Optimal Utility of Vehicles in LTE-V Scenario: An Immune Clone-Based Spectrum Allocation Approach

With the surge service requirements from vehicular users, especially for automated driving, providing real-time high-rate wireless connections to fast-moving vehicles is ever demanding. This motivates the development of the emerging LTE-V network, a 5G cellular-based vehicular technology. However, note that the vehicular user group is typically of a very large scale, whereas the bandwidth spectrum available for vehicular communications is very limited. To efficiently allocate and utilize the slim spectrum resource to vehicle users are therefore important. This paper develops a service priority oriented spectrum allocation scheme in an LTE-V network, which explores the features of vehicular networks toward economic yet QoS guaranteed spectrum allocation. Specifically, the work exploits two features of the vehicular networks. First, vehicles in the proximity typically have similar information requirements, e.g., road conditions. As a result, the location-based multicast (i.e., geocast) could be applied to save the spectrum. Second, different types of vehicles, e.g., ambulances, buses, and private cars, are of different bandwidth and service requirements. Therefore, differential services and spectrum allocations should be applied. By jointly considering the above features, the authors develop a 2-D service importance oriented framework for LTE-V network spectrum allocations. The spectrum allocation issue is finally modeled as a mixed integer programming problem to maximize the system utility, and solved using an immune clonal based algorithm. The convergence of the proposed algorithm is proved, and using numerical results, the authors show that their proposal can outperform the typical heuristics-based spectrum resource allocation in terms of convergence and average delay.

Language

  • English

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Filing Info

  • Accession Number: 01707518
  • Record Type: Publication
  • Files: TLIB, TRIS
  • Created Date: May 2 2019 3:00PM