Md. Zoheb Hassan’s research while affiliated with Université Laval and other places

Internet-of-Vehicles (IoV) is envisioned to connect vehicles with each other, the surrounding environment, and central control centers. Spectrum sharing among active vehicular links is imperative to enhance the utilization of the spectrum licensed to IoV networks. However, co-channel interference among neighboring vehicular communication links poses a fundamental challenge when enabling spectrum sharing in IoV networks. This paper introduces a resource optimization framework, entitled PRISM (Proactive Resource optimization for Interference and Spectrum Management), to mitigate co-channel interference in IoV networks. PRISM proactively allocates resources among a set of Vehicle-to-Infrastructure (V2I) communication links by accurately predicting the links’ positions and multi-path channel gains, thereby preventing outdated resource scheduling in dynamic IoV networks. PRISM is a three-step approach. In the first step, a multi-layer long short-term memory neural network and transfer learning are employed to predict the vehicles’ positions. In the second step, a digital twin network incorporating high-fidelity 3D maps and a ray tracing tool entitled SionnaTM is used to predict the V2I links’ multi-path channel gains. In the third step, a resource allocation algorithm is executed to efficiently determine V2I clusters and their transmit power allocations to maximize the overall system capacity. Simulation results show that PRISM enhances IoV network’s capacity up to 33% compared to non-proactive schemes, as validated through a simulation framework using real-world vehicular mobility traces.

The exponential growth of data traffic beyond the 5G era necessitates improved resource utilization for the integrated terrestrial and non-terrestrial networks (ITNTN). In this work, we consider a multi-user multiple input multiple output (MU-MIMO)-empowered 5G ITNTN network consisting of terrestrial 5G and multi-beam geostationary earth orbit (GEO) satellite-based gNBs and develop an interference management framework that allows multiple users to receive downlink data over the same resource blocks (RB) simultaneously. Our developed framework first employs a traffic offloading algorithm by leveraging the reference signal received power (RSRP) and celledge width criteria to offload traffic from terrestrial to NTN networks. Subsequently, we formulate the resultant interference management as a joint power allocation and user-RB scheduling optimization problem to maximize the network’s spectral efficiency. Since the joint optimization problem is NP-hard and computationally intractable, a fractional programming-based solution is developed to obtain sub-optimal yet efficient transmit power allocation and user scheduling at terrestrial and satellite gNBs. A realistic ITNTN simulator is developed for performance evaluation by considering 3GPP channel models, antenna gains, and 5G RB numerology in rural terrestrial-GEO coexistence scenarios. Extensive simulation results confirm the efficacy of the proposed framework in managing interference and improving resource utilization at 5G ITNTN networks.

The Internet of Vehicles (IoV) generates massive data traffic and demands reliable end-to-end connectivity to achieve multi-Gbps throughput between vehicles and roadside units. Millimeter-wave (mmWave) bands, with their abundant bandwidth, are promising for high-throughput IoV networks. However, in this context, significant propagation losses, intermittent line-of-sight availability, and dynamic topology changes due to vehicle mobility present critical challenges. This paper introduces RAVEN (Resource Allocation for VEhicular Networks), a centralized resource management framework designed to address these challenges effectively. RAVEN leverages a digital twin network (DTN) to optimize the end-to-end system capacity of multi-hop mmWave IoV networks by effectively managing co-channel interference among vehicles. RAVEN comprises the following three steps: (i) a channel prediction step that utilizes DTN’s awareness of vehicular mobility and environmental contexts to predict site-specific channel gains for vehicular communication links; (ii) a clustering step that partitions vehicles into non-overlapping clusters, allowing vehicles within each cluster to share the same mmWave channel for data transmission, while simultaneously reducing co-channel interference; (iii) a multi-hop connectivity optimization step that provides a connected vehicular networking topology by jointly optimizing vehicle-to-vehicle and vehicle-to-infrastructure connectivity using a graph theory approach. A proof-of-concept of RAVEN is developed by implementing a DTN on the Microsoft Azure Digital Twins platform while integrating real-world vehicular mobility traces, edge-cloud collaboration, and parallel computing. Extensive simulations demonstrate that RAVEN outperforms several benchmark schemes, and offers scalability and near real-time decision-making capabilities for managing interference in large-scale IoV networks.

Ensuring global connectivity and bridging the digital divide among urban, rural, and remote communities are the fundamental visions of 6G networks. Although various technologies, such as nonterrestrial networks, small cells, and wireless backhaul, are envisioned to enable global connectivity, their coexistence with the conventional cellular network architecture requires investigations. Meanwhile, 6G architecture is expected to accommodate a user-centric cell-free network, thanks to its robustness against inter-cell interference and offering macro-diversity. In this article, we propose a convergence of the conventional cellular and evolving cell-free communication networks to provide seamless coverage over vast geographical areas. The paper makes the following contributions. It introduces an architecture for integrated cell-free and cellular (ICFC) networks, incorporating digital twin technology and context-aware design. The paper emphasizes artificial intelligence-driven methods for managing radio resources and ensuring security. Additionally, we explore research avenues to enhance ICFC networks for seamless connectivity in the 6G era.