BPSK and 16-QAM modulated OFDM signal transmission for IoT devices over Rayleigh flat fading channels in the Wi-Fi Halow frequency band

PythonCodeForDataGeneration.py: A comprehensive simulation framework designed to model IoT devices competing for channel access within the 902–928 MHz unlicensed band. 50,000 IoT devices are evenly divided into two groups. One group uses Binary Phase Shift Keying (BPSK), while the other uses 16-Quadrature Amplitude Modulation (16-QAM). Furthermore, the IoT devices are categorized into three data rate–based classes in line with common IoT standards.: 1000 kbps (Long Term Evolution Category 1, (LTE Cat1)), 160 kbps (Narrowband IoT, (NB-IoT)), and 72 kbps (Extended Coverage GSM (EC-GSM)). Signal transmission employs an Orthogonal Frequency Division Multiplexing (OFDM) system with parameters fixed as follows: cyclic prefix length of 16, Fast Fourier Transform (FFT) size of 64, and number of sub-carriers is 64. On the transmitter side, a random binary sequence to be transmitted is generated. Then, the binary sequence is modulated, either BPSK or 16-QAM, depending on the specifications of the device transmitting. Next, the modulated symbols are assigned to OFDM data sub-carriers and the cyclic prefix is added. To model realistic propagation conditions, a Rayleigh flat fading channel is assumed. To take into account the effect of the channel, each OFDM symbol is convolved with a 4-tap Rayleigh fading channel. After that, additive white gaussian noise (AWGN) is added. On the receiver side, for each sub-band, the following features from the data obtained after adding the AWGN is calculated: total signal power; sums of the real and imaginary components; variances of the real and imaginary parts; spans of the real and imaginary parts; as well as the skewness and kurtosis of both the real and imaginary components. Each 1 kHz sub-band 902–928 MHz unlicensed frequency band is labelled according to its usage status during the simulation: sub-bands carrying IoT device transmission subjected to Rayleigh flat fading and AWGN, are labelled as ‘1’, indicating signal presence; sub-bands containing only AWGN without any IoT transmission are labelled as ‘0’, indicating signal absence. PandasCodeForDataProcessing.ipynb: Selects the first 5000 sub-bands labelled as '1' and the first 5000 sub-bands labelled as '0'. Then Min-Max normalisation is performed. Final_Dataset_SNR_Minus5.csv, Final_Dataset_SNR_Minus10.csv, Final_Dataset_SNR_Minus15.csv, Final_Dataset_SNR_Minus20.csv and Final_Dataset_SNR_Minus25.csv: Simulations are conducted over a range of SNRs from -5 dB down to -25 dB by varying the transmitted signal power in PythonCodeForDataGeneration.pyPythonCodeForDataGeneration.py：一款用于建模902–928 MHz免授权频段内物联网（Internet of Things, IoT）设备信道接入竞争行为的综合仿真框架。该框架包含50000台物联网设备，均分为两组：一组采用二进制相移键控（Binary Phase Shift Keying, BPSK）调制方式，另一组采用16正交幅度调制（16-Quadrature Amplitude Modulation, 16-QAM）调制方式。此外，物联网设备还依据数据速率划分为三类，以契合主流物联网标准：1000 kbps（长期演进类别1，LTE Cat1）、160 kbps（窄带物联网（Narrowband IoT, NB-IoT））以及72 kbps（扩展覆盖全球移动通信系统（Extended Coverage GSM, EC-GSM））。信号传输采用正交频分复用（Orthogonal Frequency Division Multiplexing, OFDM）系统，参数固定如下：循环前缀长度为16，快速傅里叶变换（Fast Fourier Transform, FFT）点数为64，子载波总数为64。发射端流程如下：先生成待传输的随机二进制序列，随后根据发射设备的规格选用BPSK或16-QAM进行调制，再将调制符号映射至OFDM数据子载波，并添加循环前缀。为模拟真实传播环境，本框架采用瑞利平坦衰落信道模型，将每个OFDM符号与4抽头瑞利衰落信道进行卷积运算，随后添加加性高斯白噪声（Additive White Gaussian Noise, AWGN）。接收端需针对每个子带，基于添加AWGN后的接收数据计算以下特征：总信号功率、实部与虚部的和、实部与虚部的方差、实部与虚部的极差、以及实部与虚部的偏度和峰度。902–928 MHz免授权频段内的每个1 kHz子带将根据仿真期间的使用状态进行标注：承载受瑞利平坦衰落与AWGN影响的物联网设备传输信号的子带标注为‘1’，代表信号存在；仅包含AWGN且无任何物联网传输的子带标注为‘0’，代表信号不存在。 PandasCodeForDataProcessing.ipynb：选取前5000个标注为‘1’的子带与前5000个标注为‘0’的子带，随后执行最小-最大归一化处理。 Final_Dataset_SNR_Minus5.csv、Final_Dataset_SNR_Minus10.csv、Final_Dataset_SNR_Minus15.csv、Final_Dataset_SNR_Minus20.csv 与 Final_Dataset_SNR_Minus25.csv：通过调整PythonCodeForDataGeneration.py中的发射信号功率，在信噪比（Signal-to-Noise Ratio, SNR）为-5 dB至-25 dB的范围内开展仿真实验。