Python实现rician莱斯衰落和rician莱斯信道

概述基本概念Example来自Github-Rician-Fading-Python2.1Rician衰落类#-*-coding:utf-8-*-"""CreatedonWedNov418:55:462020@author:JonathanBrowning"""importnumpyasnpfromscipy.statsimportgaussian_kdeaskdffromsci 基本概念Example

来自Github-Rician-Fading-Python

2.1 Rician衰落类

# -*- Coding: utf-8 -*-"""Created on Wed Nov  4 18:55:46 2020@author: Jonathan browning"""import numpy as npfrom scipy.stats import gaussian_kde as kdffrom scipy import special as spclass Rice:    # numSamples = 2*(10**6)  # the number of samples used in the simulation    # numSamples = 64 #表示要产生的随机数的长度    r = np.linspace(0, 6, 6000) # theoretical envelope pdf x axes    theta = np.linspace(-np.pi, np.pi, 6000)    # theoretical phase pdf x axes        def __init__(self, K, r_hat_2, phi,numSamples):                # # user input checks and assigns value        self.K = self.input_Check(K, "K", 0, 50)        self.r_hat_2 = self.input_Check(r_hat_2, "\hat{r}^2", 0.5, 2.5)          self.phi = self.input_Check(phi, "\phi", -np.pi, np.pi)        self.numSamples = numSamples        # user input checks and assigns value                # simulating and theri densitIEs         self.multipathFading = self.complex_Multipath_Fading()        self.xdataEnv, self.ydataEnv = self.envelope_Density()        self.xdataPh, self.ydataPh = self.phase_Density()                # theoretical pdfs calculated        self.envelopeProbability = self.envelope_pdf()        self.phaseProbability = self.phase_pdf()    def input_Check(self, data, inputname, lower, upper):        # input_Check checks the user inputs                # has a value been entered        if data == "":            raise ValueError(" ".join((inputname, "must have a numeric value")))                # incase of an non-numeric input         try:            data = float(data)        except:                raise ValueError(" ".join((inputname, "must have a numeric value")))            # data must be within the range        if data < lower or data > upper:            raise ValueError(" ".join((inputname, f"must be in the range [{lower:.2f}, {upper:.2f}]")))                return data    def calculate_Means(self):        # calculate_means calculates the means of the complex Gaussians representing the        # in-phase and quadrature components                p = np.sqrt(self.K * self.r_hat_2 / (1+self.K)) * np.cos(self.phi)        q = np.sqrt(self.K * self.r_hat_2 / (1+self.K)) * np.sin(self.phi)                return p, q        def scattered_Component(self):        # scattered_Component calculates the power of the scattered signal component                sigma = np.sqrt(self.r_hat_2 / ( 2 * (1+self.K) ) )                return sigma        def generate_Gaussians(self, mean, sigma):        # generate_Gaussians generates the Gaussian random variables                gaussians = np.random.default_rng().normal(mean, sigma, self.numSamples)                return gaussians        def complex_Multipath_Fading(self):        # complex_Multipath_Fading generates the complex fading random variables                p, q = self.calculate_Means()        sigma = self.scattered_Component()        multipathFading = self.generate_Gaussians(p, sigma) + (1j*self.generate_Gaussians(q, sigma))                return multipathFading        def envelope_pdf(self):        # envelope_pdf calculates the theoretical envelope pdf                pdf = 2 * (1+self.K) * self.r / self.r_hat_2 \            * np.exp(- self.K - ((1+self.K)*self.r**2)/self.r_hat_2) \            * np.i0(2 * self.r * np.sqrt(self.K*(1+self.K)/self.r_hat_2))                    return pdf    def phase_pdf(self):        # phase_pdf calculates the theoretical phase pdf                def q_func(x):        # Q-function                    return 0.5-0.5*sp.erf(x/np.sqrt(2))                     pdf = (1/(2*np.pi))* np.exp(- self.K) * (1 + (np.sqrt(4*np.pi*self.K) \              * np.exp(self.K * (np.cos(self.theta-self.phi))**2) *np.cos(self.theta-self.phi)) \              * (1 - q_func(np.sqrt(2*self.K) * np.cos(self.theta-self.phi))))                return pdf                def envelope_Density(self):        # envelope_Density finds the envelope pdf of the simulated random variables                R = np.sqrt((np.real(self.multipathFading))**2 + (np.imag(self.multipathFading))**2)        kde = kdf(R)        x = np.linspace(R.min(), R.max(), 100)        p = kde(x)                return x, p        def phase_Density(self):        # phase_Density finds the phase pdf of the simulated random variables                R = np.angle(self.multipathFading)        kde = kdf(R)        x = np.linspace(R.min(), R.max(), 100)        p = kde(x)                return x, p  

写一个Demo测试程序

from rice import *K =1 # K因子hat = 1 # 振幅phi = 1 # 相位numSamples = 16	# 随机样本的长度s = Rice(1,1,1,16)# rician衰落随机数print(s.multipathFading)

2.2 Ricican信道

# signal输入的信号# channelResponse rician衰落随机数,就是以上代码求的s.multipathFading# SNRdb,信噪比def rician_channel(signal, channelResponse, SNRdb):	# 卷积    convolved = np.convolve(signal, channelResponse)    signal_power = np.mean(abs(convolved**2))    sigma2 = signal_power * 10**(-SNRdb / 10)    # 噪声    noise = np.sqrt(sigma2 / 2) * (np.random.randn(*convolved.shape) + 1j * np.random.randn(*convolved.shape))    return convolved + noise 

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