In this article, we propose a blind wideband signal detection algorithm to detect and locate occupied channels in cellular bands without a priori knowledge of the band channelization. This is formulated as a change-point detection problem. The proposed algorithm is validated using over-the-air measurements taken at different cellular bands and shown to work in a global system for mobile communication, long-term evolution (LTE), and universal mobile telecommunications system bands.
- Change-point detection,
- experimental measurements,
- wideband signal detection
- Signal detection,
- 3G mobile communication,
- Linear programming,
- Long Term Evolution,
- Frequency measurement
Signal detection is an important function for several radio applications in both military and commercial fields, such as spectrum surveillance and adaptive spectrum use . Wideband signal detection aims at detecting and locating occupied channels within large capture bandwidths. It consists of two main stages: detection and channelization. In the detection stage, a large capture band that is likely to contain multiple signals is analyzed to decide if the band is idle or occupied. In the later stage, the band is channelized to mark active channels within the band. In , a large observation band is divided into several smaller subbands with equal widths. The occupancy of each subband is then tested based on the eigenvalues of the covariance matrix of the received signal. The algorithm has been validated through several experimental tests to obtain an acceptable trade-off between correct detection percentage and computational complexity. This technique, however, requires the knowledge of band channelization which may not be known a priori in certain applications. A lower complexity algorithm that evaluates the histogram of the power spectral density (PSD) bins is proposed in . In , a goodness-of-fit test is employed on over-the-air captured signals to detect the presence of a modulated signal in each frequency bin. In the aforementioned method, testing the occupancy of each frequency bin leads to exponential growth in the implementation complexity as the number of the frequency bins increases. In , the histogram of the PSD values is calculated. The location of the left-most peak in the histogram is used as an estimate of the noise floor level. A detection is declared for a bin if the PSD value of that bin is higher than the estimated noise floor plus a threshold set by the user. A two-stage method for dynamic spectrum access in cognitive radio applications is proposed in . In , a standalone sensor for spectrum occupancy monitoring in a dynamic spectrum access framework is developed. The main contributions of this paper are as follows:
- A novel blind wideband signal detection method for cellular bands with no a priori knowledge of the band channelization is proposed. In a classical approach, a detection decision is made by comparing the PSD measurements against a threshold value, whereas this decision is made based on the shape of a metric obtained from PSD measurements in our proposed technique. Both analytical results and measurements confirm the practical applicability of the proposed technique. • The decision metric is calculated by modeling the PSD measurement as a change point process, which yields its low-complexity, and thus, the main advantage for online implementation. • The proposed method can detect and locate GSM, LTE, and UMTS signals and it outperforms the histogram-based method in the low SNR ranges of practical interest.
- PROBLEM FORMULATION
Wideband signal detection can be formulated as a multiple change-point detection problem in PSD values in which bandlimited communications signals introduce abrupt changes against the background noise floor . It is rather difficult to solve multiple change-point problems when the number of change points (model order) is unknown. In this paper, we propose the use of order statistics to fix the model order. Specifically, we sort the PSD values and then model the sorted sequence as a single change-point process, where the change-point indicates the level where the transition from signal to noise occurs. Bin indices that are higher and lower than the estimated change-point index are assigned as signal and noise-only bins, respectively.
In this paper, a blind wideband signal detection algorithm was proposed to detect and locate occupied channels in cellular bands. Distinct from conventional algorithms, the proposed detection scheme does not require setting an absolute threshold power level; therefore, it is robust to changes in the electromagnetic environment. Our results show that the proposed algorithm can detect and locate GSM, LTE, and UMTS channels without a priori knowledge of the channelization of the captured band.
The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.
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FULL Paper PDF file:al-habob2020
Blind Signal Detection in Cellular Bands
in IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 3, pp. 657-659, March 2020,
PDF reference and original file: Click here
Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.