rmance of LDM in an uncoded OFDM system in the AWGN channel. We have obtained the equation for symbol error rate (SER) of an uncoded QAM OFDM system from [18] as (9.6) where (9.7) and Pk is the SNR of the kth symbol. By assuming Gray coding for QAM constellation, which gives a single bit error for each symbol error, we get the relation between BER and SER as BER = SER/M. An analytical model for the BER calculation of the UL is developed from the equations above. For evaluation, no intersymbol interference is assumed. LL has lower power than the UL; therefore, the value of g is always negative in dB. We apply this setup for all the different QAMs used. We identified that the UL data rate and g follow the relation in an uncoded OFDM system as g = -4 Mc (9.8) where Mc is the QAM order for the UL. Now, the UL SNR is calculated from channel SNR as (9.9) Using the values of UL SNR from (9.9) in (9.6), we calculate the SER of the UL data as (9.10) M. S. Islam et al. 9.5.2 Channel Capacity Distribution The channel capacity of an AWGN channel can be written as (9.11) where Ps is the signal power and Pn is the noise power. This shows the dependency between channel SNR and capacity and can be used to calculate the channel capacity for LDM layers as [10] (9.12) (9.13) As the power ratio g is known, we can evaluate the UL's maximum system capacity with a fixed value of g from Cul.=10gs(1+1). (9.14) The following section considers a model where both user and IoT receivers are mobile. A detailed analysis of LDM pair sustainability with mobility is presented in the next section. 9.6 Mobility Model We are considering both user and IoT mobility in this analysis. In ATSC 3.0, a 4k ultrahigh-definition TV or multiple enhanced HDTV services are transmitted using LL to a fixed receiver with advanced antennas[19]. The proposed model is developed to test the UL and LL layers' performance with various mobility models for users and IoT devices. Moreover, the future wireless network is expected to use advanced transmission technology such 