Skip to main content

Ultra-Wideband | Positioning, Frequency Range, Power and AoA & AoD detection



UWB functions with the signal's so-called Time of Flight rather than RSSI (Received Signal Strength Indication), which makes technology more precise and enables it to conduct extremely precise ranging measurements. This is in contrast to traditional radio technologies (like Bluetooth or Wi-Fi).

Key Features of UWB Bands

  • UWB in order to bring decimeter-level positioning to the market
  • There is almost no interference with other radio communication systems
  • Multipath signal propagation resistance 
  • resistance to noise 
  • Low-power transceiver required


Ultra Wide Band or UWB comes under the Super High Frequency Band (SHF) range, as SHF ranges from 3 to 30 GHz.

UWB frequency range: 3.1 GHz to 10.6 GHz

Ultra-wideband or UWB technology is used for high-speed short-range wireless communication protocol. Now, it is a globally accepted protocol used in Mobile Telephony, AirTags, Medical fields, and NFC (near-field communication), and serves a variety of security services. etc. We need more spectral resources or bandwidth to meet the constantly expanding data traffic demands. On the other hand, wireless communication is gaining popularity in the industrial setting, particularly for industrial automation. The spectral resource of very high frequencies, such as ultra-wideband and millimeter wave, is huge. But unfortunately, it cannot be used with Wi-Fi to some limitations in UWB transmission.

In 1960, the ultra-wideband (UWB) was invented. This band is ideal for communication over short distances. As a result, it can be used for both indoor and short-range outdoor communication. Because of its larger bandwidth and reduced latency, it is suitable for industrial automation.


Here, in the above figure, it is shown that GSM uses a bandwidth of 200 KHz. But it uses maximum energy among the three compared communication bands to overcome the noise level. But in the case of UWB, it transmits less power for short-range communication. As here communication range is limited, so it hardly interacts with other networks. But we can experience high data rate communication here because the available bandwidth is huge.
What is the significance of Ultra Wide Band (UWB)

The difference between a communication band's highest and lowest frequencies is used to compute electronic communication bandwidth. The ratio of the highest frequency to the lowest operating frequency in a communication band is substantially higher in a wideband transmission. Similarly, the signal is described as a narrow band if the highest to lowest frequency ratio is close to one.

The highest operational frequency for UWB transmission is much higher than the lowest operating frequency. UWB signals are sent as narrow pulses ranging up to a few picoseconds. As a result of the narrower pulses, it implies operating at higher frequencies. As a result, there is plenty of scope for high bandwidth allocation because it is wideband.
Why Choose Ultra Wide Band (UWB)

There are several compelling reasons to use UWB for modern wireless communication. The following are the reasons:

1. Huge spectrum resource

2. When two UWB devices get close together, they begin to range.

3. High positional precision

4. Can detect angle of arrival (AoA) and angle of departure (AoD)


1. Huge spectrum resource:

UWB systems transmit signals in the form of pulse pattern radio-based technology in the time domain. UWB band's frequency span 3.1 to 10.6 GHz. We transfer very narrow pulses in the time domain, so it contains huge bandwidth. In the following paras, we've discussed about the energy efficiency of UWB. We've already discussed in the above para that ultra-wideband communication is wideband communication itself because its highest operating frequency is much higher than the lowest operating frequency. So, here available spectrum resources are huge.


2. Live tracking (positioning) Property of Ultra Wide Band (UWB):

UWB is used in tracking devices like the -- Apple Air-Tag, Samsung galaxy smart Tag plus, etc. Keyless entry technologies (e.g., RFID) or digital key technologies are adopting ultra wideband or UWB.

Currently, UWB operates in the 3–10 GHz spectrum. The positioning accuracy of this band is great. Because the wavelength is so short, it provides a higher detection resolution of objects. As a result, when two UWB devices get close enough, they start ranging. The ranging is done using time of flight (ToF), which is the amount of time it takes for packets to perform a round trip between initiator and responder devices. It can track devices in real-time, improving the connection's reliability.

People are good at skipping over material they already know!

