## 5 Simulation Results  
We consider a massive MIMO system consisting of \(N=50\) receive antennas arranged in a uniform linear fashion and separated by a half wavelength. We choose the compression ratio to be \(N/M=5\), which yields the dimension of the compressed measurement \(M=10\). The number of impinging sources in the massive MIMO system is considered between 1 and 9. The sources impinge from angular bins discretized by a \(\Delta\bar{\theta}=0.1^{\circ}\) interval and within an angular range between \(-90^{\circ}\) and \(90^{\circ}\). As a result, there are 1801 components in the Gaussian mixture model. The number of snapshots is assumed to be \(T=100\). We consider a 4-layer LSTM unit with 200 nodes in each layer, and a DNN with 3 layers and 500 nodes. The selection of the number of layers and nodes for both models is made to achieve a good balance between the predictability and generalization capability of the networks. A training dataset is created with 10,000 scenarios, each containing 1 to 9 sources randomly sampled from a uniform distribution ranging between \(-90^{\circ}\) and \(90^{\circ}\). The input signal-to-noise ratio (SNR) is randomly selected between 0 dB and 20 dB for each scenario. The test dataset consists of 1000 scenarios, which are generated using a similar approach. We evaluate the performance of the proposed model against two related approaches as described in [21; 34]. The non-neural network approach presented in [21] optimizes CMM \(\mathbf{\Phi}\) iteratively based on mutual information maximization, while the approach described in [34] uses an FCDNN to update the posterior distribution of the DOAs of the impinging signals. To compare these methods, we consider a test example with nine sources and their corresponding signal DOAs are \(-55^{\circ}\), \(-48^{\circ}\), \(-44^{\circ}\), \(-20^{\circ}\), \(8^{\circ}\), \(20^{\circ}\), \(31^{\circ}\), \(41^{\circ}\), and \(45^{\circ}\). Figure 5 shows the estimated spectra obtained from the methods where the input SNR is 5 dB. As demonstrated in this figure, the proposed method, depicted in (a), shows a cleaner spectrum compared to [21; 34], as illustrated in (b) and (c), in a low SNR scenario. Figure 6 demonstrates the reduction in the loss function as the number of epochs increases. It is evident from the figure that the model converged well within the first 200 epochs. Figure 5: Comparison of the estimated spatial spectra. (**a**) Proposed method. (**b**) Method in [21]. (**c**) Method in [34]. In order to assess the methods' performance under different conditions, including varying input SNR levels, number of snapshots, and dimension of compressed measurement (number of front-end circuits), we compared their performance using the root mean squared error (RMSE), defined as  
\[\text{RMSE}=\sqrt{\frac{1}{QD}\sum_{q=1}^{Q}\sum_{d=1}^{D}(\hat{\theta}_{q,d}- \theta_{d})^{2}}, \tag{21}\]  
where \(Q\) is the number of trials and \(\hat{\theta}_{q,d}\) is the estimated DOA for the \(d\)th source of the \(q\)th Monte Carlo trial. In total, 1000 Monte Carlo trials are used to compute the RMSE values. Figure 7 presents the RMSE values with respect to input SNR, number of snapshots, and dimension of compressed measurement, and clearly shows that the proposed LSTM-based approach outperforms the other methods. Additionally, the Cramer-Rao bound (CRB) is included in Figure 7 for comparison. To obtain the CRB, we first denote the unknown parameters in this problem, which include the signal DOAs and power of \(D\) sources as \(\mathbf{\theta}=[\theta_{1},\cdots,\theta_{D}]^{\text{T}}\) and \(\mathbf{p}=[\sigma_{1}^{2},\cdots,\sigma_{D}^{2}]^{\text{T}}\), respectively. We also define the noise power as \(\sigma_{n}^{2}\), and \(\mathbf{\omega}=[\omega_{1},\cdots,\omega_{D}]^{\text{T}}\) as the spatial frequencies, where \(\omega_{d}=\sin(\theta_{d})/2\) is the spatial frequency of the \(d\)th source. Then, the unknown parameter vectors are grouped as \(\psi=[\mathbf{w}^{\text{T}}\ \ \mathbf{p}^{\text{T}}\ \sigma_{n}^{2}]^{\text{T}}\).