate cost, which we characterize exactly under a standard high-rate assumption. On $96$ Hessians of Qwen3 and Llama-3.1 the rotated gap has median $0.04$ bit and maximum $0.36$ bit, against $2$--$5$ bits in the worst case for the same condition numbers, and the limit matches it to within $0.009$ bit.

cs.IT · cs.LG

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版本更新 · v3arXiv:2606.31466

Antenna Orientation Optimization for Rotatable Antenna-Enabled ISAC Systems

Qingjie Wu, Beixiong Zheng, Guangchi Zhang, Robert Schober

原文摘要

Non-fixed flexible antenna architectures, such as fluid antenna system (FAS), movable antenna (MA), and pinching antenna, have garnered significant interest in recent years. In this paper, we deploy a rotatable antenna (RA) array at the base station (BS) to improve the integrated sensing and communication (ISAC) performance by exploiting the additional spatial degrees of freedom (DoFs) introduced by antenna rotation. To enhance the sensing performance over an extended region containing a potential target while meeting the communication requirements of multiple users, we aim to maximize the minimum echo signal power within the sensing region, subject to required minimum communication rates of the users. For the special case of a single user and a point target, we develop a tractable approximation of the original problem, leading to independent per-RA subproblems. We characterize the geometric structure of these subproblems and derive closed-form solutions. Under the far-field approximation, the per-RA subproblems become approximately identical, allowing a common pointing vector to be adopted for all RAs. For the general multi-user and regional sensing case, we propose an alternating optimization (AO) algorithm that alternately optimizes the transmit beamforming for communication, the covariance matrix of the probing signal, and the pointing vectors of the RAs in an iterative manner. Simulation results demonstrate that the proposed RA-enabled ISAC system significantly outperforms various benchmark schemes with restricted antenna-orientation flexibility, while the evaluation of the Cramér-Rao bound (CRB) further verifies its benefits for target-position estimation accuracy.

cs.IT

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新论文 · v1arXiv:2611.01650

Worst-User Outage Probability Minimization in Active RIS-Aided Systems

Donghwan Shin, Hyeongtaek Lee, Junil Choi

原文摘要

In this paper, a worst-user outage probability minimization problem in active reconfigurable intelligent surface (RIS)-aided systems is addressed. Since the resulting problem involves a discontinuous objective function over an unknown channel distribution, a sequence of reformulations is applied by integrating a probability-simplex user weighting, a softplus surrogate for differentiability, and entropic tail-risk aggregation for rare but severe outage events. Based on the reformulated problem, a data-driven stochastic optimization technique that relies on historical channel samples is adopted to jointly optimize the beamformer and reflection coefficients with projected adaptive moment estimation. Simulation results show advantages of the proposed technique for reliability-oriented active RIS transmission design.

cs.IT · eess.SP

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新论文 · v1arXiv:2611.01590

6G Uplink Waveform and Rank Selection under Distributed Reception

Yewen Cao, Fen Hou, Yulin Shao

原文摘要

The development of 6G uplink is expanding waveform and spatial transmission options, while distributed reception makes their relative merits depend on the spatial and frequency structure of the aggregate channel. We study the joint selection of cyclic prefix orthogonal frequency-division multiplexing and discrete Fourier transform spread OFDM at rank 1 and noncoherent rank 2 under common user equipment power constraints and centralized linear minimum mean-square error reception. We derive waveform switching thresholds at both ranks and a rank selection margin, establishing conditions for selecting among the four modes based on available transmit power, DFT despreading loss, and spatial multiplexing gain. We then characterize how port gains and correlations at individual access points (APs) form aggregate spatial separation and influence mode selection. At fixed port gains and available transmit powers, improving spatial separation without reducing it on any subcarrier increases the rank selection margin, while its effect on the preferred waveform within rank 2 depends on the frequency distribution of the improvement. Numerical results show that different AP deployments can favor different waveforms and ranks, even with the same receive antenna budget and comparable aggregate channel gains, including waveform switches at a fixed preferred rank.

cs.IT · eess.SP

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新论文 · v1arXiv:2611.01583

Second-Order Asymptotics for the Scalar Gaussian Broadcast Channel

Vincent Y. F. Tan

原文摘要

This paper characterizes the second-order coding region of the scalar Gaussian broadcast channel along the curved boundary of its capacity region. The characterization holds under both average and maximal error criteria and allows correlation between the receiver noises. At each interior point of this boundary, the second-order region is a halfspace, as in the work of Scarlett and the author on Gaussian multiple-access channels with degraded message sets. Achievability follows from spherical superposition coding with a blocklength-dependent power allocation. The main innovation lies in the converse. Using Ornstein--Uhlenbeck entropy dissipation and Gaussian functional inequalities, we construct an auxiliary observation through an additive Gaussian noise channel and establish quantitative relative entropy bounds for the channel output distributions conditioned on this observation. The strong functional representation lemma of Li and El Gamal then allows us to select a realization of its independent random seed to obtain a deterministic partition of the retained receiver-1 message set. The component label plays a role analogous to the auxiliary random variable in the capacity characterization, while providing the distributional control needed at the second-order scale. These properties guide the construction of auxiliary output measures whose likelihood ratios admit the cancellations and normal approximation needed to evaluate an information spectrum converse.

cs.IT

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新论文 · v1arXiv:2611.01546

MDS Algebraic Geometry Codes of Square-Root Length on Hyperelliptic Curves

Chuangqiang Hu, Guorui Ma

原文摘要

For every fixed genus $g\geq2$ and every $0<ρ<1$, we construct maximum distance separable (MDS) algebraic geometry codes over $\F_q$ for all sufficiently large odd squares $q$. Their parameters are $\bigl[n,\lfloorρn\rfloor,n-\lfloorρn\rfloor+1\bigr]_q$ with $n=\sqrt q+1+O_g(q^{1/4})$. The codes use full Riemann--Roch spaces on genus-$g$ hyperelliptic curves. They are not monomially equivalent to generalized Reed--Solomon (GRS) codes when $q$ is sufficiently large. An infinite subfamily has exact length $n=\sqrt q+2gq^{1/4}+1$.

cs.IT · math.AG

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