ISM Bands Spectrum Sensing based on Versatile Sensor Node Platform Miha Smolnikar, Marko Mihelin, Gregor Berke, Gorazd Kandus and Mihael Mohorcic Department of Communication Systems Jozef Stefan Institute Ljubljana, Slovenia
[email protected] Abstract—The increased utilization of wireless technologies operating in the ISM frequency bands causes cross-interference and performance degradation. A preliminary step to achieve coexistence and spectrum sharing among the ISM band technologies, supporting the idea of cognitive radio networks, is to obtain knowledge of the wireless medium utilization by making use of spectrum sensing. For this purpose we developed a low-cost spectrum sensing device for Industrial, Scientific and Medical (ISM) bands, based on the Versatile Sensor Node platform with dual frequency agile radio modules. In the paper we describe spectrum sensing based on the Received Signal Strength Indicator (RSSI), show spectrum sensing measurements results for the 868 MHz ISM band and pay special attention to use cases of such frequency spectrum sensing devices. Keywords- Spectrum Sensing, ISM bands, Wireless Sensor Network, Versatile Sensor Node
I.
INTRODUCTION
The inherent nature of wireless medium is that the radio spectrum is shared among all users. It is thus a limited resource, requiring coordinated usage. The transmission of radio waves is traditionally regulated by international and national regulatory authorities, which determine frequency allocation and licensing to different technologies and systems. With the proliferation of new wireless technologies and the increasing bandwidth demands, licensed spectrum for exclusive use became a scarce resource, although some measurement campaign studies show that its spatial-temporal usage is not excessive. In fact, spectrum occupancy measurement campaigns carried out in different parts of the world indicate that in very attractive frequency bands up to approximately 3 GHz only between 15% and 85% of the assigned spectrum is actually utilized, with variations depending on spatial and temporal usage. This motivated the concepts of dynamic spectrum access and cognitive radio networks, where licensed spectrum assigned to primary users can be used under certain conditions by secondary (nonlicensed) users with frequency agile radios. This usually means that secondary user has to release the used frequency spectrum to the primary user instantly upon detection of his activity. However, recently there is an increasing level of understanding that a certain level of mutual interference may have to be tolerated for practical implementation of dynamic spectrum access techniques. In detecting and adapting to the primary
spectrum usage, spectrum sensing plays a crucial role, though relying solely on this information and using the spectrum in true ad-hoc manner may give rise to the well-known hidden node problem. Nevertheless, the US Federal Communications Commission (FCC) decided to eliminate the spectrum sensing requirements for devices operating in the TV white spaces, instead making them fully dependent on spatial-temporal map of spectrum usage. In addition to licensed spectrum in sub 3 GHz frequency bands there are also very attractive so-called Industrial, Scientific and Medical (ISM) bands, which have been approved by regulatory authorities for license-free operation if only the devices comply with the statutory rules. The attractiveness of ISM bands, and particularly the one at 2.4 GHz, caused a spread of numerous wireless technologies, but consequently resulted in considerable coexistence problems due to time-varying and unpredictable interference. These bands can also benefit from spectrum sensing and utilization of frequency agile radios for spectrum sharing among unlicensed users [1], [2], [3]. In this paper we present a spectrum sensing system based on wireless sensor network (WSN) platform, which scans the ISM band frequencies. Its aim is to identify spectrum holes and enable interference-free parallel operation of WSN, to implement advanced clear channel assessment (CCA) and time-synchronization protocols, or to build a spatial-temporal map of spectrum occupancy that other technologies can use as a look-up table in adapting their spectrum [4]. The rest of the paper is structured as follows. Section II summarizes the key ISM band technologies and regulatory restrictions. The WSN platform is described in Section III, with the main emphasis given on the radio interface. Energy detection spectrum sensing is explained in Section IV, while Section V discusses measurement results and use cases. Section VI concludes the paper. II.
