Related Experiment Video
Updated: Jan 19, 2026
07:45
Quasi-light Storage for Optical Data Packets
Published on: February 6, 2014
11.3K
A Policy for Optimizing Sub-Band Selection Sequences in Wideband Spectrum Sensing
Yangyi Chen1, Shaojing Su2, Junyu Wei3
1College of Intelligent Science and Technology, National University of Defense Technology, Changsha 410073, China. chenyangyi09@nudt.edu.cn.
Sensors (Basel, Switzerland)
|September 25, 2019
Summary
This study introduces a new method for cognitive radio wideband spectrum sensing by optimizing sub-band selection. The novel approach, using a non-stationary multi-arm bandit model, significantly reduces cumulative regret in sensing.
Area of Science:
- Wireless Communication
- Signal Processing
- Cognitive Radio
Background:
- Wideband spectrum sensing is crucial for cognitive radio technology.
- Electronic component limitations necessitate splitting wideband signals into sub-bands for sensing.
- The order of sub-band sensing critically impacts overall performance.
Purpose of the Study:
- To develop an optimized sub-band selection strategy for wideband spectrum sensing.
- To improve the efficiency and performance of cognitive radio spectrum sensing.
Main Methods:
- A novel approach utilizing the non-stationary multi-arm bandit (NS-MAB) model.
- Implementation of the order-optimal discounted upper confidence bound (D-UCB) policy.
- Design of various discount functions and exploration bonuses tailored to D-UCB for parameter tuning.
Main Results:
- The proposed policy demonstrated lower cumulative regret compared to existing methods.
- Effective optimization of sub-band selection sequence for wideband spectrum sensing.
- Adaptability of the policy through adjustable discount functions and exploration bonuses.
Conclusions:
- The developed D-UCB-based policy offers a superior approach to sub-band selection in wideband spectrum sensing.
- This method enhances cognitive radio performance by addressing sensing sequence challenges.
- The tailored D-UCB policy provides a flexible and effective solution for dynamic spectrum access.
Related Concept Videos
01:17Bandpass Sampling
479
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
479
01:22Upsampling
593
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
593
01:20Downsampling
619
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
619
01:18Aliasing
578
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
578
01:15Sampling Theorem
1.3K
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
1.3K
01:21Frequency-dependent Selection
23.1K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
23.1K