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RIS_SUB_DIR_MIMO_NFWB_SNR.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Jul 12 14:29:40 2021
@author: 5106
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops.layers.torch import Rearrange
from einops import rearrange
import einops
from Transformer_model import *
# from RIS import *
from NFBF_RIS_R3 import Channel_BS_RIS_NLOS, Channel_RIS_UE_MIMO_NLOS, Channel_RIS_UE_MIMO_LOS, Channel_BS_UE_MIMO_NLOS, Channel_BS_RIS_LOS_PLA
from scipy.linalg import dft
from GFNet import Block
from PolarizedSelfAttention import ParallelPolarizedSelfAttention,SequentialPolarizedSelfAttention
from math import *
import random
import datetime
max_time_delay = torch.tensor(5e-9)
random.seed(2023)
def dft_matrix(n):
DFT = np.empty((n, n), dtype=np.complex128)
w = np.exp(-2j * np.pi / n)
for i in range(n):
for j in range(n):
DFT[i][j] = w ** (i * j)
return DFT / np.sqrt(n)
# Convolutional module
class conv_block1(nn.Module):
"""
Convolution Block
"""
def __init__(self, in_ch, out_ch, strides,pads, dilas):
super(conv_block1, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=strides, padding=pads, dilation=dilas,bias=True),
nn.BatchNorm2d(out_ch),
nn.LeakyReLU(negative_slope=0.3)
)
def forward(self, x):
x = self.conv(x)
return x
class FeedForward(nn.Module):
def __init__(self, dim, hidden_dim, dropout = 0.):
super().__init__()
self.net = nn.Sequential(
nn.Linear(dim, hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, dim),
nn.Dropout(dropout)
)
def forward(self, x):
return self.net(x)
class MixerBlock(nn.Module):
def __init__(self, dim, num_patch, token_dim, channel_dim, dropout = 0.):
super().__init__()
self.token_mix = nn.Sequential(
nn.LayerNorm(dim),
Rearrange('b n d -> b d n'),
FeedForward(num_patch, token_dim, dropout),
Rearrange('b d n -> b n d')
)
self.channel_mix = nn.Sequential(
nn.LayerNorm(dim),
FeedForward(dim, channel_dim, dropout),
)
def forward(self, x):
x = x + self.token_mix(x)
x = x + self.channel_mix(x)
return x
class MLPMixer(nn.Module):
def __init__(self, dim, num_patch,num_output, depth, token_dim, channel_dim):
super().__init__()
self.mixer_blocks = nn.ModuleList([])
for _ in range(depth):
self.mixer_blocks.append(MixerBlock(dim, num_patch, token_dim, channel_dim))
self.layer_norm = nn.LayerNorm(dim)
self.mlp_head = nn.Sequential(
nn.Linear(dim, num_output)
)
def forward(self, x): # (batch, 8, 26, 32)
for mixer_block in self.mixer_blocks:
x = mixer_block(x)
x = self.layer_norm(x) # (batch, patch, dim)
#x = x.mean(dim=1)
return self.mlp_head(x)
class UL_CE(nn.Module):
def __init__(self, param_list, param_list1):
super(UL_CE,self).__init__()
self.fc = param_list[0]
self.B = param_list[1]
self.Nc = param_list[2]
self.M1 = param_list[3]
self.M2 = param_list[4]
self.N1 = param_list[5]
self.N2 = param_list[6]
self.D_ant = param_list[7]
self.R1 = param_list[8]
self.R = param_list[9]
self.h1 = param_list[10]
self.h2 = param_list[11]
self.aod_azi = param_list[12]
self.aod_ele = param_list[13]
self.aoa_azi = param_list[14]
self.aoa_ele = param_list[15]
self.tau = param_list[16]
self.L1 = param_list[17]
self.L2 = param_list[18]
self.K = param_list[19]
self.Nr = param_list[20]
self.RF_chain = param_list1[0]
self.BS_TDDs = param_list1[1]
self.RIS_TDDs = param_list1[2]
self.L = param_list1[3]
self.UL = param_list1[4]
self.Bit = param_list1[5]
self.sigma = param_list1[6]
self.batch = param_list1[7]
self.upsnr = param_list1[8]
self.downsnr = param_list1[9]
self.M_ant = self.M1*self.M2
