Sener Dikmese

Efficient Energy Detection Methods for Spectrum Sensing Under Non-Flat Spectral Characteristics

Cognitive radio is an emerging wireless technology that is capable of efficiently coordinating the use of the currently scarce spectrum resources, and spectrum sensing constitutes its most crucial operation. This paper proposes wideband multichannel spectrum sensing methods utilizing fast Fourier transform or filter-bank-based methods for spectrum analysis. Fine-grained spectrum analysis facilitates optimal energy detection in practical scenarios where the transmitted signal, channel frequency response, and/or receiver frequency response do not follow the commonly assumed boxcar model, which typically assumes, among other things, narrow-band communications with flat spectral characteristics. Such sensing schemes can be tuned to the spectral characteristics of the target primary user signals, allowing simultaneous sensing of multiple target primary signals with low additional complexity. This model is also extended to accounting for the specific scenario of detecting a reappearing primary user during secondary transmission, as well as in spectrum sensing scenarios where the frequency range of a primary user is unknown. Novel analytic expressions are derived for the corresponding probability of false alarm and probability of detection in each case, while the useful concept of the area under the receiver operating characteristics curve is additionally introduced as a single scalar metric for evaluating the overall performance of the proposed spectrum sensing algorithms and scenarios. The derived expressions have a rather simple algebraic representation, which renders them convenient to handle both analytically and numerically. The offered results are also extensively validated through comparisons with respective results from computer simulations and are subsequently employed in evaluating each technique analytically, which provides meaningful insights that are anticipated to be useful in future deployments of cognitive radio systems.

Energy Detection under IQ Imbalance with Single- and Multi-Channel Direct-Conversion Receiver: Analysis and Mitigation

Direct-conversion radio receivers can offer highly integrated low-cost hardware solutions for cognitive radio (CR) devices. Such receivers are, however, also very sensitive to various radio frequency (RF) impairments such as IQ imbalance, which can considerably limit the spectrum sensing capabilities. Most of the existing spectrum sensing studies in literature assume an ideal RF receiver and hence neglect the impacts of such practical RF hardware limitations. In this article, we study energy detection (ED) based spectrum sensing in both single-channel and multi-channel direct-conversion receiver scenarios impaired by IQ imbalance. With complex Gaussian primary user (PU) signal models, we first derive the detection and false alarm probabilities in closed-form for both receiver scenarios. The analytical results, confirmed through extensive simulations, show that while the single-channel receiver scenario is fairly robust to IQ imbalance, the wideband multi-channel sensing receiver is very sensitive to the image channel crosstalk induced by IQ imbalance. More specifically, it is shown that the false alarm probability of multi-channel energy detection increases significantly, compared to ideal RF receiver case, and the exact performance depends on the image channel power level and IQ imbalance values. In order to prevent such degradation in the ability to identify free spectrum, a waveform level interference cancellation method is then proposed to mitigate the image channel crosstalk. The optimum cancellation coefficient yielding interference-free signal is first derived, being also complemented with a practical coefficient sample estimator. An explicit condition is also derived for which the proposed cancellation scheme deploying the practical coefficient sample estimator provides performance gain in the sensing decisions compared to uncompensated energy detection. Extensive computer simulations with various signal and imbalance conditions are provided which demonstrate that the proposed enhanced energy detection can suppress the image channel crosstalk efficiently, yielding detection and false alarm probabilities essentially identical to those of an ideal RF receiver.

Spectrum sensing and resource allocation for multicarrier cognitive radio systems under interference and power constraints

Multicarrier waveforms have been commonly recognized as strong candidates for cognitive radio. In this paper, we study the dynamics of spectrum sensing and spectrum allocation functions in cognitive radio context using very practical signal models for the primary users (PUs), including the effects of power amplifier nonlinearities. We start by sensing the spectrum with energy detection-based wideband multichannel spectrum sensing algorithm and continue by investigating optimal resource allocation methods. Along the way, we examine the effects of spectral regrowth due to the inevitable power amplifier nonlinearities of the PU transmitters. The signal model includes frequency selective block-fading channel models for both secondary and primary transmissions. Filter bank-based wideband spectrum sensing techniques are applied for detecting spectral holes and filter bank-based multicarrier (FBMC) modulation is selected for transmission as an alternative multicarrier waveform to avoid the disadvantage of limited spectral containment of orthogonal frequency-division multiplexing (OFDM)-based multicarrier systems. The optimization technique used for the resource allocation approach considered in this study utilizes the information obtained through spectrum sensing and knowledge of spectrum leakage effects of the underlying waveforms, including a practical power amplifier model for the PU transmitter. This study utilizes a computationally efficient algorithm to maximize the SU link capacity with power and interference constraints. It is seen that the SU transmission capacity depends critically on the spectral containment of the PU waveform, and these effects are quantified in a case study using an 802.11-g WLAN scenario.

Optimized FFT and filter bank based spectrum sensing for Bluetooth signal

Wireless Local Area Networks (WLAN) and Wireless Personal Area Networks (WPAN) such as the Bluetooth are designed to operate in 2.4 GHz ISM band. Since both IEEE 802.15 based Bluetooth and IEEE 802.11 WLAN devices, among various others, use the same frequency band, interference may lead to significant performance degradation. Hence, Cognitive Radio (CR) technology has been considered for coordinating better the spectrum use in this band. Spectrum sensing is one of the most important functions in a CR. In this study, energy detector based spectrum sensing techniques are optimized for detecting Bluetooth signals, considering both the effect of non-flat power spectrum and frequency hopping characteristics. To reduce complexity and required number of samples for effective spectrum sensing, optimum weighting process is proposed for subband based spectrum sensing. Bluetooth sensing is analyzed also in the presence of WLANs at nearby frequencies.

