RSS Threshold Optimization for D2D-Aided HTC/MTC in Ultra-Dense NOMA System

With the rapid development of ultra-dense networks (UDNs) and random access technologies, device-to-device (D2D) and non-orthogonal multiple access (NOMA) techniques will incorporated into future UDNs supporting both human-type communications (HTC) and machine-type communications (MTC) to fulfill the stringent requirements brought by various potential Internet of Everything (IoE) applications. Nevertheless, the combination of D2D and NOMA will make the network management more complicated. In view of this, we optimize the received signal strength (RSS) threshold value of each small base stations (SBSs) in the UDN where HTC and MTC coexist. Considering the computational complexity, we employ a multi-agent reinforcement learning based RSS threshold value selection scheme, in which each SBS acts as an agent and chose the optimal RSS threshold value to achieve maximum system throughput performance by interacting with the environment. Extensive numerical results show our proposed scheme can greatly improve the system throughput by enhancing the connectivity of massive HTC users and MTC devices via D2D and NOMA techniques.