Skip to main content
Home Wireless Communication Modulation MATLAB Beamforming Project Ideas MIMO Computer Networks Lab 🚀

Present and Future Wireless Communication Systems


1. Overview of 5G:

Looking back in time, we can see that we have adopted a new evolution or G in each decade. We were first introduced to 4G technology in 2010. However, we now need to make some changes to our current network. We're looking for two things in particular: 1. A network that is extremely dense, and 2. Broadband connectivity through cellular networks. Around 2020, 5G technology was commercialized. By 2025, it is anticipated that extensive adaption will be achievable. [Read More about 5G]


2. Limitations of 4G LTE:

Previously, with 4G LTE, a single base station (BS) could connect hundreds of devices at once. In the current situation, we need to expand the capacity of our system. Because the amount of bandwidth needed by various devices is continually rising. Every decade, it grows by a factor of 1000. As a result, every ten years, an entirely new evolution of G is required. [Read More]


3. The reason of the increasing data demand:

The number of wireless devices is increasing every day, yet the internet-based services, such as self-driving cars, streaming ultra-high-definition video, andIoTsensors, need both high data rates and extremely low latency to function in real time. Between 2011 and 2022, mobile data traffic will increase at a compound annual growth rate of 46%. It would have reached 2.58 exabytes (EB) daily by 2022. Statistics show that by 2022, the amount of internet protocol (IP) traffic worldwide is expected to exceed 4.8 zettabytes (ZBs) annually.


4. High data rates and more connections are offered to users with 5G:

Thousands of devices per square kilometer are projected to be supported by 5G. We urgently require it since the number of internet-connected devices, IoTs, and PDAs is continuously expanding, necessitating a large amount of bandwidth to operate them. Because 5G employs extremely high frequency or millimeter wave, it is capable of doing so. Previously, we've seen bandwidth allotment of roughly 2GHz per channel in WI PAN applications employing the 60 GHz millimeter wave spectrum. In the case of a cellular 5G network, we will now ‎utilize‎ this millimeter wave spectrum. That is very incredible. We'll use massive MIMO to make better use of the spectrum resource because millimeter wave has a lot of promise for greater bandwidth. Massive MIMO is an excellent way to boost system capacity even more. Using those incredible core technologies, we've almost reached the Shannon limit in 5G communication.

Our economy will be greatly impacted by 5G. Automation may be seen in a variety of sectors and industries. Machine-to-machine communication, augmented reality (AR), and virtual reality will all be common in the future. We will be able to control machines from afar and in real time. For many years, internet-connected high-speed vehicles, such as bullet trains, have been a major source of concern. Everything is feasible thanks to the ultra-low latency of the 5G millimeter wave spectrum. Communication latency will be decreased to 1 ms in 5G, compared to 40 ms in 4G.

Although 5G has a lot of potential, it also has several drawbacks, such as a complex channel model (sparse channel matrix), high propagation path loss, and so on. We've talked about a lot of problems and potential remedies.


5. Upcoming Wireless Mobile Generations, Millimeter Wave Band, and Massive MIMO: 
 
We are consistently upgrading our cellular wireless network's generation(G in telecom) and the IEEE body is releasing new WLAN technology, all to satisfy the demand for high data traffic from various internet-connected devices. As a result, we're moving to 5G, The essential technology for 5G connectivity is the millimeter wave (mmWave) band. The frequency range for mm-Wave is 30 to 300 GHz. To address the rising demand for data traffic on a worldwide scale, other spectrum bands need to be investigated. The millimeter wave band with massive MIMO antenna allows for a directed and narrow beam, which boosts the received signal power to an adequate level. Wi-Max, and other technologies to give greater connectivity to the fast-growing number of internet-connected devices. The fundamental goal of upgrading communication systems or the evolution of G is to offer enough bandwidth for all devices to connect with BSs seamlessly (due to the large amount of bandwidth available in the mm-wave band,Ultra-Wide Band (UWB),or microwave link communication) as well as to improve bandwidth efficiency (by applying new modulation techniques or designing antenna more properly for those systems, etc.).

