Rafid I. Abd

Smart cities and intelligent transportation systems (ITS) confront substantial urban mobility challenges, with parking management emerging as a particularly complex subsystem. The operational efficacy of these systems is fundamentally constrained by dynamic stochastic variables, including spatiotemporal resource distribution, demand volatility, multimodal traffic interdependencies, and competing urban development priorities. These factors generate nonlinear system behaviors that manifest as chronic inefficiencies in parking resource allocation, ultimately degrading overall urban mobility performance. The Hydra radio access network (Hydra-RAN) is envisioned as a next-generation multifunctional (NG-MF) platform. A comprehensive solution designed to consolidate existing networks and technologies into a cohesive, integrated framework. This advanced architecture promotes a synergistic environment, enabling the simultaneous operation of multiple networks and applications. While Hydra-RAN supports a broad spectrum of applications, this study focuses specifically on its perceptive parking management -an innovative solution enabled by the network’s distinctive integration of multi-sparse input processing and multi-task learning (SMTL) paradigms. This approach provides intelligent real-time classification and dynamic allocation of available parking spaces through edge network nodes. The system’s advanced capabilities stem from three key technological integrations: (1) continuous processing of real-time urban data streams, (2) a hierarchical framework for computational task distribution across three integrated tiers: edge computing (EC), fog computing (FC), and cloud computing (CC), and (3) semantic communication protocols, collectively representing a paradigm shift in intelligent parking management. Our proposed solution demonstrated a 50% reduction in communication overhead, 75% improved real-time decision-making accuracy, and enhanced scalability in modern urban environments. These results have significant implications, e.g., for reducing operational costs, improving resource utilization, and supporting sustainable urban development.

Hydra radio access networks (H-RANs) are poised to revolutionize the way data is gathered, transmitted, and analyzed, ushering in an era of unprecedented connectivity, efficiency, and intelligence. By proactively managing changes and addressing potential disruptions, H-RAN networks aim to provide consistent service quality, even under varied operating conditions. This means that networks can automatically detect issues, assess their impact, implement corrective measures, and modify operating parameters in response to variations in user behavior or environmental factors. However, conventional RANs typically implement a uniform strategy when dealing with diverse communication scenarios. This one-size-fits-all methodology limits their ability to autonomously adjust to the unique requirements of the millimeter and terahertz wave (MMW/THz) communications environment, which is characterized by high sensitivity to environmental factors. As a result, these systems face significant challenges in maintaining a reliable and efficient communication link under varying conditions. Furthermore, conventional RANs typically require considerable manual tuning and configuration, making them time-consuming and costly. Indeed, while the potential of H-RAN networks encompasses numerous improvements and innovations across various aspects of next-generation technologies, this paper addresses the initial access implementation and data transmission approaches under the non-line-of-sight (NLoS) of the level₁ case study. Our solution contains several novelties that enhance both overhead and model accuracy. To this end, we have developed a novel adaptive environment-aware blockage beamforming codebook structure. This codebook consists of edge-line-of-sight (ELoS) vectors and reconfigurable intelligent surface (RIS) vectors, RIS-LoS (SLoS). These elements are integral to optimizing beamforming in scenarios where direct line-of-sight (LoS) is obstructed by level₁ blockage scenario. Furthermore, we employed a multi-sparse input and multi-task learning (SMTL) framework in the Hydra distributed unit (H-DU) artificial intelligence and machine learning (AI/ML) (AI/ML D-engine), where each task is tailored to be executed in a particular environment based on online feedback. The SMTL model employs an approach to handling sparse and heterogeneous input data while performing multiple tasks based on the current network condition. More specifically, a novel task₂ is developed in this study to generate a list of recommended beamforming vectors and patterns designed to address the challenges arising from blockage scenarios of level₁ in MMW/THz environments.

