Overview of spectrum sensing

Spectrum sensing [1] is the cornerstone of a successful CR based communication. This task consists of obtaining awareness about the spectrum occupancy over multiple dimensions like time and space to be able to detect and access the vacant frequency sub-channels in licensed bands. Recent literature has proven an increasing interest in using centralized and distributed spectrum sensing to enhance the reliability of sensing measurements [2] [3] [4]. More precisely, the physical layer regularly performs a suite of measurements to obtain information about the presence of the primary signal, the position of primary and secondary nodes and sub-channel characteristics such as fading and noise.

Based on the amount of information needed for the establishment of sensing operations, sensing techniques falls into three classes (Fig. 1):

1. The blind detection that senses the surrounding environment without using any preliminary information on the source signal or the noise power.

2. The noise dependent detection making use of information on the noise properties without any assumptions on the primary signal.

3. The feature detection that uses the prior knowledge on both the source signal and the noise power characteristics.

Figure 1: Spectrum sensing techniques.

To search for spectrum opportunities, different dimensions have to be explored to find the abundant spectrum holes. Legacy sensing algorithms monitor and supervise the spectrum through three conventional dimensions: frequency, time and space domains. However, other degrees of freedom (DoF) such as the used code and the angle of arrival may be used to create new spectrum opportunities and optimize the utilization of spectral resources [5].

In practice, using access technologies based on the opportunistic spectrum sharing raises some technical challenges like the sensing reliability and the hidden node problem.  For clarity, the hidden node issue [6][7] is where a transmitter-receiver pair of secondary users is in the shadow of a primary user, perhaps behind a building, and as a result their sensing does not detect the licensed user. Since, no primary transmissions are detected in this band and in those locations, the transmitter-receiver pair starts communicating. However, the primary user may have a receiver that is outside the shadow of the building and in range of the cognitive transmission. So, it is interfered with. Cognitive peers are expected to collaborate in spectrum sensing and monitoring operations or, as another alternative, a centralized station has to be designated to coordinate the measurements and broadcast feedback to different nodes in order to be able to alleviate the consequences of the hidden node happening. Geographic coordination through a central database to identify the vacant sub-channels is a good substitute for the spectrum sensing concept, a combination of both methods may be also envisaged.  For instance, Google, Spectrum Bridge and Telcordia [8] are among the database administrators granted approval to provide such coordination to track frequencies in use.

On the other hand, due to the large range of frequency bands to be explored and sensed to find gaps in spectrum and in order to avoid penalizing the effective data transmission time, digital signal processors need to be faster, more accurate and more robust. Recent developments in hardware must fulfill the challenge of low power consumption to improve the battery life of cognitive mobile devices.

Finally, it appears evident that the spectrum sensing concept faces the challenge of building a strong trustworthiness from a license holder viewpoint.

BIBLIOGRAPHY

[1] D. Cabric, S.M. Mishra, and R.W. Brodersen, "Implementation issues in spectrum sensing for cognitive radios," in Proceedings of the Asilomar conference on signals, systems and computers. IEEE, November 2004, vol. I, pp. 772-776.

[2] Chunyi Peng, Haitao Zheng, and Ben Y Zhao, "Utilization and fairness in spectrum assignment for opportunistic spectrum access," Mobile Networks and Applications, vol. 11, no. 4, pp. 555-576, 2006.

[3] Ian F Akyildiz, Won-Yeol Lee, Mehmet C Vuran, and Shantidev Mohanty, "A survey on spectrum management in cognitive radio networks," IEEE Communications Magazine, IEEE, vol. 46, no. 4, pp. 40-48, 2008.

[4] Raza Umar and Asrar UH Sheikh, "A comparative study of spectrum awareness techniques for cognitive radio oriented wireless networks," Physical Communication, 2012.

[5] T. Yucek and H. Arslan, "A survey of spectrum sensing algorithms for cognitive radio applications," IEEE Communications Surveys and Tutorials, vol. 11(1), pp. 116-130, 2009.

[6] A.S. Kamil and I. Khider, "Open research issues in cognitive radio," in Proceedings of the 16th Telecommunications forum TELFOR, November 2008, pp. 25-27.

[7] M. Barbiroli, C. Barbiroli, A. Guidotti, and D. Guiducci, "Evaluation and analysis of the hidden node margin for cognitive radio system operation in a real scenario," in Proceedings of the 5th European Conference on Antennas and Propagation. IEEE, April 2011, pp. 1309-1313.

[8] http://www.fcc.gov/encyclopedia/white-space-database-administration.