# Joint Downlink Scheduling for File Placement and Delivery in Cache-Assisted Wireless Networks with Finite File Lifetime

## TL;DR

This paper develops a low-complexity scheduling policy for cache-assisted wireless networks that optimizes downlink transmission, reducing resource use by combining reactive and proactive multicasting based on user request modeling.

## Contribution

It introduces a novel dynamic programming approach with a low-complexity approximation and a learning algorithm for unknown user distributions, enhancing downlink scheduling efficiency.

## Key findings

- Reactive multicast policy reduces base station resource consumption.
- Proactive multicast further improves network performance.
- Proposed methods outperform baseline strategies in simulations.

## Abstract

In this paper, downlink transmission scheduling of popular files is optimized with the assistance of wireless cache nodes. Specifically, the requests of each file, which is further divided into a number of segments, are modeled as a Poisson point process within its finite lifetime. Two downlink transmission modes are considered: (1) the base station reactively multicasts the file segments to the requesting users and selected cache nodes; (2) the base station proactively multicasts some file segments to the selected cache nodes without requests. The cache nodes with decoded file segments can help to offload the traffic via other spectrum. Without the proactive multicast, we formulate the downlink transmission resource minimization as a dynamic programming problem with random stage number, which can be approximated via a finite-horizon Markov decision process (MDP) with fixed stage number. To address the prohibitively huge state space, we propose a low-complexity scheduling policy by linearly approximating the value functions of the MDP, where the bound on the approximation error is derived. Moreover, we propose a learning-based algorithm to evaluate the approximated value functions for unknown geographical distribution of requesting users. Finally, given the above reactive multicast policy, a proactive multicast policy is introduced to exploit the temporal diversity of shadowing effect. It is shown by simulation that the proposed low-complexity reactive multicast policy can significantly reduce the resource consumption at the base station, and the proactive multicast policy can further improve the performance.