Skip to main content

5G : Theoretical Aspects | Frequency & Spectrum, Speed, Massive MIMO & OFDM


 

5G technology is a brand-new technology that will supply us with data rates that are significantly faster than 4G. It works at frequencies below 6 GHz in many countries. But in future, 5G frequencies will range from 26 to 100 GHz (These frequencies will be used for the 5G backhaul connection, and the end user will connect to a local cell tower utilizing somewhat lower frequency bands, specifically the 1 to 7 GHz bands.). With a 1 millisecond latency, it can deliver multi-gigabit per second data speeds (over the air). The millimeter wave band is chosen for 5G technology. However, 5G is currently being deployed in a large number of countries (almost 60+). Because it operates at the EHF band and has very low on-the-air latency, 5G will lead automation in industries, internet connected vehicles for smooth traffic, tele-medicine, augmented reality (AR), and virtual reality (VR). Three key technologies that will enable 5G are millimeter wave spectrum, OFDM, and massive MIMO. One of the most prominent reasons for developing 5G technology is that the number of internet-connected devices is continually expanding. Due of its large available bandwidth, 5G can manage more devices connecting to the BS at the same time. It has the capacity to handle thousands of devices per square kilometer that are connected to the 5G network.

What's new in 5G Technology

1. Enhanced Mobile Broadband (EMBB)

Users of 4G receive about 10 megabits per second, whereas 5G users receive 100 megabits per second. 5G is predicted to have a peak data throughput of 10 GBits/s, compared to 1 GBit/s for 4G. 5G is expected to have ten times the connection density of 4G. In comparison to 4G, 5G is expected to require less power.

2. Infrastructure for 5G Technology

Because it is a new technology, the infrastructure, equipment, and so on will be considerably different from the current network. In 5G, the coverage zone under a cell will be relatively tiny Because higher frequencies may only travel a limited distance in the earth's atmosphere. Tiny cells are commonly

Also read about what is 5g RAN?
 
referred to as a microcell. Gases, vapor, and other substances in the atmosphere will absorb very high frequency waves. It also has a hard time penetrating thick obstacles because to the increased frequency. As a result, the microcell will be mostly coupled to user devices like PDAs. After that, the microcells will be linked to BS. Then one BS will be connected to another BS through backhaul.

Backhaul is a concept in which a free space LOS channel connects two high BS towers. Simply said, the line of sight path of two high BSs will be unobstructed. However, because the millimeter wave spectrum has more bandwidth, it can accommodate higher data rates. In backhaul communication, the use of wires and fiber optics is reduced. As a result, communication is completely wireless.

3. Dense connectivity and large network capacity

5G is planned to support a connection density up to 10^(6) per square kilometer, which is about ten times more than 4G. The important technologies that will boost the capacity of the 5G network are discussed below.

4. Interference in 5G Network

Interference is a concern since the number of internet-connected gadgets per square kilometer is in the thousands. As a result, it is necessary to eliminate interferences between devices in a very intelligent manner. Precoding in massive MIMO and beamforming will be quite beneficial in this situation.

Key Technologies to enable 5G Technology

1. Extremely high frequency & bandwidth

2. OFDM

3. Massive MIMO

1. Extremely high frequency & bandwidth

In general millimetre wave band is suitable for high data rate communication. Although some frequency band, like, 60 GHz band is easily absorbed by oxygen in atmosphere, but it a good plus for indoor communication.

In comparison to 30 - 60 GHz electromagnetic bands, oxygen in the environment absorbs 60 GHz frequency more. As a result, the 60 GHz millimeter wave band is typically appropriate for indoor communication. Indoor communication has a much shorter range than outdoor communication. Because 60 GHz attenuates significantly with distance, it rarely interacts with outdoor frequency bands. In 60 GHz indoor communication, however, device to device or D2D interference is less. So, it is a big plus for that.

2. OFDM

We've already written an article about OFDM. We covered how OFDM suppresses inter-symbol interferences. When it comes to frequency selective fading, OFDM offers an excellent resistance. It also improves spectrum efficiency.

3. How Massive MIMO increases data rate in 5G

Massive MIMO is critical for 5G communications. Let's pretend that there's simply one transmitter and one reception antenna. Between the transmitter and the receiver, there is only one communication path or data stream accessible. There are four simultaneous paths or data streams between the transmitter and receiver if 2*2 MIMO is used. However, you should be aware that there are two independent paths that a transmitter and receiver could take. Similarly, there are three antennas on the transmitter side and two antennas on the receiver side for 3*2 MIMO. The maximum number of simultaneous data streams between TX and RX is defined as.


