53results about How to "Improve Spectrum Sensing Performance" patented technology

The invention discloses an energy detection method based on weighting matrix filtering. The energy detection method based on weighting matrix filtering comprises the steps that sampling is conducted on signals of a monitored frequency band, so that sampled signals are obtained; signals of all sampling points of the sampled signals are grouped, and each group of signals of the sampling points form a column vector; a weighting matrix used for filtering is obtained through the convex optimization technology according to the frequency range of a monitored channel needing spectrum sensing and the total number of the signals of the sampling points of each group; the energy of the monitored channel needing spectrum sensing is worked out through the weighting matrix and all the column vectors; through comparison between the energy of the monitored channel and a judgment threshold, whether an authorized user signal exists in the monitored channel or not is judged, and spectrum sensing is achieved. The energy detection method based on weighting matrix filtering has the advantages that according to the obtained weighting matrix, the energy of the monitored channel can be directly obtained; in addition, when the order of the weighting matrix used for filtering is equal to the order of an FIR filter, higher spectrum sensing performance can be obtained.

The invention discloses an energy detection method for restraining direct current bias. A sensor node is used for performing sampling on signals of a monitoring channel, sampled signals are obtained, then, difference operation is performed on signals of two adjacent sampling points in the sampled signals, a differential signal of the signals of the two adjacent sampling points in the sampled signals is obtained, then the total energy of all the differential signals is calculated, finally, the total energy of all the differential signals is compared with a judgment threshold, whether a user is authorized with the signals in the monitoring channel to achieve spectrum sensing or not is judged, and the spectrum sensing performance is high. In addition, due to the fact that when the judgment threshold is calculated, only noise power and false alarm probability need to be known, power of the direct current bias does not need to be known, in this way, when the power of the direct current bias is increased, the spectrum sensing performance can not be reduced, but when the power of the direct current bias is larger than one fourth of the noise power, the method has the better spectrum sensing performance compared with an existing energy detection method.

The present invention relates to a wireless communication system and, more particularly, to a spectrum sensing method in cognitive radio communication. According to an aspect of the present invention, a method of canceling an introduced transmission signal includes generating a first signal, having an inverse phase, using a data transmission signal transmitted by a data transmission unit, adjusting a path and / or an amplitude of the first signal, and generating a first signal, having an inverse phase, using a data transmission signal transmitted by a data transmission unit, adjusting a path and / or an amplitude of the first signal, and canceling a second signal, introduced through a data receiving unit, and the first signal by the data transmission signal.

Aiming at the problem of spectrum detection in the case of unknown noise variance dynamics in practical applications, the present invention first proposes a dynamic state space system model to reflect its internal mechanism: the authorized user state and the time-varying noise variance are regarded as two hidden states, and two states are adopted respectively. A first-order Markov and autoregressive model models its dynamic migration properties. On this basis, a novel spectrum sensing method device is designed and proposed. The method is rooted in Bayesian statistical reasoning theory, and adopts marginalized particle filter technology (see attached figure), which can realize joint estimation of authorized users and noise variance. In particular, the present invention proposes a two-stage adaptive prediction coefficient adjustment method for marginalized particle filtering, which can fully utilize the time-varying characteristics of noise variance and can achieve accurate tracking of noise variance. Using the obtained noise variance information, the algorithm device can be extended to (but not limited to) a single-node single-antenna sensing system, and obtains good spectrum sensing performance.

The invention discloses an OFDM signal spectrum sensing method for time synchronization and no frequency offset situations. The method comprises the following steps: sampling a received signal from a monitoring channel to obtain a sampling signal; then performing estimation by using the autocorrelation of cyclic prefixs of OFDM signals in the received signal according to the sampling values of all sampling points in the sampling signal in time synchronization and no frequency offset situations to obtain noise power; calculating a covariance matrix of the sampling signal according to the sampling values of the sampling points in the sampling signal; then calculating test statistics according to the noise power and the covariance matrix of the sampling signal; and finally comparing the test statistics with an obtained judgment threshold, and judging whether an authorized user signal exists in the monitoring channel to realize spectrum sensing; and the OFDM signal spectrum sensing method has the advantages of effectively improving the spectrum sensing performance of OFDM signals and ensuring low computational complexity.

