Skip to main content

Discussion of some confusing questions about mm-wave 5G

 

When the channel matrix is sparse, why will we use massive MIMO?

It's used for beamforming purposes. We'll be able to build a directional beam toward the users/receivers via massive MIMO. On the other hand, we'll be able to use the spatial multiplexing property to enable simultaneous data streams from BSs or APs (access points) to numerous receivers. Let me offer you a simple example. Assume that the BS has 16 MIMO antennas and that four users want to connect to it. Now, to enable proper beamforming (on the BS side), we can use each of the four antenna elements to construct a beam that is supposed to be targeted at a specific user. Then we may utilize three more beams in the same way to link the rest three users. This is a case of spatial multiplexing in action. Obviously, need to deploy a precoding method to cancel interferences between different simultaneous paths. 


Why are we going to use mm-Wave even if it experiences a high path loss?

The main reason for choosing mm-Wave is because of its huge bandwidth capacity. In electronics, communication bandwidth is measured by a range of frequencies within a band. Here, one thing is clear: we can't increase the range of frequencies beyond transmitting or operating frequencies. For example, a 10% bandwidth at 100 KHz is only 10 KHz while a 10% bandwidth of 1 GHz is 100 MHz. Hope you got the point.


Because the coverage area of 5G is less than that of 4G, we'll be placing billions of APs (access points) everywhere, such as on the tops of street poles or on buildings. So, how can 5G maintain its ultra-low latency when there are a large number of APs and APs are connected to BSs? I hope you understood the question. Because there are many APs, the link speed may be reduced owing to a lot of processing before reaching the core network, and the same is true for the reverse connection path.   

For 5G communication, many countries are still using the sub-6 GHz spectrum. As a result, the latency will always be lower than that of the current 4G technology. However, the aforementioned issue may arise when we use the millimeter wave band for 5G connectivity. In those cases, however, APs can be connected to BSs via fiber wires. It will have the ability to behave as a simple bridge.
Backhaul, on the other hand, can be used in the future to connect BSs to BSs.  Because the millimeter wave spectrum has such a great potential for a high data transfer rate, it has a lot of potentials. Backhauls may be positioned at high altitudes. As a result, they will communicate with one another in the manner of free space communication. There will be no obstacle between the two backhauls.



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). ...

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...

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...

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 ...