- Research Article
- Open Access
Cyclostationarity Detectors for Cognitive Radio: Architectural Tradeoffs
© Dominique Noguet et al. 2010
- Received: 17 November 2009
- Accepted: 15 July 2010
- Published: 3 August 2010
Cyclostationarity detectors have been studied in the past few years as an efficient means for signal detection under low-SNR conditions. On the other hand, some knowledge about the signal is needed at the detector. This is typically the case in Cognitive Radio spectrum secondary usage, where the primary system is known. This paper focuses on two hardware architectures of cyclostationarity detectors for OFDM signals. The first architecture aims at secondary ISM band use, considering IEEE802.11a/g as the primary system. In this scenario, low latency is required. The second architecture targets TV band secondary usage, where DVB-T signals must be detected at very low SNR. The paper focuses on the architectural tradeoffs that the designer has to face, and how his/her choices will influence either performance or complexity. Hardware complexity evaluation on FPGA is provided for detectors that have been tested in the laboratory under real conditions.
- Cognitive Radio
- Secondary Usage
- Primary System
- Cyclic Prefix
- Hardware Complexity
Recently, there has been a growing interest in signal detection in the context of Cognitive Radio , and more specifically in that of opportunistic radio, where secondary Cognitive Radio Networks (CRNs) can be operated over frequency bands allocated to some primary system in so far as this primary system is absent or, in a more general case, whenever harmful interference with primary systems can be avoided. In most cases, the presence of the primary system is assessed through direct detection of its communication signal, although beaconing is sometimes considered . Thus, in many situations, the primary system detection problem is transposed to the problem of detecting a communication signal in the presence of noise. Surveys of signal detection in the context of spectrum sensing have been proposed in the literature [3, 4]. These detectors operate according to the a priori knowledge they have about the signal and the model of this signal. Telecommunication signals are modulated by sine wave carriers, pulse trains, repeated spreading, hopping sequences, or exhibit cyclic prefixes. Thus, these signals are characterized by the fact that their momentum (mean, autocorrelation, etc.) exhibits periodicity. This built-in periodicity, which of course is not present in noise, can be exploited to detect signals in the presence of noise even at a low Signal-to-Noise Ratio (SNR) . Using this model, the signal detection process becomes a test for presence of cyclostationary characteristics of the tested signal [6–8].
Many scenarios have been investigated in the context of CRN over the past years. The two most likely to occur in the short term are, on the one hand, the unlicensed usage of TV bands and, on the other hand, the opportunistic use of unlicensed bands by nonlegacy secondary systems. The first scenario, often referred to as the TV White Space (TVWS) scenario, was made possible by the FCC in the US in 2008, with some restrictions which include high-sensitivity requirements for primary user detection . In the context of this scenario, standardization has been very active, especially under the IEEE802.22 banner . Industry fora, like the White Space Coalition, have given more momentum to this option. The second scenario is, for obvious regulatory reasons, the first that can be practically experimented and used .
In this context, implementation of blind cyclostationarity detectors has been proposed. In , a detector based on Cyclostationary Spectrum Density (CSD) is suggested. The CSD theoretically makes it possible to explore the presence of cyclic frequencies for any autocorrelation lag at any frequency (also referred to as 2D CSD). However, the comprehensive 2D CSD is never implemented in practice due to its huge implementation cost. To sort out this issue, 1D CSDs are preferred to limit implementation cost. The CSD can be performed on the time domain autocorrelation [13, 14], or through the analysis of signal periodicity redundancy in the frequency domain . In both cases however, a large FFT operator (512 to 2048) needs to be implemented, leading to significant hardware complexity. The approach described hereafter goes one step further in narrowing down the CSD domain. Indeed, in both scenarios of interest, the primary systems (which are the ones requiring the highest detection sensitivity) are known. Therefore, analysis of the primary signal nature helps narrow down the CSD search to very specific cyclic frequencies, thereby avoiding implementation of a large FFT.
