mobicom11-peng-greenbs-final(編輯修改稿)
2025-01-31 12:28 本頁面
【文章內(nèi)容簡介】 ith 24hourlag Traffic is predictable. Case for traffic profiling UCLA WiNG Spatial Dynamics ? Deployment varies at locations ? Dense in big cities ? 20+ neighbor (1KM) Mobi 2023 C Peng (UCLA) 13 Rich BS redundancy ensures coverage. UCLA WiNG Spatialtemporal Dynamics ? Traffic is also diverse at various locations ? Peak hours are different ? Multiplexing gain ~ 2 at peak hours ? Lower bound for the ratio of capacity to traffic Mobi 2023 C Peng (UCLA) 14 Multiplexing gain: sum(maxTraffic)/sum(traffic) Large saving potential even at peak hours UCLA WiNG Roadmap ? Characterizing multidimensional dynamics ? Exploiting dynamics in design ? Working with 3G standards ? Evaluation Mobi 2023 15 C Peng (UCLA) UCLA WiNG Issue I: How to Satisfy Locationdependent Coverage Capacity Constraints? ? Once a BS turns off, clients in its original coverage should still be covered Mobi 2023 C Peng (UCLA) 16 ? ? ? ? ? ? ? ? ? ? ? Even if the total capacity is enough, it may fail to serve mobile clients due to coverage issue. ?provide locationdependent capacity UCLA WiNG Solution I: Building Virtual Grids ? Divide into BS virtual grids ? BSes within a grid cover each other ? Decouple coverage constraint ? Locationdependent capacity meets locationdep. traffic Virtual BS Grids Mobi 2023 17 C Peng (UCLA) turn on/off BSes . cap = load j i ri + d(i,j) Ri rj + d(i,j) Rj ? ? ? ? ? ? ? ? ? ? ? ? ? ? UCLA WiNG Issue II: How to Estimate Traffic Load? ? At what time scale is traffic load predictable? ? Exploit near periodicity over consecutive timeoftheday ? What to estimate? Instantaneous traffic load vs. traffic upperenvelope ? Choices between accuracy and overestimate ? Tradeoff between energy efficiency and missrate
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