Long-Term Sum-Rate Maximization in Irregular RIS-Aided V2X Communications Using Lyapunov Optimization

In emerging smart cities and intelligent transportation systems (ITS) ecosystems, sixth-generation (6G) vehicular networks demand sustainable wireless environments capable of adapting to high mobility and dynamic propagation conditions. In dynamic vehicular networks, irregular reconfigurable intelligent surface (IRIS) with non-uniformly distributed elements over a larger surface offer improved coverage, reduced interference, and lower power consumption. This paper undertakes a rigorous investigation into IRIS-assisted downlink vehicular communication in the context of Doppler shift, with the objective of optimizing the long-term net weighted average sum-rate under both perfect and imperfect channel state information (CSI). This task entails a comprehensive joint optimization of IRIS topology selection, phase-shift matrix, and beamforming vectors, while ensuring queue stability. To address this stochastic network optimization problems involving time-varying system dynamics, a Lyapunov optimization-based framework is proposed. This framework transforms the stochastic problem into a deterministic form, ensuring queue stability without requiring knowledge of future channel conditions. Subsequently, a joint optimization strategy based on the block coordinate descent (BCD) algorithm is applied to make optimal decisions of IRIS topology selection, phase-shift matrix, and beamforming vectors in each time slot. To validate the effectiveness of the proposed framework, extensive simulations were conducted and benchmarked against two established schemes: one employing conventional IRIS configurations in wireless networks and the other utilizing non-orthogonal multiple access (NOMA) techniques. The results consistently demonstrate that the proposed IRIS-assisted Lyapunov optimization framework significantly outperforms both benchmarks in terms of achievable sum-rate and maintains queue stability.