However, when the CSD is narrowed down, the algorithm becomes more specific to the signal to detect. For this reason, this paper will analyze two different implementation options depending on the aforementioned scenarios. The main reason justifying different types of implementation in the WiFI and TVWS scenarios is the sensitivity level required in each case. In the case of TVWS, the guarantee that secondary CRN will not interfere with licensed systems (TV, microphones) leads to high-sensitivity requirements. On the other hand, unlicensed band networks, such as IEEE802.11x, have lighter coexistence constraints. These specific requirements lead to architectural tradeoffs which are examined in this paper. First, the principle of prefix-based cyclostationarity detection will be recapped. Then, the two aforementioned scenarios will be analyzed by pinpointing their impact on the sensor requirement. Considering these requirements, two hardware implementation architectures will be described and evaluated. These approaches will be compared and discussed before concluding the paper.
In both scenarios considered in this paper, in the primary system—DVB-T broadcast system on the one hand, IEEE802.11a/g networks on the other—the signal is modulated using Orthogonal Frequency Digital Multiplexing (OFDM); see, for example, . The OFDM signal is a compound signal consisting of multiple frequency carriers, also called subcarriers or tones, that are each modulated in phase or in phase and amplitude. From a practical outlook, the modulated tones are multiplexed at the transmitter using an inverse FFT. Conversely, the subcarriers are de-multiplexed at the receiver end by an FFT. The size of the FFT N, which defines that of the OFDM symbols, depends on the system. In the case of IEEE802.11a/g systems, 64 subcarriers are used whereas the DVB-T signal uses 1024, 2048, 4096, or 8192 tones. In order to avoid intersymbol interference, a Guard Interval (GI) is introduced. In the case of OFDM, this GI is designed as a copy of the last samples of the OFDM symbol. This approach provides the symbol with a cyclic nature which simplifies the receiver. For this reason, this D long GI is called the Cyclic Prefix (CP).
The cyclostationarity detector for IEEE802.11a/g signals is specified considering the scenario presented in . In this scenario, the detector is used to check the presence of WiFi signals in order to trigger data transmission from a secondary system which is completely independent from the primary system (no messaging exchanged, no synchronization performed). Besides, in order to achieve the highest spectrum efficiency, the secondary system is expected to exploit time gaps (opportunities) in the time domain rather than to leave the channel to find a vacant one. Although this strategy may lead to some collisions, it is found acceptable due to the nature of the primary (unlicensed system) and in so far as the impact is not significant at application level .
This architecture is directly derived from (6). The top left corner block computes one single observation of the autocorrelation function. Each grey block then computes the Fourier coefficients in parallel. Each of these branches is accumulated over the observation time before being aggregated by the sum blocks on the right of the figure.
Selecting = 0 corresponds to considering the fundamental frequency only, which is equivalent to performing energy detection. Detector performance is maximized for a small value, which implies that performance can be maximized for a limited hardware complexity. Aggregating harmonics still further causes performance to decrease since high harmonics, of low amplitude, are strongly impacted by noise. This shows that performance can be optimized with respect to while preserving a limited hardware complexity.
Another important parameter for the detector is the size of the integration window (where denotes the number of OFDM symbols considered for integration). Although this parameter has a more limited impact on hardware complexity (only the accumulators are slightly larger), has a strong influence on latency, another major requirement in the scenario. As expected, increasing does indeed improve performance significantly as shown in Figure 4.
Limiting detector latency while preserving performance of long observation time is possible by trading U against ( MHz). Oversampling is expected to have a similar influence on performance in that it increases U, except for the fact that cannot be reasonably increased to a similar extent to . Therefore, whenever latency is not critical, increasing should be considered. Besides, increasing directly impacts the length of the delay line of the correlator, as well as the look-up tables used for storage of the sine waveforms, whereas had only a slight impact on the complexity of the accumulators in each branch. Thus, priority should be given to increasing in so far as detector latency fits into the latency specification. In the case of the WiFi detector, 5 OFDM symbols correspond to a latency of 20 s. Additional performance can then be ensured by a reasonable increase in to limit the additional complexity drawback. Figure 5 shows the influence of .