Number of simultaneous data stream = min ( M, N)

where, M = number of antennas at transmitter side
N= number of antennas at receiver side

More examples:
If 4*4 MIMO or number of transmitter antenna (antenna element) equal to 4 and number of receiver side antenna = 4; then number is simultaneous data stream between transmitter and receiver is 4.
Similarly, for 5*6 MIMO, number of simultaneous data stream = 5
for 6*6 MIO, it is 6.

In a huge MIMO system, we can get independent eigen pathways using SVD. Signal processing becomes more simple as a result of these independent paths.

Full-duplex radio technology in 5G


In full duplex radio, transmit and receive in the same frequency bands at the same time. Unlike FDD and TDD, when both links use the entire bandwidth at the same time. As a result, self-interference is a critical challenge in full duplex transmission.

#Documentation of next-g wireless communication 5g technology
How many companies have developed multibeam backhaul or point to multipoint wireless products in E band frequency?
What are the handover authentication protocols used in 5g network?






Contact Us

Name

Email *

Message *

Popular Posts

MATLAB code for BER vs SNR for M-QAM, M-PSK, QPSK, BPSK (with Simulation)

🧮 MATLAB Code for BPSK, M-ary PSK, and M-ary QAM Together 🧮 MATLAB Code for M-ary QAM 🧮 MATLAB Code for M-ary PSK 📚 Further Reading MATLAB Script for BER vs. SNR for M-QAM, M-PSK, QPSK, BPSK % Written by Salim Wireless clc; clear; close all; snr_db = -5:2:25; psk_orders = [2, 4, 8, 16, 32]; qam_orders = [4, 16, 64, 256]; ber_psk_results = zeros(length(psk_orders), length(snr_db)); ber_qam_results = zeros(length(qam_orders), length(snr_db)); for i = 1:length(psk_orders) ber_psk_results(i, :) = berawgn(snr_db, 'psk', psk_orders(i), 'nondiff'); end for i = 1:length(qam_orders) ber_qam_results(i, :) = berawgn(snr_db, 'qam', qam_orders(i)); end figure; semilogy(snr_db, ber_psk_results(1, :), 'o-', 'LineWidth', 1.5, 'DisplayName', 'BPSK'); hold on; for i = 2:length(psk_orders) semilogy(snr_db, ber_psk_results(i, :), 'o-', 'DisplayName', sprintf('%d-PSK', psk_or...

Theoretical BER vs SNR for BPSK

Theoretical Bit Error Rate (BER) vs Signal-to-Noise Ratio (SNR) for BPSK in AWGN Channel Let’s simplify the explanation for the theoretical Bit Error Rate (BER) versus Signal-to-Noise Ratio (SNR) for Binary Phase Shift Keying (BPSK) in an Additive White Gaussian Noise (AWGN) channel. Key Points Fig. 1: Constellation Diagrams of BASK, BFSK, and BPSK [↗] BPSK Modulation Transmits one of two signals: +√Eb or −√Eb , where Eb is the energy per bit. These signals represent binary 0 and 1 . AWGN Channel The channel adds Gaussian noise with zero mean and variance N₀/2 (where N₀ is the noise power spectral density). Receiver Decision The receiver decides if the received signal is closer to +√Eb (for bit 0) or −√Eb (for bit 1) . Bit Error Rat...

PSD Calculation with FFT: MATLAB Tutorial for Signal Analysis

  Implementation Steps 1. FFT Computes the Frequency Content of a Signal FFT converts a time-domain signal to the frequency domain. If: The signal is sampled at rate $f_s$ You compute an $N_{\text{FFT}}$-point FFT Then each FFT bin corresponds to a frequency resolution of: $$\Delta f = \frac{f_s}{N_{\text{FFT}}}$$ So the FFT gives you accurate frequency content, assuming the signal is stationary and adequately sampled (Nyquist criterion met).  2. Magnitude Squared Gives Power (Not Amplitude) $$P[k] = |X[k]|^2$$ This gives power at each frequency bin, not just amplitude. It represents how much energy is present at each frequency. It's a key step for PSD.  3. Normalization Makes the PSD Physically Meaningful The equation: $$\text{PSD}[k] = \frac{|X[k]|^2}{N_{\text{FFT}} \cdot f_s \cdot U}$$ is derived from first principles and ensures that the u...