The maximum bandwidth of the LTE cellular system, which operates at a sub-6 GHz operating frequency, is 200 MHz. However, WPAN, which operates in the 60 GHz unsilenced millimeter wave range, can give each channel a bandwidth of 2 GHz. The ITU classifies the millimeter wave band, which spans frequencies from 30 to 300 GHz, as extremely high frequency (or EHF). It is referred to as a millimeter wave since its wavelength varies from 1 millimeter to 10 millimeter. By providing high data rate wireless communication, where traffic from mobile and wireless devices will account for 71% of overall IP traffic, millimeter wave with massive MIMO will be crucial in meeting these demands.

N.B. We don't spam. Various posts about modern wireless communication systems, WLAN, 5G, IoTs, MIMO technology, Web design, programming, and other topics are published here. Don't forget tosubscribefor our newsletter.


Also read about

[1] 1G to 5G Technology - Evolution ofMobile Wireless Generations
[2] Important Wireless Communication Terms




People are good at skipping over material they already know!

View Related Topics to







Admin & Author: Salim

profile

  Website: www.salimwireless.com
  Interests: Signal Processing, Telecommunication, 5G Technology, Present & Future Wireless Technologies, Digital Signal Processing, Computer Networks, Millimeter Wave Band Channel, Web Development
  Seeking an opportunity in the Teaching or Electronics & Telecommunication domains.
  Possess M.Tech in Electronic Communication Systems.


Contact Us

Name

Email *

Message *

Popular Posts

BER vs SNR for M-ary QAM, M-ary PSK, QPSK, BPSK, ...

Modulation Constellation Diagrams BER vs. SNR BER vs SNR for M-QAM, M-PSK, QPSk, BPSK, ... 1. What is Bit Error Rate (BER)? The abbreviation BER stands for bit error rate, which indicates how many corrupted bits are received (after the demodulation process) compared to the total number of bits sent in a communication process. It is defined as,  In mathematics, BER = (number of bits received in error / total number of transmitted bits)  On the other hand, SNR refers to the signal-to-noise power ratio. For ease of calculation, we commonly convert it to dB or decibels.   2. What is Signal the signal-to-noise ratio (SNR)? SNR = signal power/noise power (SNR is a ratio of signal power to noise power) SNR (in dB) = 10*log(signal power / noise power) [base 10] For instance, the SNR for a given communication system is 3dB. So, SNR (in ratio) = 10^{SNR (in dB) / 10} = 2 Therefore, in this instance, the signal power i

Comparisons among ASK, PSK, and FSK | And the definitions of each

Modulation ASK, FSK & PSK Constellation MATLAB Simulink MATLAB Code Comparisons among ASK, PSK, and FSK    Comparisons among ASK, PSK, and FSK Comparison among ASK,  FSK, and PSK Performance Comparison: 1. Noise Sensitivity:    - ASK is the most sensitive to noise due to its reliance on amplitude variations.    - PSK is less sensitive to noise compared to ASK.    - FSK is relatively more robust against noise, making it suitable for noisy environments. 2. Bandwidth Efficiency:    - PSK is the most bandwidth-efficient, requiring less bandwidth than FSK for the same data rate.    - FSK requires wider bandwidth compared to PSK.    - ASK's bandwidth efficiency lies between FSK and PSK. Bandwidth Calculator for ASK, FSK, and PSK The baud rate represents the number of symbols transmitted per second Select Modulation Type: ASK FSK PSK Baud Rate (Hz):

MATLAB code for BER vs SNR for M-QAM, M-PSK, QPSk, BPSK, ...