self.N_ant = self.N1*self.N2
L = self.UL
# self.S_digital = torch.nn.Parameter(torch.randn(L,Nc,RF_chain,Nr)+1j*torch.randn(L,Nc,RF_chain, Nr)).cuda() #L个时隙BS导频,这里假设频率选择
self.S_analog = torch.nn.Parameter(torch.randn(L,self.Nc,self.M_ant,self.RF_chain)).cuda()
self.S_analog_TDD = torch.nn.Parameter(torch.randn(L,self.Nc,self.BS_TDDs,self.RF_chain)).cuda()
# self.Phase = torch.nn.Parameter(torch.randn(L,Nc, N_ant)).cuda() #L个时隙RIS相位
self.Phase1 = torch.nn.Parameter(torch.randn(L, self.N_ant)).cuda() #L个时隙RIS相位
self.Phase2 = torch.nn.Parameter(torch.randn(L, self.N_ant)).cuda()
self.Phase_TDD = torch.nn.Parameter(torch.randn(L, self.RIS_TDDs)).cuda()
self.B_quan = 2
# self.trans = TRANS_BLOCK(self.K*self.RF_chain*self.Nr*2*L,4*L,256,1)
# # self.linear = nn.Linear(2*L*self.Nc, self.Bit//self.B_quan)
# # self.bn = nn.BatchNorm1d(self.Bit//self.B_quan)
# depth = 2
# self.blocks = nn.ModuleList([
# Block(dim=self.Nc, mlp_ratio=2, h=self.K*self.RF_chain*self.Nr*2, w=L)
# for i in range(depth)
# ])
self.psa = ParallelPolarizedSelfAttention(channel=self.Nc)
# self.QL = QuantizationLayer(self.B_quan)
self.conv1 = conv_block1(self.Nr*self.RF_chain, 2,1,1,1)
self.sig = nn.Sigmoid()
def forward(self, param_list, param_list1, H_BR, H_RU, H_BU):
M_ant = self.M1*self.M2
N_ant = self.N1*self.N2
L = self.UL
n = (torch.arange(0, self.Nc)+1).cuda()
nn = -(self.Nc+1)/2
deta = self.B/self.Nc
fm=(nn+n)*deta+self.fc
Y = torch.zeros(H_RU.shape[0], self.Nc, self.Nr, self.RF_chain, L).cuda() + 1j
Theta = torch.zeros(self.Nc, self.N_ant, self.N_ant).cuda() + 1j
F_RF_TTDs = torch.zeros(self.Nc,self.M_ant,self.RF_chain).cuda() + 1j
# self.upsnr = 0
# self.upsnr = param_list1[8]
train_snr = param_list1[10]
snr_index = random.randint(0, len(train_snr)-1)
self.upsnr = train_snr[snr_index]
SNR_linear=10**(-1*self.upsnr/10.)
for l in range(L):
# H_equ = (torch.zeros(batch,Nc,RF_chain,K) + 0j).cuda()
# for i in range(K):
# H_equ[:,:,i] = (H_RU[:,i] @ Phi @ H_BR @ F_RF).reshape(-1,Nc,RF_chain)
Theta1 = self.Phase1[l,:] # L,Nc, N_ant
# Theta2 = torch.exp(1j*self.Phase2[l,:]) # L,Nc, N_ant
# Phase_TDD = torch.exp(1j*self.Phase_TDD[l,:]) # L,Nc, N_ant
S_analog = torch.exp(1j*self.S_analog[l,:,:,:])/sqrt(self.M_ant)
Phi_T = torch.zeros(self.N_ant).cuda() + 1j
subarray = self.N_ant//self.Nc
for nc in range(self.Nc):
Phi_T[nc*subarray:(nc+1)*subarray] = torch.exp(1j*2*pi*fm[nc]/self.fc*Theta1[nc*subarray:(nc+1)*subarray])
for nc in range(self.Nc):
# self.Phase_TDD[l,:]= torch.abs(self.Phase_TDD[l,:])
# self.Phase_TDD[l,:]= max_time_delay*self.Phase_TDD[l,:]/torch.max(self.Phase_TDD[l,:])
a1 = max_time_delay * self.sig(self.Phase_TDD[l,:])
# Phi_T = torch.exp(1j*2*pi*fm[nc]*a1)
# self.S_analog_TDD[l,nc,:,:]= torch.abs(self.S_analog_TDD[l,nc,:,:])
# self.S_analog_TDD[l,nc,:,:]= max_time_delay*self.S_analog_TDD[l,nc,:,:]/torch.max(self.S_analog_TDD[l,nc,:,:])
b = max_time_delay * self.sig(self.S_analog_TDD[l,nc,:,:])
S_analog_T = torch.exp(1j*2*pi*fm[nc]*b)
# Phi_TDD = torch.kron(Phi_T.reshape(1,self.RIS_TDDs),torch.ones(1,self.N_ant//self.RIS_TDDs).cuda())
F_RF_TDD = torch.kron(S_analog_T,torch.ones(M_ant//self.BS_TDDs,1).cuda())
F_RF_TTDs[nc,:,:] = S_analog[nc,:,:]*F_RF_TDD
Theta[nc,:,:] = torch.diag_embed(Phi_T)
# Theta = torch.diag(self.Phase[l,:])
a = H_RU[:,self.K-1,:,:,:] @ Theta# batch Nc 1 N_ant
a = a @ H_BR + H_BU[:,self.K-1,:,:,:] # batch Nc 1 M
# a = a @ torch.exp(1j*self.S_analog[l,:,:,:]) # batch Nc 1 RF_chain
a = a @ F_RF_TTDs # batch Nc 1 RF_chain
Power = (torch.sum(torch.abs(a)**2,[2,3])).reshape(-1,self.Nc,1,1)
