Skip to main content

5G : Channel modelling for millimeter wave


Channel modelling for millimeter wave 5G communication:

In general, we employ 1. analytical channel modelling; 2. map based channel modelling; and 3. sinusoidal channel modelling for wireless communication channel modelling. Analytical modelling is based on measurements such as pathloss, rms delay spread, and so on. Map-based channel modelling, on the other hand, is focused on the geographical architecture of a specific location. When we derive a channel model for a specific frequency band, we use these two models. We'll focus on channel modelling for millimetre wave communication, which is a promising contender for enabling 5G communications.

When interacting with metal, glass, and other surfaces, mm Wave signals have a higher reflectivity and are more easily absorbed by air, rain, and other elements than signals in lower frequency bands. Furthermore, its diffraction ability is reduced. As we aforementioned channel modelling approaches fall into one of three categories: analytical modelling, map-based modelling, and stochastic modelling. Analytical modelling uses a set of established parameters, whereas ray-tracing-based modelling focuses on locating signal paths in the environment. For applications such as massive MIMO and enhanced beam formation, the map-based model delivers precise and realistic spatial channel features.


Analytical Channel Modelling:

The appropriate statistical parameters such as number of pathways, root-mean-square (RMS) delay spread, path loss, and shadowing of the propagation channel can be produced using the analytical modelling approach, which is based on the data of measurements or statistical characteristics of the scenario. Without taking into account the specifics of the environment, this method can be represented using a given set of parameters. As a result, in an anisotropic radio environment, the analysis result may be inaccurate.


Map-based Channel Modelling:

For applications such as massive MIMO and sophisticated beamforming, the map-based model delivers precise and realistic spatial channel features. It automatically generates spatially consistent modelling for difficult instances like D2D and V2V links with dual-end mobility. Ray tracing is used in conjunction with a reduced 3D geometric description of the propagation environment to create the model. Diffraction, specular reflection, diffuse scattering, and blocking are all considered important propagation mechanisms. The electromagnetic material properties of building walls are modelled as rectangular surfaces. There is no explicit path loss model in the map-based model. Instead, path loss, shadowing, and other propagation features are defined by the map layout and, optionally, a random distribution of objects that account for people, automobiles, and trees, among other things.


General description:

A geometrical representation of the environment – such as a map or a building layout expressed in a three-dimensional (3D) Cartesian coordinate system – is required for any ray-tracing-based model. It is not necessary to have a high level of map detail. Building walls and potentially other fixed structures are the only things that need to be defined.

Here in the above figure signal reaches to cell phone via MPCs where paths are either reflected or reflected. The probability of LOS path decreases as operating frequency increases.


Creation of the environment:

When walls are modelled as rectangular surfaces, a 3D map comprising coordinate points of wall corners is constructed. Both outside and indoor maps, as well as the position of indoor walls within a building block, are defined in the outdoor-to-indoor instance. The map is then strewn with random scattering/shadowing objects that depict persons, automobiles, and other items. The item positions can then be defined either based on a known regular pattern, such as the spectator seats in a stadium, or randomly selected from a uniform distribution with a set situation dependent density.


Determination of propagation pathways:

Direct, diffraction, specular reflection, and diffuse scattering must all be represented for this purpose, as seen in Figure above. The diffuse scattering caused by rough surfaces is compensated for by placing point scatterers on the external walls' surface.

Here in millimeter wave channel modelling map-based channel modeling is very important because here types of obstacle's surfaces, constructional architecture of a area, angle of arrival and departure (AoA and AoD) matters a lot.


Stochastic Channel Modelling:

The stochastic model is based on the Geometry-based Stochastic Channel Models (GSCMs) family, which includes 3GPP 3D Channel Models. It concentrates on path loss, the sum-of-sinusoids approach for calculating large-scale parameters, and so on.

#beamforming

Next Page>>

People are good at skipping over material they already know!

View Related Topics to







Contact Us

Name

Email *

Message *

Popular Posts

Theoretical BER vs SNR for binary ASK, FSK, and PSK with MATLAB Code + Simulator

📘 Overview & Theory 🧮 MATLAB Codes 📚 Further Reading Bit Error Rate (BER) Equations In ASK, noise directly affects the signal amplitude, making it the most vulnerable since the data is carried in amplitude changes. In FSK, data is represented by frequency variations, and because noise typically impacts amplitude more than frequency, FSK is more robust than ASK. In PSK, data is encoded in the signal phase, and BPSK specifically uses 180-degree phase shifts, creating the greatest separation between signal points and therefore achieving the lowest bit error rate (BER) for the same power level. BER formulas for ASK, FSK, and PSK modulation schemes. ASK BER = 0.5 × erfc(0.5 × √SNR) FSK BER = 0.5 × erfc(√(SNR / 2)) PSK BER = 0.5 × erfc(√SNR) Theoretical BER ...

BER vs SNR for M-ary QAM, M-ary PSK, QPSK, BPSK, ...(MATLAB Code + Simulator)

Bit Error Rate (BER) & SNR Guide Analyze communication system performance with our interactive simulators and MATLAB tools. 📘 Theory 🧮 Simulators 💻 MATLAB Code 📚 Resources BER Definition SNR Formula BER Calculator MATLAB Comparison 📂 Explore M-ary QAM, PSK, and QPSK Topics ▼ 🧮 Constellation Simulator: M-ary QAM 🧮 Constellation Simulator: M-ary PSK 🧮 BER calculation for ASK, FSK, and PSK 🧮 Approaches to BER vs SNR What is Bit Error Rate (BER)? The BER indicates how many corrupted bits are received compared to the total number of bits sent. It is the primary figure of merit for a...

