def make_gmsk(samples_per_symbol, BT): # M, K, P, h, L are fixed for GMSK M = 2 # number of bits per symbol K = 1 P = 2 # h=K/P, K and P are relatively prime h = (1.0 * K) / P L = 3 Q = samples_per_symbol frac = 0.99 # fractional energy component deemed necessary to consider in the trellis, for finding dimensionality fsm = trellis.fsm(P, M, L) tt = numpy.arange(0, L * Q) / (1.0 * Q) - L / 2.0 p = ( 0.5 * scipy.stats.erfc(2 * math.pi * BT * (tt - 0.5) / math.sqrt(math.log(2.0)) / math.sqrt(2.0)) - 0.5 * scipy.stats.erfc(2 * math.pi * BT * (tt + 0.5) / math.sqrt(math.log(2.0)) / math.sqrt(2.0)) ) / 2.0 p = p / sum(p) * Q / 2.0 q = numpy.cumsum(p) / Q q = q / q[-1] / 2.0 (f0T, SS, S, F, Sf, Ff, N) = fsm_utils.make_cpm_signals(K, P, M, L, q, frac) constellation = numpy.reshape(numpy.transpose(Sf), N * fsm.O()) Ffa = numpy.insert(Ff, Q, numpy.zeros(N), axis=0) MF = numpy.fliplr(numpy.transpose(Ffa)) return (fsm, constellation, MF, N, f0T)
def make_gmsk(samples_per_symbol, BT): #M, K, P, h, L are fixed for GMSK M = 2 #number of bits per symbol K = 1 P = 2 #h=K/P, K and P are relatively prime h = (1.0 * K) / P L = 3 Q = samples_per_symbol frac = 0.99 #fractional energy component deemed necessary to consider in the trellis, for finding dimensionality fsm = trellis.fsm(P, M, L) tt = numpy.arange(0, L * Q) / (1.0 * Q) - L / 2.0 p = (0.5 * scipy.stats.erfc(2 * math.pi * BT * (tt - 0.5) / math.sqrt( math.log(2.0)) / math.sqrt(2.0)) - 0.5 * scipy.stats.erfc( 2 * math.pi * BT * (tt + 0.5) / math.sqrt(math.log(2.0)) / math.sqrt(2.0))) / 2.0 p = p / sum(p) * Q / 2.0 q = numpy.cumsum(p) / Q q = q / q[-1] / 2.0 (f0T, SS, S, F, Sf, Ff, N) = fsm_utils.make_cpm_signals(K, P, M, L, q, frac) constellation = numpy.reshape(numpy.transpose(Sf), N * fsm.O()) Ffa = numpy.insert(Ff, Q, numpy.zeros(N), axis=0) MF = numpy.fliplr(numpy.transpose(Ffa)) return (fsm, constellation, MF, N, f0T)
def run_test(seed,blocksize): tb = gr.top_block() ################################################## # Variables ################################################## M = 2 K = 1 P = 2 h = (1.0*K)/P L = 3 Q = 4 frac = 0.99 f = trellis.fsm(P,M,L) # CPFSK signals #p = numpy.ones(Q)/(2.0) #q = numpy.cumsum(p)/(1.0*Q) # GMSK signals BT=0.3; tt=numpy.arange(0,L*Q)/(1.0*Q)-L/2.0; #print tt p=(0.5*scipy.special.erfc(2*math.pi*BT*(tt-0.5)/math.sqrt(math.log(2.0))/math.sqrt(2.0))-0.5*scipy.special.erfc(2*math.pi*BT*(tt+0.5)/math.sqrt(math.log(2.0))/math.sqrt(2.0)))/2.0; p=p/sum(p)*Q/2.0; #print p q=numpy.cumsum(p)/Q; q=q/q[-1]/2.0; #print q (f0T,SS,S,F,Sf,Ff,N) = fsm_utils.make_cpm_signals(K,P,M,L,q,frac) #print N #print Ff Ffa = numpy.insert(Ff,Q,numpy.zeros(N),axis=0) #print Ffa MF = numpy.fliplr(numpy.transpose(Ffa)) #print MF E = numpy.sum(numpy.abs(Sf)**2,axis=0) Es = numpy.sum(E)/f.O() #print Es constellation = numpy.reshape(numpy.transpose(Sf),N*f.O()) #print Ff #print