# Power = (torch.mean(torch.abs(a)**2,[1,2,3])).reshape(-1,1,1,1)
# Power = torch.tensor(1)
# a = a @ self.S_analog
# F_SDMA = torch.exp(1j*self.S_analog[l,:,:,:]) @ self.S_digital[l,:,:,k] # L Nc M 1
# Power = (torch.sum(torch.abs(F_SDMA)**2,[2,3])).reshape(-1,Nc,1,1)
# self.S_digital = self.S_digital[l,:,:,:] / torch.sqrt(Power)
# a = a @ self.S_digital[l,:,:,k] # batch Nc 1 1
n = (torch.randn(H_RU.shape[0],self.Nc,self.Nr,self.RF_chain) + 1j*torch.randn(H_RU.shape[0],self.Nc,self.Nr,self.RF_chain)).cuda()*torch.sqrt(Power*SNR_linear/2)
y = a + n # batch Nc 1 RF_chain
# y = a
# y = y.reshape(self.batch, self.Nc, self.RF_chain)
# y = y.permute(0,1,3,2)
Y[:,:,:,:,l] = y
Y = Y.reshape(H_RU.shape[0], self.Nc, self.K*self.RF_chain*self.Nr,L)
x = torch.cat((torch.real(Y),torch.imag(Y)), 3) #[batch, K*RF_chain, Nc, 2*L]
x = self.psa(x)
# # x = self.conv1(x)
# # x = x.reshape(batch, Nc, K*RF_chain*Nr*2*L)
# x = x.reshape(self.batch, self.K*self.RF_chain*self.Nr*2*self.L, self.Nc)
# for blk in self.blocks:
# x = blk(x)
# x = x.reshape(self.batch, self.Nc, self.K*self.RF_chain*self.Nr*2*L)
# x = self.trans(x)
# x = x.reshape(-1,2*Nc*L)
# x = self.linear(x)
# x = self.bn(x)
# x = torch.sigmoid(x)
# x = self.QL(x)
return x
class DL_BF_TTD(nn.Module):
def __init__(self, param_list, param_list1):
super(DL_BF_TTD,self).__init__()
self.fc = param_list[0]
self.B = param_list[1]
self.Nc = param_list[2]
self.M1 = param_list[3]
self.M2 = param_list[4]
self.N1 = param_list[5]
self.N2 = param_list[6]
self.D_ant = param_list[7]
self.R1 = param_list[8]
self.R = param_list[9]
self.h1 = param_list[10]
self.h2 = param_list[11]
self.aod_azi = param_list[12]
self.aod_ele = param_list[13]
self.aoa_azi = param_list[14]
self.aoa_ele = param_list[15]
self.tau = param_list[16]
self.L1 = param_list[17]
self.L2 = param_list[18]
self.K = param_list[19]
self.Nr = param_list[20]
self.RF_chain = param_list1[0]
self.BS_TDDs = param_list1[1]
self.RIS_TDDs = param_list1[2]
self.L = param_list1[3]
self.UL = param_list1[4]
self.Bit = param_list1[5]
self.sigma = param_list1[6]
self.batch = param_list1[7]
self.upsnr = param_list1[8]
self.downsnr = param_list1[9]
self.M_ant = self.M1*self.M2
self.N_ant = self.N1*self.N2
self.L = self.UL
# self.B_quan = 2
# self.DQL = DequantizationLayer(self.B_quan)
# self.FC1 = nn.Linear(self.Bit//self.B_quan,2*self.Nc*L)#全连接
# self.bn1 = nn.BatchNorm1d(2*self.Nc*L)
# self.trans = TRANS_BLOCK(self.K*self.RF_chain*self.Nr*2*self.L,self.N_ant,256,2) # batch, Nc, K*N_ant
self.FC = nn.Linear(self.K*self.RF_chain*self.Nr*2*self.L, self.N_ant)
self.linear_H = nn.Linear(self.N_ant, 32)
self.linear_RIS = nn.Linear(self.Nc*32, self.N_ant) #生成RIS相位
self.linear_RIS2 = nn.Linear(self.Nc*32, self.N_ant) #生成RIS相位
self.LN_RIS = nn.LayerNorm(self.N_ant)
self.linear_RIS_T = nn.Linear(self.N_ant, self.RIS_TDDs)
self.linear_BS_RF1 = nn.Linear(self.Nc, self.RF_chain)
self.linear_BS_RF2 = nn.Linear(self.N_ant, self.M_ant)
self.linear_BS_T = nn.Linear(self.N_ant, self.RF_chain*self.BS_TDDs)
self.linear_BS_BB = nn.Linear(self.N_ant, 2*self.RF_chain*self.Nr)
self.trans_RIS1 = TRANS_BLOCK(self.N_ant,self.N_ant,256,1)
self.trans_RIS2 = TRANS_BLOCK(self.N_ant,self.N_ant,256,1)
self.LN1 = nn.LayerNorm(self.N_ant)
# self.trans_RIS_T = TRANS_BLOCK(self.RIS_TDDs,self.RIS_TDDs,256,1)
self.LN_RIS_T = nn.LayerNorm(self.RIS_TDDs)
# self.trans_BS_RF1 = TRANS_BLOCK(self.RF_chain,self.RF_chain,256,1)
self.LN_BS_RF1 = nn.LayerNorm(self.RF_chain)
