IEEE 802.11 PHY supports multiple transmission rates according to multiple different modulations and coding schemes. Each WiFi station selects its own transmission rate according to its own
algorithm; in particular, the IEEE 802.11 standards do not specify the bit-rate selection method. Although many adaptive bit-rate selection algorithms have been proposed, there is limited research
and evaluation on the performance of such algorithms for roadside networks, especially in cases with multi-vehicle roadside multi-vehicular WiFi networks.
In this thesis we propose an opportunistic highest bit-rate algorithm, Opportunistic Highest Bit-Rate Multi-Vehicular WiFi Networks (OHBR-MVN), specifically for roadside multi-vehicular WiFi networks. Our proposal is based on three key characteristics of such networks: (1) vehicles will drive closer to, and eventually pass, the roadside WiFi station, experiencing a progressively better
transmission environment; (2) the vast majority of data transmitted in single-vehicle drive-by downloading scenarios occurs at the maximum transmission rate; (3) vehicles that transmit at less than the maximum rate do so at the expense of those that could send more data at a higher
transmission rate. We therefore believe that transmitting only at the highest possible bit-rate is the preferred algorithm for such networks. Further, this approach keeps the bit-rate selection extremely simple, avoiding the complexity and resulting problems of adaptive approaches.
Through a series of experiments that compare the throughput of both fixed and adaptive bit-rate
selection algorithms we show that our approach yields both higher throughput and better fairness characteristics, while being significantly simple, and thus more robust.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OWTU.10012/5774 |
| Date | 21 January 2011 |
| Creators | Son, Giyeong |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
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