  Non-orthogonal multiple access (NOMA) is a key technology to enable massive
machine type communications (mMTC) in 5G networks and beyond. In this paper,
NOMA is applied to improve the random access efficiency in high-density
spatially-distributed multi-cell wireless IoT networks, where IoT devices
contend for accessing the shared wireless channel using an adaptive
p-persistent slotted Aloha protocol. To enable a capacity-optimal network, a
novel formulation of random channel access management is proposed, in which the
transmission probability of each IoT device is tuned to maximize the geometric
mean of users' expected capacity. It is shown that the network optimization
objective is high dimensional and mathematically intractable, yet it admits
favourable mathematical properties that enable the design of efficient
data-driven algorithmic solutions which do not require a priori knowledge of
the channel model or network topology. A centralized model-based algorithm and
a scalable distributed model-free algorithm, are proposed to optimally tune the
transmission probabilities of IoT devices and attain the maximum capacity. The
convergence of the proposed algorithms to the optimal solution is further
established based on convex optimization and game-theoretic analysis. Extensive
simulations demonstrate the merits of the novel formulation and the efficacy of
the proposed algorithms.

Networking and Internet Architecture;Artificial Intelligence;Systems and Control;Systems and Control