Name
Abstract | ||
|---|---|---|
In this paper, the downlink packet scheduling problem for cellular networks is modeled, which jointly optimizes throughput, fairness and packet drop rate. Two genie-aided heuristic search methods are employed to explore the solution space. A deep reinforcement learning (DRL) framework with Advantage actor-critic (A2C) algorithm is proposed for the optimization problem. Several methods have been utilized in the framework to improve the sampling and training efficiency and to adapt the algorithm to a specific scheduling problem. Numerical results show that DRL outperforms the baseline algorithm and achieves similar performance as genie-aided methods without using the future information. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/WCNC45663.2020.9120729 | 2020 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC) |
Keywords | DocType | ISSN |
radio resource scheduling, deep reinforcement learning, cellular networks, multi-objective optimization | Conference | 1525-3511 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen Xu | 1 | 3 | 1.40 |
| Jian Wang | 2 | 302 | 48.27 |
| Yu Tianhang | 3 | 0 | 0.34 |
| Kong Chuili | 4 | 0 | 0.34 |
| Yourui Huangfu | 5 | 0 | 0.68 |
| Rong Li | 6 | 31 | 17.93 |
| Yiqun Ge | 7 | 10 | 4.64 |
| Jun Wang | 8 | 9228 | 736.82 |