Cache-aided wireless device-to-device (D2D) networks allow significant throughput increase, depending on the concentration of the popularity distribution of files. Many studies assume that all users have the same preference distribution; however, this may not be true in practice. This work investigates whether and how the information about individual preferences can benefit cache-aided D2D networks. We examine a clustered network and derive a network utility that considers both the user distribution and channel fading effects into the analysis. We also formulate a utility maximization problem for designing caching policies. This maximization problem can be applied to optimize several important quantities, including throughput, energy efficiency (EE), cost, and hit-rate, and to solve different tradeoff problems. We provide a general approach that can solve the proposed problem under the assumption that users coordinate, then prove that the proposed approach can obtain the stationary point under a mild assumption. Using simulations of practical setups, we show that performance can improve significantly with proper exploitation of individual preferences. We also show that different types of tradeoffs exist between different performance metrics and that they can be managed through caching policy and cooperation distance designs.
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Noncoordinated Individual Preference Aware Caching Policy in Wireless D2D Networks
Recent investigations showed that cache-aided device-to-device (D2D) networks can be improved by properly exploiting the individual preferences of users. Since in practice it might be difficult to make centralized decisions about the caching distributions, this paper investigates the individual preference aware caching policy that can be implemented distributedly by users without coordination. The proposed policy is based on categorizing different users into different reference groups associated with different caching policies according to their preferences. To construct reference groups, learning-based approaches are used. To design caching policies that maximize throughput and hit-rate, optimization problems are formulated and solved. Numerical results based on measured individual preferences show that our design is effective and exploiting individual preferences is beneficial.
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- NSF-PAR ID:
- 10176187
- Date Published:
- Journal Name:
- ICC 2020 - 2020 IEEE International Conference on Communications (ICC)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICC40277.2020.9149007
