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This is a supplementary document to the paper: Multi-channel Opportunistic Access for Heterogeneous Networks Based on Deep Reinforcement Learning. In this document, we derive the optimal network throughputs when the Multi-channel Deep-reinforcement Learning Multiple Access (MC-DLMA) node coexists with the nodes using other protocols in various multi-channel heterogeneous wireless network (HetNet) scenarios. Then we use these optimal network throughputs as upper bound benchmarks for our paper.