 Research
 Open Access
Spectrum sensing and resource allocation for multicarrier cognitive radio systems under interference and power constraints
 Sener Dikmese^{1}Email author,
 Sudharsan Srinivasan^{1},
 Musbah Shaat^{2},
 Faouzi Bader^{3} and
 Markku Renfors^{1}
https://doi.org/10.1186/16876180201468
© Dikmese et al.; licensee Springer. 2014
 Received: 10 December 2013
 Accepted: 15 April 2014
 Published: 12 May 2014
Abstract
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 detectionbased 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 blockfading channel models for both secondary and primary transmissions. Filter bankbased wideband spectrum sensing techniques are applied for detecting spectral holes and filter bankbased multicarrier (FBMC) modulation is selected for transmission as an alternative multicarrier waveform to avoid the disadvantage of limited spectral containment of orthogonal frequencydivision 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 802.11g WLAN scenario.
Keywords
 CR
 OFDM
 FBMC
 Filter bank
 Spectrum sensing
 Energy detector
 Spectrum utilization
 Loading algorithms
 Multicarrier
1 Introduction
One of the major challenges in cognitive radio (CR) operation is to utilize the available whitespace with minimal interference to the primary or prioritized secondary transmission systems [1]. Several spectrum sensing techniques have been proposed, e.g., in [2–5] to facilitate CR operation. Especially, energy detectorbased spectrum sensing algorithms have been widely considered due to low computational complexity. On the other hand, the fading channel capacity has already been studied from an information theoretic perspective, e.g., in [6, 7] in terms of resource allocation. Recently, the secondary user (SU) capacity has been widely studied. The SU channel capacity for additive white Gaussian noise (AWGN) channels under different power constraint is studied in [8]. The effect of various types of fading channels on the CR capacity has been studied in [9] under optimal power allocation strategy for the CR and subjected to an interference power constraint at the coexisting primary. Further, [10] discusses the effects of peak power and average interference power constraints on the outage capacity. In [11], the ergodic capacity, the delaylimited capacity, and the outage capacity of the CR in blockfading channels under spectrum sharing are discussed.
In this paper, we investigate two important features of the cognitive radio. We begin with the spectrum sensing function and later study the spectrum utilization function implementing optimized resource allocation under power and interference constraints. Instead of elaborate spectrum sensing techniques, such as cyclostationary and eigenvaluebased methods [2, 3], energy detectorbased spectrum sensing is utilized. This is motivated by subbandbased energy detector's capability to implement the needed spectrum analysis functions for identifying the available spectral slots and for estimating the signaltointerferenceplusnoise (SINR) ratios at subcarrier level for resource allocation purposes.
For a CR system, multicarrier modulation techniques are generally better suited as they are spectrally more efficient than single carrier systems and have the flexibility to allocate resources to the available spectral gaps and among different users to maximize system throughput. There are various ways of improving the spectral containment of multicarrier waveforms, including methods to suppress the strong side lobes of the orthogonal frequencydivision multiplexing (OFDM) spectrum [12–14]. Filter bank multicarrier (FBMC) is another multicarrier modulation scheme which has significantly reduced spectrum leakage compared to the cyclic prefixbased OFDM systems [15]. Also, the analysis filter bank (AFB) module of an FBMC receiver can be easily used for spectrum analysis purposes [15–22].
This paper includes a brief summary of our earlier studies concerning simple energy detectionbased wideband multichannel spectrum sensing techniques for identifying the spectrum holes, considering the 2.4GHz ISM band as a case study. We apply an AFBbased energy detector, which averages the subband sample energies. By this way, multiple center frequencies, bandwidths, and multiple spectral gaps can be identified rapidly, efficiently, and flexibly for potential use by the CR. A similar fast Fourier transform (FFT)based scheme is considered as a reference.
At the resource allocation stage, the transmit power of the subcarriers must be adjusted according to the channel state information (CSI) and the location of subcarriers with respect to the primary user's (PU) spectrum. In [23], an optimal and two suboptimal power loading algorithms are developed. These algorithms use Lagrange formulation which maximizes the downlink capacity of the CR keeping the interference to the primary transmission below a threshold, without considering the total power constraint. In [23, 24], the spectral hole and the signaltonoise (SNR) are fixed to simplify the model. In [25], a lowcomplexity suboptimal algorithm is proposed. The algorithm gives maximum power to each subcarrier based on the results from conventional water filling and then modifies these values by applying power reduction algorithm in such a way that the interference constraint is satisfied. In [23, 25], the used signal models are closer to the ideal signal model, e.g., assuming fixed spectral hole bandwidth, instead of a realistic system model. In reality, the spectral hole bandwidth varies with the SNR. A proper system model should include also a practical power amplifier model. Our study focuses on the missing aspects of these studies.