Power Spectral Density Calculation Using FFT in MATLAB

📘 📘 Overview 🧮 🧮 Steps to calculate 💻 🧮 MATLAB Codes 📚 📚 Further Reading Power spectral density (PSD) tells us how the power of a signal is distributed across different frequency components, whereas Fourier Magnitude gives you the amplitude (or strength) of each frequency component in the signal. Steps to calculate the PSD of a signal Firstly, calculate the fast Fourier transform (FFT) of a signal. Then, calculate the Fourier magnitude (absolute value) of the signal. Square the Fourier magnitude to get the power spectrum. To calculate the Power Spectral Density (PSD), divide the squared magnitude by the product of the sampling frequency (fs) and the total number of samples (N). Formula: PSD = |FFT|^2 / (fs * N) Sampling frequency (fs): The rate at which the continuous-time signal is sampled (in Hz). ...

MUSIC Algorithm Explained (with MATLAB + Simulator)

Practical Implementation of the MUSIC Algorithm The focus is on how the algorithm works computationally , not just theory, and it explains the denominator (a H E n E n H a) mathematically and intuitively. 1. Introduction The MUSIC (Multiple Signal Classification) algorithm is a high-resolution method used in signal processing and array processing to estimate the Direction of Arrival (DOA) of signals received by a sensor array. Unlike classical beamforming methods, MUSIC uses eigenvector decomposition of the covariance matrix to separate the signal subspace and noise subspace , allowing it to achieve much higher angular resolution. In practical implementations, MUSIC works by: Simulating or collecting array signals Computing the covariance matrix Performing eigenvalue decomposition Separating signal and noise subspaces Scanning possible angles using a steering vector Constructing a pseudo-spectrum where peaks indicate signal directions 2. Signal Mo...

MATLAB Code for MUSIC

  MATLAB Code clc; clear; close all ; %% Step 1: Define Parameters M = 8; % Number of array sensors d = 0.5; % Sensor spacing (lambda/2) K = 2; % Number of signals N = 200; % Number of snapshots theta = [-20 30]; % True signal angles (degrees) SNR = 10; % Signal-to-noise ratio (dB) fprintf( 'Step 1: Parameters Initialized\n' ); %% Step 2: Generate Signal Sources t = 1:N; s1 = exp(1j*2*pi*0.05*t); s2 = exp(1j*2*pi*0.1*t); S = [s1; s2]; figure; plot(real(S(1,:))) title( 'Signal 1 (Real Part)' ) xlabel( 'Samples' ) ylabel( 'Amplitude' ) figure; plot(real(S(2,:))) title( 'Signal 2 (Real Part)' ) xlabel( 'Samples' ) ylabel( 'Amplitude' ) fprintf( 'Step 2: Source Signals Generated\n' ); %% Step 3: Construct Steering Matrix A = zeros(M,K); for k = 1:K A(:,k) = exp(-1j*2*pi*d*(0:M-1)'*sin(theta(k)*pi/180)); end fprintf( 'Step 3: Steering Matr...

Direction of Arrival (DoA) Online Simulator (using MUSIC)

Interactive DOA Simulator X-axis XY angle (deg): 45 XZ angle (deg): 30 Noise: 0.05 Y-axis XY angle (deg): 60 YZ angle (deg): 45 Noise: 0.05 Z-axis XZ angle (deg): 60 YZ angle (deg): 30 Noise: 0.05 Estimated DOA (deg): 0 Simulation Workflow and Mathematical Background This simulator demonstrates Direction of Arrival (DOA) estimation using three-axis sensor signals (X, Y, Z), Maximal Ratio Combining (MRC) , and the MUSIC algorithm . It allows interactive control of signal angles and noise for teaching purposes. 1. Signal Generation A pure sinewave signal of frequency f is projected onto three axes using user-defined angles in different planes: X-axis: θ XY , θ XZ Y-axis: θ XY , θ YZ Z-axis: θ XZ , θ YZ Mathematically, for each time sample t : x(t) = s(t) * cos(θ_xy_x) * cos(θ_xz_x) + n_x(t) y(t) = s(t) * sin(θ_xy_y) * cos(θ_yz_y) + n_y(t) z(t) = s(t) * sin(θ_xz_z) * sin(θ_yz_z) + n_z(t) wh...

UGC NET Electronic Science Previous Year Question Papers with Solutions

Home / Engineering & Other Exams / UGC NET 2026 PYQ ⬇️ Download Papers and Solutions 📋 Exam Pattern 💡 Preparation Tips ❓ FAQs 📊 Exam Highlights: Electronic Science (88) Feature Details Junior Research Fellowship (JRF) ₹37,000 + HRA per month Eligibility M.Sc/M.Tech in Electronics (55%) Validity of Certificate JRF (3 Years) | Lectureship (Lifetime) 📥 Download UGC NET Electronics PDFs Complete collection of previous year question papers, answer keys and explanations for Subject Code 88. Start Downloading 📂 View All Question Papers June 2025 - Question Paper Download PDF June 2025 - Solved Paper + Explanation ...