# self.trans_BS_T = TRANS_BLOCK(self.RF_chain*self.BS_TDDs,self.RF_chain*self.BS_TDDs,256,1)
self.LN_BS_T = nn.LayerNorm(self.RF_chain*self.BS_TDDs)
# self.trans_BS_BB = TRANS_BLOCK(2*self.RF_chain*self.Nr,2*self.RF_chain*self.Nr,256,1)
self.LN_BS_BB = nn.LayerNorm(2*self.RF_chain*self.Nr)
self.sig = nn.Sigmoid()
# self.tran = TRANS_BLOCK(self.K*self.RF_chain*self.Nr*2*self.L,self.N_ant,256,2)
# self.linear = nn.Linear(2*L*self.Nc, self.Bit//self.B_quan)
# self.bn = nn.BatchNorm1d(self.Bit//self.B_quan)
depth = 2
self.blocks = nn.ModuleList([
Block(dim=self.Nc, mlp_ratio=2, h=self.K*self.RF_chain*self.Nr*2, w=self.L)
for i in range(depth)
])
d_model = self.K*self.RF_chain*self.Nr*2*self.L
out_len = self.Nc
self.projection2 = MLPMixer(dim= d_model, num_patch = out_len, num_output = self.N_ant, depth=2, token_dim=64, channel_dim=64)
# self.linear_RF = nn.Linear(Nc*32, M_ant*K)
# self.trans2 = TRANS_BLOCK(2*Nr*Nr,4*Nr+1,256,1)
def forward(self, param_list, param_list1, x):
# x = self.DQL(x)-0.5
# x = self.FC1(x)
# x = self.bn1(x)
# x = torch.relu(x)
# x = x.reshape(batch,Nc,K*2*L)
x = x.reshape(x.shape[0], self.K*self.RF_chain*self.Nr*2*self.L, self.Nc)
for blk in self.blocks:
x = blk(x)
x = x.reshape(x.shape[0], self.Nc, self.K*self.RF_chain*self.Nr*2*self.L)
# x_init = self.tran(x)
x_init = self.projection2(x)
# x_init = self.FC(x_init)
# x_init = self.LN1(x_init)
# x_init = self.trans(x) # batch, Nc, K*N_ant
x_R = self.linear_H(x_init) #[batch,Nc,32]
out_RIS = x_R.reshape(-1,self.Nc*32)
Phi_phase = self.linear_RIS(out_RIS)
Phi_phase = self.LN_RIS(Phi_phase).reshape(-1,self.N_ant)
# Phi_phase = self.trans_RIS1(Phi_phase)
# Phi_phase = torch.exp(1j*Phi_phase.reshape(-1,self.N_ant))
n = (torch.arange(0, self.Nc)+1).cuda()
nn = -(self.Nc+1)/2
deta = self.B/self.Nc
fm=(nn+n)*deta+self.fc
subarray = self.N_ant//self.Nc
Phi_T = torch.zeros(Phi_phase.shape[0],self.N_ant).cuda() + 1j
for nc in range(self.Nc):
Phi_T[:,nc*subarray:(nc+1)*subarray] = torch.exp(1j*2*pi*fm[nc]/self.fc*Phi_phase[:,nc*subarray:(nc+1)*subarray])
Phi_VIR = torch.diag_embed(Phi_T)
Phi_WB = torch.zeros(Phi_phase.shape[0],self.Nc,self.N_ant,self.N_ant).cuda() + 1j
# for nc in range(Nc):
# Phi_T = torch.exp(1j*2*pi*fm[nc]*x_T[:,nc,:]).reshape(-1,1,N_ant)
# Phi_WB[:,nc,:,:] = torch.diag_embed(Phi_phase*Phi_T).reshape(-1,N_ant,N_ant)
# for nc in range(Nc):
# Phi_T = torch.exp(1j*2*pi*fm[nc]*x_T[:,nc,:])
# for ba in range(Phi.shape[0]):
# Phi_TDD = torch.kron(Phi_T[ba,:].reshape(1,RIS_TDDs),torch.ones(1,N_ant//RIS_TDDs).cuda())
# Phi_WB[:,nc,:,:] = torch.diag_embed(Phi_phase*Phi_TDD*Phi_phase2).reshape(-1,N_ant,N_ant)
RF_phase = self.linear_BS_RF2(x_init) # batch N_ant RF_chains
RF_phase = RF_phase.reshape(-1,self.M_ant,self.Nc)
RF_phase = self.linear_BS_RF1(RF_phase) # batch N_ant RF_chains
# RF_phase = self.trans_BS_RF1(RF_phase)
RF_phase = self.LN_BS_RF1(RF_phase)
F_RF = torch.exp(1j*RF_phase.reshape(-1,1,self.M_ant,self.RF_chain)) #[batch,1,M_ant,K]
RF_delay = self.linear_BS_T(x_init) # batch Nc BS_TDDs*RF_chains
# RF_delay = self.trans_BS_T(RF_delay)
RF_delay = self.LN_BS_T(RF_delay)
RF_delay = RF_delay.reshape(-1,self.Nc,self.BS_TDDs,self.RF_chain)
# F_RF_delay = torch.exp(1j*2*pi*fm*RF_delay) #[batch,1,M_ant,K]
F_RF_TTDs = torch.zeros(Phi_phase.shape[0],self.Nc,self.M_ant,self.RF_chain).cuda() + 1j
for nc in range(self.Nc):
# RF_delay[:,nc,:,:]= torch.abs(RF_delay[:,nc,:,:])
# RF_delay[:,nc,:,:]= max_time_delay*RF_delay[:,nc,:,:]/torch.max(RF_delay[:,nc,:,:])
b = max_time_delay * self.sig(RF_delay[:,nc,:,:])
