  Massive interconnection has sparked people's envisioning for next-generation
ultra-reliable and low-latency communications (xURLLC), prompting the design of
customized next-generation advanced transceivers (NGAT). Rate-splitting
multiple access (RSMA) has emerged as a pivotal technology for NGAT design,
given its robustness to imperfect channel state information (CSI) and
resilience to quality of service (QoS). Additionally, xURLLC urgently appeals
to large-scale access techniques, thus massive multiple-input multiple-output
(mMIMO) is anticipated to integrate with RSMA to enhance xURLLC. In this paper,
we develop an innovative RSMA-assisted massive-MIMO xURLLC (RSMA-mMIMO-xURLLC)
network architecture tailored to accommodate xURLLC's critical QoS constraints
in finite blocklength (FBL) regimes. Leveraging uplink pilot training under
imperfect CSI at the transmitter, we estimate channel gains and customize
linear precoders for efficient downlink short-packet data transmission.
Subsequently, we formulate a joint rate-splitting, beamforming, and transmit
antenna selection optimization problem to maximize the total effective
transmission rate (ETR). Addressing this multi-variable coupled non-convex
problem, we decompose it into three corresponding subproblems and propose a
low-complexity joint iterative algorithm for efficient optimization. Extensive
simulations substantiate that compared with non-orthogonal multiple access
(NOMA) and space division multiple access (SDMA), the developed architecture
improves the total ETR by 15.3% and 41.91%, respectively, as well as
accommodates larger-scale access.
