The shift toward 6 G architectures demands a transition from human-centric management to AI-native, zero-touch orchestration to navigate extreme network complexity. While the Open RAN (O-RAN) framework utilizes the near-real-time RAN intelligent controller (RIC) to host third-party xApps for resource optimization, the lack of coordination between multivendor applications often triggers operational collisions and resource competition. This paper introduces a novel ML-driven network orchestrator designed as an intelligent interceptor layer that decouples xApps from the RIC. The proposed framework transforms asynchronous xApp requests into a collective optimization problem, evaluating the combinatorial impact of control actions before execution. The system leverages high-speed eXtreme Gradient Boosting (XGBoost) predictors and an inmemory feature store to forecast network utility within a 5 -second prediction horizon, ensuring stability through a dedicated cooling mechanism for rejected requests. Experimental evaluations conducted via ns-3 and FlexRIC integration to demonstrate that the orchestrator effectively prevents performance degradation, yielding an increase in mobility stability and an additional gain in overall throughput. Our results validate that this scalable orchestration logic provides a resilient foundation for autonomous 6G operations by prioritizing global system utility over individual xApp objectives while effectively mitigating xApp conflicts and preventing resource contention.
Trusted xApp distribution is a prerequisite for O-RAN adoption, but current proposals rely on logically centralized stores, a single point of trust unsuitable for multi-operator deployments. We propose a decentralized xApp marketplace governed by Ethereum smart contracts, where a consortium of operators jointly controls xApp onboarding, validation, licensing, and revocation with no central authority; artifact hashes are stored on-chain while binaries reside on IPFS. Three smart contracts are deployed on Sepolia testnet and evaluated for gas cost across all lifecycle operations and for resilience against six threat classes (Sybil attacks, artifact injection, governance takeover, replay, key compromise, and admin role abuse). Post-approval deployment latency is statistically indistinguishable from a centralized baseline $(\Delta=-1.8 \text{ms})$, while the on-chain audit trail provides immutable provenance that centralized stores cannot offer.
In this paper, we introduce a dynamic approach to routing in SDNs and VNF placement, using a parameterized multi-objective optimization model, which can handle different weights for objectives such as energy, delay, congestion, and resource constraints. This approach allows the network to behave dynamically in response to changing conditions, since the optimization and objective weighting are decoupled processes. The online control layer, which uses rule-based approaches and reinforcement learning, can dynamically control the objectives in real time and improve decision-making efficiency. The results demonstrate the effectiveness of the proposed approach in terms of the costs of service deployment and overall network performance.
This paper explores the application of Digital Twins in 6 G wireless communications. Simulating these environments is crucial for performance evaluation and sensing applications. We introduce Raycom, a Command Line Interface (CLI) tool based on the Sionna RayTracing engine, designed to automate wireless channel evaluation and generate datasets. Utilizing Raycom, we tackle a complex inverse problem: reconstructing a visual representation of an indoor space entirely from Radio Frequency ($\mathbf{R F}$) channel state data, enabling privacypreserving spatial awareness. By employing a Conditional Generative Adversarial Network (Pix2Pix), we map lowresolution RF heatmaps to high-resolution ($256 \times 256$) optical images. The network converges over 200 epochs on 400 spatial samples. We present a limitation analysis on complex geometries across multiple viewpoints. Our results highlight the application potential and the informational bottlenecks imposed by small antenna arrays ($5 \times 5$), laying the groundwork for high-fidelity wireless vision using Massive MIMO systems.
Recent advances in communication technologies and networked intelligent systems are enabling new paradigms for connected mobility, where distributed sensing and real-time data processing play a critical role. In such environments, efficient data transmission and low-latency operation are essential, particularly for UAV-assisted applications that rely on continuous environmental monitoring. To address these requirements, perception is increasingly performed at the edge, allowing UAV platforms to operate as intelligent sensing nodes that process visual data locally and transmit only relevant semantic information. Within this context, fisheye cameras provide wide-area coverage suitable for mobility monitoring, while introducing additional challenges due to geometric distortion. This work presents a comparative evaluation of fisheye-based object detection methods under realistic deployment conditions on an embedded platform (NVIDIA Jetson Orin Nano), analyzing distortion-aware and foundation-model-based approaches and highlighting accuracy-efficiency trade-offs for scalable and communication-efficient intelligent transportation systems.
