mmserv

commit 3a5a8bba62c8a41af7336f57d473d536277afad6
parent bf6a94e5a9d53997aaf89d915327aba66411079c
Author: Egor Achkasov <eaachkasov@edu.hse.ru>
Date:   Fri,  8 Nov 2024 00:01:59 +0100

Add mmse_nosqrt.py

Diffstat:
A
scripts/mmse_nosqrt.py
 | 
60
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

1 file changed, 60 insertions(+), 0 deletions(-)
diff --git a/scripts/mmse_nosqrt.py b/scripts/mmse_nosqrt.py
@@ -0,0 +1,60 @@
+#!/usr/bin/python
+
+from util import read_defines, deinterleave, interleave
+
+import numpy as np
+#from scipy.linalg import ldl
+from os import path, makedirs
+
+NUM_RX_ANT, NUM_TX_ANT, NUM_SC = read_defines()
+
+# Read the data from the files
+x = deinterleave(np.fromfile("data/x.bin", dtype=np.float32), (NUM_TX_ANT, NUM_SC))
+H = deinterleave(np.fromfile("data/H.bin", dtype=np.float32), (NUM_RX_ANT, NUM_TX_ANT, NUM_SC))
+R = deinterleave(np.fromfile("data/R.bin", dtype=np.float32), (NUM_TX_ANT, NUM_TX_ANT, NUM_SC))
+y = deinterleave(np.fromfile("data/y.bin", dtype=np.float32), (NUM_RX_ANT, NUM_SC))
+
+x_mmse = np.empty((NUM_TX_ANT, NUM_SC), np.complex64)
+
+def ldl(A):
+    n = A.shape[0]
+    D = np.zeros_like(A)
+    L = np.eye(n, dtype=A.dtype)
+    for i in range(n):
+        for j in range(i):
+            s = 0.+0j
+            for l in range(j):
+                s += L[i][l] * L[j][l].conj() * D[l][l]
+            L[i][j] = (A[i][j] - s) / D[j][j]
+        s = 0.+0.j
+        for j in range(i):
+            s += L[i][j] * L[i][j].conj() * D[j][j]
+        D[i][i] = A[i][i] - s
+    return L, D
+
+
+for sc in range(NUM_SC):
+    HH = H[..., sc].conj().T
+    L, D = ldl(HH @ H[..., sc] + R[..., sc])
+    HHy = np.einsum("ij,j->i", HH, y[:, sc])
+    print("\nHHy:\n", HHy)
+    print("\nL:\n", L)
+    print("\nD:\n", D)
+
+    z = np.empty((NUM_TX_ANT,), dtype=np.complex64)
+    for i in range(NUM_TX_ANT):
+        z[i] = (HHy[i] - np.sum([L[i, j]*z[j] for j in range(i)]))
+    print("\nz:\n", z)
+
+    LH = L.conj().T
+    for i in range(NUM_TX_ANT-1, -1, -1):
+        x_mmse[i, sc] = z[i] / D[i, i] - np.sum([L[i, j] * x[j, sc] for j in range(i+1, NUM_TX_ANT)])
+    print("\nx_mmse:\n", x_mmse)
+
+# Output the data
+x_mmse_data = interleave(x_mmse)
+out_dir = path.join(path.dirname(__file__), "..", "out")
+if not path.exists(out_dir):
+    makedirs(out_dir)
+with open(path.join(out_dir, "x_mmse_python.bin"), "wb") as f:
+    f.write(x_mmse_data.tobytes())