Finally, the last parameter that needs to be determined is W, the width of the binary word representing the input data. Assuming that the full dynamic range is preserved throughout the architecture, it is obvious that this parameter will significantly impact hardware complexity. However, the impact on detector performance is less obvious, and some simulations must be quantified. These simulation results are provided in Figure 6.
Figure 6 shows that near optimal performance can be obtained where . However, to preserve some additional margin, a value of 8 is preferred, with rescaling after each macro block to guarantee a good performance/complexity tradeoff. With these parameters, detector overall latency has been measured at 40.5 s.
In the same way as IEEE802.11a/g, the physical layer of DVB-T is based on an OFDM modulation. However, some key elements differ from WiFi systems. First, the DVB-T standard defines four FFT sizes: , 2048, 4096, or 8192, and MHz. The cyclic prefix over FFT size ratio can also vary: 1/32, 1/16, 1/8, and 1/4. However, in practice, implementation considers a smaller set of parameters depending on the country.
For instance, in France, the set of parameters used is , . Another key difference, which will be exploited in the architecture design, stems from the broadcast nature of the DVB-T signal. This means that detector sensitivity can be increased significantly by very long integration time which cannot be considered in the case of short signal bursts occurring in WiFi. This is, of course, a relevant feature since sensitivity requirements for primary user detection are very demanding (typically SNR = 10 dB, to which an additional margin for detector Noise Figure must be added ).
For large values, the expression in (13) tends to 2.3 . Estimator performance is increased by increasing the integration ability of the filter. This is, however, at the cost of long integration time. Thus, this approach is to be considered for "always on" kind of systems, such as DVB-T broadcast signals to guarantee reliable detection under low SNR-conditions.
Complexity evaluation of the DVB-T detector.
RAM blocks of 18 kbits
Depends on n
This paper presents 2 cyclostationarity detectors targeting different scenarios. It is shown in the paper that selection of the scenario has a strong influence on architecture and its performance tradeoffs. First, when aiming at secondary usage of ISM bands with time leftover reuse, latency is the key parameter. With this architecture, latency as low as 40.5 s was measured. Besides, the cyclostationary detectors of this paper outperform classical energy detectors in terms of probability of detection (e.g., Pd is increased by 0.4 where SNR = 17 dB in the WiFi case). This has led to a parallel design in which sensitivity is traded against low latency as collisions with the primary system may be tolerated. On the other hand, when considering secondary spectrum usage of licensed bands, collisions are not permitted and much attention must be paid to sensitivity. This is achieved through long integration time which relies on the assumption that the signal is either "always on" or absent. This assumption makes the second architecture ideally suited to broadcast signal detection (e.g., DVB-T), but would be inapplicable to the first scenario.
The authors would like to acknowledge the ORACLE European IST project of the 6th Framework Program and the French ANR INFOP project for supporting the work presented in this paper.
