# **meeting 11/14**
**Advisor: Prof. Chih-Yu Wang \
Presenter: Shao-Heng Chen \
Date: Nov 14, 2023**
<!-- Chih-Yu Wang -->
<!-- Wei-Ho Chung -->
## **Channel model**
### **mmWave Systems**
- Because of its small wavelength, mmWave encounters challenges in diffracting around obstacles. Consequently, mmWave channels display a sparse multipath structure, commonly described by the **Saleh-Valenzuela (SV)** channel model.
- P. Wang, J. Fang, L. Dai and H. Li, "[Joint Transceiver and Large Intelligent Surface Design for Massive MIMO mmWave Systems](https://ieeexplore.ieee.org/document/9234098)," in *IEEE Transactions on Wireless Communications*, vol. 20, no. 2, pp. 1052-1064, Feb. 2021. (Cited by 80)
- K. Ying, Z. Gao, S. Lyu, Y. Wu, H. Wang and M. -S. Alouini, "[GMD-Based Hybrid Beamforming for Large Reconfigurable Intelligent Surface Assisted Millimeter-Wave Massive MIMO](https://ieeexplore.ieee.org/abstract/document/8964330)," in *IEEE Access*, vol. 8, pp. 19530-19539, 2020. (Cited by 91)
- The $\mathbf{H}_1 \in \mathbb{C}^{N_s \times N_t}, \hat{\mathbf{h}}_{k, 2} \in \mathbb{C}^{1 \times N_s}$ and $\hat{\mathbf{h}}_{k, 3} \in \mathbb{C}^{1 \times N_t}$ are modeled as
$$
\begin{align*}
\mathbf{H}_1 &= \sqrt{\frac{N_t N_s}{L_1 + 1}} \sum_{i = 0}^{L_1} \kappa_\mathrm{LoS} \ \textbf{a}_\mathrm{RIS}(\gamma_i^r, \eta_i^r)\textbf{a}_\mathrm{BS}(\theta_i^t)^H, \\
\hat{\mathbf{h}}_{k, 2} &= \sqrt{\frac{N_s N_r}{L_2 + 1}} \sum_{i = 0}^{L_2} \kappa_\mathrm{LoS} \ \textbf{a}_\mathrm{UE}(\theta_i^r)\textbf{a}_\mathrm{RIS}(\gamma_i^t, \eta_i^t)^H, \\
\hat{\mathbf{h}}_{k, 3} &= \sqrt{\frac{N_t N_r}{L_3 + 1}} \sum_{i = 0}^{L_3} \kappa_\mathrm{NLoS} \ \textbf{a}_\mathrm{UE}(\theta_i^r)\textbf{a}_\mathrm{BS}(\theta_i^t)^H,
\end{align*}
$$
- where $L_1, L_2$ and $L_3$ are the total number of signal paths between BS-RIS, RIS-UE and BS-UE channel, respectively
- $\kappa_{LoS}$ and $\kappa_{LoS}$ are the complex channel gains modeled by $\mathcal{CN}(0, 1)$ and $\mathcal{CN}(0, 10^{-0.1\mu})$
- with Ricain factor $\mu = 10 dB$
- $\textbf{a}_{BS}, \textbf{a}_{RIS}$ and $\textbf{a}_{UE}$ are the steering vectors at the BS, RIS, and UE, respectively
- the values of $AoD$ and $AoA$ are randomly generated between $0$ and $2\pi$
- $\theta_i^t$ and $\theta_i^r$ denote the angles of departure $(AoD)$ at the BS and angles of arrival $(AoA)$ at the UE
- $\gamma_i^t$ and $\gamma_i^r$ are the azimuth $AoD$ and $AoA$ at the RIS
- $\eta_i^t$ and $\eta_i^r$ are the elevation $AoD$ and $AoA$ at the RIS
#### **Channel matrices**

### **Apply GPU acceleration**
#### **Array response implementations**




## **Training**
### **Last week**
Training for ```100``` episodes, with each episode running for ```10,000``` time steps

### **This week**
Training for ```100``` episodes, with each episode running for ```10,000``` time steps

Training for ```10``` episodes, with each episode running for ```100,000``` time steps

## **Future works**
- Incorporating ```pathloss``` and configuring the ```BS-RIS-UE topology``` settings into the channel model
- Derive a new SINR $\rho_{k}$ to obtain the resulting downlink rate $R_k = \log_2(1 + \rho_{k})$ and sum-rate $\sum\limits_{k = 1}^{N_k} R_{k}$ equations
- Implement new reward funstions ```compute_SumRate()``` and ```compute_min_DownLinkRate()```
- Consider using '```conventional SVD beamforming``` + equal power allocation' as an alternative to '```MRT beamforming```.'
<!-- - O. E. Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi and R. W. Heath, "[Spatially Sparse Precoding in Millimeter Wave MIMO Systems](https://ieeexplore.ieee.org/document/6717211)," in *IEEE Transactions on Wireless Communications*, vol. 13, no. 3, pp. 1499-1513, March 2014. (Cited by 2444) -->
<!--
##
W. Guan, J. Tian, T. A. Tsiftsis and C. Pan, "[Deep Learning-based Joint Transmit and Reflective Beamforming Design for IRS-Aided MISO Multiuser Systems Under Statistical CSI](https://ieeexplore.ieee.org/abstract/document/10233381)," *2023 IEEE/CIC International Conference on Communications in China (ICCC)*, Dalian, China, 2023, pp. 1-6. -->