# a = max_time_delay * self.sig(x_T[:,nc,:])
F_RF_delay = torch.exp(1j*2*pi*fm[nc]*b)
# x_T[:,nc,:]= torch.abs(x_T[:,nc,:])
# x_T[:,nc,:]= max_time_delay*x_T[:,nc,:]/torch.max(x_T[:,nc,:])
# Phi_T = torch.exp(1j*2*pi*fm[nc]*a)
for ba in range(Phi_phase.shape[0]):
F_RF_TDD = torch.kron(F_RF_delay[ba,:,:],torch.ones(self.M_ant//self.BS_TDDs,1).cuda())
F_RF_TTDs[ba,nc,:,:] = F_RF[ba,0,:,:]*F_RF_TDD
# Phi_TDD = torch.kron(Phi_T[ba,:].reshape(1,self.RIS_TDDs),torch.ones(1,self.N_ant//self.RIS_TDDs).cuda())
# Phi_WB[:,nc,:,:] = torch.diag_embed(Phi_phase*Phi_TDD*Phi_phase2).reshape(-1,self.N_ant,self.N_ant)
Phi_WB[:,nc,:,:] = Phi_VIR
F_BB = self.linear_BS_BB(x_init) # batch Nc 2*K*RF_chains
# F_BB = self.trans_BS_BB(F_BB)
F_BB = self.LN_BS_BB(F_BB)
# F_BB = F_BB.reshape(-1,Nc,2*K,RF_chain)
# F_BB_SDMA = (F_BB[:,:,0:K,:] + 1j*F_BB[:,:,K:K*2,:])
F_BB = F_BB.reshape(-1,self.Nc,self.RF_chain,2*self.Nr)
F_BB_SDMA = (F_BB[:,:,:,0:self.Nr] + 1j*F_BB[:,:,:,self.Nr:self.Nr*2])
# F_RF_WB = F_RF*(torch.kron(F_RF_delay,torch.ones(F_RF_delay.size(0),Nc,BS_TDDs,1).cuda()))
F_SDMA = F_RF_TTDs @ F_BB_SDMA
Power = (torch.sum(torch.abs(F_SDMA)**2,[2,3])).reshape(-1,self.Nc,1,1)
F_BB_SDMA = F_BB_SDMA / torch.sqrt(Power)*sqrt(self.Nr)
return Phi_WB,F_RF_TTDs,F_BB_SDMA,Phi_phase #Phi[batch,1,M_ant,M_ant] F_RF[batch,1,M_ant,K] F_BB_SDMA[batch,Nc,K,K] F_BB_RS[batch,Nc,K,1]
class NFWB_RIS(nn.Module):
def __init__(self, param_list,param_list1):
super(NFWB_RIS,self).__init__()
self.encoder = UL_CE(param_list,param_list1)
self.decoder = DL_BF_TTD(param_list,param_list1)
def forward(self, param_list,param_list1,H_BR, H_RU1, H_BU):
out = self.encoder(param_list,param_list1,H_BR,H_RU1, H_BU)
Phi,F_RF_WB,F_BB_SDMA,Phi_T = self.decoder(param_list,param_list1,out)
return Phi,F_RF_WB,F_BB_SDMA,Phi_T
def SE_RS_BB(batch,Nc,K,Nt,N_RS1,N_RS2,RS_cor,H,F_SDMA,F_RS,sigma2):
H_SDMA = H @ F_SDMA #[batch,Nc,K,K]
R = 0
R_SA = 0
R_SDMA = 0
Nr = H_SDMA.shape[3]
R_k = torch.zeros(batch,Nr).cuda()
for a in range(N_RS1):
for b in RS_cor[a]:
signal = torch.abs(H_SDMA[:,:,b,b]*H_SDMA[:,:,b,b])#[batch,Nc]
Interfer = torch.zeros(batch,Nc).cuda()
for c in range(N_RS1):
if c!=a:
Interfer = Interfer + torch.abs(H_RS[:,:,b,c]*H_RS[:,:,b,c])
for c in range(K):
if c!=b:
Interfer = Interfer + torch.abs(H_SDMA[:,:,b,c]*H_SDMA[:,:,b,c])
SINR = signal/(Interfer+sigma2)#[batch,Nc]
# SINR = signal/(Interfer)#[batch,Nc]
R_k[:,b] = torch.mean(torch.log2(1+SINR),1)#[batch]
# R_min,aa = torch.min(R_k,1) #[batch]
R_min = torch.sum(R_k,1)
R_SDMA = R_SDMA + torch.mean(R_min)
Loss = -R_SDMA
# print(torch.mean(torch.log2(1+SINR)))
return R_SDMA,Loss
class SE_RS(torch.nn.Module):
def __init__(self):
super(SE_RS, self).__init__()
def forward(self, param_list, param_list1,H_BR,H_RU,H_BU,Phi,F_RF,F_BB_SDMA,F_BB_RS,dSNR):
fc = param_list[0]
B = param_list[1]
Nc = param_list[2]
M1 = param_list[3]
M2 = param_list[4]
N1 = param_list[5]
N2 = param_list[6]
D_ant = param_list[7]
R1 = param_list[8]
R = param_list[9]
h1 = param_list[10]
h2 = param_list[11]
aod_azi = param_list[12]
aod_ele = param_list[13]
aoa_azi = param_list[14]
aoa_ele = param_list[15]
tau = param_list[16]
L1 = param_list[17]
L2 = param_list[18]
K = param_list[19]
Nr = param_list[20]
RF_chain = param_list1[0]
BS_TDDs = param_list1[1]
RIS_TDDs = param_list1[2]
L = param_list1[3]
UL = param_list1[4]
Bit = param_list1[5]
sigma = param_list1[6]
batch = param_list1[7]
upsnr = param_list1[8]
downsnr = param_list1[9]
batch = H_RU.shape[0]
N_RS1 = 1
N_RS2 = K
