The proposed dynamic algorithm can provide suboptimal resource allocation at 0.001 s, whereas the Max SE MILP model provides the optimal resource allocation in around 1 min, thus, the proposed dynamic scheme can be used in real-time applications.
Abstract
The intelligent transportation systems (ITS), including autonomous driving technologies, have increased the need for a capable communication system. The optical domain offers a promising spectrum for supporting multi-connection and high-data-rate applications. This paper proposes a dynamic resource allocation algorithm for vehicle-to-vehicle (V2V) 6G visible light communication (VLC) systems. A wavelength division multiple access (WDMA) method is utilized as a technique for supporting multiple connections. Two optimization objectives of the resource allocation are evaluated, which are referred to as Max SNR and Max spectral efficiency (SE) objectives. The best resource assignment for each vehicle is obtained by using the optimized resource allocation model. Five scenarios are examined where vehicles are moved in this work. The Max SE objective shows a fair allocation of resources based on the SNR compared to the Max SNR objective. In addition, a dynamic algorithm is developed for real-time solutions. The proposed dynamic algorithm can provide suboptimal resource allocation at 0.001 s, whereas the Max SE MILP model provides the optimal resource allocation in around 1 min. Thus, the proposed dynamic scheme can be used in real-time applications.
A mixed-integer linear programming (MILP) model is developed that jointly optimizes access points (APs) selection, wavelength assignment and fog computing resources allocation to serve the requests of users and achieves lower average total power consumption.
Wafaa B. M. Fadlelmula, S. Mohamed, T. El-Gorashi et al.· 0 citations
The results demonstrate that the proposed PP-SAPF is suitable for real-time deployment in intelligent transportation systems (ITS) and autonomous vehicles where low latency, reliable connectivity, and adaptive resource management is significant.
Irshad Khan, Neetha Papanna Umalakshmi, Somshekhar Durgaiah et al.· Bulletin of Electrical Engin...· 0 citations
This paper proposes an advanced resource allocation technique based on multi-objective optimization (MOO) to jointly optimize spectrum and power, mitigating nonlinear impairments and enhancing network performance, thereby improving the optical signal-to-noise ratio.
S. A. Silva, Carmelo J. A. Bastos-Filho, Danilo R. B. Araújo et al.· Journal of Microwaves, Optoe...· 0 citations
A Dual-Stage Multi-Time-Scale Temporal Attention-Based LSTM network (D-MTSTA-LSTM) has been architected, which effectively learns short- and long-term relationships in network trends, thereby precisely predicting optimal communication routes and associated power and spectrum allocation.
Nishu Gupta, Rupali Bhartiya, S. Rathod et al.· Scientific Reports· 0 citations
The rapid growth of Internet of Vehicles (IoV) applications has imposed strict requirements on low-latency and energy-efficient computing services. This letter investigates a multi-Uncrewed Aerial Vehicle (UAV)-assisted IoV system, where multiple Mobile Edge Computing (MEC)-enabled UAVs (MUs) collaboratively provide computing services for vehicular terminals (VTs). To improve service capability, we propose an energy-efficient task offloading and load balancing scheme that jointly considers vehicle mobility, task offloading and migration, and computing resource allocation to formulate an optimization problem. To solve this problem, a collective learning (CL)-enabled multi-agent reinforcement learning (CL-MARL) algorithm is proposed, where each agent learns optimal policies through centralized training and collective cooperative learning. Simulation results demonstrate that the proposed scheme outperforms benchmark strategies in terms of energy efficiency, task completion rate, and load balancing.
Yongbin Wang, Peng Lin, Yan Liu et al.· IEEE Wireless Communications...· 0 citations
Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are two emerging technologies envisioned for sixth-generation (6G) wireless systems. These technologies enhance conventional cellular networks by extending coverage and enabling ubiquitous connectivity. However, spectrum scarcity and interoperability challenges create a strong need for efficient spectrum sharing among cellular users supported by these technologies. In this context, this paper considers a dynamic spectrum-sharing method in which data rate-aware spectrum sharing plays a critical role in managing base station power consumption and mitigating interference. We investigate a UAV-RIS-assisted cellular system where legacy cellular users share the spectrum with cellular Internet-of-Things (IoT) devices. To enhance the overall system sum data rate, we propose a joint user pairing, spectrum, power allocation, and RIS phase shift optimization approach based on matching theory. Simulation results demonstrate the effectiveness of the proposed method in improving resource allocation efficiency and significantly enhancing the sum data rate performance of wireless communication systems.
Lilatul Ferdouse, Mashiwat Tabassum Waishy· International Conference on...· 0 citations