References & Further Reading
References
- M. A. Maddah-Ali and U. Niesen, Fundamental Limits of Caching, 2014
The paper that invented coded caching. Contains the MAN scheme in §III, the proof of the rate formula in Theorem 1, and the classical converse in §IV. Required reading.
- M. A. Maddah-Ali and U. Niesen, Decentralized Coded Caching Attains Order-Optimal Memory-Rate Tradeoff, 2015
The decentralized version of MAN (Exercise 2.11). Each user independently caches bits with probability $M/N$; the rate matches centralized MAN asymptotically as $K \to \infty$. Eliminates the need for coordinated placement.
- Q. Yu, M. A. Maddah-Ali, and A. S. Avestimehr, The Exact Rate-Memory Tradeoff for Caching with Uncoded Prefetching, 2018
Proves the MAN scheme is exactly optimal under uncoded placement. The converse uses a clever averaging argument over demand types. This is the Chapter 3 headline result; the scheme side is from this chapter.
- T. M. Cover and J. A. Thomas, Elements of Information Theory, Wiley-Interscience, 2nd ed., 2006
For the entropy bounds used in §2.2 and the general information- theoretic setup.
- M. Ji, A. M. Tulino, J. Llorca, and G. Caire, Order-Optimal Rate of Caching and Coded Multicasting with Random Demands, 2017
Caire group's analysis of MAN with random (Zipf) demands. Shows that the MAN scheme's gain $1 + KM/N$ survives under random demand in the asymptotic regime. Important for bridging worst-case and average-case analyses.
- Q. Yan, M. Cheng, X. Tang, and Q. Chen, On the Placement Delivery Array Design for Centralized Coded Caching Scheme, 2017
Introduces placement delivery arrays (PDAs) — a finite-size construction that recovers MAN-like gains with polynomial subpacketization. Foundational for Chapter 14.
- Z. Chen, P. Fan, and K. B. Letaief, Fundamental Limits of Caching: Improved Bounds For Small Buffer Users, 2016
Early improvement on MAN for small $M$. Extends the basic combinatorial placement with additional flexibility in subfile assignment.
- K. Shanmugam, M. Ji, A. M. Tulino, J. Llorca, and A. G. Dimakis, Finite Length Analysis of Caching-Aided Coded Multicasting, 2016
Non-asymptotic analysis of coded multicast gain. Important for finite subpacketization regimes where MAN's idealized assumptions start to break.
- S. Katti, H. Rahul, W. Hu, D. Katabi, M. Médard, and J. Crowcroft, XORs in the Air: Practical Wireless Network Coding, 2008
Practical demonstration that XOR multicasting works in real wireless networks. Precedes coded caching by five years; the technical link is that both exploit receiver-side information.
- R. Ahlswede, N. Cai, S.-Y. R. Li, and R. W. Yeung, Network Information Flow, 2000
Foundational paper on network coding. The butterfly example foreshadows XOR multicasting; coded caching's delivery phase is structurally related.
- A. El Gamal and Y.-H. Kim, Network Information Theory, Cambridge University Press, 2011
Chapter 5 (broadcast) and Chapter 9 (network coding) cover the multiuser information theory that the shared-link model specializes.
Further Reading
Resources for readers who want to master coded caching before moving to the converse in Chapter 3.
The MAN paper itself
Maddah-Ali & Niesen, 'Fundamental Limits of Caching,' IEEE Trans. IT, 2014
Section III has the scheme; Theorem 1 has the rate. Everything in this chapter is a reorganization of that paper with more pedagogical scaffolding.
Decentralized coded caching
Maddah-Ali & Niesen, 'Decentralized Coded Caching...' IEEE/ACM TON, 2015
The 'we-don't-need-coordination' version of MAN. Crucial reading before Chapter 13 (non-uniform demand), where decentralized placement is the practical default.
Placement delivery arrays
Yan, Cheng, Tang, Chen, 'PDA Design for Centralized Coded Caching,' IEEE Trans. IT, 2017
The right next paper after MAN for students concerned about subpacketization. The construction reduces $F$ from $\binom{K}{t}$ to a polynomial in $K$ at a constant-factor rate cost.
An engineer's view of coded caching
Paridi, Caire, Molisch: 'Coded Caching for Content Delivery' (tutorial), IEEE ComSoc
Tutorial-level treatment focused on practical systems. Good for bridging the information-theoretic idealization to deployable designs.