# Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences arXiv: https://arxiv.org/abs/2305.00135 ยท HTML: https://mlsanity.com/paper/2305.00135/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 20 of its 50 references that are in the map. - First posted on arXiv 29 Apr 2023. ## Builds on (most cited first, 20 of 20) - [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015) - [Tensor Decomposition for Signal Processing and Machine Learning](https://mlsanity.com/paper/1607.01668/) (2016) - [Sparse Sequence-to-Sequence Models](https://mlsanity.com/paper/1905.05702/) (2019) - [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019) - [Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability](https://mlsanity.com/paper/1703.06182/) (2017) - [NAOMI: Non-Autoregressive Multiresolution Sequence Imputation](https://mlsanity.com/paper/1901.10946/) (2019) - [Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms](https://mlsanity.com/paper/1805.09717/) (2018) - [Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution](https://mlsanity.com/paper/2301.02068/) (2023) - [Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks](https://mlsanity.com/paper/2006.09066/) (2020) - [Uniqueness of Tensor Decompositions with Applications to Polynomial Identifiability](https://mlsanity.com/paper/1304.8087/) (2013) - [An Overview of Signal Processing Techniques for Terahertz Communications](https://mlsanity.com/paper/2005.13176/) (2020) - [Next Generation Terahertz Communications: A Rendezvous of Sensing, Imaging, and Localization](https://mlsanity.com/paper/1909.10462/) (2019) - [Ultra-Massive MIMO Systems at Terahertz Bands: Prospects and Challenges](https://mlsanity.com/paper/1902.11090/) (2019) - [THz ISAC: A Physical-Layer Perspective of Terahertz Integrated Sensing and Communication](https://mlsanity.com/paper/2209.03145/) (2022) - [Near-field Tracking with Large Antenna Arrays: Fundamental Limits and Practical Algorithms](https://mlsanity.com/paper/2102.05890/) (2021) - [Joint Sensing and Communication for Situational Awareness in Wireless THz Systems](https://mlsanity.com/paper/2111.14044/) (2021) - [Terahertz-Band Integrated Sensing and Communications: Challenges and Opportunities](https://mlsanity.com/paper/2208.01235/) (2022) - [A Line-of-Sight Channel Model for the 100-450 Gigahertz Frequency Band](https://mlsanity.com/paper/2002.04918/) (2020) - [Reconfigurable Intelligent Surface Assisted NOMA Empowered Integrated Sensing and Communication](https://mlsanity.com/paper/2208.04786/) (2022) - [Robust Integrated Sensing and Communication Beamforming for Dual-functional Radar and Communications: Method and Insights](https://mlsanity.com/paper/2303.07652/) (2023) ## Built on by (most cited first, 10 of 10) - [Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G](https://mlsanity.com/paper/2405.02336/) (2024) - [Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks](https://mlsanity.com/paper/2309.13223/) (2023) - [Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challenges](https://mlsanity.com/paper/2405.17454/) (2024) - [R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Models](https://mlsanity.com/paper/2407.11654/) (2024) - [Design Optimization of NOMA Aided Multi-STAR-RIS for Indoor Environments: A Convex Approximation Imitated Reinforcement Learning Approach](https://mlsanity.com/paper/2406.13280/) (2024) - [PDSR: Efficient UAV Deployment for Swift and Accurate Post-Disaster Search and Rescue](https://mlsanity.com/paper/2410.22982/) (2024) - [Large Multimodal Model-Based Environment-Aware Mobility Management](https://mlsanity.com/paper/2607.09795/) (2026) - [AGI Enabled Solutions For IoX Layers Bottlenecks In Cyber-Physical-Social-Thinking Space](https://mlsanity.com/paper/2506.22487/) (2025) - [Vehicle-to-Infrastructure Collaborative Spatial Perception via Multimodal Large Language Models](https://mlsanity.com/paper/2509.03837/) (2025) - [Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks](https://mlsanity.com/paper/2606.05208/) (2026) ## Abstract (arXiv) In this paper a novel joint sensing, communication, and artificial intelligence (AI) framework is proposed so as to optimize extended reality (XR) experiences over terahertz (THz) wireless systems. The proposed framework consists of three main components. First, a tensor decomposition framework is proposed to extract unique sensing parameters for XR users and their environment by exploiting then THz channel sparsity. Essentially, THz band's quasi-opticality is exploited and the sensing parameters are extracted from the uplink communication signal, thereby allowing for the use of the same waveform, spectrum, and hardware for both communication and sensing functionalities. Then, the Cramer-Rao lower bound is derived to assess the accuracy of the estimated sensing parameters. Second, a non-autoregressive multi-resolution generative artificial intelligence (AI) framework integrated with an adversarial transformer is proposed to predict missing and future sensing information. The proposed framework offers robust and comprehensive historical sensing information and anticipatory forecasts of future environmental changes, which are generalizable to fluctuations in both known and unforeseen user behaviors and environmental conditions. Third, a multi-agent deep recurrent hysteretic Q-neural network is developed to control the handover policy of reconfigurable intelligent surface (RIS) subarrays, leveraging the informative nature of sensing information to minimize handover cost, maximize the individual quality of personal experiences (QoPEs), and improve the robustness and resilience of THz links. Simulation results show a high generalizability of the proposed unsupervised generative AI framework to fluctuations in user behavior and velocity, leading to a 61 % improvement in instantaneous reliability compared to schemes with known channel state information.