  As a main use case of 5G and Beyond wireless network, the ever-increasing
machine type communications (MTC) devices pose critical challenges over MTC
network in recent years. It is imperative to support massive MTC devices with
limited resources. To this end, Non-orthogonal multiple access (NOMA) based
random access network has been deemed as a prospective candidate for MTC
network. In this paper, we propose a deep reinforcement learning (RL) based
approach for NOMA-based random access network with truncated channel inversion
power control. Specifically, each MTC device randomly selects a pre-defined
power level with a certain probability for data transmission. Devices are using
channel inversion power control yet subject to the upper bound of the
transmission power. Due to the stochastic feature of the channel fading and the
limited transmission power, devices with different achievable power levels have
been categorized as different types of devices. In order to achieve high
throughput with considering the fairness between all devices, two objective
functions are formulated. One is to maximize the minimum long-term expected
throughput of all MTC devices, the other is to maximize the geometric mean of
the long-term expected throughput for all MTC devices. A Policy based deep
reinforcement learning approach is further applied to tune the transmission
probabilities of each device to solve the formulated optimization problems.
Extensive simulations are conducted to show the merits of our proposed
approach.