ISM BAND REGULATORY AND TECHNOLOGIES
In the ISM band devices may operate without approval if only they comply with statutory rules including effective radiated power (ERP) or effective isotropic radiated power (EIRP), bandwidth, spurious emission, and duty cycle. Due to license exempt operation the ISM bands are excessively used,
Figure 1. Versatile Sensor Node – radio (VSR) and core (VSC) modules comprising spectrum sensing platform
Figure 2. VSN setup for spectrum measurements
thus one has to take into account certain level of mutual interference among radio systems operating in parallel.
The core module is based on a high performance ARM Cortex-M3 microcontroller from ST, which offers a very good trade-off between processing power and energy consumption. It supports clock frequencies up to 72 MHz, has 512 kB of program memory and 64 kB of data memory. For storing large sets of data it also incorporates a mini SD card interface supporting the SD specification 2.0, which means that up to 32 GB of memory can be addressed. For attaching peripheral devices it offers analog-to-digital converter, a large number of general purpose input-output (GPIO) pins and interfaces like SPI, I2C and UART.
The 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 (US/EU), where the power is limited to 10 mW ERP and duty cycle to 10%. This band 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 for instance used by Wireless M-BUS technology. The spectrum is divided into sub-bands with maximum power ranging from 5 mW ERP to 500 mW ERP and duty cycle from 1% to 100%. 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. In EU the power in this frequency band is limited to 10 mW EIRP and may for some applications be increased to 100 mW EIRP, whereas in US the power is limited to 1 mW EIRP and may for particular applications be increased to 1 W EIRP. This band is used by wireless technologies such as IEEE 802.11 (Wi-Fi) [5], IEEE 802.15.1 (Bluetooth) [6], IEEE 802.15.4 (ZigBee, 6LoWPAN) [7], as well as microwave ovens and active RFIDs. Besides respecting duty cycle limitations and controlling output power, different medium access techniques are used to enable parallel operation of overlapping systems in the same ISM band. These include frequency hopping spread spectrum (FHSS), direct sequence spread spectrum (DSSS) and orthogonal frequency division multiplexing (OFDM). III.
a)
VERSATILE SENSOR NODE PLATFORM
The spectrum sensing system was implemented on a Versatile Sensor Node (VSN), which is a WSN platform with high processing capability, long-term autonomy and flexible radio. It supports a broad portfolio of sensors and actuators, while its modular approach allows adaptation to diverse application requirements. In this respect the platform consists of the core module – VSC and a set of special feature modules (radio module – VSR, expansion modules – VSE, power module – VSP) that are used as/if needed. The core module can be powered by batteries, solar panel or external power supply and together with radio module supports wireless sensor networks technologies such as ZigBee, 6LoWPAN and Wireless M-Bus. The VSC and VSR modules are depicted in Figure 1.
b) Figure 3. Filter responses
A. Radio Interface For the purpose of this study we used the VSN as a lowcost spectrum sensing platform. We designed a dedicated VSR module, composed of two radio interfaces from Texas Instruments. As indicated in Figure 2, the RF transceiver CC1101 [8] was selected for the sub-GHz ISM frequencies, while the design for 2.4 GHz ISM band is based on RF transceiver CC2500 [9]. Both interfaces are connected to the VSC module through the same SPI interface. The CC1101 [8] is a low-cost sub-GHz RF transceiver intended for low-power wireless applications in the ISM frequency bands at 315 MHz, 433 MHz, 868 MHz and 915 MHz. It provides excellent receiver selectivity and offers high sensitivity of -116 dBm at 433 MHz and -112 dBm at 868 MHz. With the programmable modulation settings the data rates can be set from 0.6 kbps to 600 kbps. The antenna analogue front end has a differential output, which is converted to a single ended output with a discrete balun. The impedance matching of the antenna and radio chip is also accomplished with the balun. For the suppression of higher frequency harmonics in transmit mode an impedance matched LC filter is added between the balun and antenna. The balun filter network is designed for the use in the 868 MHz or 915 MHz frequency band. The balun and filter are constructed as suggested in Texas Instruments reference design [8]. Since the radio chip allows selection of ISM band carrier frequency lower than 868 MHz, spectrum sensing at these frequencies is limited by the filter balun. An ideal element