# Intelligent Radio Frequency Identification for URLLC in Industrial IoT Networks
## 5 Results and Discussion
### Uplink Transmission Performance of Multiple Devices with Massive MIMO  
Impact spatial correlation \(Corr\) and number of antennas \(M\) are investigated here. As shown in Figure 8, we assumed that there existed two smart devices connected to a single BS at \((0,100)\). A horizontal uniform linear array (ULA) was established to achieve massive MIMO. Furthermore, we assumed that channel scatters were randomly distributed in the path of BS and devices [42]. Then, we could describe the angular distribution by \((\theta_{i}\pm\delta)\), where \(\theta_{i}\) denotes the angle of arrival (AoA) of the \(i\)-th device, and \(\delta\) is the scatter spread. The large-scale fading coefficient was modeled in [43]. The transmission power of uplink was assumed to be 10 mW, and the noise level of receiver to be about \(-100\) dBm. A typical uplink transmission contains about 100 bits of information. In Figure 10, the impact of antenna number \(M\) with different positions is investigated. Results demonstrates that error probability sharply deteriorated with the shortening of the distance between two devices, and a larger number of antennas provide better system performance.  
In order to validate the asymptotic analysis of channel correlation, we numerically evaluated the uplink transmission error probability when two devices transmit at the same  
Figure 10: Uplink transmission error probability against different positions of Device 1 with \(n=100\) and fixed transmission power. Device 2 located at \((57.7,0)\).  
Figure 9: Average error probability of uplink transmission with massive MIMO. Single UE is assumed to connect with BS and \(\mathbf{h}\sim\mathcal{CN}(\mathbf{0}_{M},\theta\mathbf{I}_{M})\). (**a**) Error probability under different numbers of antennas and approximation methods with \(R=0.6\). (**b**) Performance of URLLCs with different \(R\), where \(M=256\).  
power and pilot sequence. Furthermore, we assumed that the devices were located at \((0,0)\), and their channel correlation could be designed for evaluating the significant impact on error probability. As shown in Figure 11a, average error probability as a function of \(M\) with different levels of channel correlation is investigated. These results demonstrate that channel correlation can directly influence system error probability. However, with the increase in \(M\), system performance can be greatly improved due to the promotion of spatial freedom.  
To gain more insight into the proposed RFI-aided URLLCs network from different perspectives, we defined network availability \(P_{Ave}=\mathbb{P}(P\leq P_{Ave})\) to measure the probability that satisfied the condition where \(P\) is calculated by uniformly distributing in the serving area of BS. As shown in Figure 11b, \(P_{Ave}\) increased with the increase in average error probability \(P_{Ave}\). Interference caused by channel correlation significantly reduced network availability, irrespective of the processing scheme. There existed a very rapid upward trend when \(P_{Ave}\) was equal to 0 or 1 because, when uniformly distributing the devices, it is almost impossible that they always be located at excellent or low-SNR serving areas of BS. Therefore, carefully deploying smart devices guarantees balancing transmission efficiency and robustness in practical URLLCs systems.