978-1-4673-0671-3/11/$26.00©2011 IEEE 282 IEEE-ICoAC 2011

Spectrum Opportunity in UHF – ISM Band of 902-928 MHz for Cognitive Radio

Dhananjay Kumar, Department of Information Technology

MIT Campus, Anna University Chennai, India

dhananjay540@gmail.com

G. Kalaichelvi, D. Saravanan, T.K. Loheswari, SAMEER-Centre for Electromagnetics, 2nd Cross Road CIT Campus, Taramani

Chennai, India kalai_g2002@yahoo.co.in

Abstract - The cognitive radio is an intelligent wireless communication system dynamically adapting itself to the operating environment by changing its parameters, towards reliable communication and efficient spectrum utilization. Radio scene analysis is the first and foremost task based on which the spectrum is sensed towards its efficient utilization. A real-time observation is needed to model the statistical data of spectrum utilization with reference to specific geographic location and time, which can be utilized in resource allocation of cognitive radio system. This paper presents typical spectrum occupancy of the 902-928 MHz ISM band obtained through signal strength measurements and its statistical study. The usage of such channel occupancy statistical data in the spectrum sensing formulation is also elaborated in a typical scenario for a frequency hopping system working in this band. The simulation result of energy based detection of the above system is presented to realize a cognitive environment.

Index Terms-Cognitive radio, ISM, Spectrum sensing, FCC, TVWS

I. INTRODUCTION Spectrum is a very valuable resource in wireless

communication systems and it has been the focal point for research and development efforts over the last several decades. But, now there is a big difference in the thinking process and the related activity towards spectrum. The traditional thinking had been that one should try to have more bandwidth and more resources. But, now everyone has come to the realization of the problem of not having enough bandwidth or resources. Today’s spectrum allocations are based on a command-and-control philosophy which means that the spectrum allocated for a particular application does not change over space and time. A lot of spectrum studies have shown that the bandwidth/resource utilization rates in many such allocated cases are too low. With the understanding that rigid, inflexible protocols and strategies often leave wasted resources, the situational information is now emphasized. Therefore, in the past decade the concept of cognitive networking and communications has offered a revolutionary perspective in the design of modern communicative architecture. [1, 2]

Today we are in a scenario, where the National Broadband Plan of Federal communications commission (FCC) has mainly recommended to free up 500 MHz of spectrum in the next 10 years and urged to initiate further proceedings on opportunistic spectrum access beyond the already completed TV White Spaces (TVWS) proceedings.[3]

Moreover, wireless communications are going to be

extensively used by emergency responders to prevent or respond to incidents and by citizens to avail their services. Therefore, cognitive radio is identified as an emerging technology towards effective and efficient usage of the spectrum in National Emergency Communications Plan (NECP) also, as in national broadband plan. With cognitive radio, public safety users can use additional spectrum for daily operation from location to location and from time to time. [3]

II. COGNITIVE RADIO The cognitive radio which is an extension of software-

defined radio, is defined as an intelligent wireless communication system aware of its operating environment, understanding and dynamically adapting to it by changing its operating parameters, with the two primary objectives in mind: Highly reliable communication and Efficient utilization of the radio spectrum resources.

Such a cognitive radio system has to perform the three

following fundamental cognitive tasks [4].

1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum

management.

Radio scene analysis is the first and foremost task based on which the spectrum is sensed towards efficient utilization. The fundamental aspect to the success of such technology is the statistical properties of the spectrum occupancy measured through Radio-scene analysis.

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III.USAGE OF ISM BANDS The Industrial, Scientific, and Medical (ISM) bands include

two sub-GHz frequency bands around 400 MHz and 900 MHz, and the well-known 2.4 GHz band. The lowest ISM band is designated at 315/433 MHz and is characterized by wide ranges but reduced data rate. The ISM band at 915/868 MHz (US/EU) is characterized by good penetration and is used by Cordless phones, Microwave ovens, Industrial heaters, etc.,The most widely used ISM frequency band is at 2.4 GHz, and is particularly attractive since it is available worldwide and offers high bandwidth and sufficient range. This band is used by wireless technologies such as IEEE 802.11 (Wi-Fi), IEEE 802.15.1 (Bluetooth), IEEE 802.15.4 (ZigBee, 6LoWPAN), as well as microwave ovens and active RFIDs.

