## 3 Spectrum Management Issues
### M-Milmo
#### 3.5.4 Channel Modeling  
In [233], the authors have investigated M-MIMO performance in a real propagation channel environment. The channel measurements take on 2.6 GHz with virtual Uniform Linear Array (ULA) and Uniform Cylindrical Array (UCA) in the presence of 128 antenna ports. However, the investigations are held on the Rayleigh channels with theoretical channels and based on measurement data. The channel behavior in three propagation scenarios is discussed, and the results are evaluated. Therefore, the outcomes are achieved for both arrays defined, as the performance is close to that of i.i.d. (independent and identically distributed) Rayleigh fading channels. A study [234] has derived an attainable aggregate rate for time selective channel model for M-MIMO systems. The authors have proved that a major difference is obtained in the sum rate between block fading and time selective models. Moreover, results have shown that for constant amplitude pilots, the asymptotic SINR performance is limited by the effect of interference from the time-selective channel. Additionally, they have suggested that in a block fading, the optimal training is not appropriate for a time-selective channel with ample antenna systems in the BS. However, given numerous antennas and multiuser systems, the signal deteriorates due to interferences, and a constructive and sharp interference cancellation scheme should be investigated. The authors in [235] have discussed the serious concern of pilot contamination in multicell M-MIMO systems under correlated channels. The output is received through simulation via MATLAB, pointing out that if channels are correlated, the performance of M-MIMO degrades massively. They have added that when the channel correlation coefficient is lesser than the threshold value, the performance of M-MIMO increases with an increase in the number of antennas. In contrast, for the large channel correlation coefficient, the performance gets better slowly and steadily by increasing the number of BS antennas. The authors in [236] have proposed a scheduling algorithm for the M-MIMO downlink system along with a ZF beamforming approach. The idea is to utilize a channel mean gain and to select a user to achieve maximum sum rate. The results are achieved in terms of error performance, sum rate, throughput, and fairness; however, the authors have suggested that the proposed algorithm must be tested on a realistic model for multiantenna users.  
Below is Table 5, which summarizes the techniques, advantages, and limitations of the above-discussed studies.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Issues/Approach** & **Methodology/Technique** & **Advantages** & \multicolumn{2}{c}{**Limitations/future Work**} & **References** \\ \hline \multirow{6}{*}{Minimizing Better Bit Error Rate (BER)} & \multirow{6}{*}{MATLAB simulations of approximate message passing algorithm for uplink detection and the excellent tradeoff between complexity and performance} & \multirow{2}{*}{[227]} & \multirow{2}{*}{[228]} \\  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \hline \multirow{6}{*}{Spectrum Sensing} & \multirow{6}{*}{Direct localization algorithm, which is based on the location to source for narrowband multipath} & \multirow{6}{*}{Decreases execution time and enhances the spectrum accuracy} & \multirow{6}{*}{[229]} \\  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \hline \multirow{6}{*}{Receiver design} & \multirow{6}{*}{Multi-user MIMO precoding schemes, i.e., Eigen Zero Forcing (EZF), Tomlinson-Harashima Precoding (THP), for different UE deployment scenarios} & \multirow{6}{*}{[231]} \\  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5