Skip to main content

Analog Beamforming vs Digital beamforming



1. Analog Beamforming:

Beamforming is a method of focusing a signal in a certain direction to provide sufficient signal strength at the receiver end of the communication process. We normally require more than one closely located antenna to form a beam in a specific direction and focus the resultant signal from antennas to use beam forming. We can also use phase shifters (PSs) to control the phases of a signal. We employ MIMO (multiple input multiple output antenna) [↗] to provide beam forming. In a MIMO system, antennas are normally positioned in a half-wavelength interval of the operating frequency.


We commonly employ beam forming when we need to send a signal over a great distance (e.g., for radar communication) and omnidirectional transmission isn't feasible. On the other hand, we can use beam forming to extend the range of our signal without boosting TX power.


Similarly, 5G communication [Read More] makes advantage of an incredibly high frequency [↗]. As a result, it suffers from severe path loss [↗], and its short wavelength is easily absorbed by air gases, vapor, and other particles. With sufficient power, such an extremely high-frequency band can only go a short distance. As a result, we use beam forming to cover greater distances. We get a stronger and narrower beam by increasing the number of antennas without raising the TX power. It is an important advantage of beamforming.

There are several methods of beam formation, but they are usually divided into three categories: 1. Analog beam forming 2. Digital Beamforming 3. Hybrid Beamforming. In analog beam formation, the beam is steered at both the transmitter and receiver end, and the best beam pairs are selected for communication. More simply, we aim to send signals at varying angles of departure, or AOD, from the transmitter side. We do the same thing using angles of arrival (AOA) on the receiver side, then only connect the best beams from both the transmitter and receiver sides. We can only change the phases (or, the direction of signal transmission) of a signal in analog beam forming, and there is only one data stream between the transmitter and receiver.
 
analog beamforming
Get MATLAB Code for Analog and Digital Beamforming


The number of antennas on both the transmitter and receiving sides may be seen in the diagram above. To form a beam, more than one neighboring antenna is required. The fact that there is just one RF Chain on both the transmitter and receiver sides is a crucial aspect of analog beam formation. The number of RF chains determines the number of simultaneous data streams accessible between the transmitter and receiver. In the diagram above, we steer the beam at the transmitter to find the optimal beam, while the receiver listens in an omnidirectional manner. The same thing happens at the receiver's end, where the receiver tries directional beams while the transmitter radiates in an omnidirectional manner. Then, using adequate feedback, the best beam pairs from both sides are connected. RF chains contain mixers, power amplifiers, etc.

In the following chapters, we'll look into digital beam forming, which allows multiple data streams to be sent and received simultaneously. We'll also talk about canceling interference between many devices and canceling interference between simultaneous data streams.



2. Digital Beamforming:

Unlike analog pre-coding, we can send signals with a variety of phases and amplitudes. Different phases signify different things, such as the ability to steer the beam in different directions. On the other hand, we can also regulate the transmitted signal's amplitude. If we need to reduce the signal's amplitude in a specific antenna element, we can easily do it.

The signal that was received is denoted by

y=√pHDs + n
where, p=average received power
H=channel matrix
D= digital or baseband pre-coder
s= symbol vector
n = additive white Gaussian noise

Each antenna is connected to a distinct transmit and receive (TR) module or RF chain in the system diagram below.


analog beamforming

Fig: Digital Beamforming


We know that point-to-point communication between MIMO is conceivable, such as h11, h12, h22, and so on. For example, 'h22' denotes channel gain or the link between the second antenna on the transmitter and the second antenna on the receiver. 



Also Read about:

more ...

📊 MATLAB & ONLINE SIMULATIONS (Web-Based)
🎓 GATE & UGC - NET SOLUTIONS
📡 WIRELESS & SIGNAL PROCESSING


Contact Us

Name

Email *

Message *

Popular Posts

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 ...
Read more

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...
Read more

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 Calculation 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 figur...
Read more

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

Constellation Diagrams: ASK, FSK, and PSK Comprehensive guide to signal space representation, including interactive simulators and MATLAB implementations. 📘 Overview 🧮 Simulator ⚖️ Theory 📈 Q-function 📚 Resources BASK Modulation Transmits one of two signals: 0 or $\sqrt{E_b}$, representing binary 0 and 1. Simple but sensitive to noise. BFSK Modulation Transmits one of two signals: $\sqrt{E_b}$ on the Y-axis or $\sqrt{E_b}$ on the X-axis. These are orthogonal signals. BPSK Modulation Transmits $+\sqrt{E_b}$ or $-\sqrt{E_b}$ (antipodal signaling). Most efficient binary scheme. ...
Read more

