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1. Traffic Priority-Aware 5G NR-U/Wi-Fi Coexistence with Deep Reinforcement Learning

   https://doi.org/10.1109/VTC2024-Fall63153.2024.10757867  

   Fasihi, Mohammad Reza; Mark, Brian L (October 2024, IEEE)

   Coexistence of 5G new radio unlicensed (NR-U) and Wi-Fi is highly prone to the collisions among NR-U gNBs (5G base stations) and Wi-Fi APs (access points). To improve performance and fairness for both networks, various collision resolution mechanisms have been proposed to replace the simple listen-before-talk (LBT) scheme used in the current 5G standard. We address two gaps in the literature: first, the lack of a comprehensive performance comparison among the proposed collision resolution mechanisms and second, the impact of multiple traffic priority classes. Through extensive simulations, we compare the performance of several recently proposed collision resolution mechanisms for NR-U/Wi-Fi coexistence. We extend one of these mechanisms to handle multiple traffic priorities. We then develop a traffic-aware multi-objective deep reinforcement learning algorithm for the scenario of coexistence of high-priority traffic gNB user equipment (UE) with multiple lower-priority traffic UEs and Wi-Fi stations. The objective is to ensure low latency for high-priority gNB traffic while increasing the airtime fairness among the NR-U and Wi-Fi networks. Our simulation results show that the proposed algorithm lowers the channel access delay of high-priority traffic while improving the fairness among both networks. 

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2. QaSAL: QoS-aware State-Augmented Learnable Algorithms for Coexistence of 5G NR-U/Wi-Fi

   https://doi.org/10.1109/CISS64860.2025.10944641  

   Fasihi, Mohammad Reza; Mark, Brian L (March 2025, IEEE)

   With the increasing demand for wireless connectivity, ensuring the efficient coexistence of multiple radio access technologies in shared unlicensed spectrum has become an important issue. This paper focuses on optimizing Medium Access Control (MAC) parameters to enhance the coexistence of 5G New Radio in Unlicensed Spectrum (NR-U) and Wi-Fi networks operating in unlicensed spectrum with multiple priority classes of traffic that may have varying quality-of-service (QoS) requirements. In this context, we tackle the coexistence parameter management problem by introducing a QoS-aware State-Augmented Learnable (QaSAL) framework, designed to improve network performance under various traffic conditions. Our approach augments the state representation with constraint information, enabling dynamic policy adjustments to enforce QoS requirements effectively. Simulation results validate the effectiveness of QaSAL in managing NR-U and Wi-Fi coexistence, demonstrating improved channel access fairness while satisfying a latency constraint for high-priority traffic. 

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3. RanCAD: Random Channel Access Deterrence Attack against Spectrum Coexistence between NR-U and Wi-Fi on the 5GHz Unlicensed Band

   https://doi.org/10.1109/ICC51166.2024.10622917  

   Rahman, Md Rashedur; Hossain, Moinul (June 2024, IEEE International Conference on Communications)

   Spectrum coexistence between 5G and Wi-Fi in the coveted 5GHz spectrum band unleashes new possibilities for more effective spectrum utilization. While the Listen-Before-Talk-based channel access mechanism with the self-deferral-based method enhances the relative fairness of this coexistence framework, it introduces new vulnerabilities yet to be addressed. This research presents a unique attack approach, Random Channel Access Deterrence (RanCAD), that exploits a novel vulnerability in the channel access mechanism. In the proposed attack, a malicious access point deceives a victim 5G base station into deferring its access to the shared channel, resulting in higher channel access delay and lower spectrum utilization. In addition, we propose a Discrete Time Markov Chain (DTMC) to study the proposed attack model, which helps illustrate the attack's impact on the victim's performance. To our knowledge, this is the first work to introduce this vulnerability in the channel access mechanism between coexisting 5G and Wi-Fi networks in the 5GHz band. 

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4. ViFiT: Reconstructing Vision Trajectories from IMU and Wi-Fi Fine Time Measurements

   https://doi.org/10.1145/3615984.3616503  

   Cao, Bryan Bo; Alali, Abrar; Liu, Hansi; Meegan, Nicholas; Gruteser, Marco; Dana, Kristin; Ashok, Ashwin; Jain, Shubham (October 2023, ACM)

   Tracking subjects in videos is one of the most widely used functions in camera-based IoT applications such as security surveillance, smart city traffic safety enhancement, vehicle to pedestrian communication and so on. In computer vision domain, tracking is usually achieved by first detecting subjects, then associating detected bounding boxes across video frames. Typically, frames are transmitted to a remote site for processing, incurring high latency and network costs. To address this, we propose ViFiT, a transformerbased model that reconstructs vision bounding box trajectories from phone data (IMU and Fine Time Measurements). It leverages a transformer’s ability of better modeling long-term time series data. ViFiT is evaluated on Vi-Fi Dataset, a large-scale multimodal dataset in 5 diverse real world scenes, including indoor and outdoor environments. Results demonstrate that ViFiT outperforms the state-of-the-art approach for cross-modal reconstruction in LSTM Encoder-Decoder architecture X-Translator and achieves a high frame reduction rate as 97.76% with IMU and Wi-Fi data. 

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5. On Modeling and Optimizing LTE/Wi-Fi Coexistence with Prioritized Traffic Classes

   https://doi.org/10.1109/DySPAN.2018.8610465  

   Hirzallah, Mohammed; Xiao, Yong; Krunz, Marwan (October 2018, 2018 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN))

   The dramatic growth in demand for mobile data service has prompted mobile network operators (MNOs) to explore new spectrum resources in unlicensed bands. MNOs have been recently allowed to extend LTE-based service called LTE-LAA over 5 GHz U-NII bands, currently occupied by Wi-Fi. To support applications with diverse QoS requirements, both LTE and Wi-Fi technologies introduce multiple priority classes with different channel contention parameters for accessing unlicensed bands. How these different priority classes affect the interplay between coexisting LTE and Wi-Fi technologies is still relatively under-explored. In this paper, we develop a simple and efficient framework that helps MNOs assess the fair coexistence between MNOs and Wi-Fi operators with prioritized channel access under the multi-channel setting. We derive an approximated closed-form solution for each MNO to pre-evaluate the probability of successful transmission (PST), average contention delay, and average throughput when adopting different priority classes to serve different traffics. MNOs and Wi-Fi operators can fit our model using measurements collected offline and/or online, and use it to further optimize their systems’ throughput and latency. Our results reveal that PSTs computed with our approximated closed-form model approach those collected from system-level simulations with around 95% accuracy under scenarios of dense network deployment density and high traffic intensity. 

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