g low- density parity-check (LDPC) coding of 4/15. They achieved the same 10 -4 bit error rate at 7 dB. They also attained a similar LL performance for 64 QAM at 20 dB. In another work, [23] used BPSK for the UL with 1/8 Turbo coding for error correction and QPSK for LL with 1/2 turbo coding and achieved a similar performance at 1 and 15 dB, respectively. We get a similar performance of LL around 40 dB channel SNR for uncoded OFDM system. Our model requires higher SNR due to lack of error correction ability as we were focused on finding the performance of LDM itself. However, we also have around 15 dB higher SNR requirement for LL than UL, which is similar to the results found in [23]. Similar to the earlier results, higher SNR is needed to achieve similar BER performance with a higher data rate. Figure 9.6 shows the channel condition required for different receivers. The receiver of the UL can be in the worse transmission area with a poor SNR, whereas the receiver of the LL needs to be in a good coverage area for successful detection. The BER performance of UL at lower SNR works well for small IoT devices as they will be distributed among different places with varying channel conditions. On the other hand, LLs need higher SNR values, as seen in Fig. 9.6, which is more suitable for users due to their better channel condition and data requirement. Channel capacity distribution between the LDM data layers is explained using (9.11), (9.12), and (9.13). Figure 9.7 shows the total channel capacity distribution between the LDM layers. The figure shows that the capacity distribution is lossless and nonlinear. At lower SNR, the UL capacity is higher than that of the LL. M. S. Islam et al. However, it gets saturated with an increase in SNR, which can be calculated using (9.14) and shows the maximum UL capacity in any given LDM configuration. This distribution works well for IoT devices in our usage model as the devices are assumed to be in different locations, which can cause bad channel conditions for some devices. Due t