## 6 Simulation Results
### Validaton of the MASSIVE Test-Bed
#### 6.1.2 Validation of the ME-SSA Receiver and Implementation Loss Analysis  
One of the main requirements of MASSIVE is to show the gain in terms of aggregated SE (in bit/s/Hz) or, equivalently, throughput (in pkts/s) of the MASSIVE solution, which is based on MSD, with respect to solutions making use of an SUMF-based receiver. The MASSIVE receiver (MSD-based) showed performance gains with respect to the SUMF-based receiver for all tested MASSIVE waveforms. For TFI#25, the performance gain, measured in terms of the aggregate throughput, was about 50% whereas for the other MASSIVE waveforms the experienced gain was less than 50%, as shown in Table 2. This was traced down to an imperfect optimization of the demodulation parameters and some hardware limitations. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**WF** & **SF** & **MODCOD** & **C/N [dB]** & \begin{tabular}{c} **MAC Load [pkt/s]** \\ **@PLR = 10\({}^{-3}\) SUMF** \\ \end{tabular} & \
\begin{tabular}{c} **MAC Load [pkt/s]** \\ **@PLR = 10\({}^{-3}\) with MSD** \\ \end{tabular} &
\begin{tabular}{c} **Percentage** \\ **Improvement** \\ \end{tabular} \\ \hline TFI#25 & 16 & QPSK1/3 & \(-12.0\) & 79 & 119 & 50.63\% \\ \hline TFI#9 & 32 & QPSK1/3 & \(-15.1\) & 102 & 124 & 21.56\% \\ \hline TFI#7 & 64 & QPSK2/3 & \(-11.8\) & 175 & 210 & 20.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Examples of throughput gain. Figure 12: (a) Implementation loss less than 1 dB at PLR = 10\({}^{-3}\) for TFI#4; (b) Implementation loss less than 1 dB at PLR = 10\({}^{-3}\) for TFI#17. During the benchmarking and testing activities, the performance of the burst demodulator based on the MSD algorithm was evaluated in the first instance in a stand-alone mode, in order to exclude any bugs in the software implementation of the MSD block only. This test gave performance outcomes similar to the ideal conditions used in [9], where the results were obtained assuming perfect knowledge of all and only interfering bursts, i.e., assuming a perfect PS, and no real time constraints. Therefore, that outcome was considered as a reference to evaluate the performance of the receiver when all its blocks are enabled. Tests were performed in two conditions: (i) an ideal PS and (ii) a real PS. As shown in Figure 13, in the latter case, as expected, the performance was worse than the reference case. Even with a perfect knowledge of the burst positions (an ideal PS) the performance did not coincide with the reference case. An in-depth analysis was carried out to identify the causes of performance degradation in both cases. The results can be summarized as follows. Specifically, three main causes emerged, one was due to the intrinsic behavior of the real PS, while the other two were instead related to the receiver's real-time operation constraints, hence affecting both the real and ideal PS results. Let us start from the first cause, related only to the use of the real PS. An adaptive threshold \(\eta_{i}\) was set to determine which power peaks of the cross-correlations in the current \(i\)-th observation window were to be considered as preambles. In the conventional SUMF receiver, the adaptive threshold value depends on the past history of the incoming signals as:  
\[\eta_{i}=\max(\zeta\cdot\rho_{i},\ 0.5\cdot\eta_{i-1}) \tag{2}\]  
where \(\zeta\) is a constant value depending on the target false alarm rate and number of frequency hypothesis in the PS, while \(\rho_{i}\) is the average value of the best cross-correlation peaks per frequency hypothesis. This method in the SUMF case works well as the miss-detection (MD) probability is several orders of magnitude lower than that of the false-alarm (FA) probability. The FAs have instead little or no impact on user performance, as they are discarded following the CRC failure, prior to any burst reconstruction and cancellation stage. Specifically, with this threshold setting, the FAs increase across the SIC loops, due to the following reason.