Cache-Aided Communications in MISO Networks with Dynamic User Behavior: A Universal Solution

arXiv (Cornell University)(2023)

引用 1|浏览14
暂无评分
摘要
A practical barrier to the implementation of cache-aided networks is dynamic and unpredictable user behavior. In dynamic setups, users can freely depart and enter the network at any moment. The shared caching concept has the potential to handle this issue by assigning $K$ users to $P$ caching profiles, where all $\eta_{p}$ users assigned to profile $p$ store the same cache content defined by that profile. The existing schemes, however, cannot be applied in general and are not dynamic in the true sense as they put constraints on the transmitter-side spatial multiplexing gain $\alpha$. Specifically, they work only if $\alpha \leq \min_{p} \eta_{p}$ or $\alpha \geq \hat{\eta}$, where in the latter case, $\gamma$ is the normalized cache size of each user, $\hat{\eta}$ is an arbitrary parameter satisfying $1 \leq \hat{\eta} \leq \max_{p} \eta_{p}$, and the extra condition of $\alpha \geq K\gamma$ should also be met. In this work, we propose a universal caching scheme based on the same shared-cache model that can be applied to any dynamic setup, extending the working region of existing schemes to networks with $\min_{p} \eta_{p} \leq \alpha \leq \hat{\eta}$ and removing any other constraints of existing schemes. We also derive the closed-form expressions for the achievable degrees-of-freedom (DoF) of the proposed scheme and show that it achieves the optimal DoF for uniform user distributions. Notably, it is the first scheme to achieve the optimal DoF of $K\gamma+\alpha$ for networks with uniform user distribution, $\alpha > \hat{\eta}$, and non-integer $\frac{\alpha}{\hat{\eta}}$, without imposing any other constraints. Finally, we use numerical simulations to assess how non-uniform user distribution impacts the DoF performance and illustrate that the proposed scheme provides a noticeable improvement over unicasting for uneven distributions.
更多
查看译文
关键词
miso networks,dynamic user behavior
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要