ceiver will have the originally transmitted signal with added noise as well as channel effects and can be expressed as NUL(k)=gXLL(k).H(k)+N(k). (9.3) The detection of LL data is done in the next phase, where the detected UL data is processed the same way as it is done at the transmitter. The reconstructed UL data was then subtracted from the original received signal. Then the remaining signal is amplified and detected in that order. Equation (9.4) shows the subtracted signal for the LL detection. YLL(k) = XUL(k). H(k) + XLL (k) H(k)+N(k)-XUL re(k). (9.4) Assuming a perfect UL detection with perfect channel estimation, we can derive LL signal as YLL(k)=gXLL(k).H(k)+N(k). (9.5) The signal from (9.5) is amplified with a factor of 1/g before the detection of LL data. As we cannot separate noise during this phase of detection, the noise is also amplified by the same ratio, and it makes the LL data detection more prone to channel noise power level. Successful UL detection is essential in this transmission framework for successful LL detection, SO the UL needs to be reliable and robust. Moreover, the LL data detection process is more computationally intensive, SO user devices require higher computation capability. The analytical model to evaluate the UL bit error rate (BER) is developed empirically and described in the following section. The channel capacity distribution and the maximum UL channel capacity are also derived. 9.5 Theoretical Evaluation We analyzed the system models described in the last section to evaluate the performance of LDM in our proposed usage models. To justify the results of this analysis and have a reference point for future work, we have developed an empirical equation to examine the performance bound of the LDM UL. Due to the incorporation of LL data, the maximum UL channel capacity is bounded by the g. This capacity bound is also defined in this section. 9 Feasibility of LDM to Serve User-IoT Pairs in the Future Wireless Network 9.5.1 Bit Error Rate of LDM Upper Layer We considered the perfo