Wiener Filter in MATLAB

  MATLAB Code  % Wiener Filter Based on Wiener-Hopf Equation % This script demonstrates how to apply the Wiener filter to recover % a reference signal from a noisy signal using the Wiener-Hopf equation. % The filter minimizes the mean squared error between the noisy signal and the reference signal. clear; close all; clc; % Signal Parameters fs = 4000; % Sampling frequency (Hz) T = 1; % Total recording time (seconds) L = T * fs; % Signal length (samples) tt = (0:L-1) / fs; % Time vector ff = (0:L-1) * fs / L; % Frequency vector % Generate Reference Signal (a sinusoid) y = sin(2 * pi * 120 * tt); % Reference sinusoidal signal y = y(:); % Ensure column vector % Create Noisy Signal by Adding Gaussian Noise x = 0.50 * randn(L, 1) + y; % Noisy signal x = x(:); % Ensure column vector % Define Filter Order (Number of Coefficients) N = 200; % Apply Wiener Filter using custom function [xest, b, MSE] = wienerFilt(x, y, N); % Plot Results figure; subplot(411); plot(tt, x, 'k'), hold on, p...
Read more

MIMO Channel Matrix | Rank and Condition Number

MIMO / Massive MIMO MIMO Channel Matrix | Rank and Condition...   The channel matrix in wireless communication is a matrix that describes the impact of the channel on the transmitted signal. The channel matrix can be used to model the effects of the atmospheric or underwater environment on the signal, such as the absorption, reflection or scattering of the signal by surrounding objects. When addressing multi-antenna communication, the term "channel matrix" is used. Let's assume that only one TX and one RX are in communication and there's no surrounding object. Here, in our case, we can apply the proper threshold condition to a received signal and get the original transmitted signal at the RX side. However, in real-world situations, we see signal path blockage, reflections, etc.,  (NLOS paths [↗]) more frequently. The obstruction is typically caused by building walls, etc. Multi-antenna communication was introduced to address this issue. It makes diversity app...
Read more

Overmodulation & Distortion in AM

Overmodulation in AM and How It Causes Distortion 1. AM Signal Equation s(t) = A c [1 + μ m(t)] cos(2Ï€ f c t) A c = carrier amplitude m(t) = normalized modulating signal (|m(t)| ≤ 1) μ = modulation index 2. Modulation Index μ = A m / A c - Normal AM: 0 < μ ≤ 1 → no distortion - Overmodulation: μ > 1 → distortion occurs 3. Envelope and Overmodulation A(t) = A c [1 + μ m(t)] - For undistorted AM: 1 + μ m(t) ≥ 0 at all times - If μ > 1: 1 + μ m(t) < 0 at negative peaks → carrier flips Example: Let m(t) = cos(2Ï€ f m t), A c = 1 V, μ = 1.2 Minimum envelope: A min = A c [1 - 1.2] = -0.2 V Negative amplitude → envelope crosses zero → 180° phase flip 4. Mathematical Consequence -A c cos(θ) = A c cos(θ + Ï€) This phase reversal is what causes distortion in the demodulated signal. 5. Instantaneous AM Signal s...
Read more

Frequency Selective Fading vs Flat Fading in MATLAB

In the MATLAB code below, a comparison between  frequency-selective fading  and  flat fading  is shown. In frequency-selective fading, multipath propagation causes multiple delayed copies of the signal to arrive at the receiver. When the channel delay spread exceeds the symbol duration, these delayed components overlap, resulting in inter-symbol interference (ISI). In flat fading, ISI does not occur because the signal bandwidth is much smaller than the channel’s coherence bandwidth . Therefore, the channel response remains approximately constant across the signal bandwidth, and all symbols experience the same fading. MATLAB Code for frequency selective fading channel % OFDM over frequency selective Rayleigh fading channel clc; clearvars; close all ; % Simulation parameters nSym = 10^4; % Number of OFDM symbols EbN0dB = 0:2:20; % Eb/N0 range MOD_TYPE = 'MPSK' ; % 'MPSK' or 'MQAM' M = 4; % QPSK N = 64; % Total number ...
Read more