Department Publications

The upcoming Sixth Generation (6G) networks aim for fully automated, intelligent network functionalities and services. Therefore, Machine Learning (ML) is essential for these networks. Given stringent privacy regulations, future network architectures should use privacy-preserved ML for their applications and services. Federated Learning (FL) is expected to play an important role as a popular approach for distributed ML, as it protects privacy by design. However, many practical challenges exist before FL can be fully utilized as a key technology for these future networks. We consider the vision of a 6G layered architecture to evaluate the applicability of FL-based distributed intelligence. In this paper, we highlight the benefits of using FL for 6G and the main challenges and issues involved. We also discuss the existing solutions and the possible future directions that should be taken toward more robust and trustworthy FL …

In this paper, we design a resource block (RB) oriented power pool (PP) for semi-grant-free non-orthogonal multiple access (SGF-NOMA) in the presence of residual errors resulting from imperfect successive interference cancellation (SIC). In the proposed method, the BS allocates one orthogonal RB to each grant-based (GB) user, and determines the acceptable received power from grant-free (GF) users and calculates a threshold against this RB for broadcasting. Each GF user as an agent, tries to find the optimal transmit power and RB without affecting the quality-of-service (QoS) and ongoing transmission of the GB user. To this end, we formulate the transmit power and RB allocation problem as a stochastic Markov game to design the desired PPs and maximize the long-term system throughput. The problem is then solved using multi-agent (MA) deep reinforcement learning algorithms, such as double deep Q …

Cycling is a popular leisure and fitness activity, making it a prime candidate for emerging virtual and augmented reality applications. This paper details the design, development, and testing of a competitive virtual cycling platform that allows two players to compete regardless of their geographical location on customized stationary bicycles. They wear head-mounted Virtual Reality (VR) devices to immerse themselves in a 360° video experience. Video playback responds to their cycling speed and head orientation. Competitors' locations are displayed on a map within their VR dashboard. The platform's functionality is tested on 4G and 5G networks and an experimental 4G network implemented on software-defined radios. To evaluate the system's usability, the critical metric of end-to-end delay is measured for the different communication architectures. Delays of under 100ms, crucial for preventing motion sickness, are …

We present SimuSIL, a software-in-the-loop (SIL) simulation platform using open-source tools to capture the behavior of connected vehicles in intelligent transportation system (ITS) applications. Connected cooperative vehicles use vehicle-to-everything (V2X) communication to enable ITS. Real-world testing of ITS applications involves significant risks. Simulating true vehicle dynamics and real-time behavior is challenging in discrete time step environments. We extend the Veins platform, integrating SUMO and OMNet++, with Webots, a simulator for robots and vehicles. Webots implements engine control unit (ECU) models in software to mimic physical vehicle behavior. We validate SimuSIL with a car-following application using Webots and demonstrate its ability to capture communication anomalies like packet losses. Additionally, we demonstrate fuel consumption and CO 2 emissions in autonomous eco-driving …