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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 » « lessFree, publicly-accessible full text available July 2, 2025
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In response to the evolving landscape of wireless communication networks and the escalating demand for unprecedented wireless connectivity performance in the forthcoming 6G era, this paper proposes a new 6G architecture to enhance the wireless network's sum rate performance. Therefore, we introduce an aerial base station (ABS) network with reconfigurable intelligent surfaces (RISs) while leveraging the multi-users multiple-input single-output (MU-MISO) antenna technology. The motivation behind our proposal stems from the imperative to address critical challenges in contemporary wireless networks and harness emerging technologies for substantial performance gains. We employ deep reinforcement learning (DRL) to jointly optimize the ABS trajectories, the active beamforming weights, and the RIS phase shifts. Simulation results show that this joint optimization effectively improves the system's sum rate while meeting minimum quality of service (QoS) requirements for diverse mobile users.more » « lessFree, publicly-accessible full text available June 9, 2025
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The open radio access network (O-RAN) offers new degrees of freedom for building and operating advanced cellular networks. Emphasizing on RAN disaggregation, open interfaces, multi-vendor support, and RAN intelligent controllers (RICs), O-RAN facilitates adaptation to new applications and technology trends. Yet, this architecture introduces new security challenges. This article proposes leveraging zero trust principles for O-RAN security. We introduce zero trust RAN (ZTRAN), which embeds service authentication, intrusion detection, and secure slicing subsystems that are encapsulated as xApps. We implement ZTRAN on the open artificial intelligence cellular (OAIC) research platform and demonstrate its feasibility and effectiveness in terms of legitimate user throughput and latency figures. Our experimental analysis illustrates how ZTRAN's intrusion detection and secure slicing microservices operate effectively and in concert as part of O-RAN Alliance's containerized near-real time RIC. Research directions include exploring machine learning and additional threat intelligence feeds for improving the performance and extending the scope of ZTRAN.more » « lessFree, publicly-accessible full text available April 1, 2025
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This demonstration explores the security concerns in 5G and beyond networks within open radio access network (O-RAN) deployments, focusing on active attacks disrupting cellular communications. An xApp developed on the open artificial intelligence cellular (OAIC) platform enables on-the-fly creation and management of network slices to mitigate such attacks. The xApp is hosted in the near-real time RAN intelligent controller (RIC) and establishes secure slices for the software radio network it controls. This solution presents a practical approach for resilient and secure network management in dynamic environments.more » « less
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The evolution of open architectures for Radio Ac-cess Networks (RANs) is revolutionizing network management and optimization. This transformation, fostered by O-RAN, expedites data acquisition and examination by exploiting newly established open interfaces. Moreover, it has led to the rise of near real-time RAN Intelligent Controllers (RICs), instigating a wave of AI-driven applications, or xApps, that employ Artificial Intelligence (AI)/Machine Learning (ML) methods. Nevertheless, deploying xApps as centralized applications presents substantial challenges, such as handling vast data transactions, potential delays, and security vulnerabilities, which are notably prominent within the multifaceted, decentralized, multivendor, and trustless nature of open networks. To alleviate these predicaments, a transition from centralized apps operating in near real-time to distributed real-time apps is imperative for augmented security and efficiency. This paper addresses these complexities by introducing an open platform that integrates a federated reinforcement learning algorithm to operate as distributed Apps (dApps) within the next-generation O-RAN architecture. We present evaluation results in a specific test environment.more » « less
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The open radio access network (O-RAN) is recognized for its modularity and adaptability, facilitating swift responses to emerging applications and technological advancements. However, this architecture's disaggregated nature, coupled with support from various vendors, introduces new security challenges. This paper proposes an innovative approach to bolster the security of future O-RAN deployments by leveraging RAN slicing principles. Central to this security enhancement is the concept of secure slicing. We introduce SliceX, an xApp designed to safeguard RAN resources while ensuring strict throughput and latency requirements are met for legitimate users. Leveraging the open artificial intelligence cellular re-search (OAIC) platform, we observed that the network latency averages around ten microseconds in a default configuration without SliceX. The latency escalates to over seven seconds in the presence of a malicious user equipment (UE) flooding the net-work with requests. SliceX intervenes, restoring network latency to normal levels, with a maximum latency of approximately 2.3 s. These and other numerical findings presented in this paper affirm the tangible advantages of SliceX in mitigating security threats and ensuring that 0- RAN deployments meet stringent performance requirements. Our research demonstrates the real-world effectiveness of secure slicing, making SliceX a valuable tool for military, government, and critical infrastructure opera-tors reliant on public wireless communication networks to fulfill their security, resiliency, and performance objectives.more » « less
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This paper defines the problem of optimizing the downlink multi-user multiple input, single output (MU-MISO) sum-rate for ground users served by an aerial reconfigurable intelligent surface (ARIS) that acts as a relay to the terrestrial base station. The deep deterministic policy gradient (DDPG) is proposed to calculate the optimal active beamforming matrix at the base station and the phase shifts of the reflecting elements at the ARIS to maximize the data rate. Simulation results show the superiority of the proposed scheme when compared to deep Q-learning (DQL) and baseline approaches.more » « less