Skip to main content

5G phased arrays (with MATLAB)


For practical 5G phased arrays, the beamforming effect is generated primarily by antenna spacing. 


1. How 5G phased arrays work

  • 5G base stations use multi-element antenna arrays (often 8×8, 16×16, or even more elements for Massive MIMO).
  • Each antenna element transmits the same signal, but the phase of each element is controlled electronically.
  • The physical spacing between antennas—usually ~0.5位 (half-wavelength) to 位—creates inherent phase differences when the signal reaches a user at an angle.

total,n = 蠁steering,n - (2蟺/位) n d sin(胃)

Where:

  • n = antenna index
  • d = antenna spacing
  • = angle of the target
  • steering,n = electronic phase shift applied to steer the beam
The steering phase compensates for the inherent phase from spacing to direct the beam toward the desired angle.

2. Why not use a single antenna with time delays?

  • Software time delays can emulate phased arrays in simulations.
  • But in real hardware:
    1. You need separate RF paths for each antenna element.
    2. Phase shifts are applied electronically using phase shifters at GHz frequencies.
    3. Physically shifting time on a single antenna would require ultra-fast RF switches—impractical at mmWave frequencies.

So, antenna spacing + electronic phase shifts is the standard method in 5G hardware.


3. Practical Summary for 5G

Aspect How It’s Done
Beamforming Electronic phase shifts per antenna element
Direction control Steering phases compensate for inherent spacing phase
Physical array Required; spacing usually 0.5位 or 位
Simulation Can use single antenna + time delays, but only for modeling

In real 5G phased arrays, the array spacing is the fundamental cause of phase differences, and beam steering is done by applying phase shifts per element, not by artificially delaying a single signal.


MATLAB Code

 
clc; clear; close all;
%% Parameters
N = 8; % Number of antenna elements
lambda = 1; % Wavelength (normalized)
d = 0.5*lambda; % Antenna spacing (half wavelength)
theta = -90:0.1:90; % Angle range in degrees
%% 1. Inherent Phase Only (No Steering)
theta_rad = deg2rad(theta);
inherent_phase = 2*pi*d/lambda * (0:N-1)' * sin(theta_rad);
AF_inherent = sum(exp(1j*inherent_phase),1); % Array factor
AF_inherent_dB = 20*log10(abs(AF_inherent)/N);
%% 2. Apply Phase Shifts to steer to 30 degrees
theta_s = 30; % Steering angle
steering_phase = -2*pi*d/lambda * (0:N-1)' * sin(deg2rad(theta_s));
AF_steered = sum(exp(1j*(inherent_phase + steering_phase)),1);
AF_steered_dB = 20*log10(abs(AF_steered)/N);
%% Plot
figure('Color','w','Position',[100 100 800 400]);
plot(theta, AF_inherent_dB,'b','LineWidth',2); hold on;
plot(theta, AF_steered_dB,'r','LineWidth',2);
grid on; xlabel('Angle (degrees)'); ylabel('Normalized Array Factor (dB)');
title('Linear Phased Array: Inherent vs Steered Phase');
legend('Inherent Phase (spacing only)','Applied Phase Shift (beam steering)');
ylim([-40 0]);
 

 Output

 

People are good at skipping over material they already know!

View Related Topics to







Contact Us

Name

Email *

Message *

Popular Posts

Constellation Diagrams of ASK, PSK, and FSK with MATLAB Code + Simulator

馃摌 Overview of Energy per Bit (Eb / N0) 馃М Online Simulator for constellation diagrams of ASK, FSK, and PSK 馃М Theory behind Constellation Diagrams of ASK, FSK, and PSK 馃М MATLAB Codes for Constellation Diagrams of ASK, FSK, and PSK 馃摎 Further Reading 馃搨 Other Topics on Constellation Diagrams of ASK, PSK, and FSK ... 馃М Simulator for constellation diagrams of m-ary PSK 馃М Simulator for constellation diagrams of m-ary QAM BASK (Binary ASK) Modulation: Transmits one of two signals: 0 or -√Eb, where Eb​ is the energy per bit. These signals represent binary 0 and 1.    BFSK (Binary FSK) Modulation: Transmits one of two signals: +√Eb​ ( On the y-axis, the phase shift of 90 degrees with respect to the x-axis, which is also termed phase offset ) or √Eb (on x-axis), where Eb​ is the energy per bit. These signals represent binary 0 and 1.  BPSK (Binary PSK) Modulation: Transmits one of two signals...