# RS_cor = [[0,1,2,3]]
RS_cor = [np.arange(Nr)]
H_BR = H_BR.cuda()
H_RU = H_RU.cuda()
# H_equ = (torch.zeros(batch,Nc,RF_chain,Nr) + 0j).cuda()
H_equ = (H_RU[:,K-1] @ Phi @ H_BR + H_BU[:,K-1])@ F_RF
H_SDMA = H_equ @ F_BB_SDMA #[batch,Nc,K,K]
Nr = H_SDMA.shape[3]
# Pt_dB=0
# sigma2 = -120
period = 2e4
# SNR_dB = Pt_dB-sigma2
# noise_dB = -174
# noise_dB = noise_dB + 10*np.log10(B/10) - 30
# noise_power = 10**(noise_dB/10)
# Pt = dSNR-30
# Pt_p = 10**(Pt/10)
# sigma = noise_power/Pt_p
SNR_dB = downsnr
sigma=10**(-1*SNR_dB/10.)
L_CP = 4
R_k = torch.zeros(batch).cuda()
SINR = 0
Loss = 0
for a in range(H_SDMA.shape[0]):
for b in range(H_SDMA.shape[1]):
# signal = torch.abs(H_SDMA[:,:,b,b]*H_SDMA[:,:,b,b])#[batch,Nc]
# signal = torch.det(torch.eye(Nr).cuda() + torch.abs(H_SDMA[a,b,:,:] * H_SDMA[a,b,:,:])/(Nr*sigma2))#[batch,Nc]
# signal = torch.det(torch.eye(Nr).cuda() + torch.real(H_SDMA[a,b,:,:] @ torch.transpose(torch.conj(H_SDMA[a,b,:,:]), 0,1))/(Nr*sigma))#[batch,Nc]
# # signal = torch.sum(torch.abs(H_SDMA[a,b,:,:] * H_SDMA[a,b,:,:])/(Nr*sigma),[0,1])
# SINR = SINR + torch.log2(signal)
signal = torch.det(torch.eye(Nr).cuda() + H_SDMA[a,b,:,:] @ torch.transpose(torch.conj(H_SDMA[a,b,:,:]), 0,1)/(Nr*sigma))#[batch,Nc]
# signal = torch.sum(torch.abs(H_SDMA[a,b,:,:] * H_SDMA[a,b,:,:])/(Nr*sigma),[0,1])
SINR = SINR + torch.real(torch.log2(signal))
R = (period-UL*Nr)*SINR/(period*H_SDMA.shape[0]*(H_SDMA.shape[1]+L_CP))
Loss = -R
# R,Loss = SE_RS_BB(batch,Nc,K,K,N_RS1,N_RS2,RS_cor,H_equ,F_BB_SDMA,F_BB_RS,sigma)
return R, Loss
def adjust_learning_rate(optimizer, epoch,learning_rate_init,learning_rate_final, epochs):
"""For resnet, the lr starts from 0.1, and is divided by 10 at 80 and 120 epochs"""
# learning_rate_init = 1e-4
# learning_rate_final = 1e-7
lr = learning_rate_final + 0.5*(learning_rate_init-learning_rate_final)*(1+math.cos((epoch*3.14)/epochs))
# lr = 0.00003* (1+math.cos(float(epoch)/TOTAL_EPOCHS*math.pi))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return lr
def NFWB_RIS_VIR(param_list,param_list1, batch,Num_batchs,model_name):
fc = param_list[0]
B = param_list[1]
Nc = param_list[2]
M1 = param_list[3]
M2 = param_list[4]
N1 = param_list[5]
N2 = param_list[6]
D_ant = param_list[7]
R1 = param_list[8]
R = param_list[9]
h1 = param_list[10]
h2 = param_list[11]
aod_azi = param_list[12]
aod_ele = param_list[13]
aoa_azi = param_list[14]
aoa_ele = param_list[15]
tau = param_list[16]
L1 = param_list[17]
L2 = param_list[18]
K = param_list[19]
Nr = param_list[20]
RF_chain = param_list1[0]
BS_TTDs = param_list1[1]
RIS_TDDs = param_list1[2]
L = param_list1[3]
UL = param_list1[4]
Bit = param_list1[5]
sigma = param_list1[6]
batch = param_list1[7]
upsnr = param_list1[8]
downsnr = param_list1[9]
train_snr = param_list1[10]
M_ant = M1*M2
N_ant = N1*N2
loss_func = SE_RS()
loss_func = loss_func.cuda()
model = NFWB_RIS(param_list,param_list1).cuda()
# BW = 7e9
# model_name1 = './models/RIS_NFWB_VIR_'+str(floor(BW))+'band'+str(floor(BS_TTDs))+'BS_TTDs_'+str(UL)+'pilots_'+str(floor(upsnr))+'UdB'+str(floor(downsnr))+'DdB'+'.pth'
# model = torch.load(model_name)
# optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),lr=0.001)
# optimizer = torch.optim.SGD(net_AE.parameters(), lr=0.0001, momentum=0.9, weight_decay=5e-4,nesterov=True)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-07,weight_decay=1e-5)
num_train = 0
train_nmse = 0
train_SE = 0
best_SE = 0
start = datetime.datetime.now()
numStep = 0
# warmup_steps = 4000
d_model = 256
LR0 = 1
Gt_gain = 10**(25/10.)