The optimal solution which maximizes the CR link capacity under both transmission power and interference constraints requires high computational complexity, and it is unsuitable for the practical applications. Low complexity algorithms are proposed in [25, 26]. However, in these methods, the interfered subcarriers are deactivated without considering optimized power and bit loading based on each subcarrier's SINR. Such optimization can be carried out using the power interference (PI) algorithm [27, 28]. The resource allocation method utilizes the results of spectrum sensing in an efficient way, so there is interdependence between the spectrum sensing and spectrum allocation functions, which has not been addressed in earlier work. We study this interdependence, focusing on its effects on efficient utilization of the sensed spectrum.
The main contributions of this study are listed as follows:

We have generalized the study for realistic signal models which can be applied to any multicarrier CR system.
Until now, simplistic CPOFDM signal models have been used as the PU and CR signal models for spectrum allocation algorithms [23–26, 29–32]. Except for [27, 28], CPOFDM has also been used for the CR. The primary knowledge we assume about the PU waveform is its transmitted power spectral density (PSD) and the receiver selectivity mask; otherwise, there are no limitations regarding the PU signal model. In our case study, we select the PU waveform either as CPOFDM following the 802.11g standard or an FBMC waveform with similar parameterization. Furthermore, a nonlinear transmitter power amplifier model (the socalled Rapp model [33]) is used for the PU system in order to obtain a realistic model for the PU spectrum. To the best of our knowledge, this aspect has not been considered in earlier work. In this way, we are able to quantify the effects of the PU spectral characteristics on the SU capacity. It is seen that the nonlinear power amplifierinduced spectral leakage (regrowth) effect, which is present in any radio communication system, has a significant impact on the SU capacity. As for the SU waveform, we have chosen the FBMC scheme for the case study because it has the sharpest spectrum, reaching the maximum spectral containment among the alternatives. However, generic multicarrier model is included in the overall system model, and the analysis and optimization methods are readily applicable for any multicarrier waveform for the CR.
Furthermore, previous studies on CR resource allocation in [23–25, 27–32] consider only flat fading channel models. However, the performance of spectrum sensing and resource allocation is affected significantly when frequencyselective fading channel is assumed. In our study, all the links within/between PU and SU systems associated with spectrum estimation and spectrum utilization are modeled as frequency selective block fading channels.

Combined spectrum sensing and resource allocation algorithms for cognitive radios.
There has been no previous work addressing the combined spectrum sensing and resource allocation algorithm in the literature. Especially, different types of spectrum sensing algorithms have been applied without considering any particular spectrum utilization techniques to make efficient use of the available spectral holes [1–5]. Similarly, resource allocation algorithms have only been applied without any spectrum sensing information so far [27–32, 34, 35]. Constant number of available subbands has been considered in the spectral hole. However, the variation of the PUs' power level affects the actual number of available subbands, and this depends critically on the spectral characteristics of the PUs. Hence, spectrum sensing plays a crucial and enabling role for spectrum utilization process. The sensing function identifies the frequency band which is considered for allocation, but it is also needed for detecting possible other PU's starting to operate in the spectral gap during the SU operation. For this purpose, we assume that there are gaps in the CR transmission. In our study, efficient spectrum utilization methods are investigated and applied for maximizing the cognitive radio's throughput based on robust spectrum sensing results. It turns out that the PI algorithm is applicable in our scenario, with all the mentioned generalizations of the system model. The main contribution of this study is evaluating the SU performance with the combination of energy detectionbased wideband sensing algorithm and the PI algorithm for spectrum utilization in a realistic cognitive radio scenario.
The rest of this paper is organized as follows. In Section 2, the signal models for the CR and the primary transmission system, along with the mutual interference model between the CR and primary are explained. The problem definition for this study is given in the same section. In Section 3, FFT and AFBbased wideband spectrum sensing is reviewed considering the spectrum analysis aspects related to the multicarrier techniques. Section 4 develops the algorithms for spectrum allocation. Section 5 gives the numeric and graphic results obtained through simulations. Finally, some concluding remarks are given about the performance of these methods, along with discussion of possible further studies in this area.
2 Signal models and problem definition
2.1 Signal model for PU
where A is the input amplitude, κ is the small signal gain, A_{0} is the saturated amplitude, and p is the amplitude smoothness factor of the transition from linear to saturated amplitude range. Three cases with respect to the PA nonlinearity are considered in this study. No regrowth is the ideal case, and the Rapp PA nonlinearity with two different backoff values of 15 dB (modest case) and 5 dB (worst case) is illustrated in Figure 2. Parameters of the Rapp model have been chosen according to the practical model for PU signals based on [37]. In our study, we use κ = 1, p = 3, and A_{0} = 1 as Rapp model simulation parameters.
The 802.11gbased WLAN signal specifications allow the spectral regrowth in this scenario to be at the level of about −20 dB, i.e., close to the worst case model. We investigate how the CR system performance is affected by improved spectral containment of the PU signal through enhanced multicarrier waveform and/or improved power amplifier linearity. These effects for both sensing and utilization functions will be addressed in the study.
