Functional split is a promising technique to flexibly balance the processing cost at remote ends and the fronthaul rate in cloud radio access networks (C-RAN). By harvesting renewable energy, remote radio units (RRUs) can save grid power and be flexibly deployed. However, the randomness of energy arrival poses a major design challenge. To maximize the throughput under the average fronthaul rate constraint in C-RAN with renewable powered RRUs, we first study the offline problem of selecting the optimal functional split modes and the corresponding durations, jointly with the transmission power. We find that between successive energy arrivals, at most two functional split modes should be selected. Then the optimal online problem is formulated as an Markov decision process (MDP). To deal with the curse of dimensionality of solving MDP, we further analyze the special case with one instance of energy arrival and two candidate functional split modes as inspired by the offline solution, and then a heuristic online policy is proposed. Numerical results show that with flexible functional split, the throughput can be significantly improved compared with fixed functional split. Also, the proposed heuristic online policy has similar performance with the optimal online one, as validated by simulations.

中文翻译：

---

用于能量收集云无线电接入网络的灵活功能拆分和功率控制

功能拆分是一种很有前途的技术，可以灵活地平衡远程端的处理成本和云无线接入网络 (C-RAN) 中的前传速率。通过收集可再生能源，远程无线电单元 (RRU) 可以节省电网功率并灵活部署。然而，能量到达的随机性带来了重大的设计挑战。为了在具有可再生供电 RRU 的 C-RAN 中在平均前传速率约束下最大化吞吐量，我们首先研究了选择最佳功能拆分模式和相应持续时间以及传输功率的离线问题。我们发现在连续的能量到达之间，最多应该选择两种功能分裂模式。然后将最优在线问题表述为马尔可夫决策过程 (MDP)。为了解决解决MDP的维数灾难，我们进一步分析了受离线解决方案启发的具有一个能量到达实例和两个候选功能拆分模式的特殊情况，然后提出了启发式在线策略。数值结果表明，与固定功能拆分相比，灵活的功能拆分可以显着提高吞吐量。此外，经模拟验证，所提出的启发式在线策略与最佳在线策略具有相似的性能。