AC analysis of the balun filter was done to determine the insertion loss at lower frequency. The results are shown in Figure 3a. The matching and filtering can also be used at 315 MHz and 433 MHz, since the insertion loss does not fall below -5 dB. A simple copper wire of 86 mm in length was used on the VSR module as a quarter-wave monopole antenna. The antenna impedance is somewhere between 30 Ω and 36 Ω [10] and the filer input-output impedance is 50 Ω, thus some loss due to impedance mismatch does occur. The CC2500 [9] is pin and function compatible RF transceiver to the CC1101, but for the operation in the ISM frequency band from 2400 MHz to 2483 MHz. It provides excellent receiver selectivity and offers high sensitivity of 104 dBm. With the programmable modulation settings the data rates can be set from 1.2 kbps up to 500 kbps. The CC2500 also has a differential antenna output, thus the impedance matching and filtering are also achieved with a balun and filter, respectively. The filter and balun are designed for the 2.4 GHz frequency band, with the results of AC analysis shown in Figure 3b. The balun filter was constructed according to the Texas Instruments reference design [9]. The balun has an output impedance of 50 Ω as is the input-output impedance of the LC filter. An omnidirectional 4.7 dBi dual-band (2.4 GHz and 5.8 GHz) antenna with 50 Ω impedance was used with this radio chip.
IV.
SPECTRUM SENSING BASED ON RSSI
In cognitive radio the spectrum measurements are usually performed locally by each secondary user device. Typically used transmitter detection methods include matched-filer, energy detection and feature-based detection. The latter relies on cyclostationary method, higher order statistics based method, eigenvalue based method or covariance method. In this study energy detection is utilized for spectrum sensing and is based on the radio chip Received Signal Strength Indicator (RSSI), which measures the transceiver input RF power and estimates the signal level in the chosen channel. To analyze the spectrum in a given frequency band, a sweep through more channels has to be performed. To correctly estimate the spectrum, the distance between the central frequencies (channel spacing) has to be set as close as possible to the bandwidth of the channel, not to miss some frequencies or measure them twice. The RSSI value is read from the CCxxxx RSSI status register as a 2’s complement number with 0.5 dB resolution. To acquire the proper RSSI value, the radio first needs to enter the receive mode and then it has to wait for a certain response time before the RSSI value in the status register is valid. After reading the register the RSSI offset has to be substracted to acquire the proper RSSI value. According to [8] and [9], the RSSI offset for CC1101 in the 868 MHz ISM band is 74 dBm. For the case of CC2500 in the 2.4 GHz ISM band the RSSI offset depends on the data rate and ranges from 69 dBm to 72 dBm. The RSSI response time is mainly dependent on the demodulator and automatic gain control (AGC) settings and can be estimated as
RSSI = T0 + T1,1 + T 2 + n × (T1, n + T 2 ) , where T0 represents the time required by the signal to propagate through the demodulator, T1,1 and T1,n represent the time AGC waits after performing gain change and T2 represents the time AGC needs to average the magnitude. The procedure is depicted in Figure 4.
Figure 4. RSSI measurements in CCxxxx transceivers [11]
Figure 5. Graphical user interface of spectrum measurements
During the spectrum sensing, the receiver was set to the maximum data rate so as to minimize the signal travel time through the demodulator. T0 mostly depends on the data rate, whereas T1,1, T1,n and T2 are mainly dependent on the bandwidth of the channel. Spectrum sweeps over large frequency ranges demand wide channel bandwidths, which are in the case of CC1101 transceiver up to 420 kHz wide, inheriting delay times of T0=66 μs, T1,1=28 μs, T1,n=27 μs, and T2=20 μs. Considering the average number of AGC updates, which according to [11] equal n=4, we can approximate the RSSI response time to about 300 μs. On the other hand, the channel bandwidth can be set as low as 70 kHz for fine spectrum sweeps over narrow bandwidths, demanding delay times of T0=177 μs, T1,1=170 μs, T1,n=164 μs and T2=114 μs, which result in RSSI response time of about 1.5 ms. In the test scenario depicted in Figure 5 we performed a frequency sweep from 868 MHz to 950 MHz over 256 channels with a bandwidth of 337.5 kHz and channel spacing of 320.5 kHz. In the ideal case these two parameters would be set to perfectly match, but due to register setting limitation there has to be few kHz difference. For the given settings the RSSI response time was 400 μs. V.