IV. OPERATION IN THE 915MHZ BAND VS 2.4GHZ The frequency band of 902 – 928 MHz is one of the ISM

bands in the US, commonly abbreviated as the 915-MHz ISM band. In this band, there are no restrictions to the application or the duty cycle for the control and periodic applications as in the other bands. Furthermore, the allowed power output is considerably higher. Because of the lack of stringent restrictions and higher allowed power, this band is very popular for unlicensed short range applications including audio and video transmission [5,6]. Due to this reason, the radio modem and related hardware are readily available and there are lot of existing usage of this band in the other countries including India irrespective of the band being licensed or not.

The 2.4-GHz ISM band is a worldwide unlicensed band.

This is an important advantage compared to the 902– 928MHz ISM band. The 2.4-GHz band also has a wider bandwidth than the 902–928 MHz band which means more available channels. The disadvantages of the 2.4GHz band are increased cost and current consumption of the active components, reduced propagation distance for the same power, and increased band congestion due to the coexistence of systems such as Bluetooth and wireless internet. [5,6, 7]

With increasing interest in implementing the ubiquitous

monitoring of patients in hospitals, Medical Body Area Networks (MBAN) is a promising solution. It is strongly felt that 2.4GHz ISM band is not suitable for life critical medical applications due to the interference and congestion from IT wireless networks in hospitals. [3]

V. SPECTRUM SENSING MEASUREMENTS IN RECENT STANDARDS

Recent increases in demand for cognitive radio technology have driven researchers and technologists to rethink the implications of the traditional engineering designs and approaches to communications and networking. As a consequence, recently developed wireless standards have started to include cognitive features [8]. It is practically

difficult to expect a wireless standard that is based on wideband spectrum sensing and opportunistic exploitation of the spectrum. However, the trend is towards achieving these goals.

A. IEEE 802.11k This is clear from IEEE 802.11k, a proposed extension to

IEEE 802.11 which defines several types of measurements including channel load report, noise histogram report and station statistics report. The noise histogram report provides methods to measure interference levels received by its subscriber unit. The measurement information is used to improve the traffic distribution within the network towards efficient utilization of the spectrum for better throughput.

B. Bluetooth In the 2.4 GHz ISM band, Bluetooth has to coexist with

IEEE802.11b/g devices, cordless telephones, and microwave ovens. To achieve higher BER avoiding narrow band interference, a new feature called Adaptive Frequency Hopping requiring a sensing algorithm is introduced in the Bluetooth standard. The sensing algorithm is based on the statistics of channel occupancy measuring RF or baseband domain parameters.

C. IEEE 802.22 IEEE 802.22 cognitive radio standard suggests spectrum

sensing of TV signals through energy and cyclostationary based detection for its wireless regional area network (WRAN) devices. In this standard, spectrum sensing is drafted in two stages namely fast and fine sensing. A coarse sensing algorithm namely an energy detector is employed for fast sensing and the fine sensing stage initiated by the fast sensing includes waveform based sensing, cyclostationary feature detection and matched filtering.

VI. SPECTRUM MEASUREMENTS IN 915 MHZ BAND As in 2.4 GHz ISM band, the spectrum knowledge and the

sensing algorithms can be used to initiate advanced algorithms as well as adaptive interference cancellation in receivers operating in UHF-ISM band of 902-928MHz. In India, due to the excellent propagation characteristics, UHF – ISM range of 902-928MHz range is still preferred as a low cost solution to a number of data acquisition/control and navigational requirements.

