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test.py
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import time
from tabulate import tabulate
from model import HeuristicNet
from search import *
from prelude import *
import matplotlib.pyplot as plt
def get_heurisitc(n_ant):
dnn = HeuristicNet(n_ant)
dnn.load()
return lambda y, R, psv: dnn.compute(y, R, psv)
def get_algorithms(n_ant, mem_size_list):
alg_list = []
hyber_accelerated_heuristic = get_heurisitc(n_ant)
alg_list.append(["MMSE", mmse_estimate])
for mem_size in mem_size_list:
alg_list.append(
(
"SMA*({})".format(mem_size),
SMAStar(capacity=mem_size, heuristic=None)
)
)
alg_list.append(
(
"HATS({})".format(mem_size),
SMAStar(capacity=mem_size, heuristic=hyber_accelerated_heuristic)
)
)
return alg_list
def test_algorithms(algorithms, snr, n_ant, packet_length, total_packets):
total_errs = np.zeros(len(algorithms))
total_nodes_expanded_list = np.zeros(len(algorithms))
total_nodes_generated_list = np.zeros(len(algorithms))
last_len = 0
for i_packet in range(total_packets):
p = 10 ** (snr / 10)
H = np.sqrt(p / n_ant) / np.sqrt(2) * complex_channel(n_ant)
Q, R = np.linalg.qr(H)
for i_timeslot in range(packet_length):
b = random_bits([2 * n_ant, 1])
x = qpsk(b)
w = np.random.randn(2 * n_ant, 1)
y = R @ x + Q.T @ w
for i_alg, (alg_name, alg) in enumerate(algorithms):
info_txt = "Testing n_ant={} snr={} packet={}/{} timeslots={}/{} algorithms={}/{} {}".format(
n_ant, snr, i_packet + 1, total_packets, i_timeslot + 1, packet_length, i_alg + 1, len(algorithms),
alg_name)
if last_len > 0:
print(" " * last_len, end="\r")
print(info_txt + "..", end="\r")
last_len = len(info_txt) + 4
x_est = alg(y, R)
err = count_errors(x, x_est)
total_errs[i_alg] += err
total_nodes_expanded_list[
i_alg] += alg.nodes_expanded if "nodes_expanded" in alg.__dict__ is not None else 0
total_nodes_generated_list[
i_alg] += alg.nodes_generated if "nodes_generated" in alg.__dict__ is not None else 0
print()
total_time_slots_now = (i_packet + 1) * packet_length
total_bits_now = total_time_slots_now * 2 * n_ant
precision = 1 / total_bits_now
bers_list = total_errs / total_bits_now
avg_nodes_expanded_list = total_nodes_expanded_list / total_time_slots_now
avg_nodes_generated_list = total_nodes_generated_list / total_time_slots_now
table = []
for i_alg, (alg_name, alg) in enumerate(algorithms):
table.append([
alg_name,
"{:e}({}/{})".format(bers_list[i_alg], total_errs[i_alg], total_bits_now),
precision,
"{:.2f}/{:.2f}".format(avg_nodes_expanded_list[i_alg], avg_nodes_generated_list[i_alg])
])
print(tabulate(
table,
headers=["NAME", "BER", "PRECISION", "STEPS"],
floatfmt=("", "", "e", ""),
stralign="left",
numalign="right"
))
print()
return bers_list, avg_nodes_expanded_list, avg_nodes_generated_list
def run_test(alg_list, snr, n_ant, packet_length, total_packets):
t_start = time.time()
results = test_algorithms(alg_list, snr, n_ant, packet_length, total_packets)
t_end = time.time()
t_total = t_end - t_start
print("{:.2f} seconds elapsed".format(t_total))
return results
def test_mimo_system(snr_list, n_ant, total_packets):
omega = [-1, 1]
m = 2 * n_ant
u = m * len(omega)
packet_length = 1024 // (2 * n_ant)
mem_list = [u, u ** 2, np.inf]
alg_list = get_algorithms(n_ant, mem_list)
alg_ber_list = np.zeros([len(alg_list), len(snr_list)])
alg_avg_nodes_generated_list = np.zeros([len(alg_list), len(snr_list)])
fmt_list = ["k-+", "b-*", "r-o", "b--*", "r--o", "b:*", "r:o"]
snr_results = []
for snr in snr_list:
snr_results.append(run_test(alg_list, snr, n_ant, packet_length, total_packets))
for i_snr, snr in enumerate(snr_list):
bers_list, avg_nodes_expanded_list, avg_nodes_generated_list = snr_results[i_snr]
for i_alg in range(len(alg_list)):
alg_ber_list[i_alg, i_snr] = bers_list[i_alg]
alg_avg_nodes_generated_list[i_alg, i_snr] = avg_nodes_generated_list[i_alg]
alg_complexity_coefficients = alg_avg_nodes_generated_list / m
fig, (ax1, ax2) = plt.subplots(1, 2)
fig.suptitle('QPSK {}x{} MIMO'.format(n_ant, n_ant))
legend = []
for i_alg, (alg_name, alg) in enumerate(alg_list):
ax1.semilogy(snr_list, alg_ber_list[i_alg, :], fmt_list[i_alg])
legend.append(alg_name)
ax1.legend(legend)
ax1.set_xlabel("SNR (dB)")
ax1.set_ylabel("BER")
ax1.set_xticks(snr_list)
ax1.set_title("BER Performance Comparison")
legend = []
for i_alg, (alg_name, alg) in enumerate(alg_list):
if alg_complexity_coefficients[i_alg, 0] > 0:
ax2.semilogy(snr_list, alg_complexity_coefficients[i_alg, :], fmt_list[i_alg])
legend.append(alg_name)
ax2.legend(legend)
ax2.set_xlabel("SNR (dB)")
ax2.set_ylabel("Complexity coefficient")
ax2.set_xticks(snr_list)
ax2.set_title("Complexity Comparison")
plt.show()
if __name__ == "__main__":
test_mimo_system(snr_list=[13, 13.25, 13.5, 13.75, 14.25, 14.5, 14.75, 15], n_ant=32, total_packets=1000)