MEASUREMENT RESULTS AND USE CASES
The low-cost ISM band spectrum sensing platform based on VSN described in Section III is currently used as a standalone setup for scanning the ISM bands, similar to the setup proposed in [12]. In this study it has been used to demonstrate the RSSI based spectrum sensing in the 868 MHz ISM band. As depicted in Figure 2, the spectrum measurements were performed using the VSN board connected with Matlab over the PC COM port. In Matlab we created a graphical user interface (GUI) in which the radio register settings are calculated according to the selected frequency sweep range and
sent to the VSN board. In the GUI we can set the lower and upper frequency bound of the spectrum sweep and according to this setting the channel spacing and bandwidth are calculated in order to acquire 256 channels in which the RSSI measurements are performed. The channel spacing and bandwidth have to be matched as much as possible not to miss some frequencies or measure them twice. The settings, which are sent to the radio, include the base frequency, which is written in the FREQ registers, the channel bandwidth, which is set in the MDMCFG4 register and the channel spacing, which is set in the MDMCFG1 and MDMCFG0 registers. When the serial connection to the VSN is established, the spectrum is continuously measured. The representative demonstration results obtained in the 868 MHz ISM band are displayed in Matlab, as depicted in Figure 5. The peak at the lower end of the frequency sweep range indicates the operation of the WSN network in the 868 MHz band, whereas spikes between 930 MHz and 940 MHz indicate the sensed GSM channels. In addition to sole tracking of ISM spectrum devices the proposed VSN platform can also be used in other use cases, most notably for coexistence of cognitive radio enabled wireless sensor nodes with other ISM band technologies based on spectrum sharing. In this case WSNs are mimicking the behavior of secondary users with Wi-Fi devices, microwave ovens and other devices operating at fixed frequencies representing primary users. Using the proposed RSSI-based spectrum sensing and appropriate decision techniques, sensor nodes can adapt to primary spectrum usage and transmit data obtained from sensors using the same or additional radio transceiver. Sensing in this case is performed locally by each secondary user device, so relying solely on this information. Using the spectrum in true ad-hoc manner may give rise to the well-known hidden node problem, calling for additional methods. One solution to the hidden node problem is to
introduce a cognitive control channel, however this makes the spectrum again centrally coordinated. This case relies on cognitive radio enabled sensor networks, with nodes scattered across the primary user coverage area. Sensor nodes are continuously sensing the spectrum and reporting on detection results back to secondary users. As such they represent an alternative enabling technology which decouples spectrum sensing from secondary user devices, essentially providing sensing as a service. Additionally to temporal usage of spectrum, localization of spectrum utilization is vital in cases when we want to build a spatio-temporal spectrum occupancy map. Building such map relies on sensor nodes placed on locations with known coordinates. Each sensor node is periodically sweeping the spectrum. Results can be stored in external SD memory card, before they are collected by the coordinator and sent to a central data base for the lookup by secondary users with no sensing capability. Operation of such sensor network requires perfect synchronization in order to avoid overlapping of spectrum sensing and transmission periods. In the case when the spatio-temporal spectrum occupancy map is only used by the wireless sensor network, there is no need for database, as it is the coordinator node that analyses the sensing results and decides if the network should move to another frequency, which is not occupied. Yet another interesting use case of exploiting RSSI is for localization purposes. During the time interval, when the air traffic is allowed, the nodes are communicating with each other in a mesh network. When a sensor node receives data from any other node, it automatically gets the RSSI appended at the end of the packet. With this method the sensor node builds another table with RSSI strengths of all nodes in the transmission range and also sends this data to the coordinator node, where the spatial relationship of the nodes is analyzed in addition to the spectrum occupancy. VI.
