Matches in SemOpenAlex for { <https://semopenalex.org/work/W4381327595> ?p ?o ?g. }
- W4381327595 endingPage "106" @default.
- W4381327595 startingPage "90" @default.
- W4381327595 abstract "Future WiFi networks require a channel access method that provides users with high capacity. Such a method must consider (1) channel bonding, which improves the transmission capacity of Access Points (APs), and (2) spatial reuse, where APs tune their Clear Channel Assessment (CCA) threshold and transmit power in order to transmit concurrently with neighboring APs. To date, there are no solutions that <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>jointly</i> optimize the channels used by an AP, and the CCA threshold and transmit power of a bonded channel. To this end, we outline a three-tier deep learning approach. Briefly, at Layer-1, it selects a set of transmitting channels. At Layer-2 and Layer-3, it respectively determines the transmit power and CCA threshold for each selected channel. An AP then employs deep reinforcement learning to learn the optimal policy for each layer given its interference intensity and queue length. The simulation results show that when compared to three competing solutions, an AP that uses our approach is able to reduce its queue length by up to 62.52% under realistic traffic load." @default.
- W4381327595 created "2023-06-21" @default.
- W4381327595 creator A5034008140 @default.
- W4381327595 creator A5065046113 @default.
- W4381327595 date "2023-01-01" @default.
- W4381327595 modified "2023-10-05" @default.
- W4381327595 title "A Three-Tier Deep Learning-Based Channel Access Method for WiFi Networks" @default.
- W4381327595 cites W2026531360 @default.
- W4381327595 cites W2041707400 @default.
- W4381327595 cites W2076337764 @default.
- W4381327595 cites W2101676293 @default.
- W4381327595 cites W2109401393 @default.
- W4381327595 cites W2133088499 @default.
- W4381327595 cites W2137661534 @default.
- W4381327595 cites W2139233035 @default.
- W4381327595 cites W2145339207 @default.
- W4381327595 cites W2293729851 @default.
- W4381327595 cites W2334782222 @default.
- W4381327595 cites W2495597049 @default.
- W4381327595 cites W2579692724 @default.
- W4381327595 cites W2604222154 @default.
- W4381327595 cites W2616985635 @default.
- W4381327595 cites W2783855483 @default.
- W4381327595 cites W2787778301 @default.
- W4381327595 cites W2804613408 @default.
- W4381327595 cites W2807900833 @default.
- W4381327595 cites W2808000686 @default.
- W4381327595 cites W2890950736 @default.
- W4381327595 cites W2964107888 @default.
- W4381327595 cites W2977044331 @default.
- W4381327595 cites W2991031935 @default.
- W4381327595 cites W2995897908 @default.
- W4381327595 cites W3013353193 @default.
- W4381327595 cites W3024077104 @default.
- W4381327595 cites W3038790644 @default.
- W4381327595 cites W3047031615 @default.
- W4381327595 cites W3099749652 @default.
- W4381327595 cites W3110964917 @default.
- W4381327595 cites W3161945723 @default.
- W4381327595 cites W3163268305 @default.
- W4381327595 cites W3194467569 @default.
- W4381327595 cites W4206795684 @default.
- W4381327595 cites W4220971535 @default.
- W4381327595 doi "https://doi.org/10.1109/tmlcn.2023.3288090" @default.
- W4381327595 hasPublicationYear "2023" @default.
- W4381327595 type Work @default.
- W4381327595 citedByCount "0" @default.
- W4381327595 crossrefType "journal-article" @default.
- W4381327595 hasAuthorship W4381327595A5034008140 @default.
- W4381327595 hasAuthorship W4381327595A5065046113 @default.
- W4381327595 hasBestOaLocation W43813275951 @default.
- W4381327595 hasConcept C127162648 @default.
- W4381327595 hasConcept C127413603 @default.
- W4381327595 hasConcept C154945302 @default.
- W4381327595 hasConcept C160403385 @default.
- W4381327595 hasConcept C177264268 @default.
- W4381327595 hasConcept C178790620 @default.
- W4381327595 hasConcept C185592680 @default.
- W4381327595 hasConcept C199360897 @default.
- W4381327595 hasConcept C206588197 @default.
- W4381327595 hasConcept C22684755 @default.
- W4381327595 hasConcept C2779227376 @default.
- W4381327595 hasConcept C31258907 @default.
- W4381327595 hasConcept C32022120 @default.
- W4381327595 hasConcept C41008148 @default.
- W4381327595 hasConcept C47798520 @default.
- W4381327595 hasConcept C548081761 @default.
- W4381327595 hasConcept C65422117 @default.
- W4381327595 hasConcept C761482 @default.
- W4381327595 hasConcept C76155785 @default.
- W4381327595 hasConcept C97541855 @default.
- W4381327595 hasConceptScore W4381327595C127162648 @default.
- W4381327595 hasConceptScore W4381327595C127413603 @default.
- W4381327595 hasConceptScore W4381327595C154945302 @default.
- W4381327595 hasConceptScore W4381327595C160403385 @default.
- W4381327595 hasConceptScore W4381327595C177264268 @default.
- W4381327595 hasConceptScore W4381327595C178790620 @default.
- W4381327595 hasConceptScore W4381327595C185592680 @default.
- W4381327595 hasConceptScore W4381327595C199360897 @default.
- W4381327595 hasConceptScore W4381327595C206588197 @default.
- W4381327595 hasConceptScore W4381327595C22684755 @default.
- W4381327595 hasConceptScore W4381327595C2779227376 @default.
- W4381327595 hasConceptScore W4381327595C31258907 @default.
- W4381327595 hasConceptScore W4381327595C32022120 @default.
- W4381327595 hasConceptScore W4381327595C41008148 @default.
- W4381327595 hasConceptScore W4381327595C47798520 @default.
- W4381327595 hasConceptScore W4381327595C548081761 @default.
- W4381327595 hasConceptScore W4381327595C65422117 @default.
- W4381327595 hasConceptScore W4381327595C761482 @default.
- W4381327595 hasConceptScore W4381327595C76155785 @default.
- W4381327595 hasConceptScore W4381327595C97541855 @default.
- W4381327595 hasLocation W43813275951 @default.
- W4381327595 hasOpenAccess W4381327595 @default.
- W4381327595 hasPrimaryLocation W43813275951 @default.
- W4381327595 hasRelatedWork W1992626567 @default.
- W4381327595 hasRelatedWork W2031553576 @default.
- W4381327595 hasRelatedWork W2048424050 @default.
- W4381327595 hasRelatedWork W260766989 @default.