diff options
Diffstat (limited to 'gr-digital/examples/berawgn.py')
| -rw-r--r-- | gr-digital/examples/berawgn.py | 34 |
1 files changed, 20 insertions, 14 deletions
diff --git a/gr-digital/examples/berawgn.py b/gr-digital/examples/berawgn.py index 082b73d83f..31ea8403b0 100644 --- a/gr-digital/examples/berawgn.py +++ b/gr-digital/examples/berawgn.py @@ -21,7 +21,6 @@ magnitude below what you chose for N_BITS. """ - import math import numpy from gnuradio import gr, digital @@ -45,20 +44,23 @@ except ImportError: N_BITS = 1e7 RAND_SEED = 42 + def berawgn(EbN0): """ Calculates theoretical bit error rate in AWGN (for BPSK and given Eb/N0) """ return 0.5 * erfc(math.sqrt(10**(float(EbN0) / 10))) + class BitErrors(gr.hier_block2): """ Two inputs: true and received bits. We compare them and add up the number of incorrect bits. Because integrate_ff() can only add up a certain number of values, the output is not a scalar, but a sequence of values, the sum of which is the BER. """ + def __init__(self, bits_per_byte): gr.hier_block2.__init__(self, "BitErrors", - gr.io_signature(2, 2, gr.sizeof_char), - gr.io_signature(1, 1, gr.sizeof_int)) + gr.io_signature(2, 2, gr.sizeof_char), + gr.io_signature(1, 1, gr.sizeof_int)) # Bit comparison comp = blocks.xor_bb() @@ -74,29 +76,32 @@ class BitErrors(gr.hier_block2): self) self.connect((self, 1), (comp, 1)) + class BERAWGNSimu(gr.top_block): " This contains the simulation flow graph " + def __init__(self, EbN0): gr.top_block.__init__(self) self.const = digital.qpsk_constellation() # Source is N_BITS bits, non-repeated - data = list(map(int, numpy.random.randint(0, self.const.arity(), N_BITS / self.const.bits_per_symbol()))) - src = blocks.vector_source_b(data, False) - mod = digital.chunks_to_symbols_bc((self.const.points()), 1) - add = blocks.add_vcc() + data = list(map(int, numpy.random.randint( + 0, self.const.arity(), N_BITS / self.const.bits_per_symbol()))) + src = blocks.vector_source_b(data, False) + mod = digital.chunks_to_symbols_bc((self.const.points()), 1) + add = blocks.add_vcc() noise = analog.noise_source_c(analog.GR_GAUSSIAN, self.EbN0_to_noise_voltage(EbN0), RAND_SEED) demod = digital.constellation_decoder_cb(self.const.base()) - ber = BitErrors(self.const.bits_per_symbol()) - self.sink = blocks.vector_sink_f() + ber = BitErrors(self.const.bits_per_symbol()) + self.sink = blocks.vector_sink_f() self.connect(src, mod, add, demod, ber, self.sink) self.connect(noise, (add, 1)) self.connect(src, (ber, 1)) def EbN0_to_noise_voltage(self, EbN0): """ Converts Eb/N0 to a complex noise voltage (assuming unit symbol power) """ - return 1.0 / math.sqrt(self.const.bits_per_symbol( * 10**(float(EbN0) / 10))) + return 1.0 / math.sqrt(self.const.bits_per_symbol(* 10**(float(EbN0) / 10))) def simulate_ber(EbN0): @@ -106,16 +111,17 @@ def simulate_ber(EbN0): fg.run() return numpy.sum(fg.sink.data()) + if __name__ == "__main__": EbN0_min = 0 EbN0_max = 15 - EbN0_range = list(range(EbN0_min, EbN0_max+1)) - ber_theory = [berawgn(x) for x in EbN0_range] + EbN0_range = list(range(EbN0_min, EbN0_max + 1)) + ber_theory = [berawgn(x) for x in EbN0_range] print("Simulating...") - ber_simu = [simulate_ber(x) for x in EbN0_range] + ber_simu = [simulate_ber(x) for x in EbN0_range] f = pyplot.figure() - s = f.add_subplot(1,1,1) + s = f.add_subplot(1, 1, 1) s.semilogy(EbN0_range, ber_theory, 'g-.', label="Theoretical") s.semilogy(EbN0_range, ber_simu, 'b-o', label="Simulated") s.set_title('BER Simulation') |