Spectrum sensing is one of the key elements in the cognitive radio (cr) communication, and it is the first step that must be followed before allowing unlicensed users to access vacant licensed channels ¹. In a cr environment, frequency bands that are legally assigned to primary users are exploited by secondary users when primary users are idle ²; nevertheless, if the primary user needs to establish the communication again, the secondary user has to vacate the channel and seek another one. Detecting the presence of primary signal (spectrum sensing) is a challenging task in cr ³. Several primary users will perform different modulation schemes, data rates and transmission powers in the presence of both variable propagation environment and interference generated by other secondary users ⁴. The spectrum sensing techniques most commonly used are: Energy Detection (ed) ⁵, Cyclostationary Detection ⁶ and Matched Filtering ⁷. Other techniques proposed to improve the poor performance of ed in noisy environ ments are the Maximum to Minimum Eigenvalue method (mme) and the Covariance method (cov) ³. All of these techniques require some previous knowledge that is not always available, regarding the received signals, e.g. the bandwidth, frequency range, modulation scheme, power, among others. Additionally, any sensing spectrum technique should be fast and effective, attempting to reduce processing time and misdetection.

This paper presents a technique for spectrum sensing based on the independent component analysis (ica) for blind source separation. The proposed scheme includes a single-to-multichannel conversion through singular spectrum analysis (ssa) as an alternative for multi-channelization.

The rest of this paper is organized as follows: first, the spectrum sensing tech niques are introduced; then, the proposed scheme for spectrum sensing is described in detail. Lastly, the effectiveness of the proposal is depicted and compared to the other outcomes by using artificial signals with different signal to noise relation (snr) levels, followed by a discussion of the results and conclusion.

The different schemes for spectrum sensing are tested with an artificial mixture signal defined by Eq. (13).

(13)

The length N of x (n) with n =1,…,N is fixed in 4,000 bins, the sampling frequency ^_fs = 10 kHz, and the parameter L= 512 for the cyclostationary analysis (smoothed window length). The white Gaussian noise ƞ(n) is a variable parameter, with SNR between 10 −and 20 dB.

In order to check the technique’s efficiency, some variations in the number of original sources and noise power ( ^_Pƞ ) are made. The amplitude of the sources is ai = 1 and the phase Φi =0 . For ED, a system with six fir filters is implemented, each one with order 100, Hamming window, bandwidth 520Hz, a center frequency of ^_fi and 10Hz of overlapping; then, the procedure recommended by (18) is followed for each channel with 1,000 Monte Carlo simulations, aiming to determine whether a channel is available or not. For CD, the parameter μ is fixed by heuristic tuning and it determines when a peak of the caf corresponds to a fundamental frequency or not.

The elapsed time that the algorithms took to run is measured in an Intel ^® Core™ i5-2,400 with 4 GB of ram memory and Python ^® of 64 bits.

Table 1 summarizes the results for the three tested schemes. The technique most sensi tive to noise is ed, with the disadvantage that if the frequency bands are unknown, the algorithm is unable of detect different frequency components. The mean time elapsed for this technique is 97.85 ± 1.02 s, and tends to increase with the number of channels. The cd and ms-ica’s are noise-robust algorithms. The time elapsed for cd is around 106.94 ± 1.77s that could be compensated by its precision. The cd sensing technique generates some false positives, but the rate of false negatives is zero. If the intention of the secondary user is to search for vacancy without disturbing the primary user, and the time is not a critical issue in the application, this technique could be a convenient solution. The only inconvenient detected is the parameter μ, because the algorithm performance depends on the choice of this parameter.

Table 1

Source: authors

The proposed ms-ica technique exhibits a suitable compromise between time and performance. The algorithm is fast enough to sense without causing long interrup tions in transmissions. The major disadvantage is the misdetection of some sources. To overcome this inconvenient, it is recommended to run the algorithm a couple of times and increase the amount of information about the frequency occupancy. The remarkable aspect is the elapsed time (0.15 ± 0.1 s), especially for online applications with demanding time issues.

A technique for spectrum sensing based on blind source separation is explored. The methodology includes a single-to-multichannel procedure through singular spectrum analysis (ssa), aiming to simulate a mimo communication system. In this work, three approaches were tested: Energy Detection, Cyclostationary Analysis and the proposed scheme with ms-ica. The methodology is proved on a collection of artificial mixture signals with different frequencies and noise power. The proposed scheme is able to detect most of the sources, even with high noise presence, with low time consumption. The most accurate technique is Cyclostationary Analysis but it is more demanding in terms of time than the others. As future work, it would be interesting to deal with other kinds of modulations, codifications and channel models.

Thisworkissupportedby “Validación de algoritmos de aprendizaje automático usados en sistemas de telecomunicaciones de radio cognitiva (Código No. 737)” financedby Universidad de Medellín and by “Ayudas para la realización del doctorado” (RR01/2011) of the Universidad Politécnica de Madrid y TEC2012-38630-C04-01 del Ministerio de Educación de España”