An efficient caching can be achieved by predicting the popularity of the files accurately. It is well known that the popularity of a file can be nudged by using recommendation, and hence it can be estimated accurately leading to an efficient caching strategy. Motivated by this, in this paper, we consider the problem of joint caching and recommendation in a 5G and beyond heterogeneous network. We model the influence of recommendation on demands by a Probability Transition Matrix (PTM). The proposed framework consists of estimating the PTM and use them to jointly recommend and cache the files. In particular, this paper considers two estimation methods namely a) Bayesian estimation and b) a genie aided Point estimation. An approximate high probability bound on the regret of both the estimation methods are provided. Using this result, we show that the approximate regret achieved by the genie aided Point estimation approach is $\mathcal{O}(T^{2/3} \sqrt{\log T})$ while the Bayesian estimation method achieves a much better scaling of $\mathcal{O}(\sqrt{T})$. These results are extended to a heterogeneous network consisting of M small base stations (sBSs) with a central macro base station. The estimates are available at multiple sBSs, and are combined using appropriate weights. Insights on the choice of these weights are provided by using the derived approximate regret bound in the multiple sBS case. Finally, simulation results confirm the superiority of the proposed algorithms in terms of average cache hit rate, delay and throughput.

翻译：通过准确预测文件流行度可以实现高效缓存。众所周知，推荐能够引导文件流行度，从而通过精确估计制定高效的缓存策略。受此启发，本文研究了5G及未来异构网络中的联合缓存与推荐问题。我们采用概率转移矩阵（PTM）建模推荐对用户需求的影响。所提出的框架包含PTM估计及其在联合推荐与缓存中的应用。具体而言，本文考虑了两种估计方法：a)贝叶斯估计，b)预言辅助点估计。给出了两种估计方法遗憾值的高概率近似上界。基于该结果，我们证明预言辅助点估计方法获得的近似遗憾值为$\mathcal{O}(T^{2/3} \sqrt{\log T})$，而贝叶斯估计方法实现了更优的$\mathcal{O}(\sqrt{T})$标度。这些结果扩展至包含M个微基站（sBS）和中央宏基站的异构网络。在多个sBS处可获得估计值，并通过适当权重进行融合。通过多sBS情形下的近似遗憾界推导，为权重选择提供了理论依据。最后，仿真结果验证了所提算法在平均缓存命中率、时延和吞吐量方面的优越性。