# FL-MD3 QN-Based IoT Intelligent Access Algorithm for Smart Construction Sites
## 4 Simulation and Analysis
### Simulation Result Analysis
Dynamic weighting necessity: Scheme 3 (single-objective) exhibits severe BER degradation (3.80 \(\times\) 10\({}^{-4}\)) despite higher rates, while Scheme 4 (unconstrained) shows intermediate performance with higher BER (2.50 \(\times\) 10\({}^{-4}\)). Theoretical validation: the proposed reward function guides policy convergence to pareto-optimal solutions, balancing spectrum efficiency, latency, and reliability. This analysis confirms that dynamic weight adjustment and BER constraints are critical for ensuring system reliability and efficiency in complex IoT environments. Table 8 compares the average transmission rates of different models across multi-modal service types. The FL-MD3 QN algorithm achieves the highest rates for all services. For text services, FL-MD3 QN reaches 5.7 Gbps, marking an 18.8% and 14% improvement over DQN (4.8 Gbps) and MD3 QN (5.0 Gbps), respectively. For audio services, FL-MD3 QN delivers 5.6 Gbps, outperforming DQN by 24.4% and MD3 QN by 19.1%. In image and video services, FL-MD3 QN maintains superior performance with rates of 4.4 Gbps and 3.8 Gbps, respectively. These results confirm FL-MD3 QN's ability to efficiently utilize spectrum resources and enhance data transmission efficiency in smart construction site IoT access scenarios. The findings validate the practicality and effectiveness of FL-MD3 QN in complex, dynamic smart construction environments. As shown in Figure 6, FL-MD3 QN exhibits minimal BER differences compared to other algorithms under low SNR conditions. At moderate SNR levels, its BER becomes significantly lower than competing approaches. Notably, under high SNR scenarios, FL-MD3 QN achieves an exceptionally low BER of 0.00045, demonstrating an 85% reduction compared to the DQN algorithm. In comparison, the baseline DQN algorithm performs poorly. The MD3 QN algorithm improves performance by introducing deterministic policy gradients but still underperforms FL-based models. Although FL-DQN and FL-DDQN incorporate FL, they do not surpass FL-MD3 QN across all conditions. These results confirm that FL-MD3 QN ensures higher reliability and stability in data transmission within smart construction site communication environments. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Average Reward Value** & **Video Latency (s)** & **Video Rate (Gbps)** & **BER (10\({}^{-4}\))** \\ \hline Scheme 1 & 5.23 & 1.50 & 3.8 & 0.45 \\ Scheme 2 & 4.12 & 2.10 & 3.2 & 1.20 \\ Scheme 3 & 3.85 & 3.50 & 4.1 & 3.80 \\ Scheme 4 & 4.75 & 1.80 & 3.5 & 2.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance analysis of different reward functions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Transmission Rate (Gbps)**} \\ \cline{2-5}  & **Text** & **Audio** & **Image** & **Video** \\ \hline DQN & 4.8 & 4.5 & 2.5 & 2.2 \\ MD3 QN & 5.0 & 4.7 & 3.3 & 2.4 \\ FL-DQN & 5.1 & 5.3 & 4.0 & 2.4 \\ FL-MD3 QN & 5.7 & 5.6 & 4.4 & 3.8 \\ FL-DDQN & 5.5 & 5.4 & 4.1 & 3.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average transmission rate comparison of different models across multi-modal service types.