A Robust Spectrum Sensing Method Based on Maximum Cyclic Autocorrelation Selection for Dynamic Spectrum Access

2015 IEEE 16th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

2015

This paper presents a weighted diversity combining technique for the cyclostationarity detection based spectrum sensing of orthogonal frequency division multiplexing signals in cognitive radio. In cognitive radio systems, secondary users must detect a desired signal in an extremely low SNR environment. In such environments, multiple antenna techniques (space diversity) such as a maximum ratio combining are not effective because an energy of target signal is also extremely weak and it is difficult to acquire the signal synchronization of some received signals. A cyclic autocorrelation function (CAF) is used for traditional cyclostationarity detection based spectrum sensing, and in this paper, the CAFs of the received signals are combined whereas the received signals themselves are combined in general space diversity techniques. In this paper, a CAF SNR is defined using the signal and noise components of the CAF, and we attempt to improve sensing performance to use a different weight for each component of the CAF. The presented results are compared with some conventional results and show that the presented technique can improve the spectrum sensing performance.Index Terms-Cognitive radio network, cyclostationarity detection based spectrum sensing, space diversity, OFDM signal.

Section: Maximum Cyclic Autocorrelation Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A weighted diversity combining technique for cyclostationarity detection based spectrum sensing in cognitive radio networks

Cho

2015 IEEE 16th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

2015

“…Secondary users (SUs) seek a vacancy spectrum for own communication, and if the spectrum is not one used for licensed primary users (PUs), the SUs can use it to communicate. In several types of spectrum sensing techniques, it is well known that a feature detection, which referred to as cyclostationarity detection (CD) [2,3,4,5] using prior knowledge of target signal, has some useful property that is robust to in-band interference signals. In digital communication, actions such as multiplexing and modulating cause cyclostationarity that can be detected by computing a cyclic autocorrelation function (CAF) [6].…”

Section: Introductionmentioning

confidence: 99%

“…However, the computational complexity of CD based sensing is greater than that of other spectrum sensing techniques, e.g., energy detection based sensing. Although several computationally efficient sensing techniques based on CD have been presented [3,4,5], none of these studies, as far we know, analyzed computation of the CAF itself. In this letter, we present a low computational complexity CAF computation technique for CD based spectrum sensing in cognitive radio.…”

Section: Introductionmentioning

confidence: 99%

Low complexity cyclic autocorrelation function computation for spectrum sensing

2017

IEICE ComEX

This letter presents a low complexity cyclic autocorrelation function (CAF) computation technique for the spectrum sensing of orthogonal frequency division multiplexing (OFDM) signals in cognitive radio. Although CAFs are frequently employed in signal cyclostationarity detection (CD) based spectrum sensing, the computational complexity of CAFs is greater than that of signal energy. The presented technique computes the CAF by multiplying a received signal, delayed one and complex periodic function where these signals and function are thinned-out so that the sampling frequency becomes quadruple of a cyclic frequency. The employed function can only take the values ²1 and 0. Because the multiplication and addition can omit in the CAF computation, the computational complexity can be reduced. We numerically verify the effectiveness of the presented CAF computation technique. Keywords: cyclic autocorrelation function, cognitive radio, cyclostationarity detection based spectrum sensing Classification: Terrestrial Wireless Communication/Broadcasting Technologies , vol. 42, no. 9, pp. 2355-2369, Sept. 1994. DOI:10.1109 L. P. Goh, Z. Lei, and F. Chin, "Feature detector for DVB-T signal in multipath fading channel," Proc. References

“…Because the communication of SUs must not interfere with that of PUs in the networks, SUs must learn the radio spectrum environment around themselves using spectrum sensing techniques [2] which can grasp the environment. In several spectrum sensing techniques, feature detection [3,4], which is referred to as cyclostationarity detection, can classify the modulation schemes and is robust to the interference signal. Furthermore, a weighted diversity combining technique based cyclostationarity detection has been presented to enhance the performance of spectrum sensing [5]; however, the technique causes an error of a false alarm probability between the obtained and designed value.…”

Section: Introductionmentioning

confidence: 99%

Improvement of false alarm probability in simple weighted diversity combining for cyclostationarity detection based spectrum sensing

Cho

2017

IEICE ComEX

This letter presents an improvement technique for a false alarm probability in simple weighted diversity combining for cyclostationarity detection based spectrum sensing. Although traditional simple weighting techniques can improve a performance of the spectrum sensing, the technique causes an error of false alarm probability between an obtained and designed value. The reason is that the technique requires two types of statistics and both probability density function (PDF) are different. The presented weighting technique makes these PDF matches and the degradation of the signal detection performance for the presented can be ignored. Simple theoretical analysis and numerical examples are shown to validate the effectiveness of the presented.