Rate Optimization for RIS-Aided mMTC Networks in the Finite Blocklength Regime

Reconfigurable intelligent surfaces (RISs) have become a promising candidate for the development of future mobile systems. In context massive machine-type communications (mMTC), RIS can be used to support transmission from group sensors collector node. Due short data packets, we focus on design maximizing weighted sum and minimum rates in finite blocklength regime. Under assumption non-orthogonal multiple access, successive interference cancelation is considered as decoding scheme mitigate interference. Accordingly, formulate optimizations non-convex problems propose two sub-optimal solutions based gradient ascent (GA) sequential optimization (SO) with semi-definite relaxation (SDR). GA, distinguish between Euclidean Riemannian gradients. For SO, derive concave lower bound throughput maximize it sequentially applying SDR. Numerical results show that SO outperform GA strategies relying classical Shannon capacity might inadequate mMTC networks.