Modulation Constellation Diagrams BER vs. SNR MATLAB code for BER vs SNR for M-QAM, M-PSK, QPSk, BPSK, ...   MATLAB Script for  BER vs. SNR for M-QAM, M-PSK, QPSk, BPSK %Written by Salim Wireless %Visit www.salimwireless.com for study materials on wireless communication %or, if you want to learn how to code in MATLAB clc; clear; close all; % Parameters num_symbols = 1e5; % Number of symbols snr_db = -20:2:20; % Range of SNR values in dB % PSK orders to be tested psk_orders = [2, 4, 8, 16, 32]; % QAM orders to be tested qam_orders = [4, 16, 64, 256]; % Initialize BER arrays ber_psk_results = zeros(length(psk_orders), length(snr_db)); ber_qam_results = zeros(length(qam_orders), length(snr_db)); % BER calculation for each PSK order and SNR value for i = 1:length(psk_orders) psk_order = psk_orders(i); for j = 1:length(snr_db) % Generate random symbols data_symbols = randi([0, psk_order-1]

FFT Magnitude and Phase Spectrum using MATLAB

MATLAB Code clc; clear; close all; % Parameters fs = 100;           % Sampling frequency t = 0:1/fs:1-1/fs;  % Time vector % Signal definition x = cos(2*pi*15*t - pi/4) - sin(2*pi*40*t); % Compute Fourier Transform y = fft(x); z = fftshift(y); % Frequency vector ly = length(y); f = (-ly/2:ly/2-1)/ly*fs; % Compute phase phase = angle(z); % Plot magnitude of the Fourier Transform figure; subplot(2, 1, 1); stem(f, abs(z), 'b'); xlabel('Frequency (Hz)'); ylabel('|y|'); title('Magnitude of Fourier Transform'); grid on; % Plot phase of the Fourier Transform subplot(2, 1, 2); stem(f, phase, 'b'); xlabel('Frequency (Hz)'); ylabel('Phase (radians)'); title('Phase of Fourier Transform'); grid on;   Output  Copy the MATLAB Code from here % The code is written by SalimWireless.Com clc; clear; close all; % Parameters fs = 100; % Sampling frequency t = 0:1/fs:1-1/fs; % Time vector % Signal definition x = cos(2*pi*15*t -

Difference between AWGN and Rayleigh Fading

Wireless Signal Processing Gaussian and Rayleigh Distribution Difference between AWGN and Rayleigh Fading 1. Introduction Rayleigh fading coefficients and AWGN, or additive white gaussian noise [↗] , are two distinct factors that affect a wireless communication channel. In mathematics, we can express it in that way.  Let's explore wireless communication under two common noise scenarios: AWGN (Additive White Gaussian Noise) and Rayleigh fading. y = hx + n ... (i) The transmitted signal  x  is multiplied by the channel coefficient or channel impulse response (h)  in the equation above, and the symbol  "n"  stands for the white Gaussian noise that is added to the signal through any type of channel (here, it is a wireless channel or wireless medium). Due to multi-paths the channel impulse response (h) changes. And multi-paths cause Rayleigh fading. 2. Additive White Gaussian Noise (AWGN) The mathematical effect involves adding Gauss

Channel Impulse Response (CIR)

Channel Impulse Response (CIR) Wireless Signal Processing CIR, Doppler Shift & Gaussian Random Variable  The Channel Impulse Response (CIR) is a concept primarily used in the field of telecommunications and signal processing. It provides information about how a communication channel responds to an impulse signal.   What is the Channel Impulse Response (CIR) ? It describes the behavior of a communication channel in response to an impulse signal. In signal processing,  an impulse signal has zero amplitude at all other times and amplitude  ∞ at time 0 for the signal. Using a Dirac Delta function, we can approximate this.  ...(i) δ( t) now has a very intriguing characteristic. The answer is 1 when the Fourier Transform of  δ( t) is calculated. As a result, all frequencies are responded to equally by  δ (t). This is crucial since we never know which frequencies a system will affect when examining an unidentified one. Since it can test the system for all freq

Simulation of ASK, FSK, and PSK using MATLAB Simulink

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 is a tool for displaying both modulated and unmodulated signals at the same time. The result section shows that binary '1' is modulated by a certain sine wave amplitude of 1 Volt, and binary '0' is modulated by zero amplitude. Simulation of Frequency Shift Keying (FSK) using MATLAB Simulink   Result The diagram above shows t