Full length articlePerformance of a frequency-domain OFDM-frame detector☆
Introduction
Recently, there has been a considerable interest to improve existing spectrum band utilization based on the concept of cognitive radio (CR): sensing the existing spectrum environment, and adapting the signal transmission to the sensing results [[1], [2], [3], [4]] so as not to interfere with other users. In the mean time, the Federal Communications Commission (FCC) has proposed an open spectrum policy allowing secondary users (SUs) to opportunistically access the spectrum bands licensed to primary users (PUs) [5]. CR network is recognized as a promising technology to mitigate spectrum shortage problems in smart grid [6], smart home and the Internet of Things (IoT) [7] applications. Among many CR-based technologies, orthogonal frequency-division multiplexing (OFDM)-based overlay technique [8] is considered a very interesting approach in CR to permit OFDM-based SUs to transmit signals simultaneously with the PUs. With this approach, the SUs can avoid interfering with the PUs by transmitting data only over a portion of subcarriers where the PUs’ signal does not exist.
One of the major obstacles to successful implementation of the OFDM-based overlay technique is the problem of signal acquisition by the OFDM receivers of SUs in the presence of narrowband interference (NBI) due to the PUs. Signal acquisition is required to reliably detect the presence of a data frame, and to estimate the transmission signal parameters, e.g., timing and frequency offsets between the transmitter and the receiver, necessary for reliable detection of the frame data. We have proposed a frequency-domain algorithm for timing-offset estimation [9], which partly solves the signal acquisition problem as it presumes that timing offset estimation is performed after presence of a data frame is declared by a hypothetical frame detector. For OFDM frame detection problem, it has been shown in [[10], [9]] that state-of-the-art time-domain correlation-based frame detection schemes such as [11] are severely vulnerable to NBI. In [12], OFDM frame detection in the discrete Fourier transform (DFT) domain is investigated. Despite its strong performance at low SIR, its performance deteriorates at high SIR due to its reliance on retrieving over-estimated sets of parameters such as NBI variances as suggested by [12]. We are interested in a frame detection algorithm based on energy detection of signal in DFT domain. Our algorithm has a very interesting feature that it can exploit uncooperative sensing results obtained by the receiver itself during the sensing period. With NBI information, the algorithm can mitigate the contribution of NBI on particular frequency bins through a set of weighting coefficients constituting a window of contiguous passband and stop-band. In this paper, the algorithm is proposed and its performance is analyzed for quasi-static frequency-selective Rayleigh fading channels with Gaussian NBI assuming an environment of a wireless local area network (WLAN). Through appropriate parameter settings considering practical CR use-cases in WLAN, the proposed algorithm exhibits strong performance without implementing coarse frequency estimate. We investigate the impact of several parameters such as NBI bandwidth, SIR, signal-to-noise ratio per active subcarrier (SNRPC) and weighting coefficients on the performance of our proposed system. In addition, the performance of our system outperforms other existing frequency-domain OFDM frame detection systems even at low SIR. Simulations results are confirmed using the numerical ones presented as a linear combination of the incomplete gamma function. The weight of each component is a function of the eigenvalues of the matrix associated with the detection metric and the number of summation is proportional to the number of non-zero eigenvalues of the matrix associated with the detection metric.
Section snippets
The frame detection algorithm
The algorithm computes a detection metric from N-point DFT of the input signal, compares the metric with a threshold, and declares presence of a frame if the metric is greater than the threshold. To keep the computational complexity at acceptable level, the metric computation is performed only once for every new window of N input samples, assuming the sampling rate is where is the OFDM subcarrier spacing. Suppose ’s, , are the N-point DFT associated with the detection metric of
Analysis
Note that, when the detection window could capture the signal provided by the preamble, would be composed of three statistically independent terms contributed by the preamble, NBI, and additive white Gaussian noise (AWGN), where , , and are the contributions of respectively the preamble, NBI, and AWGN. Hence, we will first discuss the model of each contribution in subsequent. We justify our model through simulation as presented in Section 3.2. Finally, based on the model
Conclusion
We have proposed a frequency-domain algorithm for OFDM-frame detection useful when the OFDM system is to operate in a known NBI channel, e.g., in a cognitive radio OFDM-based overlay system. The algorithm is based on detection of signal energy from the DFT-domain signal with application of weighting numbers to combat NBI. A method to compute the performance of the algorithm for quasi-static frequency-selective Rayleigh fading channels with Gaussian NBI has been presented. The computed
Chantri Polprasert (S’01-M’07) received the B.S. degree in electrical engineering from Chulalongkorn University, Bangkok, Thailand, in 1999, the M.S. degree in telecommunications from Asian Institute of Technology (AIT), Bangkok, Thailand, in 2000, and the Ph.D. degree in electrical engineering from the University of Washington, Seattle, in 2009. From 2010 to 2014, he was a researcher at the National Electronics and Computer Technology Center (NECTEC), Wireless Innovation and Security Lab,
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Chantri Polprasert (S’01-M’07) received the B.S. degree in electrical engineering from Chulalongkorn University, Bangkok, Thailand, in 1999, the M.S. degree in telecommunications from Asian Institute of Technology (AIT), Bangkok, Thailand, in 2000, and the Ph.D. degree in electrical engineering from the University of Washington, Seattle, in 2009. From 2010 to 2014, he was a researcher at the National Electronics and Computer Technology Center (NECTEC), Wireless Innovation and Security Lab, Pathumthani, Thailand.
Since 2015, he has been a faculty member at the Department of Computer Science, Srinakharinwirot University, Bangkok, Thailand. His research interests include communications over time- and frequency-selective fading channels, channel estimation and equalization, acoustical signal processing, computer networks and machine learning.
Tanee Demeechai received the B.Eng. degree in electrical engineering from Chulalongkorn University, Bangkok, Thailand, in 1991 and the M.Eng. and the D.Eng. degrees from Asian Institute of Technology, Pathumthani, Thailand, in 1994 and 2000 respectively. From 1995 to 2010, he was with Mahanakorn University of Technology, Bangkok, Thailand, where he was an Assistant Professor in the Department of Telecommunication Engineering. He was with Chung-Ang University, Seoul, Korea, in 2008, as a Distinguished Visiting Professor under the support program of IITA. Since 2011, he has been with National Electronics and Computer Technology Center, Thailand, where he is currently a researcher.
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All research data presented in this manuscript is available at https://www.dropbox.com/sh/5f79e81ue1pe07n/AAApR0gSAxwMBI_3Ee_guldAa?dl=0.