7,427 research outputs found
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
Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges
Machine learning (ML) is widely used for key tasks in Connected and Automated
Vehicles (CAV), including perception, planning, and control. However, its
reliance on vehicular data for model training presents significant challenges
related to in-vehicle user privacy and communication overhead generated by
massive data volumes. Federated learning (FL) is a decentralized ML approach
that enables multiple vehicles to collaboratively develop models, broadening
learning from various driving environments, enhancing overall performance, and
simultaneously securing local vehicle data privacy and security. This survey
paper presents a review of the advancements made in the application of FL for
CAV (FL4CAV). First, centralized and decentralized frameworks of FL are
analyzed, highlighting their key characteristics and methodologies. Second,
diverse data sources, models, and data security techniques relevant to FL in
CAVs are reviewed, emphasizing their significance in ensuring privacy and
confidentiality. Third, specific and important applications of FL are explored,
providing insight into the base models and datasets employed for each
application. Finally, existing challenges for FL4CAV are listed and potential
directions for future work are discussed to further enhance the effectiveness
and efficiency of FL in the context of CAV
A Holistic Investigation on Terahertz Propagation and Channel Modeling Toward Vertical Heterogeneous Networks
User-centric and low latency communications can be enabled not only by small
cells but also through ubiquitous connectivity. Recently, the vertical
heterogeneous network (V-HetNet) architecture is proposed to backhaul/fronthaul
a large number of small cells. Like an orchestra, the V-HetNet is a polyphony
of different communication ensembles, including geostationary orbit (GEO), and
low-earth orbit (LEO) satellites (e.g., CubeSats), and networked flying
platforms (NFPs) along with terrestrial communication links. In this study, we
propose the Terahertz (THz) communications to enable the elements of V-HetNets
to function in harmony. As THz links offer a large bandwidth, leading to
ultra-high data rates, it is suitable for backhauling and fronthauling small
cells. Furthermore, THz communications can support numerous applications from
inter-satellite links to in-vivo nanonetworks. However, to savor this harmony,
we need accurate channel models. In this paper, the insights obtained through
our measurement campaigns are highlighted, to reveal the true potential of THz
communications in V-HetNets.Comment: It has been accepted for the publication in IEEE Communications
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A Survey on Cooperative Longitudinal Motion Control of Multiple Connected and Automated Vehicles
When Cellular Meets WiFi in Wireless Small Cell Networks
The deployment of small cell base stations(SCBSs) overlaid on existing
macro-cellular systems is seen as a key solution for offloading traffic,
optimizing coverage, and boosting the capacity of future cellular wireless
systems. The next-generation of SCBSs is envisioned to be multi-mode, i.e.,
capable of transmitting simultaneously on both licensed and unlicensed bands.
This constitutes a cost-effective integration of both WiFi and cellular radio
access technologies (RATs) that can efficiently cope with peak wireless data
traffic and heterogeneous quality-of-service requirements. To leverage the
advantage of such multi-mode SCBSs, we discuss the novel proposed paradigm of
cross-system learning by means of which SCBSs self-organize and autonomously
steer their traffic flows across different RATs. Cross-system learning allows
the SCBSs to leverage the advantage of both the WiFi and cellular worlds. For
example, the SCBSs can offload delay-tolerant data traffic to WiFi, while
simultaneously learning the probability distribution function of their
transmission strategy over the licensed cellular band. This article will first
introduce the basic building blocks of cross-system learning and then provide
preliminary performance evaluation in a Long-Term Evolution (LTE) simulator
overlaid with WiFi hotspots. Remarkably, it is shown that the proposed
cross-system learning approach significantly outperforms a number of benchmark
traffic steering policies
Interaction-aware Kalman Neural Networks for Trajectory Prediction
Forecasting the motion of surrounding obstacles (vehicles, bicycles,
pedestrians and etc.) benefits the on-road motion planning for intelligent and
autonomous vehicles. Complex scenes always yield great challenges in modeling
the patterns of surrounding traffic. For example, one main challenge comes from
the intractable interaction effects in a complex traffic system. In this paper,
we propose a multi-layer architecture Interaction-aware Kalman Neural Networks
(IaKNN) which involves an interaction layer for resolving high-dimensional
traffic environmental observations as interaction-aware accelerations, a motion
layer for transforming the accelerations to interaction aware trajectories, and
a filter layer for estimating future trajectories with a Kalman filter network.
Attributed to the multiple traffic data sources, our end-to-end trainable
approach technically fuses dynamic and interaction-aware trajectories boosting
the prediction performance. Experiments on the NGSIM dataset demonstrate that
IaKNN outperforms the state-of-the-art methods in terms of effectiveness for
traffic trajectory prediction.Comment: 8 pages, 4 figures, Accepted for IEEE Intelligent Vehicles Symposium
(IV) 202
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