Modeling and simulation of nonlinear systems provide communication system designers with a tool to predict and verify overall system performance under nonlinearity and complex communication signals. Traditionally, RF system designers use deterministic signals (discrete tones), which can be implemented in circuit simulators, to predict the performance of their nonlinear circuits/systems. However, RF system designers are usually faced with the problem of predicting system performance when the input to the system is real-world communication signals which have a random nature.
標(biāo)簽: Nonlinear_Distortion_in_Wireless_ Systems
上傳時(shí)間: 2020-05-31
上傳用戶:shancjb
Before delving into the details of orthogonal frequency division multiplexing (OFDM), relevant background material must be presented first. The purpose of this chapter is to provide the necessary building blocks for the development of OFDM principles. Included in this chapter are reviews of stochastic and random process, discrete-time signals and systems, and the Discrete Fourier Transform (DFT). Tooled with the necessary mathematical foundation, we proceed with an overview of digital communication systems and OFDM communication systems. We conclude the chapter with summaries of the OFDM wireless LAN standards currently in existence and a high-level comparison of single carrier systems versus OFDM.
上傳時(shí)間: 2020-05-31
上傳用戶:shancjb
Part I provides a compact survey on classical stochastic geometry models. The basic models defined in this part will be used and extended throughout the whole monograph, and in particular to SINR based models. Note however that these classical stochastic models can be used in a variety of contexts which go far beyond the modeling of wireless networks. Chapter 1 reviews the definition and basic properties of Poisson point processes in Euclidean space. We review key operations on Poisson point processes (thinning, superposition, displacement) as well as key formulas like Campbell’s formula. Chapter 2 is focused on properties of the spatial shot-noise process: its continuity properties, its Laplace transform, its moments etc. Both additive and max shot-noise processes are studied. Chapter 3 bears on coverage processes, and in particular on the Boolean model. Its basic coverage characteristics are reviewed. We also give a brief account of its percolation properties. Chapter 4 studies random tessellations; the main focus is on Poisson–Voronoi tessellations and cells. We also discuss various random objects associated with bivariate point processes such as the set of points of the first point process that fall in a Voronoi cell w.r.t. the second point process.
標(biāo)簽: Stochastic Geometry Networks Wireless Volume and
上傳時(shí)間: 2020-06-01
上傳用戶:shancjb
General paradigm in solving a computer vision problem is to represent a raw image using a more informative vector called feature vector and train a classifier on top of feature vectors collected from training set. From classification perspective, there are several off-the-shelf methods such as gradient boosting, random forest and support vector machines that are able to accurately model nonlinear decision boundaries. Hence, solving a computer vision problem mainly depends on the feature extraction algorithm
標(biāo)簽: Convolutional Networks Neural Guide to
上傳時(shí)間: 2020-06-10
上傳用戶:shancjb
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上傳時(shí)間: 2021-10-22
上傳用戶:
PW2330 develops a high efficiency synchronous step down DC-DC converter capable of delivering3A output current. PW2330 operates over a wide input voltage range from 4.5V to 30V andintegrates main switch and synchronous switch with very low RDS(ON) to minimize the conductionloss. PW2330 adopts the proprietary instant PWM architecture to achieve fast transient responsesfor high step down applications and high efficiency at light loads. In addition, it operates atpseudo-constant frequency of 500kHz under continuous conduction mode to minimize the size ofinductor and capacitor
標(biāo)簽: pw2330
上傳時(shí)間: 2022-02-11
上傳用戶:
PW2205 develops a high efficiency synchronous step-down DC-DC converter capable of delivering5A output current. PW2205 operates over a wide input voltage range from 4.5V to 30V andintegrates main switch and synchronous switch with very low RDS(ON) to minimize the conductionloss.PW2205 adopts the instant PWM architecture to achieve fast transient responses for high step downapplications and high efficiency at light loads. In addition, it operates at pseudo-constant frequencyof 500kHz under continuous conduction mode to minimize the size of inductor and capacitor
標(biāo)簽: pw2205
上傳時(shí)間: 2022-02-11
上傳用戶:
MT2625 DatasheetVersion: 1.2Release date: 31 January 2018NB-IoT transceiver? Compliant with 3GPP R13/R14 NB-IoT standard? Supports DL 200kHz bandwidth/UL single tone and multi-tone? Supported RF bands: B1/B2/B3/B5/B8/B11/B12/B13/B17/B18/B19/B20/B21/B25/B26/B28/B31/B66/B70/B71? Supports PSM and eDRX modeMicrocontroller subsystem? ARM? Cortex?-M4 with FPU and MPU? 14 DMA channels? One RTC timer, one 64-bit and five 32-bit general purpose timers? Development support: SWD, JTAG? Crypto engineo AES 128, 192, 256 bitso DES, 3DESo MD5, SHA-1, 224, 256, 384, 512? True random number generator? JTAG password protection
標(biāo)簽: MT2625
上傳時(shí)間: 2022-07-04
上傳用戶:
實(shí)驗(yàn)二、Python 運(yùn)算符、內(nèi)置函數(shù)實(shí)驗(yàn)?zāi)康模?、熟練運(yùn)用 Python 運(yùn)算符。2、熟練運(yùn)用 Python 內(nèi)置函數(shù)。實(shí)驗(yàn)內(nèi)容:1、編寫程序,輸入任意大的自然數(shù),輸出各位數(shù)字之和。2、編寫程序,輸入兩個(gè)集合 setA 和 setB,分別輸出它們的交集、并集和差集 setA-setB。3、編寫程序,輸入一個(gè)自然數(shù),輸出它的二進(jìn)制、八進(jìn)制、十六進(jìn)制表示形式。實(shí)驗(yàn)三、使用蒙特·卡羅方法計(jì)算圓周率近似值實(shí)驗(yàn)?zāi)康模?、理解蒙特·卡羅方法原理。2、理解 for 循環(huán)本質(zhì)與工作原理。3、了解 random 模塊中常用函數(shù)。實(shí)驗(yàn)內(nèi)容:蒙特·卡羅方法是一種通過概率來得到問題近似解的方法,在很多領(lǐng)域都有重要的應(yīng)用,其中就包括圓周率近似值的計(jì)算問題。假設(shè)有一塊邊長(zhǎng)為 2 的正方形木板,上面畫一個(gè)單位圓,然后隨意往木板上扔飛鏢,落點(diǎn)坐標(biāo)(x, y)必然在木板上(更多的時(shí)候是落在單位圓內(nèi)),如果扔的次數(shù)足夠多,那么落在單位圓內(nèi)的次數(shù)除以總次數(shù)再乘以 4,這個(gè)數(shù)字會(huì)無(wú)限逼近圓周率的值。這就是蒙特·卡羅發(fā)明的用于計(jì)算圓周率近似值的方法,如圖所示。
標(biāo)簽: python
上傳時(shí)間: 2022-07-09
上傳用戶:
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