An Efficient Multiple-Groupcast Coded Multicasting Scheme for Finite Fractional Caching
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Coded multicasting has been shown to improve the caching performance of content delivery networks with multiple caches downstream of a common multicast link. However, the schemes that have been shown to achieve order-optimal perfor- mance require content items to be partitioned into a number of packets that grows exponentially with the number of users [1]. In this paper, we first extend the analysis of the achievable scheme in [2] to the case of heterogeneous cache sizes and demand distribu- tions, providing an achievable scheme and an upper bound on the limiting average performance when the number of packets goes to infinity while the remaining system parameters are kept constant. We then show how the scheme achieving this upper bound can very quickly loose its multiplicative caching gain for finite content packetization. To overcome this limitation, we design a novel polynomial-time algorithm based on greedy local graph-coloring that, while keeping the same content packetization, recovers a significant part of the multiplicative caching gain. Our results show that the achievable schemes proposed to date to quantify the limiting performance, must be properly designed for practical finite system parameters.
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Cited by 1 Pith paper
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Learning Selective Merge Policies for Deadline-Constrained Coded Caching via Deep Reinforcement Learning
A DRL policy with graph attention learns selective merging for deadline-constrained coded caching, cutting packet expiration ratio by 40.9% versus SACM++ while merging only about 32% of the time.
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