Simulation of ASK, FSK, and PSK using MATLAB Simulink (with Online Simulator)

📘 Overview 🧮 How to use MATLAB Simulink 🧮 Simulation of ASK using MATLAB Simulink 🧮 Simulation of FSK using MATLAB Simulink 🧮 Simulation of PSK using MATLAB Simulink 🧮 Simulator for ASK, FSK, and PSK 🧮 Digital Signal Processing Simulator 📚 Further Reading ASK, FSK & PSK HomePage MATLAB Simulation Simulation of Amplitude Shift Keying (ASK) using MATLAB Simulink In Simulink, we pick different components/elements from MATLAB Simulink Library. Then we connect the components and perform a particular operation. Result A sine wave source, a pulse generator, a product block, a mux, and a scope are shown in the diagram above. The pulse generator generates the '1' and '0' bit sequences. Sine wave sources produce a specific amplitude and frequency. The scope displays the modulated signal as well as the original bit sequence created by the pulse generator. Mux i...

Online Simulator for ASK, FSK, and PSK

Try our new Digital Signal Processing Simulator!   •   Interactive ASK, FSK, and BPSK tools updated for 2025. Start Now Interactive Modulation Simulators Visualize binary modulation techniques (ASK, FSK, BPSK) in real-time with adjustable carrier and sampling parameters. 📡 ASK Simulator 📶 FSK Simulator 🎚️ BPSK Simulator 📚 More Topics ASK Modulator FSK Modulator BPSK Modulator More Topics Simulator for Binary ASK Modulation Digital Message Bits Carrier Freq (Hz) Sampl...

OFDM Waveform with MATLAB Code (with Simulator)

  In OFDM (Orthogonal Frequency Division Multiplexing) , we transmit multiple orthogonal subcarriers simultaneously. Since the subcarriers are orthogonal , they do not interfere with each other, which is one of the main advantages of OFDM. Practically, OFDM converts a wideband signal into multiple narrowband orthogonal subcarriers. For typical wireless communication, if the signal bandwidth (or symbol duration) exceeds the coherence bandwidth of the channel, the signal experiences frequency-selective fading . Fading distorts the signal, making it difficult to recover the original information. By using OFDM, we transmit the same wideband signal across multiple orthogonal narrowband subcarriers, reducing the effect of fading. For example, if we want to transmit a signal of bandwidth 1024 kHz , we can divide it into N = 8 subcarriers . Each subcarrier is then spaced by: Δf = Total Bandwidth N = 1024 8 kHz...

MATLAB Code for Constellation Diagram of QAM configurations such as 4, 8, 16, 32, 64, 128, and 256-QAM

📘 Overview of QAM 🧮 4-QAM MATLAB 🧮 16-QAM MATLAB 🚀 Online Simulator 📂 Other Topics on Constellation Diagrams... ▼ 🧮 MATLAB Code for 4-QAM 🧮 MATLAB Code for 16-QAM 🧮 MATLAB Code for m-ary QAM 🧮 Simulator for m-ary PSK 🧮 Simulator for m-ary QAM 🧮 Overview of Energy per Bit (Eb / N0) 🧮 Simulator for ASK, FSK, and PSK Overview of QAM One of the best-performing modulation techniques is QAM [↗] . Here, we modulate the symbols by varying the carrier signal's amplitude and phase in response to the variation in the message signal (or voltage variation). So, we may say that QAM is a combination of phase and amplitude modulation. Additionally, it performs better than ASK or PSK [↗] . In fact, any constellation for any type of modulatio...

How to use MATLAB Simulink

Introduction to MATLAB Simulink MATLAB Simulink is a popular add-on of MATLAB. Here, you can use different blocks like modulator, demodulator, AWGN channel, etc. And you can do experiments on your own. Steps to Get Started 1. Go to the 'Simulink' tab at the top navbar of MATLAB. If not found, click on the add-on tab, search 'Simulink,' and then click on it to add. 2. Once you installed the simulation, click the 'new' tap at the top left corner. 3. Then, search the required blocks in the 'Simulink library.' Then, drag it to the editor space. 4. You can double-click on the blocks to see the input parameters. 5. Then, connect the blocks by dragging a line from one block's output terminal to another block's input. 6. If the connection is complete, click the 'run' tab in the middle of the top navbar. 7. After clicking on the run ...

FastAPI Static Files – Overview

FastAPI Static Files Often, a web application needs to include resources that do not change, even when dynamic data is rendered. These resources are called static assets . Examples of static files include: Images ( .png , .jpg ) JavaScript files ( .js ) Stylesheets ( .css ) Installing Required Library To handle static files in FastAPI, you need the aiofiles library. pip install aiofiles Mounting Static Files FastAPI uses the StaticFiles class to serve static content. You mount a folder (usually named static ) so that all files inside it can be accessed via a URL. from fastapi import FastAPI from fastapi.staticfiles import StaticFiles app = FastAPI() app.mount("/static", StaticFiles(directory="static"), name="static") Example 1: Using an Image Place an image file (for example, fa-logo.png ) inside the static folder. main.py from fastapi import FastAPI, Request from fastapi.responses import HTMLRespon...