Programmable and customized intelligence for traffic steering in 5G networks using open RAN architectures

5G and beyond mobile networks will support heterogeneous use cases at an unprecedented scale, thus demanding automated control and optimization of network functionalities customized to the needs of individual users. Such fine-grained control of the Radio Access Network (RAN) is not possible with the current cellular architecture. To fill this gap, the Open RAN paradigm and its specification introduce an “open” architecture with abstractions that enable closed-loop control and provide data-driven, and intelligent optimization of the RAN at the userlevel. This is obtained through custom RAN control applications (i.e., xApps) deployed on near-real-time RAN Intelligent Controller (near-RT RIC) at the edge of the network. Despite these premises, as of today the research community lacks a sandbox to build data-driven xApps, and create large-scale datasets for effective Artificial Intelligence (AI) training. In this paper, we address this by introducing ns-O-RAN , a software framework that integrates a real-world, production-grade near- RT RIC with a 3GPP-based simulated environment on ns-3, enabling at the same time the development of xApps and automated large-scale data collection and testing of Deep Reinforcement Learning (DRL)- driven control policies for the optimization at the user-level. In addition, we propose the first user-specific O-RAN Traffic Steering (TS) intelligent handover framework. It uses Random Ensemble Mixture (REM), a Conservative Q-learning (CQL) algorithm, combined with a state-of-the-art Convolutional Neural Network (CNN) architecture, to optimally assign a serving base station to each user in the network. Our TS xApp, trained with more than 40 million data points collected by ns-O-RAN, runs on the near-RT RIC and controls the ns-O-RAN base stations. We evaluate the performance on a large-scale deployment with up to 126 users with 8 base stations, showing that the xApp-based handover improves throughput and spectral efficiency by an average of 50% over traditional handover heuristics, with less mobility overhead

Preserving Data Privacy During Data Transfer in MANETs

Mobile adhoc networks consists of large number of mobile nodes, and is usually deployed to transfer data from a sender to a receiver using multi-hop routing. The data being transmitted may contain sensitive information, and undesired disclosure of information can lead to launching of various attacks, thus breaching the data privacy. Earlier works achieve data privacy by using approaches such as data transformation and data perturbation. However, these approaches introduce higher overheads and delays. We propose a computational intelligence based data privacy preserving scheme, where rough set theory is used to anonymize the data during data transfer. Data packets are enclosed within capsules that can be opened only by the designated node, thus preventing the undesired leakage of the data. Also, route between a sender and a receiver is changed dynamically by selecting more than one trusted 1-hop neighbor nodes in each routing step. The proposed data privacy preserving scheme is tested by considering different case studies in a MANET deployed for stock market. Theoretical analysis for data privacy is presented in terms of Information Gain by an attacker and Attacker Overhead, and the performance of proposed scheme against some of the attacks is also discussed. The simulation results show the effectiveness of proposed scheme

Utility-driven k-anonymization of public transport user data

In this paper, we propose a k-anonymity approach that prioritizes the generalization of attributes based on their utility. We focus on transport data, which we consider a special case in which many or all attributes are quasi-identifiers (e.g., origin, destination, ride start time), as they allow correlation with easily observable auxiliary data. The novelty in our approach lies in introducing normalization techniques as well as distance and utility metrics that allow the consideration of not only numerical attributes but also categorical attributes by representing them in tree or graph form. The prioritization of the attributes in the generalization process is based on the attributes’ utility and can further be influenced by either automatically or manually assigned attribute weights. We evaluate and compare different options for all components of our mechanism as well as present an extensive performance evaluation of our approach using real-world data. Lastly, we show in which cases suppression of records can counter-intuitively lead to higher data utility.National Research Foundation (NRF)Published versionThis work was supported by the Singapore National Research Foundation through the Campus for Research Excellence and Technological Enterprise (CREATE) Programme