CONCLUSIONS
An ISM bands spectrum sensing method based on RSSI was presented in this article. The proposed method was implemented on a Versatile Sensor Node (VSN) platform using two Texas Instruments low-power transceivers as the RF frontend, namely CC1101 for sub-GHz ISM bands at 400 MHz and 900 MHz, and CC2500 for the ISM band at 2.4 GHz. Both transceivers are characterized by high sensitivity and support sweeping over carrier frequencies in their respective nominal bands. The measurement setup presented as proof of concept consisted of a VSN node, which was controlled via PC COM port using graphical user interface purposely built in Matlab. As a demonstration a spectrum sweep from 868 MHz to 950 MHz was presented, identifying the WSN network operating at 868 MHz along with GSM channels between 930 MHz and 940 MHz. Finally, we argue that RSSI based
spectrum sensing using low-cost VSN platform has many applications from demonstrating cognitive radio network procedures to sensing of spectrum utilization, localization and providing sensing service for building of spatial-temporal spectrum occupancy maps. Spectrum sensing with the VSN platform, as proposed in this paper, is purely energy based, i.e. only measuring the RF power input to the transceiver and subsequently estimating the signal level in the wireless channel. As part of the future work, however, we intend to enhance the current setup so as to be able to extract features of detected systems and support coexistence of wireless sensor network with heterogeneous ISM band technologies. REFERENCES [1]
S. Pollin, I. Tan, B. Hodge, C. Chun, A. Bahai, "Harmful Coexistence Between 802.15.4 and 802.11: A Measurement-based Study," 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008), May 2008. [2] K. Yu-Kwong, M.C.-H.Chek, "Design and evaluation of coexistence mechanisms for Bluetooth and IEEE 802.11b systems," 15th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2004), Sept. 2004. [3] M. Timmers, S. Pollin, A. Dejonghe, L. Van der Perre, F. Catthoor, "Exploring vs exploiting: Enhanced distributed cognitive coexistence of 802.15.4 with 802.11," IEEE Sensors, Oct. 2008. [4] G. Vijay, E. Bdira, M. Ibnkahla, "Cognition in Wireless Sensor Networks: A Perspective," IEEE Sensors Journal, accepted for publication in a future issue. [5] IEEE Std 802.11 – 2007, IEEE Standard for Information technology – Telecommunications and information exchange between systems – Local and metropolitan area networks – Specific requirements – Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, 12 June 2007. [6] IEEE Std 802.15.1 – 2005, IEEE Standard for Information technology – Telecommunications and information exchange between systems – Local and metropolitan area networks – Specific requirements – Part 15.1: Wireless medium access control (MAC) and physical layer (PHY) specifications for wireless personal area networks (WPANs), 14 June 2005. [7] IEEE Std 802.15.4 – 2006, IEEE Standard for Information technology – Telecommunications and information exchange between systems – Local and metropolitan area networks – Specific requirements – Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (WPANs), 8 September 2006. [8] "CC1101: Low-Power Sub-1 GHz RF Transceiver," Texas Instruments, 2010. [9] "CC2500: Low-Power Sub-2.4 GHz RF Transceiver," Texas Instruments, 2010. [10] "ISM-Band and Short Range Device Antennas," Texas Instruments, August 2005. [11] "RSSI Interpretation and Timing," Texas Instruments, 2010. [12] L. Stabellini, J. Zander, "Energy-Aware Spectrum Sensing in Cognitive Wireless Sensor Networks: A Cross Layer Approach," IEEE Wireless Communications and Networking Conference (WCNC2010), Apr. 2010.
