interference mitigation

This study addresses reliability challenges in maritime wireless communications under shared-spectrum conditions, focusing on signal degradation induced by dynamic obstructions and complex marine environments. We extend laboratory-based electromagnetic propagation measurement methodologies—previously confined to controlled settings—to real-world deployments on an electric research vessel, conducting systematic indoor–outdoor comparative experiments. These include line-of-sight obstruction tests, multi-location received signal strength (RSS) measurements, and empirical signal attenuation analysis. We quantitatively characterize the impact of obstacles (e.g., lab objects and ship structures), transmission distance, and device mounting height on path loss. Results reveal that obstructions significantly exacerbate path loss; distance and antenna height exhibit nonlinear effects on link margin; and typical ship structures introduce up to 20 dB additional attenuation. The findings provide reproducible empirical evidence and actionable design guidelines for robust maritime wireless network deployment.

To address anti-jamming communication challenges in spectrum-constrained scenarios under strong interference (e.g., jammer-to-signal ratio as low as 5 dB), this paper proposes a low-complexity, robust modulation scheme leveraging computational antennas and 1-bit reconfigurable intelligent surfaces (RIS). Unlike spread-spectrum or frequency-hopping techniques that consume substantial spectral resources, the proposed method achieves interference suppression without additional bandwidth overhead by employing time-averaged channel modeling and time-domain modulation optimization. Its core innovation lies in the co-design of computational antenna theory and ultra-low-cost 1-bit RIS hardware, establishing a software–hardware joint anti-jamming architecture operating in the time domain. Experimental validation on a USRP platform demonstrates that, under 5 dB interference, the scheme reduces bit error rate by up to 80.9% and successfully reconstructs severely distorted images—confirming its high robustness and engineering feasibility.

This study identifies a critical gap in practical interference management within operational 4G/5G cellular networks: widespread inter-cell interference persists due to uncoordinated reuse of identical time-frequency resource blocks—even when spectrum is underutilized—severely degrading UE signal quality, especially under frequency-selective fading. Method: Leveraging large-scale real-world measurements, we systematically model and analyze interference characteristics across four dimensions: network deployment, channel assignment, time-frequency scheduling, and configuration parameters. Contribution/Results: We demonstrate that current networks lack effective interference coordination mechanisms. Crucially, even lightweight coordination strategies yield substantial improvements in SINR and user-perceived QoE. This work provides the first empirical, system-level characterization of inter-cell interference in live deployments, bridging a key gap in the literature. It delivers actionable, quantitatively grounded insights and a practical optimization pathway for intelligent, deployment-aware interference coordination in modern cellular systems.

To address degraded spectral reuse efficiency caused by inter-cell interference (ICI), this paper proposes a user equipment (UE)-centric, lightweight ICI suppression framework. Methodologically, it introduces a Z-optimized deep single-class support vector data description (Deep SVDD) model for high-accuracy ICI anomaly detection with minimal training overhead; further integrating interference whitening with UE-side autonomous sensing enables real-time, precise interference identification and mitigation under constrained time-frequency resources. Experimental evaluation across multiple 3GPP channel models demonstrates substantial performance gains over state-of-the-art baselines; hardware validation on commercial 5G baseband chips confirms strong robustness and practical deployability. The core contribution lies in the first integration of optimized deep one-class learning with interference whitening—establishing a novel, efficient, low-complexity, and edge-deployable ICI suppression paradigm.

The absence of a standardized system-level simulation framework hinders rigorous performance evaluation of Reconfigurable Intelligent Surfaces (RIS) in 6G multi-RIS, multi-base-station networks. Method: This work establishes the first 3GPP-compliant system-level simulator for RIS-aided networks. It integrates geometrically random RIS deployment, configurable panel density and size, and—novelly within a standardized framework—jointly models key non-ideal hardware effects: near-field propagation, inter-panel interference, phase quantization error, and unit failure. These factors are rigorously incorporated into path loss and large-scale fading modeling for both RIS-reflected and direct links. Results: Simulation results demonstrate that strategic RIS deployment significantly enhances Reference Signal Received Power (RSRP), Signal-to-Interference-plus-Noise Ratio (SINR), spectral efficiency, and cell coverage. The platform provides a reproducible, quantitative performance benchmark and design guidelines to support standardization of RIS technology in 6G systems.

This work addresses the scarcity of real-world interference data and the challenges in annotating such data for robust evaluation of anti-jamming techniques in wireless and satellite navigation systems. To this end, the authors present S-ICDF, the first large-scale indoor interference dataset generated using the GPU-accelerated Sionna physical-layer simulation framework. S-ICDF encompasses 102 distinct interference configurations combined with diverse channel models and antenna array setups, enabling research on interference detection, classification, feature extraction, and direction-of-arrival estimation. The study establishes comprehensive benchmarks by integrating classical direction-finding algorithms—MUSIC, ESPRIT, and CAPON—with modern machine learning approaches. Both the S-ICDF dataset and baseline performance results are publicly released to foster standardized, reproducible research in interference monitoring.

This work addresses the challenge of interference and packet collisions in 6G-V2X autonomous mode (NR-V2X Mode 2), where dynamic mobility and limited resource awareness degrade communication reliability. To overcome this, the study proposes the first integration of successive interference cancellation (SIC) with Mode 2’s multi-copy repetition transmission mechanism. By leveraging successfully decoded replicas at the receiver to cancel their contributions from other overlapping signal copies, the approach transforms redundant transmissions into a performance gain. Notably, this method incurs no additional signaling overhead and, in high-interference highway scenarios, achieves over 100% improvement in communication performance compared to conventional Mode 2 schemes, substantially enhancing the reliability of sidelink communications.

本文研究了OFDM信号中导频和数据资源如何共同影响感知性能，并提出了一种优化框架来最小化综合旁瓣水平，从而提高感知精度。

This study addresses interference propagation induced by user mobility in large-scale multi-RIS wireless communication systems. For the first time, the epidemic SIS model is introduced into wireless interference analysis, combined with stochastic geometry to characterize spatial deployments: base stations are modeled via a Matérn hard-core point process, while reconfigurable intelligent surfaces (RISs) follow a Poisson point process. By constructing a dynamic interference propagation model, the work introduces the notion of “interference propagation intensity” and derives closed-form expressions for both received signal and interference power, along with a novel coverage probability formula. Numerical evaluations validate the theoretical analysis and uncover key factors governing interference propagation, thereby offering foundational insights and design guidelines for interference management and deployment optimization in multi-RIS networks.

This study addresses the challenge of accurately evaluating key performance indicators for unmanned aerial vehicles (UAVs) in 5G New Radio (NR) networks, where their altitude-induced coverage and interference characteristics differ significantly from those of terrestrial users. The authors develop a 3GPP-compliant system-level simulation framework incorporating three-dimensional antenna radiation patterns, LOS/NLOS probability models, and multi-site tri-sector deployments. For the first time, they identify the critical altitude at which UAVs transition from coverage-limited to interference-limited regimes and quantify the coupled effects of inter-site distance and UAV height on RSRP, RSRQ, and SINR. Results reveal that SINR degrades sharply with increasing altitude due to dominant line-of-sight interference, while larger inter-site distances—despite reducing received power—effectively improve both SINR and RSRQ.