Cognitive Radio System with Advanced Environment Awareness Engine Using Integrated Propagation

Models and Multiple Antennas

Nuri Celik*, Hao Xu, Trevor Wilkey, Zhengqing Yun, and Magdy F. Iskander Hawaii Center for Advanced Communications, College of Engineering

University of Hawaii at Manoa Honolulu, Hawai’i, USA

celik@hawaii.edu

Abstract—Cognitive radio systems adaptively adjust their radio parameters based on the feedback from the electromagnetic environment. Therefore, it is expected that cognitive radio systems will have better performance if they are integrated with accurate site-specific propagation modeling. Our group has long standing expertise in propagation modeling utilizing geo-spatial tools and indoor building models, and has focus on integrating these models as part of the environment awareness engine in cognitive radios. In this paper, we present the preliminary results for a multi-antenna based spectrum sensing algorithm with adaptive thresholds. The results show that, an almost fixed detection rate of 85% can be achieved for a mobile cognitive radio in an urban environment when the thresholds are set based on the path loss information provided by site-specific propagation modeling. On the other hand, the spectrum sensing algorithm with a fixed threshold shows a great variability in the detection performance, going as low as 65% in regions with heavy fading, which leads to harmful interference.

I. INTRODUCTION Cognitive Radios (CR) are intelligent and flexible devices

that interact with their environment in real time, by sensing the electromagnetic environment to determine the available spectrum resources, and deciding on the appropriate communication parameters so as to adapt to the dynamic radio environment and user’s communication needs [1]. This approach of sensing “spectrum holes” and selecting the best communication parameters is very important for efficient use of scarce spectrum resources. In order to achieve adaptation to the dynamic radio environment, the CR employs a cognitive engine with functionalities shown in black in Fig. 1 [2].

The information about the dynamic radio environment for CR operation, such as the path loss, delay spread, and the angle of arrivals can be provided by accurate and site specific propagation modeling leading to significant performance gains. This integration of accurate, site-specific and computationally efficient propagation modeling into the CR, specifically as part of the Environment Awareness Engine as shown in Fig. 1, is the focus of the research in our group. Such integration is expected to eliminate the resource hungry and interference generating approaches, and to provide improved sensing capabilities [3].

To enable accurate, site-specific and computationally

efficient propagation modeling in mobile CRs, we have focused our efforts in integrating an innovative “multi-level detailed” version of the fast, accurate, and geospatial resources-based propagation models of realistic environments, developed by our group [4-5], and multiple antenna based sensing algorithms as parts of the Environment Awareness Engine.

In this paper, we present the preliminary results for a robust multi-antenna based spectrum sensing algorithm which adaptively adjusts its sensing thresholds based on the path loss information provided by the propagation modeling engine. The results show that almost fixed detection performance of 85% and higher can be achieved by switching between two sensing thresholds as opposed to a highly variable (as low as 65%) sensing performance achieved with the fixed threshold sensing.

II. SPECTRUM SENSING WITH MULTIPLE ANTENNAS AND PROPAGATION INFORMATION

An important task in the CR operation is the determination and characterization of the available spectrum resources, i.e. the spectrum holes. There are lots of works in literature proposing several spectrum sensing methods with various complexity and apriori knowledge requirements about the transmit signal [3]. To provide robust spectrum sensing and ensure proper CR operation in various environments, the spectrum awareness engine should be proactive, especially for mobile CRs, in selecting the optimal sensing parameters for the current and future CR locations/environment. However, in

Environment Awareness Engine

Spectrum Awareness Engine

Location Awareness Engine

Learning/Decision Making Engine

Cognitive Engine

Propagation Modeling Engine

Fig. 1. Proposed cognitive engine employing advanced and accurate propagation modeling as a building block.

2016978-1-4673-5317-5/13/$31.00 ©2013 IEEE AP-S 2013

literature, the sensing parameters are often set to fixed values irrespective of the environment, using statistical methods and noise statistics, resulting in a fluctuating detection performance (probability of MD) with changes in the CR environment [3]. This can create harmful interference, and consequently limit the capability and effective practical use of CR systems.

