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Memory-Guided Trust-Region Bayesian Optimization (MG-TuRBO) for High Dimensions EngageTriBoost: Predictive Modeling of User Engagement in Digital Mental Health Intervention Using Explainable Machine Learning Reservoir observer enhanced with residual calibration and attention mechanism Efficient RL Training for LLMs with Experience Replay Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learning Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control IKKA: Inversion Classification via Critical Anomalies for Robust Visual Servoing Adaptive Simulation Experiment for LLM Policy Optimization EvoLen: Evolution-Guided Tokenization for DNA Language Model Smartwatch-Based Sitting Time Estimation in Real-World Office Settings Structural Evaluation Metrics for SVG Generation via Leave-One-Out Analysis Loom: A Scalable Analytical Neural Computer Architecture Spectral Geometry of LoRA Adapters Encodes Training Objective and Predicts Harmful Compliance Finite-Sample Analysis of Nonlinear Independent Component Analysis:Sample Complexity and Identifiability Bounds How does Chain of Thought decompose complex tasks? Uncertainty-Aware Transformers: Conformal Prediction for Language Models Adaptive Candidate Point Thompson Sampling for High-Dimensional Bayesian Optimization Using Synthetic Data for Machine Learning-based Childhood Vaccination Prediction in Narok, Kenya Delve into the Applicability of Advanced Optimizers for Multi-Task Learning Bridging SFT and RL: Dynamic Policy Optimization for Robust Reasoning Multi-Agent Decision-Focused Learning via Value-Aware Sequential Communication Predictive Entropy Links Calibration and Paraphrase Sensitivity in Medical Vision-Language Models Efficient Hierarchical Implicit Flow Q-learning for Offline Goal-conditioned Reinforcement Learning Modality-Aware Zero-Shot Pruning and Sparse Attention for Efficient Multimodal Edge Inference The nextAI Solution to the NeurIPS 2023 LLM Efficiency Challenge Feature-Label Modal Alignment for Robust Partial Multi-Label Learning Integrated electro-optic attention nonlinearities for transformers Toward World Models for Epidemiology Tracing the Chain: Deep Learning for Stepping-Stone Intrusion Detection Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods
Cross-Technology Interference: Detection, Avoidance, and ...
Zegeye Mekasha Kidane, Waltenegus Dargie · 2025-01-11 · via cs.LG updates on arXiv.org

A large number of heterogeneous wireless networks share the unlicensed spectrum designated as the ISM (Industry, Scientific, and Medicine) radio band. These networks do not adhere to a common medium access rule and differ in their specifications considerably. As a result, when concurrently active, they cause cross-technology interference (CTI) on each other. The effect of this interference is not reciprocal, the networks using high transmission power and advanced transmission schemes often causing disproportionate disruptions to those with modest communication and computation resources. CTI corrupts packets, incurs packet retransmission cost, introduces end-to-end latency and jitter, and make networks unpredictable. The purpose of this paper is to closely examine its impact on low-power networks which are based on the IEEE 802.15.4 standard. It discusses latest developments on CTI detection, coexistence and avoidance mechanisms as well on messaging schemes which attempt to enable heterogeneous networks directly communicate with one another to coordinate packet transmission and channel assignment.