Sf #print constellation #print numpy.max(numpy.abs(SS - numpy.dot(Ff , Sf))) EsN0_db = 10.0 N0 = Es * 10.0**(-(1.0*EsN0_db)/10.0) #N0 = 0.0 #print N0 head = 4 tail = 4 numpy.random.seed(seed*666) data = numpy.random.randint(0, M, head+blocksize+tail+1) #data = numpy.zeros(blocksize+1+head+tail,'int') for i in range(head): data[i]=0 for i in range(tail+1): data[-i]=0 ################################################## # Blocks ################################################## random_source_x_0 = blocks.vector_source_b(data.tolist(), False) digital_chunks_to_symbols_xx_0 = digital.chunks_to_symbols_bf((-1, 1), 1) filter_interp_fir_filter_xxx_0 = filter.interp_fir_filter_fff(Q, p) analog_frequency_modulator_fc_0 = analog.frequency_modulator_fc(2*math.pi*h*(1.0/Q)) blocks_add_vxx_0 = blocks.add_vcc(1) analog_noise_source_x_0 = analog.noise_source_c(analog.GR_GAUSSIAN, (N0/2.0)**0.5, -long(seed)) blocks_multiply_vxx_0 = blocks.multiply_vcc(1) analog_sig_source_x_0 = analog.sig_source_c(Q, analog.GR_COS_WAVE, -f0T, 1, 0) # only works for N=2, do it manually for N>2... filter_fir_filter_xxx_0_0 = filter.fir_filter_ccc(Q, MF[0].conjugate()) filter_fir_filter_xxx_0_0_0 = filter.fir_filter_ccc(Q, MF[1].conjugate()) blocks_streams_to_stream_0 = blocks.streams_to_stream(gr.sizeof_gr_complex*1, int(N)) blocks_skiphead_0 = blocks.skiphead(gr.sizeof_gr_complex*1, int(N*(1+0))) viterbi = trellis.viterbi_combined_cb(f, head+blocksize+tail, 0, -1, int(N), constellation, digital.TRELLIS_EUCLIDEAN) blocks_vector_sink_x_0 = blocks.vector_sink_b() ################################################## # Connections ################################################## tb.connect((random_source_x_0, 0), (digital_chunks_to_symbols_xx_0, 0)) tb.connect((digital_chunks_to_symbols_xx_0, 0), (filter_interp_fir_filter_xxx_0, 0)) tb.connect((filter_interp_fir_filter_xxx_0, 0), (analog_frequency_modulator_fc_0, 0)) tb.connect((analog_frequency_modulator_fc_0, 0), (blocks_add_vxx_0, 0)) tb.connect((analog_noise_source_x_0, 0), (blocks_add_vxx_0, 1)) tb.connect((blocks_add_vxx_0, 0), (blocks_multiply_vxx_0, 0)) tb.connect((analog_sig_source_x_0, 0), (blocks_multiply_vxx_0, 1)) tb.connect((blocks_multiply_vxx_0, 0), (filter_fir_filter_xxx_0_0, 0)) tb.connect((blocks_multiply_vxx_0, 0), (filter_fir_filter_xxx_0_0_0, 0)) tb.connect((filter_fir_filter_xxx_0_0, 0), (blocks_streams_to_stream_0, 0)) tb.connect((filter_fir_filter_xxx_0_0_0, 0), (blocks_streams_to_stream_0, 1)) tb.connect((blocks_streams_to_stream_0, 0), (blocks_skiphead_0, 0)) tb.connect((blocks_skiphead_0, 0), (viterbi, 0)) tb.connect((viterbi, 0), (blocks_vector_sink_x_0, 0)) tb.run() dataest = blocks_vector_sink_x_0.data() #print data #print numpy.array(dataest) perr = 0 err = 0 for i in range(blocksize): if data[head+i] != dataest[head+i]: #print i err += 1 if err != 0 : perr = 1 return (err,perr)