A Continuous Spectrum Sensing Method Based on Iterative Detection of Ensemble Mean

Abstract

The invention relates to a continuous spectrum sensing method based on iterative detection of set mean values, which belongs to the field of wireless communication. The method includes: S1: obtaining a noise sample set; S2: forming an observation set from the average power values ​​corresponding to the K time slots observed in the sliding window, and merging the noise set with it into a set to be processed. Sort in ascending order; S3: detect and process the elements of the large set one by one through iterative detection of the set average value, and obtain the time slot subset occupied by the PU signal; S4: use the set mapping relationship to obtain the time stamp information of all time slots occupied by the PU; S5 : Obtain the PU signal occupancy state corresponding to each time slot in the current observation window, and SU accesses the authorized frequency band for wireless transmission or remains in a silent waiting state; S6: Sliding the sliding window forward by one time slot to perform the next spectrum sensing process. The invention improves the detection ability and response speed of the SU to the PU state change.

Description

technical field [0001] The invention belongs to the field of wireless communication, and relates to spectrum sensing (Spectrum Sensing) technology in cognitive radio. Background technique [0002] With the rapid development of mobile communications, user services will have higher and higher requirements on mobile networks. In order to cope with the explosive growth of mobile data traffic, massive device connections and new business and application scenarios, the fifth generation mobile communication system (5G) came into being. Compared with 4G, 5G will support more diverse scenarios, integrate multiple wireless access methods, and make full use of spectrum resources from low frequency to high frequency, greatly improving spectrum efficiency, energy efficiency and cost efficiency. In order to meet the huge demand for spectrum, in addition to the licensed frequency band, the way of sharing the use of spectrum will be a major means. Among them, cognitive radio (cognitive rad...

AI Technical Summary

Problems solved by technology

However, this assumption is difficult to realize in practice for the following reasons: First, in order to achieve the expected detection performance, the detection window (detection time interval) is generally set to be relatively long in practice

Compared with SU, PU as a heterogeneous asynchronous system may start and stop signal transmission at any time, which may lead to inaccurate spectrum judgment results; secondly, it is impossible for SU to accurately align the spectrum sensing time with the arrival and departure timing of PU signals

Therefore, the actual spectrum sensing performance is also lower than the corresponding theoretical expected value

Third, the time interval between two spectrum sensings is generally not set too short, otherwise, the detection time overhead of the SU will be too large, resulting in too fragmented access time of the SU to the licensed frequency band, which affects the licensed frequency band. Inefficient use of opportunistic access

[0007] (2) If a longer detection time interval is designed, it will make SU's monitoring of PU activity changes dull

[0008] In summary, the traditional LBT mechanism may cause mutual interference between the SU and the PU, and make the SU's capture of blank spectrum opportunities inefficient, resulting in the degradation of the quality of the SU's access to the licensed frequency band.

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