FFT and filter bank based spectrum sensing for WLAN signals

As wireless communication devices have increased rapidly, much attention has been paid to the spectrum resources. Cognitive Radio (CR) technology has received increasing attention as a potential approach to utilize more effectively the radio frequency bands. CRs need to dynamically and reliably determine spectral holes which could be used for secondary transmissions. In this study, wideband multichannel spectrum sensing techniques are considered, using either FFT or filter bank based spectrum analysis. We focus on detecting spectral gaps between OFDM-based WLAN signals in the 2.4 GHz ISM band. It is found out that the limited spectral purity of WLAN signals, allowed by the 802.11g specifications, significantly restricts the ability to detect possible weaker signals in the spectral gaps between WLAN channels. Improved spectral purity of the primary signals greatly enhances the sensitivity of spectrum sensing, provided that spectrally well-contained filter bank based spectrum analysis methods are used.

Subband Energy Based Reduced Complexity Spectrum Sensing Under Noise Uncertainty and Frequency-Selective Spectral Characteristics

The present work proposes a subband energy detection method that performs efficiently under noise uncertainty (NU) and frequency-selective channels. The critical impact of detrimental modeling uncertainties, such as NU, is analytically quantified and it is shown that the introduced method is robust to both NU and frequency-selectivity conditions. This is also the case for eigenvalue based sensing techniques, in contrast to traditional energy detector based sensing. Connections of the subband energy based approach and existing eigenvalue based methods are established analytically, which leads to a novel reduced complexity processing technique based on the difference between maximum and minimum subband energies. The proposed method is capable of providing accurate and robust performance with low signal-to-noise ratios (SNR) in the presence of NU. Closed-form expressions are derived for the corresponding probability of false alarm and probability of detection under frequency selectivity due to the primary signal spectrum and/or the transmission channel. The validity of the offered expressions is justified through comparisons with respective results from computer simulations. The sensing performance is evaluated in different communication scenarios, with different frequency-selective channel models and primary user waveforms. The offered results indicate that the proposed methods provide quite significant savings in complexity, e.g., 78% reduction in the considered example case, while also improving the detection performance at low SNRs and in the presence of NU.

Analysis and mitigation of RF IQ imbalance in eigenvalue based multichannel spectrum sensing

Direct-conversion radio receivers can provide highly-integrated and low-cost hardware solutions for cognitive radio (CR) devices. However, such receiver structures are also known to suffer from various RF circuit imperfections, especially IQ imbalance. When studying the performance of various spectrum sensing methods, most existing work in literature neglect the effects of such RF imperfections. In this paper, we analyze the performance of a well-known eigenvalue based sensing method, namely maximum-to-minimum eigenvalue test, in the challenging multichannel spectrum sensing context under sensing receiver RF IQ imbalance. It is analytically shown that under RF IQ imbalance, the false alarm probability of the spectrum sensor is always higher compared to ideal RF front-end case. Then, in order to compensate for such degradation in the ability to identify free spectrum, a waveform level interference cancellation solution is deployed to enhance the performance, and optimum cancellation coefficient together with a practical sample estimator are formulated. Extensive computer simulations are provided to illustrate the degradation of false alarm probability in the presence of practical IQ imbalance values. Furthermore, the simulations also show that the proposed interference cancellation method can efficiently suppress the IQ imbalance effects, achieving identical false alarm probability with the ideal RF front-end case.

Spectrum sensing and spectrum utilization model for OFDM and FBMC based cognitive radios

OFDM based 802.11g Wireless Local Area Networks (WLAN) operate in the 2.4 GHz ISM band. Various other wireless systems use the same band which causes interference and leads to significant performance degradation. Hence, Cognitive Radios (CR) could better determine free spectrum and coordinate the spectrum usage in this band. Apart from this, to reduce the interference due to spectral leakage, Filter Bank Multicarrier (FBMC) type of system is considered as an alternative to FFT based systems. In this study, FFT and filter bank based spectrum sensing methods are compared by applying them for detecting spectral holes between WLAN and FBMC channels, considering also the spectral leakage effects appearing practical WLANs. Also the performance of alternative multicarrier techniques regarding the efficiency of spectrum utilization is studied.

FFT and filter bank based spectrum sensing and spectrum utilization for cogntive radios

Wireless Local Area Networks (WLAN) such as the OFDM based 802.11g system is designed to operate in 2.4 GHz ISM band. IEEE 802.11 devices are used at the same frequency band with various other applications, which causes interference between different users and leads to significant performance degradation. Hence, Cognitive Radio (CR) technology has been considered for coordinating the spectrum usage in a better way in this band. Spectrum sensing is one of the most important functions in a CR. In this study, filter bank based wideband spectrum sensing techniques are applied for detecting WLAN signals and spectral holes. Also filter bank based multicarrier techniques, together with a water-filling based subcarrier loading algorithm, are considered for effective exploitation of the detected holes. Clear benefit is demonstrated for using spectrally well-contained subband processing instead of basic FFT approaches.

Which number format to use for baseband Wimax Modem implementation on an FPGA

FFT/IFFT is the most important module for OFDM based baseband Wimax Modem architecture. However, computational complexity of this module is larger compared to other blocks in an OFDM system. It is critically important for the realization of this module on an FPGA that is much faster than traditional processors. In this paper, computation of CORDIC algorithm which is essential for the implementation of FFT module on FPGA, implementation using floating point and signed magnitude is studied and their performance comparison is given.