- Mitola J: Cognitive radio: an integrated agent architecture for software defined radio, Ph.D. thesis. Royal Institute of Technology, Stockholm, Sweden; May 2000.Google Scholar
- Berlemann L, Mangold S: Cognitive Radio and Dynamic Spectrum Access. John Wiley & Sons, New York, NY, USA; 2009.View ArticleGoogle Scholar
- Sahai A, Cabric D: A tutorial on spectrum sensing: fundamental limits and practical challenges. Proceedings of the IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN '05), November 2005, Baltimore, Md, USAGoogle Scholar
- Noguet D, et al.: Sensing techniques for cognitive radio—state of the art and trends. April 2009, http://grouper.ieee.org/groups/scc41/6/documents/white_papers/P1900.6_WhitePaper_Sensing_final.pdf
- Gardner WA: Statistical Spectral Analysis: A Nonprobabilistic Theory. Prentice-Hall, Englewood Cliffs, NJ, USA; 1988.MATHGoogle Scholar
- Živanović GD, Gardner WA: Degrees of cyclostationarity and their application to signal detection and estimation. Signal Processing 1991, 22(3):287-297. 10.1016/0165-1684(91)90016-CView ArticleGoogle Scholar
- Gardner WA, Spooner CM: Signal interception: performance advantages of cyclic-feature detectors. IEEE Transactions on Communications 1992, 40(1):149-159. 10.1109/26.126716MATHView ArticleGoogle Scholar
- Gardner W, William A: Cyclostationarity in Communications and Signal Processing. IEEE Press, New York, NY, USA; 1994.MATHGoogle Scholar
- FCC adopts rules for unlicensed use of television white spaces Official announcement of FCC, November 2008, http://www.fcc.gov/
- IEEE 802.22 : Wireless Regional Area Networks ("WRANs"). http://www.ieee802.org/22/
- Biard L, Noguet D, Gernandt T, Marques P, Gameiro A: A hardware demonstrator of an opportunistic radio system using temporal opportunities. Proceedings of the 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom '09), June 2009, Hanover, Germany 1-6.Google Scholar
- Ye Z, Grosspietsch J, Memik G: Spectrum sensing using cyclostationary spectrum density for cognitive radios. Proceedings of the IEEE Workshop on Signal Processing Systems, October 2007, Shanghai, ChinaGoogle Scholar
- Turunen V, Kosunen M, Huttunen A, Kallioinen S, Ikonen P, Pärssinen A, Ryynänen J: Implementation of cyclostationary feature detector for cognitive radios. Proceedings of the 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom '09), June 2009, Hannover, GermanyGoogle Scholar
- Turunen V, Kosunen M, Kallioinen S, Parssinen A, Ryynanen J: Spectrum estimator and cyclostationary detector for cognitive radio. European Conference on Circuit Theory and Design (ECCTD '09), August 2009, Antalya, Turkey 283-286.Google Scholar
- Tachwali Y, Chmeiseh M, Basma F, Refai HH: A frequency agile implementation for IEEE 802.22 using software defined radio platform. Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM '08), December 2008, New Orleans, La, USA 3128-3133.Google Scholar
- Prasad R: OFDM for Wireless Communications Systems. Artech House, Norwood, Mass, USA; 1999.Google Scholar
- Jallon P: A spread signals detection algorithm based on the second order statistics in semi-blind contexts. Proceedings of the 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom '08), May 2008Google Scholar
- Dandawaté AV, Giannakis GB: Statistical tests for presence of cyclostationarity. IEEE Transactions on Signal Processing 1994, 42(9):2355-2369. 10.1109/78.317857View ArticleGoogle Scholar
- Lundén J, Koivunen V, Huttunen A, Poor HV: Spectrum sensing in cognitive radios based on multiple cyclic frequencies. Proceedings of the 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom '07), August 2007, Orlando, Fla, USA 37-43.Google Scholar
- Ghozzi M, Dohler M, Marx F, Palicot J: Cognitive radio: methods for detection of free bands. Elsevier Science Journal 2006, 7: 794-805.Google Scholar
- Bouzegzi A, Jallon P, Ciblat P: A second order statistics based algorithm for blind recognition of OFDM based systems. Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM '08), December 2008 3257-3261.Google Scholar
- ORACLE D6.1 Deliverable : Definition of test scenarios for the demonstrator. August 2007, http://www.ist-oracle.org/
- Shellhammer S: Spectrum sensing in IEEE802.22. Proceedings of the 1st IAPR Workshop on Cognitive Information Processing (CIP '08), June 2008, Santorini, GreeceGoogle Scholar
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