View Related Topics to







Contact Us

Name

Email *

Message *

Popular Posts

OFDM Symbols and Subcarriers Explained

This article explains how OFDM (Orthogonal Frequency Division Multiplexing) symbols and subcarriers work. It covers modulation, mapping symbols to subcarriers, subcarrier frequency spacing, IFFT synthesis, cyclic prefix, and transmission. Step 1: Modulation First, modulate the input bitstream. For example, with 16-QAM , each group of 4 bits maps to one QAM symbol. Suppose we generate a sequence of QAM symbols: s0, s1, s2, s3, s4, s5, …, s63 Step 2: Mapping Symbols to Subcarriers Assume N sub = 8 subcarriers. Each OFDM symbol in the frequency domain contains 8 QAM symbols (one per subcarrier): Mapping (example) OFDM symbol 1 → s0, s1, s2, s3, s4, s5, s6, s7 OFDM symbol 2 → s8, s9, s10, s11, s12, s13, s14, s15 … OFDM sym...

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. BER = (number of bits received in error) / (total number of tran...

Constellation Diagrams of ASK, PSK, and FSK

📘 Overview of Energy per Bit (Eb / N0) 🧮 Online Simulator for constellation diagrams of ASK, FSK, and PSK 🧮 Theory behind Constellation Diagrams of ASK, FSK, and PSK 🧮 MATLAB Codes for Constellation Diagrams of ASK, FSK, and PSK 📚 Further Reading 📂 Other Topics on Constellation Diagrams of ASK, PSK, and FSK ... 🧮 Simulator for constellation diagrams of m-ary PSK 🧮 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...

Power Spectral Density Calculation Using FFT in MATLAB

📘 Overview 🧮 Steps to calculate the PSD of a signal 🧮 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 first Fourier transform (FFT) of a signal Then, calculate the Fourier magnitude of the signal The power spectrum is the square of the Fourier magnitude To calculate power spectrum density (PSD), divide the power spectrum by the total number of samples and the frequency resolution. {Frequency resolution = (sampling frequency / total number of samples)} Sampling frequency (fs): The rate at which the continuous-time signal is sampled (in Hz). ...

Online Channel Impulse Response Simulator

  Fundamental Theory of Channel Impulse Response The fundamental theory behind the channel impulse response in wireless communication often involves complex exponential components such as: \( h(t) = \sum_{i=1}^{L} a_i \cdot \delta(t - \tau_i) \cdot e^{j\theta_i} \) Where: \( a_i \) is the amplitude of the \( i^{th} \) path \( \tau_i \) is the delay of the \( i^{th} \) path \( \theta_i \) is the phase shift (often due to Doppler effect, reflection, etc.) \( e^{j\theta_i} \) introduces a phase rotation (complex exponential) The convolution \( x(t) * h(t) \) gives the received signal So, instead of representing the channel with only real-valued amplitudes, each path can be more accurately modeled using a complex gain : \( h[n] = a_i \cdot e^{j\theta_i} \) 1. Simple Channel Impulse Response Simulator  (Here you can input only a unit impulse signal) Input Signal (Unit Impu...

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...

Calculation of SNR from FFT bins in MATLAB

📘 Overview 🧮 MATLAB Code for Estimation of SNR from FFT bins of a Noisy Signal 🧮 MATLAB Code for Estimation of Signal-to-Noise Ratio from Power Spectral Density Using FFT and Kaiser Window Periodogram from real signal data 📚 Further Reading   Here, you can find the SNR of a received signal from periodogram / FFT bins using the Kaiser operator. The beta (β) parameter characterizes the Kaiser window, which controls the trade-off between the main lobe width and the side lobe level in the frequency domain. For that you should know the sampling rate of the signal.  The Kaiser window is a type of window function commonly used in signal processing, particularly for designing finite impulse response (FIR) filters and performing spectral analysis. It is a general-purpose window that allows for control over the trade-off between the main lobe width (frequency resolution) and side lobe levels (suppression of spectral leakage). The Kaiser window is defined...

MATLAB code for BER vs SNR for M-QAM, M-PSK, QPSk, BPSK, ...

🧮 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; num_symbols = 1e5; snr_db = -20:2:20; 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) psk_order = psk_orders(i); for j = 1:length(snr_db) data_symbols = randi([0, psk_order-1], 1, num_symbols); modulated_signal = pskmod(data_symbols, psk_order, pi/psk_order); received_signal = awgn(modulated_signal, snr_db(j), 'measured'); demodulated_symbols = pskdemod(received_signal, psk_order, pi/psk_order); ber_psk_results(i, j) = sum(data_symbols ~= demodulated_symbols) / num_symbols; end end for i...