Optimizing Age of Information for IRS-Assisted NOMA-Based Short Packet Communication Systems

To address the stringent requirements for massive connectivity and high information freshness in the Industrial Internet of Things (IIoT), the non-orthogonal multiple access (NOMA) and intelligent reflecting surface (IRS) are introduced as a promising solution. In this paper, we investigate a downlink IRS-assisted NOMA-based short packet communication system, where the age of information (AoI) is analyzed to characterize the freshness of information on the devices. We aim to minimize the maximum average AoI among devices by jointly optimizing the power allocation factors, the IRS phase shifts and the time block length. However, the successive interference cancelation (SIC) error makes the expression of packet error rate (PER) extremely complicated, and thus the formulated problem is difficult to solve. To overcome this challenge, an approximate expression of PER at high signal-to-noise ratio (SNR) is derived for converting the original problem and thus obtaining a suboptimal solution. Simulation results show that the proposed optimization scheme achieves lower AoI, requires less power and supports larger number of users. Moreover, it is interesting to find that increasing the number of IRS reflecting elements has a tradeoff effect on the AoI performance. Although more reflecting elements can enhance the beamforming gain which improves the AoI performance, it also introduces higher channel estimation overhead and longer pilot transmission time, which wil impair the AoI performance. Therefore, when the number of reflecting elements exceeds a certain value, the AoI performance no longer improves but deteriorates, which is different from the observations in conventional setups wherein the achievable rate or energy efficiency increases monotonically with the number of IRS reflecting elements.