  Rate Splitting Multiple Access (RSMA) has recently emerged as a promising
technique to enhance the transmission rate for multiple access networks. Unlike
conventional multiple access schemes, RSMA requires splitting and transmitting
messages at different rates. The joint optimization of the power allocation and
rate control at the transmitter is challenging given the uncertainty and
dynamics of the environment. Furthermore, securing transmissions in RSMA
networks is a crucial problem because the messages transmitted can be easily
exposed to adversaries. This work first proposes a stochastic optimization
framework that allows the transmitter to adaptively adjust its power and
transmission rates allocated to users, and thereby maximizing the sum-rate and
fairness of the system under the presence of an adversary. We then develop a
highly effective learning algorithm that can help the transmitter to find the
optimal policy without requiring complete information about the environment in
advance. Extensive simulations show that our proposed scheme can achieve
positive covert transmission rates in the finite blocklength regime and
non-saturating rates at high SNR values. More significantly, our achievable
covert rate can be increased at high SNR values (i.e., 20 dB to 40 dB),
compared with saturating rates of a conventional multiple access scheme.
