Statistical Delay Guarantee for the URLLC in IRS-Assisted NOMA Networks With Finite Blocklength Coding

One of the essential factors for enabling sixth-generation systems is efficiently ensuring diverse quality-of-service (QoS) performance metrics to support the upcoming massive ultra-reliable low-latency communication (URLLC). This work proposes efficient transmission control in intelligent reflecting surface (IRS)-assisted nonorthogonal multiple access (NOMA) networks in the finite blocklength (FBL) regime that statistically guarantee stringent URLLC QoS requirements. Thus, we formulate a nonconvex problem that maximizes the sum effective capacity (SEC) while ensuring statistical delay QoS constraints. To make the problem more tractable, we propose a tight upper bound for the objective function based on Jensen’s inequality and employ the concept of opportunistically minimizing an expectation. Then, we decompose the problem into two subproblems: active beamforming at the base station and phase-shift optimization at the IRS. Each subproblem is convexified by employing slack variables, penalty functions, and linear approximation, and solved using successive convex approximations. The subproblems are iteratively solved until convergence using alternating optimization. The convergence to a suboptimal stationary solution and the computing complexity of the proposed algorithm are rigorously analyzed. Finally, extensive numerical evaluations confirm that the proposed control in the FBL regime significantly improves the SEC under various QoS parameters compared to existing benchmark schemes. In particular, as the number of antennas and IRS elements increases, the proposed method becomes more efficient than the semi-definite relaxation-based approach in terms of complexity and performance.