Optimal and Suboptimal Dynamic Cache Update Algorithms for Wireless Cellular Networks

In this letter, we study the dynamic cache update problem for wireless content caching networks. The cost minimization perspective is considered, wherein the system cost contains three components, including the cache cost, the retrieval cost, and the update cost. Particularly, the update cost lies at the root of the coded caching strategy. To address the non-convex optimization problem, we propose both suboptimal and optimal algorithms. More specifically, we prove that with previous caching state, the cost minimization problem for the current time interval is a 0-1 Knapsack problem, which can be optimally solved by a dynamic programming with pseudo polynomial time complexity. On this basis, the original optimization problem is solved by an iterative manner, resulting in an suboptimal solution. Thereafter, we design the optimal dynamic caching policy, which is derived from the recursive algorithm. Simulation result shows that the suboptimal solution achieves near-optimal performance. It also demonstrates the superiority of our developed strategies compared to various baselines in terms of cost saving. Moreover, counterintuitive insight is judiciously acquired, such as that the high cache capacity does not always reveal positive effect on system's performance when update cost is taken into consideration.

Automated summary

In this study, we examine the problem of dynamic cache update in wireless content caching networks from a cost minimization perspective. We propose suboptimal and optimal algorithms to address the non-convex optimization problem. The results show that the suboptimal solution achieves near-optimal performance, and high cache capacity does not always have a positive effect on system performance when update cost is considered.