Next-Generation Radio Access Networks (NG-RANs) aim to facilitate high data rates, low-latency applications, and dense mobile connectivity — benefit from the integration of Artificial Intelligence and Machine Learning (AI/ML) to enhance performance and efficiency. Nevertheless, the dynamic service demands within NG-RAN (namely Open RAN) lead to AI/ML performance degradation known as drift, resulting in violations of Service Level Agreements (SLA) and issues like over-or under-provisioning of resources. Detecting and adapting to drift becomes crucial to meet the diverse requirements of intelligent networks. Due to frequent retraining, the existing threshold and classifier-based approaches have potential disadvantages such as SLA violations and resource inefficiency. This paper introduces a novel approach that exploits the Generative Adversarial Network (GAN) architecture to determine the drift and anomaly. The proposed approach is evaluated for a throughput prediction use case over a real-time dataset and compared to the threshold and classifier-based approaches. The results show that the proposed approach outperforms the threshold and classifier-based approaches.

Fixed mobile convergence is a topic that is receiving significant attention from the research community as well as telecom equipment vendors around the world. The paper surveys standardization activities and proposes an end-to-end architecture that will support this architecture. The role of traffic management IP-based quality of service is recognized as a vital component for the success of a multiservice, IP-based and converged network

In this paper we describe an efficient algorithm for the constrained shortest path problem which is defined as follows. Given a directed graph with two weights on each link e, a cost l/sub e/, and a delay t/sub e/, find the cheapest path from a source to all destinations such that the delay of each path is no more than a given threshold. The constrained shortest path problem arises in quality-of-service-sensitive routing in data networks and is of particular importance in real time services. The problem formulation and the algorithmic framework presented are quite general; they apply to IP, ATM, and optical networks. Unlike previous algorithms, our algorithm generates paths from one source to all destinations. Our algorithm is strongly polynomial, and is asymptotically faster than earlier algorithms. We corroborate our analysis by a preliminary simulation study.