Split Learning-Based Robust Resource Allocation for Consumer Electronics in Smart Cities

In the smart city, high-density deployment of consumer electronics (CE) may lead to mutual interference, resulting in imperfect estimation of the channel state information (CSI). To tackle the problem, this paper proposes a split learning-based robust resource allocation for CEs in smart cities. We constructed an interference hypergraph model and divided resource allocation conflicts in overlapping areas into multiple virtual sub-cells (VSCs) to reduce the impact of mutual interference for the CSI. Then, we take into account the imperfect CSI and design a robust optimization model to maximize the throughput of the network in the VSCs. Due to the imperfections of CSI and the introduction of random channel parameters, solving robust optimization models is challenging. Hence, we propose the split robust learning algorithm based on interference hypergraph (SRLA-IH), which utilizes split learning theory to learn models and obtain more accurate uncertainty sets, effectively reducing the problems caused by imperfect CSI in smart cities. Numerical results demonstrate that compared with other algorithms, our proposed algorithm can achieve excellent network throughput and improve resource allocation utilization even under imperfect CSI.