In our study, aimed at exploring the spectrum opportunity in

the interested band of 902MHz to 928MHz, the signal strengths over the frequency band were measured using a measurement set-up. A spectrum analyzer was used to measure the signal strengths gathered through a CC-BW-Ltype antenna with vertical polarisation. Fig.1 shows a typical spectrum

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analyser screen during the measurements covering the entire frequency range of 902 MHz to 928MHz.

Fig. 1. A typical spectrum measurement log.

As it is required to evaluate the spectrum opportunity and spectrum sensing concepts by considering different dimensions of the spectrum space, in our study also, the occupancy of channels were observed over a period and the signal strengths were measured at various instances of time. The following Fig.2 shows the occupancy of channels deduced from the signal strength measurements of the band of interest, namely 902 to 928 MHz, over a period of time through a number of trials. Received Signal Strength Values are used to classify channels as good - depicted in white with no existing signals, bad depicted in Black - with signals all the time or unknown depicted in Grey - with intermittent signals, equivalent to depiction of TV white spaces. The centre frequencies of all the time occupied band and the range of frequencies of unoccupied and intermittently occupied band of frequencies are mentioned in this figure with the corresponding bandwidths mentioned inside the bars.

Fig. 2. Spectrum occupancy in 902-928 MHz

Estimation of spectrum usage in multiple dimensions includes time, frequency, space, angle and code, identifying opportunities in these dimensions. In this study also, the data collected over a period is used to arrive at the statistical results of channel occupancy. The following Fig.3 gives the spectrum usage plot in 3 dimensions namely relative time and frequency and the correspondingly measured signal strength.

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Frequency in MHz

Fig. 3. Spectrum Occupancy - Frequency Vs

Power level with time The data collected over a period of time helps the user to

arrive at statistical results which will help in understanding the occupancy of the band to avoid or use particular frequencies at various instances of time. The following figure namely Fig.4 gives the probability of percentage of occupancy calculated based on the availability of signals from signal strength measurements performed over a period of time.

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Fig. 4. % of Occupancy Vs Frequency in MHz

VII. FORMULATION OF SPECTRUM SENSING IN COGNITIVE RADIO

Usage of a sensing algorithm on the received data determines whether there are other devices present in the ISM band and whether or not to avoid them. The sensing algorithm is based on the channel occupancy statistics gathered to determine which channels are occupied and which channels are empty. The least demanding approach of a spectrum sensing algorithm, from an a priori information point of view is energy detection. Moreover, there is no knowledge about the transmission systems and signal types to be detected, to employ other methods of spectrum listening.

An energy detector measures the energy in a radio spectrum and compares the value against a threshold. If the measured energy is below the threshold, the radio resource is declared as not occupied and made available for opportunistic use.

When the signal x(t) is transmitted through the channel having gain h, the received signal y(t) at the receiver is given by y(t) =h x(t) + �(�) with additive white Gaussian noise (AWGN) with mean zero and one-sided power spectral density No.

The detection of the signal is the test of the following two

hypotheses:

H0: y(t) = �(�) signal absent (1)H1: y(t) =h x(t) + �(�) signal present H0 is a null hypothesis, meaning that there is no

existing/primary user present in the band and H1 means the primary/existing user’s presence. The detection statistics of the energy detector can be average energy of N observed samples [2].

(2)

In energy detection, the received signal is first pre-filtered

by an ideal band pass filter which has bandwidth w, and the output of this filter is then squared and integrated over a time interval t to produce the test statistics.

The test statistic, T is compared with a predefined threshold

value �, The performance of the detector are based on two probabilities: the probability of false alarm PF and detection probability PD which can be evaluated as ��(T > �� H0) and ��(T > �� H1), respectively.

Using chi square distribution with PD, probability of

detection and V = tw [9].

