Memory Enabled Bumblebee-Based Dynamic Spectrum Access for Platooning Environments

In this paper, the authors propose a novel memory-enabled non-uniform sampling-based bumblebee foraging algorithm (MEB) designed for optimal channel selection in a distributed Vehicular Dynamic Spectrum Access (VDSA) framework employed in a platoon operating environment. Given how bumblebee behavioral models are designed to support adaptation in complex and highly time-varying environments, these models can be employed by connected vehicles to enable their operation within a dynamically changing network topology and support their selection of optimal channels possessing low levels of congestion to achieve high throughput. As a result, the proposed VDSA-based optimal channel selection employs fundamental concepts from the bumblebee foraging model. In the proposed approach, the Channel Busy Ratio (CBR) of all channels is computed and stored in memory to be accessed by the MEB algorithm to make the necessary channel switching decisions. Two averaging techniques, Sliding Window Average (SWA) and Exponentially Weighted Moving Average (EWMA), are employed to leverage past samples and are evaluated against the no-memory case. Due to the high variability of the environment (e.g., high velocities, changing density of vehicles on the road), the authors propose to calculate the CBR by employing non-uniform channel sampling allocations as well as evaluate it using both simplified numerical and realistic Vehicle-to-Vehicle (V2V) computer simulations. The numerical simulation results show that gains in the probability of the best channel selection can be achieved relative to a uniform sampling allocation approach. By utilizing memory, the authors observe an additional increase in the channel selection performance. Similarly, the authors see an increase in the probability of successful reception when utilizing the bumblebee algorithm via a system-level simulator.

• Record URL:
  http://dx.doi.org/10.1109/TVT.2023.3236035
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Gill, Kuldeep S
  • Kryszkiewicz, Pawel
  • Sroka, Paweł
  • Kliks, Adrian
  • Wyglinski, Alexander M
• Publication Date: 2023-5

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 5612-5627
• Serial:
  • IEEE Transactions on Vehicular Technology
  • Volume: 72
  • Issue Number: 5
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 0018-9545
  • Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25

Subject/Index Terms

• TRT Terms: Algorithms; Connected vehicles; Mobile communication systems; Traffic platooning
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01883345
• Record Type: Publication
• Files: TRIS
• Created Date: May 24 2023 9:07AM