CoherentOFDM-IdealRayleigh-BER-diversity.py

This webpage introduces an API example for the cohrent OFDM scenario, which uses CoherentMLDSimulator. Other examples are found in CoherentMLDSimulatorTest.

This example evaluates the BER performance of the coherent subcarrier-index modulation scheme, where three subcarrier activation patterns are considered. The simulation parameters are given in the table below.

Performance Results

Simulation Parameters

Parameter	Value
Channel	Ideal Rayleigh fading
Number of subcarriers	\(M=16\)
Number of activated subcarriers	\(K=8\)
Number of activation patterns	\(Q=16\)
Constellation size	\(L=1\)
Transmission rate	\(R=0.25\) [bps/Hz]

Reproducible Code

import sys
import matplotlib.pyplot as plt
from imtoolkit import Parameters, IMCode, IdealOFDMChannel, CoherentMLDSimulator

plt.switch_backend('agg')
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
plt.rcParams['markers.fillstyle'] = 'none'

def simulateBER(argstr):
    params = Parameters(argstr)
    code = IMCode(params.dm, params.M, params.K, params.Q, params.mod, params.L, meanPower=params.M)
    channel = IdealOFDMChannel(params.ITi, params.M)
    sim = CoherentMLDSimulator(code.codes, channel)
    return sim.simulateBERParallel(params, outputFile=False, printValue=False)

if __name__ == '__main__':
    fig, ax = plt.subplots()
    ax.set_xlabel("SNR [dB]")
    ax.set_ylabel("BER")
    ax.set_xlim(-10, 40)
    plt.ylim(1e-7, 1e0)
    plt.yscale("log")
    ax.tick_params(pad=8)

    ret = simulateBER("BERP_sim=coh_code=index_dm=dic_M=16_K=8_Q=16_L=1_mod=PSK_N=16_ITo=1e3_ITi=1e4_snrfrom=-10.00_to=40.00_len=26")
    ax.plot(ret["snr_dB"], ret["ber"], color="k", marker="s", linestyle="-", label="Combinatorial design [2]")

    ret = simulateBER("BERP_sim=coh_code=index_dm=wen_M=16_K=8_Q=16_L=1_mod=PSK_N=16_ITo=1e3_ITi=1e4_snrfrom=-10.00_to=40.00_len=26")
    ax.plot(ret["snr_dB"], ret["ber"], color="b", marker="o", linestyle="-", label="Equiprobable design [3]")

    ret = simulateBER("BERP_sim=coh_code=index_dm=opt_M=16_K=8_Q=16_L=1_mod=PSK_N=16_ITo=1e3_ITi=1e4_snrfrom=-10.00_to=40.00_len=26")
    ax.plot(ret["snr_dB"], ret["ber"], color="r", marker="^", linestyle="-", label="ILP design [6]")

    handles, labels = ax.get_legend_handles_labels()
    legend = ax.legend(handles, labels, loc="best", frameon=True)
    frame = legend.get_frame()
    frame.set_facecolor('white')
    frame.set_edgecolor('white')

    #plt.show()
    plt.savefig(sys.argv[0].replace(".py", ".svg"))

Related Publications

• [1] R. Abu-Alhiga and H. Haas, “Subcarrier-index modulation OFDM,” in IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications, 2009.

• [2] E. Basar, U. Aygolu, E. Panayirci, and H. V. Poor, “Orthogonal frequency division multiplexing with index modulation,” IEEE Trans. Signal Process., vol. 61, no. 22, pp. 5536–5549, 2013.

• [3] M. Wen, Y. Zhang, J. Li, E. Basar, and F. Chen, “Equiprobable subcarrier activation method for OFDM with index modulation,” IEEE Commun. Lett., vol. 20, no. 12, pp. 2386–2389, 2016.

• [4] N. Ishikawa, S. Sugiura, and L. Hanzo, “Subcarrier-index modulation aided OFDM – will it work?,” IEEE Access, vol. 4, pp. 2580–2593, 2016.

• [5] N. Ishikawa, S. Sugiura, and L. Hanzo, “50 years of permutation, spatial and index modulation: From classic RF to visible light communications and data storage,” IEEE Commun. Surv. Tutorials, vol. 20, no. 3, pp. 1905–1938, 2018.

• [6] N. Ishikawa, “IMToolkit: An open-source index modulation toolkit for reproducible research based on massively parallel algorithms,” IEEE Access, vol. 7, pp. 93830–93846, July 2019.