GRIS_gain = 10**(5/10.)
Gr_gain = 10**(20/10.)
# Gt_gain = 1
# Gr_gain = 1
flag = 2
# if flag == 1:
# H_RU = Channel_RIS_UE_MIMO_NLOS(param_list,batch,flag)
# H_RU1 = H_RU.reshape(batch,K,Nc,Nr,N_ant)
# # H_BR = (Channel_BS_RIS_LOS(param_list,flag)+Channel_BS_RIS_NLOS(param_list,flag))
# H_BR = Channel_BS_RIS_NLOS(param_list,flag)
# else:
# H_RU = Channel_RIS_UE_MIMO_NLOS(param_list,batch,flag)* 1e3
# H_RU1 = H_RU.reshape(batch,K,Nc,Nr,N_ant)
# H_BR = (Channel_BS_RIS_LOS(param_list,flag)+Channel_BS_RIS_NLOS(param_list,1))* 1e3
# H_BR = Channel_BS_RIS_LOS(param_list,flag)
scaler = torch.cuda.amp.GradScaler()
Num_epochs = 10
num_epoch = 0
test_ESE = []
for num_batch in range(Num_batchs//Num_epochs):
lr = adjust_learning_rate(optimizer, num_batch, 5e-3, 5e-5, Num_batchs//Num_epochs)
model.train()
H_RU1 = sqrt(Gr_gain*GRIS_gain)*(Channel_RIS_UE_MIMO_LOS(param_list,batch,flag)+Channel_RIS_UE_MIMO_NLOS(param_list,batch,flag))
H_BU = sqrt(Gt_gain*Gr_gain)*Channel_BS_UE_MIMO_NLOS(param_list,batch,flag)
H_BU = H_BU.reshape(batch,K,Nc,Nr,M_ant)
H_RU1 = H_RU1.reshape(batch,K,Nc,Nr,N_ant)
H_BR = sqrt(Gt_gain*GRIS_gain)*(Channel_BS_RIS_LOS_PLA(param_list,flag)+Channel_BS_RIS_NLOS(param_list,flag))
for num_epoch in range(Num_epochs):
snr_index = random.randint(0, len(train_snr)-1)
# if (num_epoch)%1==0:
# if flag == 1:
# H_RU1 = Channel_RIS_UE_MIMO_NLOS(param_list,batch,flag)
# H_BU = Channel_BS_UE_MIMO_NLOS(param_list,batch,flag)
# H_BU = H_BU.reshape(batch,K,Nc,Nr,M_ant)
# H_RU1 = H_RU1.reshape(batch,K,Nc,Nr,N_ant)
# # H_BR = (Channel_BS_RIS_LOS(param_list,flag)+Channel_BS_RIS_NLOS(param_list,flag))
# else:
# H_RU1 = sqrt(Gr_gain*GRIS_gain)*(Channel_RIS_UE_MIMO_LOS(param_list,batch,flag)+Channel_RIS_UE_MIMO_NLOS(param_list,batch,flag))
# H_BU = sqrt(Gt_gain*Gr_gain)*Channel_BS_UE_MIMO_NLOS(param_list,batch,flag)
# H_BU = H_BU.reshape(batch,K,Nc,Nr,M_ant)
# H_RU1 = H_RU1.reshape(batch,K,Nc,Nr,N_ant)
# H_BR = sqrt(Gt_gain*GRIS_gain)*(Channel_BS_RIS_LOS_PLA(param_list,flag)+Channel_BS_RIS_NLOS(param_list,flag))
# optimizer.param_groups[0]['lr'] = (d_model**(-0.5))*min(numStep**(-0.5),numStep*warmup_steps**(-1.5))*LR0
numStep = numStep+1
Phi,F_RF,F_BB_SDMA,F_BB_RS = model(param_list, param_list1, H_BR, H_RU1, H_BU)
R_sum, Loss = loss_func(param_list, param_list1, H_BR,H_RU1,H_BU,Phi,F_RF,F_BB_SDMA,F_BB_RS, train_snr[snr_index])
loss = -R_sum
num_train = num_train + 1
# train_nmse = train_nmse + nmse.detach_()
train_SE = train_SE + R_sum.detach_()
# print(train_nmse,nmse.detach_(),num_train)
#Adaptive mixed precision method
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
# loss.backward()
# optimizer.step()
# optimizer.zero_grad()
# print('num_epoch:',num_epoch,'loss',Loss.detach_(),'SE',R_sum.detach_())
if (num_batch+1)%1==0:
with torch.no_grad():
model.eval()
test_SE = 0
test_SDMA = 0
test_loop = 5