The invention discloses a method for sensing an OFDM (Orthogonal Frequency Division Multiplexing) spectrum under conditions of time asynchronization and unknown cyclic prefix length. The processing procedure of the method comprises the following steps: at first, sampling a receipt signal from a monitoring channel to obtain a sampled signal; then, computing an autocorrelation function of the sampled signal according to the sampled value in the sampled signal; next, computing test statistic according to an autocorrelation coefficient in the autocorrelation function of the sampled signal under the condition of time asynchronization; and at last, judging whether the monitoring channel is in the idle state or not according to the test statistic and the judgment threshold. The method has the advantages that the spectrum sensing can be carried out by directly using the sampled signal, without the need of time synchronization, so that the computation complexity is reduced effectively. When the cyclic prefix length of an OFDM signal is unknown, the method can still realize the spectrum sensing of the OFDM signal, and further improves the spectrum sensing performance of the OFDM signal.

The invention discloses an OFDM signal spectrum sensing method for time synchronization in the presence of frequency offset. The method comprises the following steps: sampling a received signal from a monitoring channel to obtain a sampling signal; then estimating noise power via the autocorrelation of a cyclic prefix of an OFDM signal in the received signal according to the sampling values of all sampling points in the sampling signal under the condition of time synchronization and in the presence of frequency offset; calculating a covariance matrix of the sampling signal according to the sampling values of all sampling points in the sampling signal; then calculating test statistics according to the noise power and the covariance matrix of the sampling signal; and finally, comparing the test statistics with an obtained judgment threshold to judge whether an authorized user signal exists in the monitoring channel so as to realize spectrum sensing. The OFDM signal spectrum sensing method has the advantages of effectively improving the spectrum sensing performance of the OFDM signal and ensuring low computation complexity.

The invention discloses a cooperative spectrum sensing method based on a free probability theory. The cooperative spectrum sensing method is suitable for an MIMO communication environment and includes the steps that received signals of a plurality of antennas of all auxiliary base stations are sampled, and sampled signals are processed in a centralized mode; according to noise variance of all the received sampled signals and channels, by means of the asymptotic freedom characteristic and the Wishart distribution characteristic of a random matrix, the average received signal power (img file=' DDA0000463198220000011. TIF' wi=' 64' he=' 54' / ) of all receiving antennas is solved by the adoption of a free deconvolution algorithm, and namely statistics are detected; according to target false alarm probability pf, a detection threshold value tau is calculated by using MonteCarlo simulation under the condition that only noise exsits; finally (img file=' DDA0000463198220000012. TIF' wi=' 41' he=' 54' / ) is compared with tau to judge whether a main base station sends signals or not. The accurate receiving power can be obtained through the received signals of the auxiliary base stations, and moreover the spectrum sensing performance can be improved effectively particularly under the condition of a low signal to noise ratio and small samples.

The invention discloses a double-feature spectrum sensing method based on maximum feature values and main feature vectors. The method comprises the steps of calculating the maximum feature values andthe corresponding main feature vectors of covariance matrixes of signals received in a current sensing time slot and a plurality of previous continuous sensing time slots; obtaining the estimated value of the main feature vector of the covariance matrix of a licensed user signal and the estimated value of the maximum feature value of the covariance matrix of white gaussian noise according to the corresponding maximum feature values and the corresponding main feature vectors in the plurality of previous continuous sensing time slots; obtaining a first test statistics value and a second test statistics value according to the maximum feature value, the main feature vector and the two estimated values of the covariance matrix of the signal received in the current sensing time slot; and comparing the first test statistics value with a first judgment threshold, and comparing the second test statistics value with a second judgment threshold, thereby realizing spectrum sensing. The method hasthe advantage of effectively improving the spectrum sensing performance.