� �� �DD PVFV

PF ����

��� ��� :1

(3) where

� � dtvet

VFP v

tV

D � ���

��

02/

2/2/)2(

)2/(2 (4)

where �(.) is the gamma function Thus, the energy detection described above is a non-specific

method, as no particular property of the signal is used. However, in the absence of primary users if the band is unlicensed or in the absence of existing users of cyclostationary nature or in the unavailability of knowledge on the existing users, can be used for declaring whether a resource is occupied or not, if not the type of system or user occupying the channel. Also, an energy detector needs to have an idea of the noise level to adjust the detection threshold.

In the simulation of the energy based detector, the equation (2) is used to calculate the detection statistics which is the average (or total) energy of N Observed samples. The threshold is calculated based on the probability of detection PD using chi square distribution as shown in (3) and (4).

TABLE I.

SIMULATION PARAMETERS Sl. No. Parameter Value

1. Channel separation 500kHz

2. Max. possible hopping channels 52

3. Probability of False alarm 0.001

4. SNR -114dBm

The following Fig.5 shows the simulation results for a typical frequency hopping system working over the band of interest with an option of 52 hopping channels separated by 500 KHz, In this result, the presence of (primary) signals are depicted, so that the presence of a spectrum sensing algorithm can help in avoiding those frequencies during frequency hopping, towards achieving a more reliable communication link. In this simulation, we find that around 14 frequencies namely 902MHz, 904MHz, 905.5MHz, 907.5MHz, 908.5MHz, 909MHz, 909.5MHz, 911MHz, 912.5MHz, 915MHz, 918.5MHz, 922.5MHz, 923MHz, 925MHz and 925.5MHz are occupied by existing users and hopping to these frequencies can be avoided to get a reliable link and to reduce interference to the existing users.�

2

1)(1 �

�

�N

tty

NT

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900 905 910 915 920 925 930

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Fig. 5 Simulation result of Energy based detection

VIII. CONCLUSION

The study presented here, involving signal strength measurements show the thin occupancy of the spectrum and a good spectrum oppurtunity in this band. The simulation results suggest that an energy detection based spectrum sensing algorithm will help in establishing a highly reliable link towards an efficient and effective utilization of overall band for cognitive radio. This work based on acquiring spectrum occupancy through measurements and the application of statistical techniques to detect spectrum holes at a given time and at a particular geographical location can be used to build an intelligent database towards realising a cognitive radio system. Developing a mathematical model based on statistical data for the spectrum utilisation for cognitive radio is the future work of the study presented here.

REFERENCES [1] Joseph Mitola III “Cognitive radio: An integrated agent architecture for

Software Defined Radio,” Dissertation, Doctor of Technology, May,2000.

[2] K.J.Ray Liu and Beibei Wang, “Cognitive Radio Networking and Security”, A Game-Theoretic View, ISBN 978-0-521-76231-1, 2011.

[3] Jianfeng Wang, Monisha Ghosh and Kiran Challapali “Emerging cognitive radio applications – A survey”, IEEE communications magazine, Mar 2011, Vol.49, No.3, pp 74-81

[4] S.Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE J. Select. Areas Commun., vol. 3, no. 2, pp. 201-220, Feb.2005.

[5] http://www.odessaoffice.com/wireless/fcc_ism.html Code of Federal Regulations, Title 47 Volume 1, Revised as of October 1, 2001, From the U.S. Government Printing Office via GPO Access,

[6] Texas Instruments, “ISM band and Short range device regulatory compliance overview”, Application report, SWRA048 – May 2005.

[7] http://210.212.79.13/, Wireless planning and coordination wing, Government of India.

[8] Tevfik Yucek and Huseyin Arslan., “A Survey of Spectrum Sensing Algorithms for cognitive Radio Applications,” IEEE Communication Surveys & Tutorials, vol. 11, no. 1,pp.116-130, first quarter 2009.

[9] Saman Atappattu, Chintha Tellambura, Hai Siang, “Energy Detection Based Cooprative Spectrum Sensing in Cognitive Radio Networks, IEEE Transaciton on wireless communications, vol 10, no. 4, pp 1232-1240,April 2011.