Skip to main content

What is the process of beamforming in MIMO / Massive MIMO systems?



Beamforming is a technology that has been around for years. Beamforming is a technique for focusing a signal in a specific direction to be received at maximum gain at the receiver side. Because signal transmission from the transmitter to the receiver is directional, the receiver receives a greater signal in this process. When we send a signal from the transmitter to the receiver, the transmitter antenna spreads the signal out in an omnidirectional pattern. It'll be much easier if you've observed an antenna's radiation pattern. Using a directional beam, you can think of beamforming as directional communication between a transmitter and receiver.

More than one antenna element is required to form a beam. They will, of course, be closely spaced for proper beam forming. The resultant phase of the signals will be fixed when we send signals from multiple nearby antennas. Simply put, directional communication is possible because the signal transmission on one side is stronger than on the other, as opposed to omnidirectional transmission.


1. Beamforming in MIMO:

We can use a MIMO system or a Massive MIMO system for better beam formation. Antennas (antenna elements) are close together here. Antenna elements are typically spaced at half-wavelength intervals. When we transmit the same signal from several antennas in a multiple input multiple output (MIMO) system, it generates a beam to the receiver in a specific direction, allowing the receiver to receive a stronger signal. It also boosts the signal-to-noise ratio (SNR) at the receiver end.
On the other hand, spatial multiplexing is one of the most essential characteristics of MIMO systems. As a result, we can send multiple data streams to the transmitter and receiver at the same time. As a result, we will be able to reach higher data rates. It will be easier to understand if you use an example. Assume there is only one transmitter and receiver antenna, and they communicate at a data rate of 150 kbps. There are numerous simultaneous data streams between the transmitter and receiver if there are multiple antennas on the transmitter and receiver sides or if MIMO antennas are available. There are two data streams available at the same time between the transmitter and the receiver. Then the communication speed between them will be two times faster than before. Then it'll be around 300 kbps.

When more antenna elements are close together, we can produce a more powerful narrow beam. There are hundreds of antenna elements in large MIMO. As a result, we can use massive MIMO to create a narrower beam. Conversely, if we broadcast signal bits at higher frequencies, we can also obtain a smaller beam. For example, in the case of 60 GHz communication rather than 28 GHz extremely high frequency (EHF) communication, we can produce a narrower beam utilizing the same size MIMO antenna.


Figure: A hybrid beamforming example using a 64 x 16 MIMO system and 4 RF chains functioning at both TX and RX at 28 GHz


2. Various Types of Beamforming in MIMO:

During the beamforming process, some issues may develop. Internal interference between multiple data streams transmitted from many antennas in a MIMO system between transmitter and receiver can be a big issue. As a result, we'll need to use a pre-coding strategy to eliminate interference between many data streams. There are various techniques for pre-coding. We'll talk about it later.

Analog, digital, and hybrid beamforming are the main examples of beamforming techniques. Beam steering is used for analog beam forming. Digital beam forming can regulate a signal's amplitude and phase, whereas analog beam forming can only adjust the phase. Hybrid beam formation is comparable to digital beamforming. However, it is less complicated. As a result, in the case of massive MIMO communication, it is a cost-effective and widely accepted technology.

# mimo beamforming  # analog beamforming

People are good at skipping over material they already know!

View Related Topics to







Admin & Author: Salim

s

  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, ...

📘 Overview of BER and SNR 🧮 Online Simulator for BER calculation of m-ary QAM and m-ary PSK 🧮 MATLAB Code for BER calculation of M-ary QAM, M-ary PSK, QPSK, BPSK, ... 📚 Further Reading 📂 View Other Topics on M-ary QAM, M-ary PSK, QPSK ... 🧮 Online Simulator for Constellation Diagram of m-ary QAM 🧮 Online Simulator for Constellation Diagram of m-ary PSK 🧮 MATLAB Code for BER calculation of ASK, FSK, and PSK 🧮 MATLAB Code for BER calculation of Alamouti Scheme 🧮 Different approaches to calculate BER vs SNR 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 ...

Theoretical vs. simulated BER vs. SNR for ASK, FSK, and PSK

📘 Overview 🧮 Simulator for calculating BER 🧮 MATLAB Codes for calculating theoretical BER 🧮 MATLAB Codes for calculating simulated BER 📚 Further Reading BER vs. SNR denotes how many bits in error are received for a given signal-to-noise ratio, typically measured in dB. Common noise types in wireless systems: 1. Additive White Gaussian Noise (AWGN) 2. Rayleigh Fading AWGN adds random noise; Rayleigh fading attenuates the signal variably. A good SNR helps reduce these effects. Simulator for calculating BER vs SNR for binary ASK, FSK, and PSK Calculate BER for Binary ASK Modulation Enter SNR (dB): Calculate BER Calculate BER for Binary FSK Modulation Enter SNR (dB): Calculate BER Calculate BER for Binary PSK Modulation Enter SNR (dB): Calculate BER BER vs. SNR Curves MATLAB Code for Theoretical BER % The code is written by SalimWireless.Com clc; clear; close all; % SNR v...

Channel Impulse Response (CIR)

Channel Impulse Response (CIR) 📘 Overview & Theory 📘 How does the channel impulse response affect the signal? 🧮 Online Channel Impulse Response Simulator 🧮 MATLAB Codes 📚 Further Reading 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(...

Drone Detection via Low Complexity Zadoff-Chu Sequence Root Estimation

Summary Based on  Yeung, 2025:  Yeung, C.K.A., Lo, B.F. and Torborg, S. Drone detection via low complexity zadoff-chu sequence root estimation. In 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC) (pp. 1-4). IEEE, 2020, January.   The rise in drone usage—from agriculture and delivery to surveillance and racing—has introduced major privacy and security challenges. Modern drones often use OFDM (Orthogonal Frequency Division Multiplexing) with Zadoff-Chu (ZC) sequences for synchronization. While powerful, detecting these sequences blindly (without knowing their parameters) remains a challenge. Aim This article presents a low-complexity solution to blindly detect ZC sequences used by unknown drones. The approach uses a novel double differential method that works without large correlation banks, making it efficient and real-time capable. ZC Sequence Fundamentals A ZC sequence of prime length P and roo...

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 add...

Gaussian minimum shift keying (GMSK)

📘 Overview & Theory 🧮 Simulator for GMSK 🧮 MSK and GMSK: Understanding the Relationship 🧮 MATLAB Code for GMSK 📚 Simulation Results for GMSK 📚 Q & A and Summary 📚 Further Reading Dive into the fascinating world of GMSK modulation, where continuous phase modulation and spectral efficiency come together for robust communication systems! Core Process of GMSK Modulation Phase Accumulation (Integration of Filtered Signal) After applying Gaussian filtering to the Non-Return-to-Zero (NRZ) signal, we integrate the smoothed NRZ signal over time to produce a continuous phase signal: θ(t) = ∫ 0 t m filtered (Ï„) dÏ„ This integration is crucial for avoiding abrupt phase transitions, ensuring smooth and continuous phase changes. Phase Modulation The next step involves using the phase signal to modulate a...

Theoretical BER vs SNR for BPSK

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 Rate (BER) The probability of error (BER) for BPSK is given by a function called the Q-function. The Q-function Q(x) measures the tail probability of the normal distribution, i.e., the probability that a Gaussian random variable exceeds a certain value x.  Understanding the Q...

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 ...