Fading : Slow & Fast and Large & Small Scale Fading (with MATLAB Code + Simulator)

馃摌 Overview 馃摌 LARGE SCALE FADING 馃摌 SMALL SCALE FADING 馃摌 SLOW FADING 馃摌 FAST FADING 馃М MATLAB Codes 馃摎 Further Reading LARGE SCALE FADING The term 'Large scale fading' is used to describe variations in received signal power over a long distance, usually just considering shadowing.  Assume that a transmitter (say, a cell tower) and a receiver  (say, your smartphone) are in constant communication. Take into account the fact that you are in a moving vehicle. An obstacle, such as a tall building, comes between your cell tower and your vehicle's line of sight (LOS) path. Then you'll notice a decline in the power of your received signal on the spectrogram. Large-scale fading is the term for this type of phenomenon. SMALL SCALE FADING  Small scale fading is a term that describes rapid fluctuations in the received signal power on a small time scale. This includes multipath propagation effects as well as movement-induced Doppler fr...

Online Simulator for ASK, FSK, and PSK

Try our new Digital Signal Processing Simulator!   Start Simulator for binary ASK Modulation Message Bits (e.g. 1,0,1,0) Carrier Frequency (Hz) Sampling Frequency (Hz) Run Simulation Simulator for binary FSK Modulation Input Bits (e.g. 1,0,1,0) Freq for '1' (Hz) Freq for '0' (Hz) Sampling Rate (Hz) Visualize FSK Signal Simulator for BPSK Modulation ...

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...

What is - 3dB Frequency Response? Applications ...

馃摌 Overview & Theory 馃摌 Application of -3dB Frequency Response 馃М MATLAB Codes 馃М Online Digital Filter Simulator 馃摎 Further Reading Filters What is -3dB Frequency Response?   Remember, for most passband filters, the magnitude response typically remains close to the peak value within the passband, varying by no more than 3 dB. This is a standard characteristic in filter design. The term '-3dB frequency response' indicates that power has decreased to 50% of its maximum or that signal voltage has reduced to 0.707 of its peak value. Specifically, The -3dB comes from either 10 Log (0.5) {in the case of power} or 20 Log (0.707) {in the case of amplitude} . Viewing the signal in the frequency domain is helpful. In electronic amplifiers, the -3 dB limit is commonly used to define the passband. It shows whether the signal remains approximately flat across the passband. For example, in pulse shapi...

Pulse Shaping using Raised Cosine Filter (with MATLAB + Simulator)

  MATLAB Code for Raised Cosine Filter Pulse Shaping clc; clear; close all ; %% ===================================================== %% PARAMETERS %% ===================================================== N = 64; % Number of OFDM subcarriers cpLen = 16; % Cyclic prefix length modOrder = 4; % QPSK oversample = 8; % Oversampling factor span = 10; % RRC filter span in symbols rolloff = 0.25; % RRC roll-off factor %% ===================================================== %% Generate Baseband OFDM Symbols %% ===================================================== data = randi([0 modOrder-1], N, 1); % Random bits txSymbols = pskmod(data, modOrder, pi/4); % QPSK modulation % IFFT to get OFDM symbol tx_ofdm = ifft(txSymbols, N); % Add cyclic prefix tx_cp = [tx_ofdm(end-cpLen+1:end); tx_ofdm]; %% ===================================================== %% Oversample the Baseband Signal %% ===============================================...

Understanding the Q-function in BASK, BFSK, and BPSK

Understanding the Q-function in BASK, BFSK, and BPSK 1. Definition of the Q-function The Q-function is the tail probability of the standard normal distribution: Q(x) = (1 / √(2蟺)) ∫ x ∞ e -t²/2 dt What is Q(1)? Q(1) ≈ 0.1587 This means there is about a 15.87% chance that a Gaussian random variable exceeds 1 standard deviation above the mean. What is Q(2)? Q(2) ≈ 0.0228 This means there is only a 2.28% chance that a Gaussian value exceeds 2 standard deviations above the mean. Difference Between Q(1) and Q(2) Even though the argument changes from 1 to 2 (a small increase), the probability drops drastically: Q(1) = 0.1587 → errors fairly likely Q(2) = 0.0228 → errors much rarer This shows how fast the tail of the Gaussian distribution decays. It’s also why BER drops drama...

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

馃摌 Overview & Theory 馃М MATLAB Codes 馃摎 Further Reading Theoretical BER vs SNR for Amplitude Shift Keying (ASK) The theoretical Bit Error Rate (BER) for binary ASK depends on how binary bits are mapped to signal amplitudes. For typical cases: If bits are mapped to 1 and -1, the BER is: BER = Q(√(2 × SNR)) If bits are mapped to 0 and 1, the BER becomes: BER = Q(√(SNR / 2)) Where: Q(x) is the Q-function: Q(x) = 0.5 × erfc(x / √2) SNR : Signal-to-Noise Ratio N₀ : Noise Power Spectral Density Understanding the Q-Function and BER for ASK Bit '0' transmits noise only Bit '1' transmits signal (1 + noise) Receiver decision threshold is 0.5 BER is given by: P b = Q(0.5 / 蟽) , where 蟽 = √(N₀ / 2) Using SNR = (0.5)² / N₀, we get: BER = Q(√(SNR / 2)) Theoretical BER vs ...