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The high Dimensionality factor of Coded Caching : Resolving Bottlenecks one Antenna at a time

Abstract : The main theme of this thesis is the combination of two opposing resources, namely the multiplexing gain, corresponding to multiple antennas, and the multicasting gain achieved in cache-aided communications. Multiple antennas, or multiple transmitters with caches, provide increased gains by separating messages, while coded caching merges messages together by exploiting cached and unwanted content to remove interference. Thus, at a first look, multiplexing and coded caching gains seem to be opposing to one another. Efforts to combine the two have shown that the gains appear to be additive. For example, in the Multiple-Input-Multiple-Output (MISO) Broadcast Channel (BC) where a Base Station, equipped with L transmit antennas, serves the demands of K receiving, single antenna users, asking files from a library of popular files, when users are equipped with caches, amounting to a total sum cache-size of t times the whole library, this network can provide the order optimal Degrees-of-Freedom (DoF) performance of D=L+t. What we will show in this thesis is that, in many scenarios, pairing the multiplexing gains with coded caching can be a much more powerful combination, which can dramatically improve major fundamental limitations of both coded caching and multiple antenna precoding. A notable example, that will be proved in this thesis, is the role of multiple antennas on dramatically ameliorating the infamous subpacketization constraint i.e., the limitation that a coded caching gain equal to t requires each file to be segmented to an exponential number of packets. This, in practical systems can easily surpass the number of bits of a file by many orders of magnitude, thus imposing hard limits on caching gains [...]
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Submitted on : Monday, November 2, 2020 - 10:35:39 AM
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  • HAL Id : tel-02985381, version 1


Eleftherios Lampiris. The high Dimensionality factor of Coded Caching : Resolving Bottlenecks one Antenna at a time. Information Theory [cs.IT]. Sorbonne Université, 2019. English. ⟨NNT : 2019SORUS234⟩. ⟨tel-02985381⟩



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