# Interference Aware Resource Control for 6G-Enabled Expanded IoT Networks
## 3 Interference-Aware Scheduling of APs and Nodes
### Proposed Algorithm
The performance of these three models is depicted in Figure 6, which plots the spectral efficiency achieved by 95% of the communication nodes. As compared to random scheduling, the proposed scheduling shows improvement of 10.9% in obtained spectral efficiency with PRZF precoding. The system model with no scheduling incorporated, the 95% likely achieved spectral efficiency is minimum, which is 0.5789 bits/s/Hz with MR precoding, 1.1105 bits/s/Hz with LPMMSE precoding and 1.6869 bits/s/Hz with PRZF and PMMSE precoding. The variation of average spectral efficiency with number of nodes is shown in Figure 7. As the number of nodes are increased from 20 to 100, the average spectral efficiency is decreased to 1.69 bits/s/Hz in MR, 3.9 bits/s/Hz in LPMMSE and 4.799 bits/s/Hz in the PRZF precoding scheme. The impact of pilot length \(\tau_{p}\) on the average spectral efficiency of the system is obtained in Figure 8. The performance of different precoding schemes under different operations of the proposed cell-free model is evaluated with the scheduling schemes. It is observed that on increasing the pilot length, the spectral efficiency first increases and then it saturates. On increasing the pilot length, the interference is reduced resulting in improved spectral efficiency performance. The AP-node association is improved as more APs can serve each node. But a further increase in \(\tau_{p}\) will lead to saturation as the transmission symbol length is reduced. Here also, the proposed scheduling method outperforms the random scheduling and no scheduling approaches being adopted in different system models, respectively. The maximum spectral efficiency of 8.712 bits/s/Hz is obtained with proposed scheduling algorithm and PRZF precoding scheme at \(\tau_{p}=10\). Figure 4: CDF of spectral efficiency achieved with precoding schemes under different operations. Figure 5: Performance of proposed scheduling scheme for different precoding methods. Figure 6: The 95% likely spectral efficiency for different scheduling schemes. ## 5 Conclusions

Next generation IoT offers connected support to the consumers for personalized experience. The digital transformation of emerging consumer devices rely on future wireless

Figure 8: Comparison of different scheduling methods under different pilot lengths. Figure 7: Average spectral efficiency as a function of number of nodes. technologies. In this paper, a 6G-enabled scalable cell-free IoT network is proposed that aims for uniform coverage to the nodes in the network. An interference-aware scheduling algorithm is proposed that enables optimal association of nodes and APs with robust interference management. The spectral efficiency performance is obtained for different precoding schemes under different cell-free operations. It is observed that the obtained value is 2.949 bits/s/Hz for MR precoding and 8.592 bits/s/Hz for scalable LPMMSE precoding. For the centralized operation, the PRZF and PMMSE precoding gives 9.207 bits/s/Hz downlink spectral efficiency. The centralized precoding outperforms the LPMMSE and MR precoding achieving 18.9% improvement with proposed scheduling. Further, the comparison with other system models reveals that as compared to random scheduling, the proposed scheduling shows improvement of 10.9% in obtained spectral efficiency by 95% of the user nodes with PRZF precoding. Also, the 95% likely achieved spectral efficiency is the minimum for the system model with no scheduling incorporated. The energy efficiency analysis of the proposed cell-free network model with interference-aware scheduling is left for future work. The energy management of the network is necessary to support green communication in future IoT networks. Conceptualization, methodology: A.T., A.A. and N.A. ; formal analysis: A.T., N.A. and A.A.; software, validation, writing--original draft: A.T. and N.A. ; writing--review and editing: A.T., N.A. and A.A.; data curation: A.T., N.A. and A.A.; supervision, funding acquisition: N.A.