147 papers
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 proposes a tiled beamspace MVDR architecture that achieves computationally efficient, high-performance wideband interference suppression for a 1024-element airborne radar by combining spatial and temporal dimensionality reduction with coordinated training across eight 128-element tiles.
This paper proposes a novel randomized space-time stacked intelligent metasurface (ST-SIM) architecture that integrates a time-varying input layer to exploit multiuser diversity and enable scalable massive downlink connectivity while significantly reducing channel state information acquisition and feedback overhead through a partial-CSIT-based beamforming scheme.
This paper proposes a computationally efficient axial self-attention transformer neural receiver that reduces complexity from to while achieving state-of-the-art Block Error Rate performance under diverse 3GPP channel conditions.
This paper proposes a POMDP framework for minimizing long-term weighted distortion and transmission costs in remote tracking of Markov sources via multiple heterogeneous sensors with state-dependent accuracy, introducing truncation-based and discounted reformulation methods to solve the resulting infinite-state belief-MDP and demonstrating their superior performance and structural insights over low-complexity baselines.
This paper provides a comprehensive statistical analysis of the "Einstein from noise" phenomenon, demonstrating how template bias causes pure noise observations to converge to the template's structural features in both Fourier phases and magnitudes, thereby revealing critical pitfalls in template matching techniques across multiple scientific disciplines.
This study evaluates a low-cost FMCW MIMO radar for non-contact vital sign monitoring, revealing that while it achieves high accuracy for average respiratory and heart rates at an optimal distance of 70 cm with sufficient chirp counts, it faces significant limitations in capturing high-resolution beat-to-beat and breath-to-breath variability.
This paper introduces a physically consistent, IEEE-inspired benchmark dataset generator for evaluating passive reconnaissance in smart-grid communications, which models adversary-induced propagation effects across tiered network topologies to enable standardized, label-free assessment of graph-temporal and federated detection methods.
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 introduces GF-BiLSTM, a two-stream gated fusion network that systematically demonstrates the critical role of incorporating Wi-Fi Channel State Information phase data alongside amplitude to significantly improve the accuracy and cross-speed robustness of robotic activity recognition.
This paper introduces the NP-complete Mobile Base Station Optimal Tour (MOT) problem for efficient data collection in wide-area IoT sensor networks and proposes a polynomial-time greedy heuristic that outperforms state-of-the-art approaches by balancing travel cost, coverage gain, and execution time while adhering to energy and restricted-area constraints.
This paper proposes a linear bandit-based beam training framework for near-field multi-path channels that utilizes Thompson Sampling with a correlated Gaussian prior and three distinct search strategies to significantly reduce pilot overhead while maximizing beamforming gain and approaching full-CSI performance.
This paper proposes a hybrid model-assisted approach that enhances the classic close-in free-space reference distance model with an environment-adaptive compensation term and optimized image organization schemes to achieve superior path loss prediction accuracy in complex suburban scenarios.
This technical report presents a novel, interpretable, and computationally efficient NI-based strategy for estimating initial parameters of trigonometric models from unevenly sampled data, enabling robust convergence to the global minimum even under conditions of strong noise, limited periods, or partial coverage with a signal-to-noise ratio as low as 1.4 dB.
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 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 proposes a low-rank cyclostationarity predictive routing method that achieves near-real-time performance with less than 1.5 minutes of average excess travel time, offering a highly effective offline alternative for transportation planning tasks.
This paper proposes a two-stage hybrid transceiver design for partially connected multi-user THz MIMO systems that utilizes low-resolution ADCs and a novel beamforming technique with few true time delay lines to mitigate the dual-wideband effect, achieving a 13% improvement in spectral efficiency over existing methods.
This paper proposes a novel Location-Agnostic Dynamic Channel Knowledge Map (LAD-CKM) framework that utilizes dynamic RF radiance field rendering, a dedicated RARE-Net, and an adaptive deformation module to predict channel state information from instantaneous uplink and partial downlink data, thereby significantly improving effective data rates in dynamic 6G scenes without requiring precise user location information.
This paper presents a policy-aware, cross-layer auditing methodology that successfully identifies Starlink service tiers and throttling states by analyzing distinct signatures in goodput, RTT, and an internal-to-user ratio derived from aligned terminal telemetry and host-side probes, without requiring operator visibility.