for i in range(test_loop):
if flag == 1:
H_RU1 = Channel_RIS_UE_MIMO_NLOS(param_list,batch,flag)
H_RU1 = H_RU1.reshape(batch,K,Nc,Nr,N_ant)
H_BU = Channel_BS_UE_MIMO_NLOS(param_list,batch,flag)
H_BU = H_BU.reshape(batch,K,Nc,Nr,M_ant)
# H_BR = (Channel_BS_RIS_LOS(param_list,flag)+Channel_BS_RIS_NLOS(param_list,flag))
else:
H_RU1 = sqrt(Gr_gain*GRIS_gain)*(Channel_RIS_UE_MIMO_LOS(param_list,batch,flag)+Channel_RIS_UE_MIMO_NLOS(param_list,batch,flag))
H_BU = sqrt(Gt_gain*Gr_gain)*Channel_BS_UE_MIMO_NLOS(param_list,batch,flag)
H_BU = H_BU.reshape(batch,K,Nc,Nr,M_ant)
H_RU1 = H_RU1.reshape(batch,K,Nc,Nr,N_ant)
H_BR = sqrt(Gt_gain*GRIS_gain)*(Channel_BS_RIS_LOS_PLA(param_list,flag)+Channel_BS_RIS_NLOS(param_list,flag))
Phi,F_RF,F_BB_SDMA,F_BB_RS = model(param_list, param_list1, H_BR, H_RU1, H_BU)
# H_RU = H_RU.reshape(batch,K,Nc,1,N_ant)
R_sum, Loss = loss_func(param_list, param_list1, H_BR,H_RU1,H_BU,Phi,F_RF,F_BB_SDMA,F_BB_RS, downsnr)
test_SE = test_SE + R_sum
test_SE = test_SE/test_loop
train_nmse = train_nmse/num_train
train_SE = train_SE/num_train
time0 = datetime.datetime.now()-start
test_ESE.append(test_SE.cpu().numpy().item())
print('num_batch:',num_batch,'time',time0,'train_SE %.5f' % train_SE.cpu(),'test_SE %.5f' % test_SE.cpu())
if test_SE > best_SE:
best_SE = test_SE
torch.save(model, model_name)
print('Model saved!')
num_train = 0
train_nmse = 0
train_SE = 0
start = datetime.datetime.now()
vadilate_SE = 'vadilate_R1_'+str(floor(BS_TTDs))+'BS_TTDs_'+str(floor(Nc))+'RIS_sub'+'.mat'
sio.savemat(vadilate_SE, {'a':test_ESE})
return best_SE
import scipy.io as sio
def NFWB_RIS_VIR_test(param_list,param_list1, batch,Num_batchs,model_name):
fc = param_list[0]
B = param_list[1]
Nc = param_list[2]
M1 = param_list[3]
M2 = param_list[4]
N1 = param_list[5]
N2 = param_list[6]
D_ant = param_list[7]
R1 = param_list[8]
R = param_list[9]
h1 = param_list[10]
h2 = param_list[11]
aod_azi = param_list[12]
aod_ele = param_list[13]
aoa_azi = param_list[14]
aoa_ele = param_list[15]
tau = param_list[16]
L1 = param_list[17]
L2 = param_list[18]
K = param_list[19]
Nr = param_list[20]
RF_chain = param_list1[0]
BS_TDDs = param_list1[1]
RIS_TDDs = param_list1[2]
L = param_list1[3]
UL = param_list1[4]
Bit = param_list1[5]
sigma = param_list1[6]
batch = param_list1[7]
upsnr = param_list1[8]
downsnr = param_list1[9]
M_ant = M1*M2
N_ant = N1*N2
loss_func = SE_RS()
loss_func = loss_func.cuda()
model = NFWB_RIS(param_list,param_list1).cuda()
model = torch.load(model_name)
# optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),lr=0.001)
# optimizer = torch.optim.SGD(net_AE.parameters(), lr=0.0001, momentum=0.9, weight_decay=5e-4,nesterov=True)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001, eps=1e-07,weight_decay=1e-5)
# Gt_gain = 10**(25/10.)
# Gr_gain = 10**(20/10.)
Gt_gain = 10**(20/10.)
GRIS_gain = 10**(5/10.)