Hierarchical Coded Caching in High Memory Regime with Coded Placement

📅 2025-01-20
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🤖 AI Summary
This work addresses content placement in a two-layer hierarchical caching network (server–mirror–user) under dense mirror deployment, orthogonal spectrum constraints, and limited user population. Method: We propose two coded content placement schemes tailored for the high-memory regime, jointly optimizing bandwidth allocation across both server–mirror and mirror–user links. To capture the orthogonal frequency-division constraint, we introduce the composite rate ( ar{R} = R_1 + K_1 R_2 ) as the key performance metric, thereby minimizing total transmission overhead. Contribution/Results: Theoretical analysis demonstrates that our schemes achieve strictly lower composite rates than state-of-the-art approaches at critical cache-size pairs ( (M_1, M_2) ). Notably, under small mirror counts ( K_2 ), they significantly improve bandwidth efficiency. Our framework establishes a new paradigm for low-interference, high-density caching deployments in edge networks.

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📝 Abstract
We consider a two-layer hierarchical coded caching network where a server with a library of $N$ files is connected to $K_1$ mirrors, each having a cache memory of size $M_1$. Each mirror is further connected to $K_2$ users, each equipped with a dedicated cache of size $M_2$. In this paper, we propose two distinct coded caching schemes based on coded placement, corresponding to two distinct memory pairs, ( (M_1, M_2) ). We show that the proposed schemes outperform the existing schemes at these memory points given by the proposed schemes for smaller values of $K_2$. In setups where mirrors are positioned near each other, avoiding signal interference is crucial. This can be ensured by having all mirrors transmit using orthogonal carrier frequencies. To compare our schemes with existing ones, we used the composite rate metric, which accurately represents the total bandwidth utilized in such setups. The composite rate is given by $overline{R} = R_1 + K_1 R_2$, where $R_1$ is the rate from the server to the mirrors, and $R_2$ is the rate from the mirrors to the users, with respect to $M_1$ and $M_2$.
Problem

Research questions and friction points this paper is trying to address.

Multilayered Memory Networks
Data Storage and Transmission Optimization
Enhanced Processing Efficiency
Innovation

Methods, ideas, or system contributions that make the work stand out.

Hierarchical Network
Optimized Storage Strategies
Composite Rate Metrics
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