Skip to main content

Equations related to Spectral Efficiency in Digital Beamforming


 
 
Fig 1: Digital Beamforming 
 

The main working principle between the beamforming (or analog beamforming) is to maximize the signal strength in a particular direction towards the receiver. For example, you can steer the antenna manually towards the transmitter to maximize the signal strength, like dish antennas. However, the approach could be more practical for mobile communications. With the help of a phase shifter, we do it electronically. On the other hand, for example, a dish antenna has an aperture that adds some gain to the received signal.Similarly, placing many antennas at a particular space instant creates a beam in a specific direction, minimizing signal strength in the rest of the directions. Here, a combination of antennas creates virtual apertures. On the transmitter side, it transmits a more robust signal toward the receiver. Oppositely, it makes a virtual aperture at the receiver and captures the signal more efficiently.

The main advantage of beamforming is maximizing the signal strength by increasing the number of antenna elements, not the total power.

In the case of analog beamforming, there is only one transmitter and one receiver, and only a single data stream is possible between them.

In the case of digital beamforming, multiple data streams are possible between transmitter and receiver or receivers. The number of users may be one or many.

If there is only one number of users, then multiple data streams between the transmitter and receiver will increase data rates through spatial multiplexing. Similarly, if multiple users exist, each data stream may be allocated to a single user. The various users communicate with the transmitter by deploying spatial multiplexing.

 

For Single User Digital Beamforming

The received signal vector y at receiver side,

                                                                                 y= √ρHDs + n

                                               Here, D = digital beamforming matrix

                                                         ρ = average received power

                                                         H =channel matrix ( Nr X Nt)

                                                         n = additive white Gaussian noise vector

                                                     Nr and Nt are the number of antenna elements at the receiver and  transmitter side respectively.

 You may think this beamforming equation is the same as analog beamforming, but this is not. Here, the number of independent data streams between transmitter and receiver is min(Nr, Nt). If there are two transmitting antennas and three receiver antennas, then two independent data streams are possible between transmitter and receiver to communicate simultaneously.
 

For Multi-user Digital Beamforming

D= [D1, D2, …, DU], & Du denotes the u th user, a digital precoder (size of NBS X NMS)

NBS and NMS are the number of antenna elements at the transmitter and receiver side respectively 

Now cancel interference at uth user due to other users, we need to design the baseband precoder in such a way that HuDn for nǂ u should be zero at the u th MS. Therefore, HuDn =0 cancels interferences at uth MS.



Contact Us

Name

Email *

Message *

Popular Posts

LDPC Encoding and Decoding Techniques

Low Density Parity Check (LDPC) Guide Comprehensive analysis of linear error-correcting block codes, Tanner graphs, and 5G-NR implementations. 📘 Overview 🧮 Encoding 🧩 Decoding 📚 Resources Theory Encoding Tech Tanner Graph 5G Encoding Decoding 'LDPC' is the abbreviation for 'low density parity check'. LDPC code H matrix contains very few amount of 1's and mostly zeroes. LDPC codes are error correcting code. Using LDPC codes, channel capacities that are close to the theoretical Shannon limit can be achieved. Low density parity check (LDPC) codes are linear error-correcting block code suitable for error correction in a large block sizes transmi...

Flat vs Frequency Selective Online Simulator

Flat vs Frequency Selective Online Simulator Channel Type Without Fading Flat Fading Multipaths Nakagami m SNR(dB) Run Simulation Input Signal Signal After Fading Constellation Diagram BER vs SNR Explore Advanced Flat vs Frequency-Selective Fading Simulator Want to see these equations in action? Visualize it. Launch Simulator Tool Return to DSP Simulations Main Page →

Online Simulator for ASK, FSK, and PSK Signal Generation

Interactive Digital Signal Processing (DSP) Tutorial and Simulator for ASK, FSK, and BPSK modulation techniques. Try our new Digital Signal Processing Simulator!   •   Interactive ASK, FSK, and BPSK tools updated for 2025. Start Now Digital Modulation Visualizer: ASK, FSK, & BPSK Simulator Learn and visualize binary modulation techniques (ASK, FSK, BPSK) in real-time with adjustable carrier and sampling parameters. Perfect for DSP students and engineers. 📡 ASK Simulator 📶 FSK Simulator 🎚️ BPSK Simulator 📚 More Topics ASK Modulator FSK Modulator BPSK Modulator Demodulation More Topics 1. ASK (Ampli...

Design of CMOS Flip-Flops (SR, D, JK)

Design of CMOS Flip-Flops (SR, D, JK) A flip-flop or latch is a circuit with two stable states, used to store state information. It is the basic storage element in sequential logic and a fundamental building block in digital electronics systems, including computers and communication devices. Flip-flops and latches act as data storage elements for states, pulse counting, and synchronization of variably-timed input signals to a reference clock. Flip-flops can be transparent/opaque (latches) or clocked (synchronous, edge-triggered). Latches are level-sensitive, while flip-flops are edge-sensitive. In sequential logic, the output depends on current inputs and previous states. Fig.1 shows a sequential circuit combining a combinational block and a memory element. ...

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 signal to produce a continuous phase signal. For GMSK, the modulation index is $h=0.5$, meaning a bit '1' results in a phase shift of $\pi/2$: θ(t) = 2πh ∫ 0 t m filtered (τ) dτ This integration is crucial for avoiding abrupt phase transitions, ensuring smooth and continuous phase changes. Phase Mo...

UGC NET Electronic Science Previous Year Question Papers with Solutions

Download Papers and Solutions Exam Pattern Preparation Tips FAQs More Home / Engineering & Other Exams / UGC NET 2026 PYQ 📊 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 - Sol...

Q-function in BER vs SNR Calculation (with Simulation)

Q-function in BER vs. SNR Calculation In digital communications and signal processing, the Q-function plays a significant role in predicting system reliability. It allows engineers to quantify the probability that Gaussian noise will exceed a specific threshold, causing a bit error. What is the Q-function? The Q-function is a mathematical function representing the tail probability of the standard normal (Gaussian) distribution. It is the complementary cumulative distribution function (CCDF) of a standard Gaussian distribution. Q(x) = (1 / √(2π)) ∫ₓ∞ e^(-t² / 2) dt The Role of the Q-function in BER vs. SNR The Q-function is the standard tool for calculating BER in systems like BPSK or QPSK over AWGN (Additive White Gaussian Noise) channels. For BPSK: In BPSK, we transmit +√E b (bit 1) and -√E b (bit 0). The decision boundary is set at 0 . If -√E b was sent, an error occurs if noise r > √...