opt.is_clip = False
opt.CR = 0 if not opt.is_clip else 1
opt.is_regu_PAPR = False
opt.is_regu_sigma = False
##############################################################################################################
if not opt.is_clip:
opt.CR = 0
######################################## OFDM setting ###########################################
size_after_compress = (opt.size //
(opt.n_downsample**2))**2 * (opt.C_channel // 2)
opt.N = opt.batchSize # Batch size
opt.P = 1 # Number of symbols
opt.M = 64 # Number of subcarriers per symbol
opt.K = 16 # Length of CP
opt.L = 8 # Number of paths
opt.decay = 4
opt.S = size_after_compress // opt.M # Number of packets
opt.is_cfo = False
opt.is_trick = True
opt.is_cfo_random = False
opt.max_ang = 1.7
opt.ang = 1.7
if opt.CE not in ['LS', 'MMSE', 'TRUE']:
raise Exception("Channel estimation method not implemented")
# Display setting
opt.checkpoints_dir = './Checkpoints/'+ opt.dataset_mode + '_' + opt.channel
opt.name = opt.gan_mode + '_C' + str(opt.C_channel) + channel_name
output_path = './Images/' + opt.dataset_mode + '_' + opt.channel + '/' + opt.name
# Choose the neural network model
opt.model = 'StoGAN'
opt.num_test = 2000
opt.how_many_channel = 5
opt.N = opt.how_many_channel
model = create_model(opt) # create a model given opt.model and other options
model.setup(opt) # regular setup: load and print networks; create schedulers
model.eval()
if os.path.exists(output_path) == False:
os.makedirs(output_path)
else:
shutil.rmtree(output_path)
os.makedirs(output_path)
PSNR_list = []
SSIM_list = []
for i, data in enumerate(dataset):
