## 5 Simulation Results  
This section presents both theoretical and simulation results to validate our theoretical analysis and emphasize the effectiveness of the proposed policy for hard-delay-constrained communication. We examine a practical scenario in URLLC that leverages finite blocklength coding. For this scenario, we set the packet size to \(A=1024\) bits, the block error probability to \(\epsilon=10^{-7}\), the power consumption per symbol to \(P=1\times 10^{-3}\) W, the channel bandwidth to \(B=1\times 10^{4}\) Hz, the coherence time to \(T=5\times 10^{-3}\) s, and the noise spectral density of the Gaussian noise process to \(N_{0}=-150\) dBm. Figure 4 illustrates the changes in average power consumption corresponding to the changes in the hard delay constraints. The red line represents the policy of Algorithm 1, which integrates both heuristic algorithms and gradient descent. The blue line corresponds to the first heuristic algorithm, while the pink line depicts the second heuristic algorithm. The black line signifies the maximum coding blocklength policy, which selectively initiates transmissions that are in proximity to the hard delay constraint, thereby maximizing the coding blocklength. The on-demand transmission policy, with a channel threshold set to zero, is denoted by the grey line. Both the heuristic algorithms and the maximum coding blocklength policy display non-smooth characteristics owing to inaccuracies in symbol block quantization. Notably, Algorithm 1 exhibits the best performance, substantiating the superiority of our proposed policy over baseline policies. Additionally, the excellent match between the theoretical and simulation results in the figure is noteworthy. It is important to highlight that as the hard delay constraint increases gradually, the average power consumption decreases. This is attributed to a larger hard delay constraint providing more flexibility in selecting transmission opportunities, allowing for the identification of time slots with superior channel quality for transmission and consequently leading to reduced power consumption. Figure 5 delineates the power consumption trend, using the first heuristic algorithm as the channel gain varies. We observe that as the channel gain threshold increases, the average power consumption first decreases and then increases. The declining trend arises as an increasing channel gain threshold in poor-quality channels allows for the avoidance of transmission, consequently saving power. However, as the channel gain threshold increases, the symbol block is eventually forced to transmit through any channel when reaching the upper limit of the hard delay constraint, disregarding channel quality. It is noteworthy that power consumption at a channel threshold of zero is higher than when the channel threshold is extremely high. A channel threshold equal to zero implies immediate transmission once symbols are encoded, resulting in a shorter coding blocklength. On the contrary, a high channel threshold demands waiting, potentially until the upper limit imposed by the hard delay constraint, before initiating transmission. In such cases, the coding blocklength is longer. Therefore, based on Equation (5), the power consumption at a zero channel threshold is higher than that at an extremely high channel threshold. Figure 6 illustrates the performance of two heuristic algorithms as the upper limit of the hard delay constraint varies. With an increase in the hard delay constraint, the number of system states also increases, thereby slowing down the convergence of the gradient descent method. Thus, only the results of the heuristic algorithms are displayed. As observed, when the upper limit of the hard delay constraint increases, the average power consumption decreases. This phenomenon can be explained from two perspectives. Firstly, a higher hard delay constraint provides more time slots for transmission, enabling the selection of better-quality channels and, consequently, reducing power consumption.