To demonstrate the performance gains by utilizing the propagation models for setting the sensing thresholds, we have employed a two-level threshold setting mechanism for the GLRT multiple antenna eigen-value based spectrum sensing algorithm [6]. In these simulations, we consider a mobile CR (moving from A to B) and a fixed transmitter in an urban environment as illustrated in Fig. 2.

We have modeled the propagation channel between the transmitter and the receiver using our advanced capabilities and calculated the expected path loss from A to B which is shown in Fig. 3. In Fig. 3, also the best and worst case path losses are indicated and the thresholds for these path losses are calculated to result in 85% detection rate according to the relations given in [6]. With these thresholds, a moving CR with 4 antennas is used to perform spectrum sensing to detect the transmitter which uses BPSK as modulation. The transmit power is adjusted to result in 0 dB SNR at the receiver with the path loss at its maximum. The sensing window for both the adaptive and the fixed threshold sensing algorithms has been set to 128 symbols, and the resulting detection and false alarm rates have been obtained using Monte-Carlo simulations for 10000 cases at each position. The resulting detection and false alarm rates are indicated in Fig. 4. The dashed green line in the top plot of Fig.4 shows the average predicted SNR for the CR path, also this plot shows the predicted SNR for the CR path calculated by subtracting the calculated path loss from the transmitter power (hence the plot is flipped vertically compared to Fig. 3) and the regions indicating the high threshold and low threshold use. The bottom plot in Fig.4 indicates the resulting detection probability and its comparison to the fixed threshold approach for Pfa=0.01. As Fig. 4 shows, the adaptive threshold setting scheme with 2 levels has resulted in a guaranteed 85% detection rate for the mobile CR as opposed to the 65% of the fixed threshold scheme.

III. CONCLUSIONS In this paper, the preliminary results for a multi-antenna based spectrum sensing algorithm with adaptive thresholds are

presented. The results show that, an almost fixed detection rate of 85% can be achieved for a mobile cognitive radio in an urban environment when the thresholds are set based on the path loss information provided by site-specific propagation modeling. On the other hand, the spectrum sensing algorithm with a fixed threshold shows a great variability in the detection performance, going as low as 65% in regions with heavy fading. These results emphasize the importance of having computationally efficient and accurate propagation modeling as an integral part of the environmental awareness engine in cognitive radio for practical and robust cognitive radio operation.

REFERENCES [1] I. F. Akyildiz, W. Y. Lee, M. C. Vuran, and S. Mohanty, “NeXt

generation / dynamic spectrum access / cognitive radio wireless networks:A survey,” Computer Networks Journal (Elsevier), Sept. 2006.

[2] S. Yarkan, and H. Arslan, “Exploiting Location Awareness toward Improved Wireless System Design in Cognitive Radio,” IEEE Comm. Magazine,pp. 128-136, Jan. 2008.

[3] T. Yucek, and H. Arslan, “A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications,” IEEE Communications, Surveys, and Tutorials, vol. 11, No: 1, pp. 116-130, 2009.

[4] Z. Yun, Z. Zhang, and M. F. Iskander, “A Ray-Tracing Method Based on the Triangular Grid Approach and Application to Propagation Prediction in Urban Environments,” IEEE Trans. on Ant. and Prop., special issue on Wireless Communication, Vol. 50, pp.750-758, 2002

[5] M. F. Iskander and Z. Yun, “Propagation Prediction Models for Wireless Communication Systems,” Invited for the 50th Anniversary Special Issue of IEEE Transactions on Microwave Theory and Techniques, Vol.50, pp.662-673, March 2002

[6] J. Font-Segura and Xiaodong Wang. GLRT-based spectrum sensing for cognitive radio with prior information. Communications, IEEE Transactions on, 58(7):2137 -2146, July 2010

Fig. 4. Comparison of the detection rates for the fixed and adaptive two-level threshold setting schemes for a moving CR receiver on the path of Fig. 2.

Fig. 3. The estimated path loss provided by propagationmodeling. The best and worst case path losses where thethresholds are calculated for are indicated by the circles.

Tx

A

B

Fig. 2. The urban environment for evaluating the spectrumsensing performance with variable thresholds.

2017