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- W3206070148 abstract "Massive Machine-type Communications (mMTC) is one of the principal features of the 5th Generation and beyond (5G+) mobile network services. Due to sparse but synchronous MTC nature, a large number of devices tend to access a base station simultaneously for transmitting data, leading to congestion. To accommodate a large number of simultaneous arrivals in mMTC, efficient congestion control techniques like access class barring (ACB) are incorporated in LTE-A random access. ACB introduces access delay which may not be acceptable in delay-constrained scenarios, such as, eHealth, self-driven vehicles, and smart grid applications. In such scenarios, MTC devices may be forced to drop packets that exceed their delay budget, leading to a decreased system throughput. To this end, in this paper a novel delay-aware priority access classification (DPAC) based ACB is proposed, where the MTC devices having packets with lesser leftover delay budget are given higher priority in ACB. A reinforcement learning (RL) aided framework, called DPAC-RL, is also proposed for online learning of DPAC model parameters. Simulation studies show that the proposed scheme increases successful preamble transmissions by up to <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$75 %$</tex-math></inline-formula> while ensuring that the access delay is well within the delay budget." @default.
- W3206070148 created "2021-10-25" @default.
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- W3206070148 date "2021-12-01" @default.
- W3206070148 modified "2023-09-30" @default.
- W3206070148 title "Delay-Aware Priority Access Classification for Massive Machine-Type Communication" @default.
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- W3206070148 doi "https://doi.org/10.1109/tvt.2021.3120280" @default.
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