Next-generation sensor and radio access networks (NG-SRANs) namely, Hydra radio access networks (H-RANs) represent a significant evolution in the telecommunications and sensor ecosystem landscape in anticipation of 6G deployment and beyond. H-RAN’s vision derives its strength from integrating various technologies and networks into a single central network with the widespread incorporation of artificial intelligence (AI) technologies throughout the network. As a result, H-RAN’s unique features and characteristics can serve as a baseline for innovating new applications and significantly enhance the overall functions of conventional open radio access networks (O-RANs). However, among the many improvements and innovations that the H-RAN architecture promises in its functionality, this paper focuses on the initial access implementation "Task1" approach. Our solution contains several novelties that enhance both overhead and model accuracy. To this end, we define a novel intelligent perception network inspired by the knowledge distribution idea for collaborative H-RAN networks. We develop sparse multi-task learning (SMTL) as part of the AI/ML D-engine for federated learning to perform multiple tasks simultaneously. The SMTL is designed to select the optimal solution from a list of recommended solutions, namely "Tasks". In the simulation, figures of merit include metrics such as top-k validation accuracy, beam selection accuracy, throughput ratios, beam sweep time, latency, and initial access times, which are used to evaluate the performance and efficiency of the proposed technologies. Simulation results demonstrate that by exploiting contextual information from distributed collaborative SRUs, and UE sends its own sensing information via a physical random-access channel in addition to using SMTL, our H-RAN-based initial access scheme can achieve 82.9% throughput of an exhaustive beam search (EBS) based-O-RAN network without any beam search overhead and 96.7% by searching among as few as 5 beams. Compared to the conventional MMW 5G-NR solution, our proposed method significantly minimizes the beam search time needed to reach the desired throughput.

After researchers devoted considerable efforts to developing 5G standards, their passion began to focus on establishing the basics for the standardization of 6G and beyond. The utilization of millimeter wave (MMW) and terahertz (THz) frequency bands, combined with sensors and artificial intelligence (AI), has gained significant attention in the research community for the development of the next generation of sensory and radio access networks (NG-SRANs). Leveraging the advantages of communication and sensor systems’ common characteristics will open horizons for merging the two networks, thereby creating a unified perceptive and intelligence network. Overall, while using MMW and THz frequencies is certainly valuable, the ability to gather and transmit data in real-time makes sensors extremely effective in communication networks. In contrast, AI, machine learning (ML), and deep learning (DL) have become predominant methods for solving data analysis problems across a wide range of domains, such as analyzing large amounts of different sensor data, decision-making, channel estimation, self-organization, and self-healing. This paper proposes a novel design for a potential 6G network and beyond called the Hydra radio access network (H-RAN) perceptual networks architecture, which is designed based on NG-SRAN. From a design perspective, H-RAN aims to merge communication and sensing networks into a single network in which two functionalities are attempted to mutually complement each other, namely communication-aided sensing and sensing-aided communications networks. However, such a network provides an adequate platform for a wide range of AL/ML algorithms, such as real-time decision-making, self-organization, and self-healing. As a result, H-RAN perceptual networks architecture is expected to be more efficient, reliable, and secure than existing conventional networks, and is likely to play a critical role in a wide range of applications, including but not limited to mobile broadband, sensing systems, smart cities, autonomous vehicles, the internet of things (IoT) connectivity, vehicle-to-everything (V2X) communication, etc. This study gives a detailed overview of how H-RAN will revolutionize conventional future sensors and cellular networks. We do so through a comprehensive analysis of H-RAN architectural components and functionalities.

A multi-gigabit (WiGig) system is designed based on IEEE 802.11ad standards by exploiting millimeter-wave (mmWave) bands to achieve extremely high data rates. Recently, IEEE 802.11bd and 3GPP NR-V2X technologies adopted new specifications for the next generation vehicle-to-everything (V2X) network with the ability to support mmWave bands. However, mmWave bands could be the solution for the high throughput and low latency. Still, the instability of blockages is a substantial challenge in mmWave bands, rendering mmWave ineffective in vehicular networks. Typically, technologies capable of operating at mmWave bands, such as IEEE 802.11ad, IEEE 802.11bd, NR-V2X, etc., share the same technical challenges due to the unique characteristics of the mmWave band. As a result, to achieve the aspirations of next generation connected mmWave V2X vehicles. This paper proposes novel classic, cooperative, and extension protocols for IEEE 802.11bd by enhancing IEEE 802.11ad. The proposed solution aims to mitigate some of the most common technical challenges associated with using mmWave bands in V2X networks, such as beam training overhead, link blockage, beam misalignment, limited coverage range, and delay. Simulation results demonstrate that the proposed approach effectively reduces the beam training overhead, achieves accurate beam alignment, increases the probability of line-of-sight (LOS), overcomes link blockage/ misalignment, extends the coverage range, and achieves low latency. Further, the proposed enhancement achieves significantly higher performance against the conventional solution and shows more link stability despite continuous changes in the density of the vehicles.