  A novel framework of intelligent reflecting surface (IRS)-aided
multiple-input single-output (MISO) non-orthogonal multiple access (NOMA)
network is proposed, where a base station (BS) serves multiple clusters with
unfixed number of users in each cluster. The goal is to maximize the sum rate
of all users by jointly optimizing the passive beamforming vector at the IRS,
decoding order, power allocation coefficient vector and number of clusters,
subject to the rate requirements of users. In order to tackle the formulated
problem, a three-step approach is proposed. More particularly, a long
short-term memory (LSTM) based algorithm is first adopted for predicting the
mobility of users. Secondly, a K-means based Gaussian mixture model (K-GMM)
algorithm is proposed for user clustering. Thirdly, a deep Q-network (DQN)
based algorithm is invoked for jointly determining the phase shift matrix and
power allocation policy. Simulation results are provided for demonstrating that
the proposed algorithm outperforms the benchmarks, while the throughput gain of
35% can be achieved by invoking NOMA technique instead of orthogonal multiple
access (OMA).
