More Like this

1. SPARC: Spatio-Temporal Adaptive Resource Control for Multi-site Spectrum Management in NextG Cellular Networks

   https://doi.org/10.1145/3696405  

   Ghosh, Ushasi; Chiejina, Azuka; Stephenson, Nathan; Shah, Vijay K; Shakkottai, Srinivas; Bharadia, Dinesh (December 2024, Proceedings of the ACM on Networking)

   This work presents SPARC (Spatio-Temporal Adaptive Resource Control), a novel approach for multi-site spectrum management in NextG cellular networks. SPARC addresses the challenge of limited licensed spectrum in dynamic environments. We leverage the O-RAN architecture to develop a multi-timescale RAN Intelligent Controller (RIC) framework, featuring an xApp for near-real-time interference detection and localization, and a xApp for real-time intelligent resource allocation. By utilizing base stations as spectrum sensors, SPARC enables efficient and fine-grained dynamic resource allocation across multiple sites, enhancing signal-to-noise ratio (SNR) by up to 7dB, spectral efficiency by up to 15%, and overall system throughput by up to 20%. Comprehensive evaluations, including emulations and over-the-air experiments, demonstrate the significant performance gains achieved through SPARC, showcasing it as a promising solution for optimizing resource efficiency and network performance in NextG cellular networks. 

   more » « less
2. Distributed Data-Driven Learning-Based Optimal Dynamic Resource Allocation for Multi-RIS-Assisted Multi-User Ad-Hoc Network

   https://doi.org/10.3390/a17010045  

   Zhang, Yuzhu; Xu, Hao (January 2024, Algorithms)

   This study investigates the problem of decentralized dynamic resource allocation optimization for ad-hoc network communication with the support of reconfigurable intelligent surfaces (RIS), leveraging a reinforcement learning framework. In the present context of cellular networks, device-to-device (D2D) communication stands out as a promising technique to enhance the spectrum efficiency. Simultaneously, RIS have gained considerable attention due to their ability to enhance the quality of dynamic wireless networks by maximizing the spectrum efficiency without increasing the power consumption. However, prevalent centralized D2D transmission schemes require global information, leading to a significant signaling overhead. Conversely, existing distributed schemes, while avoiding the need for global information, often demand frequent information exchange among D2D users, falling short of achieving global optimization. This paper introduces a framework comprising an outer loop and inner loop. In the outer loop, decentralized dynamic resource allocation optimization has been developed for self-organizing network communication aided by RIS. This is accomplished through the application of a multi-player multi-armed bandit approach, completing strategies for RIS and resource block selection. Notably, these strategies operate without requiring signal interaction during execution. Meanwhile, in the inner loop, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm has been adopted for cooperative learning with neural networks (NNs) to obtain optimal transmit power control and RIS phase shift control for multiple users, with a specified RIS and resource block selection policy from the outer loop. Through the utilization of optimization theory, distributed optimal resource allocation can be attained as the outer and inner reinforcement learning algorithms converge over time. Finally, a series of numerical simulations are presented to validate and illustrate the effectiveness of the proposed scheme. 

   more » « less
3. Intelligent Dynamic Resource Allocation and Puncturing for Next Generation Wireless Networks

   https://doi.org/10.1109/JIOT.2024.3422350  

   AlQwider, Walaa; Abdalla, Aly Sabri; Rahman, Talha Faizur; Marojevic, Vuk (July 2024, IEEE Internet of Things Journal)

   As we progress from 5G to emerging 6G wireless, the spectrum of cellular communication services is set to broaden significantly, encompassing real-time remote healthcare applications and sophisticated smart infrastructure solutions, among others. This expansion brings to the forefront a diverse set of service requirements, underscoring the challenges and complexities inherent in next-generation networks. In the realm of 5G, Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) have been pivotal service categories. As we venture into the 6G era, these foundational use cases will evolve and embody additional performance criteria, further diversifying the network service portfolio. This evolution amplifies the necessity for dynamic and efficient resource allocation strategies capable of balancing the diverse service demands. In response to this need, we introduce the Intelligent Dynamic Resource Allocation and Puncturing (IDRAP) framework. Leveraging Deep Reinforcement Learning (DRL), IDRAP is designed to balance between the bandwidth-intensive requirements of eMBB services and the latency and reliability needs of URLLC users. The performance of IDRAP is evaluated and compared against other resource management solutions, including Intelligent Dynamic Resource Slicing (IDRS), Policy Gradient Actor-Critic Learning (PGACL), System-Wide Tradeoff Scheduling (SWTS), Sum-Log, and Sum-Rate.The results show an improved Service Satisfaction Level (SSL) for eMBB users while maintaining the essential SSL threshold for URLLC services. 

   more » « less
4. O-RAN Performance Analyzer: Platform Design, Development, and Deployment

   https://doi.org/10.1109/MCOM.002.2300822  

   Kouchaki, Mohammadreza; Natanzi, Seyed_Bagher Hashemi; Zhang, Minglong; Tang, Bo; Marojevic, Vuk (September 2024, IEEE Communications Magazine)

   The open radio access network (O-RAN) represents a paradigm shift in RAN architecture, integrating intelligence into communication networks via xApps -- control applications for managing RAN resources. This integration facilitates the adoption of AI for network optimization and resource management. However, there is a notable gap in practical network performance analyzers capable of assessing the functionality and efficiency of xApps in near real-time within operational networks. Addressing this gap, this article introduces a comprehensive network performance analyzer, tailored for the near-real time RAN intelligent controller. We present the design, development, and application scenarios for this testing framework, including its components, software, and tools, providing an end-to-end solution for evaluating the performance of xApps in O-RAN environments. 

   more » « less
5. Integrated LLM-Based Intrusion Detection with Secure Slicing xApp for Securing O-RAN-Enabled Wireless Network Deployments

   https://doi.org/10.1109/ICCWorkshops67674.2025.11162166  

   Moore, Joshua; Abdalla, Aly Sabri; Khanal, Prabesh; Marojevic, Vuk (June 2025, IEEE International Conference on Communications workshops)

   The Open Radio Access Network (O-RAN) architecture is reshaping telecommunications by promoting openness, flexibility, and intelligent closed-loop optimization. By decoupling hardware and software and enabling multi-vendor deployments, O-RAN reduces costs, enhances performance, and allows rapid adaptation to new technologies. A key innovation is intelligent network slicing, which partitions networks into isolated slices tailored for specific use cases or quality of service requirements. The RAN Intelligent Controller further optimizes resource allocation, ensuring efficient utilization and improved service quality for user equipment (UEs). However, the modular and dynamic nature of O-RAN expands the threat surface, necessitating advanced security measures to maintain network integrity, confidentiality, and availability. Intrusion detection systems have become essential for identifying and mitigating attacks. This research explores using large language models (LLMs) to generate security recommendations based on the temporal traffic patterns of connected UEs. The paper introduces an LLM-driven intrusion detection framework and demonstrates its efficacy through experimental deployments, comparing non-fine-tuned and fine-tuned models for task-specific accuracy. 

   more » « less