Polyphase filter bank based multi‐band spectrum sensing in cognitive radio systems

Abstract

Spectrum sensing has been identified as an essential enabling functionality for cognitive radio (CR) systems to guarantee that CR users could share the spectrum resource with licensed users on a non-interfering basis. Recently, simultaneous sensing of multi-band licensed user activity has been attracting more and more research interest. Generally, the multi-band sensing is implemented through energy detection by estimating power spectral density. In this paper, we investigate a multi-band energy detection architecture based on different polyphase filter banks (PFBs), which aim to reliably sense multiple active bands by exploiting the low power leakage property of PFB. We have theoretically derived the closed-form expressions of detection probability and false alarm probability for PFBs and fast Fourier transform based detectors, respectively. Theoretical detection thresholds are therefore computed, which ensures a fair comparison for different detectors. Final experimental results are presented to verify our theoretical analysis and demonstrate that PFBs based sensing architecture exhibits better sensing performance than the conventional FFT.Spectrum sensing on a single frequency band has a relatively rich literature [29,30]. However, the literature of multi-band sensing that monitors multiple frequency bands simultaneously is very limited. The basic concept of multi-band sensing is to firstly estimate the power spectral density (PSD) and then power detection (which is simple and can locate spectrum occupancy information quickly) is applied in the frequency domain based on the observed power spectrum [31][32][33]. A spectrum sensing algorithm for wideband CR systems was proposed in [34] by exploiting the sensed spectrum discontinuity properties. In [35], a wideband dual-stage sensing technique: a coarse and a fine spectrum sensing architecture was proposed. These two sensing stages collaborate with each other to enhance the accuracy of spectrum sensing performance. In [36], three widely used spectrum estimation methods, weighted overlapped segment averaging approach, multi-taper spectrum estimator, and multiple signal classification algorithm, were introduced and compared for wideband detection. In [37], a novel approach called segmented periodogram for wideband spectrum segmentation was proposed. The proposed scheme is based on the posterior expectation of the piece-wise flat realizations of the underlying signal spectrum, which is obtained using the reversible jump Markov chain Monte Carlo technique. The authors in [38] considered joint decisions over multiple frequency bands. The spectrum sensing problem is formulated as a class of optimization problems in interference limited cognitive radio networks. The theoretical analysis of spectrum sensing using multitaper spectrum was investigated in [39], where analytic detection probabilities have been derived for multitaper-based detection schemes, which is demonstrated to be more robust to the noise uncertainty compared with energy-based detectors. In [40], the aut...

Wideband Spectrum Sensing Based on Reconfigurable Filter Bank in Cognitive Radio

“…Due to the lower spectral leakage property compared with taking fast Fourier transform (FFT) at the ADC sampling rate, the polyphase filter bank (PFB) is widely applied to wideband spectrum sensing. Study in [7] presents a multiband energy detection method based on PFB and the closed-form expressions of detection probability and false alarm probability for PFBs are derived, which performs better than the conventional FFT. A two-stage wideband spectrum-sensing utilizing PFB in [8] reduces the number of binary detections when the number of occupied primary channels is small, otherwise, the number of binary detections is larger than the total number of primary channels.…”

Sensitivity of filter bank SCFDMA to carrier frequency offset and its compensation using firefly algorithm

“…The uncompensated MAI introduces error floor, especially for large CFO values. [23][24][25] The role of various parameters in the performance of FBMC system is analyzed in Kang and Vig 26 along with their optimization in CR environment. 10,19,20 The role of OBE in curtailing the error floor introduced by MAI and reducing the complexity of ICI compensation scheme is described in Farhang et al 21 Thus, the recent trend in interference cancellation is to design multicarrier systems with well-localized subcarrier filters such that the OBE and MAI sensitivity is minimum and low complexity SI compensation is possible at the receiver.…”

“…22 Filter bank multicarrier (FBMC) system addresses these issues effectively and has been considered a reliable physical layer strategy for future communication systems. [23][24][25] The role of various parameters in the performance of FBMC system is analyzed in Kang and Vig 26 along with their optimization in CR environment. The interference analysis of FBMC system is presented in Saeedi-Sourck et al 27 and Lin et al, 28 whereas Saeedi-Sourck et al 29 focuses on the bit error rate (BER) performance, ICI compensation, and complexity.…”

Spectral Efficiency Analysis of Filter Bank Multi‐Carrier (FBMC)‐ Based 5G Networks with Estimated Channel State Information (CSI)

“…This results in a serious conflict between the target for better communication and the fact for spectrum scarcity. Cognitive radio (CR) [7][8][9] and FBMC [10][11][12][13][14][15][16] techniques, which are capable of efficiently exploiting the spectrum hole, can be considered to apply in 5G networks. CR technology is considered to be one of the most important technologies to improve the spectral efficiency.…”