2.2 Signal model for cognitive radio
In this work, the CR waveform is chosen as FBMC due to its high spectral containment. Offset quadrature amplitude modulation (OQAM) is used for FBMCbased CRs to achieve orthogonality of subcarriers, as discussed in [18, 19, 38]. In Figure 1, the channels h_{0} and h_{1} are the channels from a cognitive transmitter to a primary receiver and a cognitive receiver, respectively. Channels h_{2} and h_{3} are from two different primary transmitters to the cognitive radio receiver. The channel estimate for h_{1} is made available by usual channel estimation procedure of the CR system. The knowledge about channel h_{0} can be obtained through the channel reciprocity in TDD operation. Here, the channel amplitude response is sufficient and the phase response is irrelevant. The amplitude responses of channels h_{2} and h_{3} are first obtained through the spectrum sensing function of the CR device and later on, during secondary transmission, through the SINR estimation function of the CR device. The effects of primary spectral dynamics at the edges of the white space play an important role both in spectrum sensing and in spectrum allocation. This dependency is captured by the analytical models and revealed by the simulations to be presented later.
Also, the nonlinear PA model can be straightforwardly included in the CR signal model and the interference models developed below. However, in the numerical studies of this paper, we consider an ideal FBMC waveform for the CR as the focus is on the SU capacity and its dependency on the PU waveform characteristics. Generally, good spectral containment is regarded as one of the key requirements for the CR transmitter. Detailed evaluation of the performancecomplexity tradeoffs for the CR implementation, including the PA linearity requirements, is a rather complicated issue and is left as a topic for future studies.
2.3 Definition of the interference problem
Here, H_{0}(f) is the channel frequency response between the CR transmitter and a primary receiver. Φ(f) represents the subcarrier power spectral density of the underlying multicarrier technique employed by the CR. Ψ(f) denotes the PU sensitivity mask characterizing the effects of the PU receiver filtering. B denotes the CR subcarrier bandwidth which is considered significant for the interference estimation. Finally, Ω_{ k } represents the combined interference factor for the k th CR subcarrier.
where H_{2}(f) is the channel frequency response between the primary transmitter and CR receiver. H_{1,k} is the channel gain between the CR transmitter and the CR receiver at the frequency of k th subcarrier. This channel can be assumed to be flatfading at the subcarrier level. Ψ_{PA}(f) is the power spectral density as seen at the output of the PU's transmitter antenna. This depends on the PU transmission power and its subcarrier spectrum, as well as on the spectral regrowth due to the PU power amplifier. Φ(f) is the CR receiver sensitivity mask characterizing the CR receiver subband filtering effects. ${\mathit{\sigma}}_{\mathit{w}}^{2}$ is the variance of the additive white Gaussian noise.
The power amplifier effects of the secondary transmission are not considered in our numerical study as they play no role in the spectrum sensing part and the effect of PArelated spectrum leakage on the interference to the PUs is expected to be relatively small. For this reason, the same Φ(f) function can be used in (5) for the CR subcarrier spectrum and in (6) for the CR receiver sensitivity mask. However, the developed generic signal model allows to include also the CR transmitter PA effects by using different CRrelated spectral functions in (5) and (6).
The interference models of (5) and (6) assume certain knowledge about PU characteristics and the channels between PUs and CRs. Regarding the interference from an active PU transmitter to a CR receiver in (6), the joint effect of transmitter power spectrum and the transmission channel can be estimated by the receiving CR station by utilizing the spectrum sensing function. This information can be communicated through the control channel to the transmitting CR station for optimizing the spectrum utilization. Regarding the channel from the CR transmitter to PU receiver, the knowledge would be available in a TDMA/TDDbased PU system (like a WLAN) based on channel reciprocity, if the PU transmission power is known. Of course, for a PU station which is in idle mode over long periods, such information is not available.
3 Filter bank energy detectorbased spectrum sensing algorithms
Energy detector, which is also known as radiometer, is the most common method of spectrum sensing due to its low computational and implementation complexity [2–5]. Furthermore, it is more generic compared to most of the other methods as it does not need any information about the PU waveform. Subbandbased energy detection, using FFT or AFB for spectrum analysis, is in the focus of this study. Basically, the energy of the received signal is compared with a threshold value which is calculated according to noise variance and desired false alarm probability in detecting spectral holes.
where S(m, k) is the transmitted WLAN or FBMC based PU signals as seen in subband k during the m th symbol interval with zero mean and variance ${\mathit{\sigma}}_{\mathrm{PU}}^{2}$. When there are no PU signals (hypothesis ℋ_{0}), the noise samples W(m, k) are modeled as AWGN with zeromean and variance ${\mathit{\sigma}}_{\mathit{w}}^{2}$. When a PU signal is present (hypothesis ℋ_{1}), the WLAN and FBMCbased PU signals can also be modeled as zeromean Gaussian distribution with variance ${\mathit{\sigma}}_{\mathrm{PU},\mathit{k}}^{2}+{\mathit{\sigma}}_{\mathit{w}}^{2}$.
In this formula, L_{ f } and L_{ t } are the window lengths in frequency and time domain averaging, respectively. The output of $\tilde{\mathit{T}}\left(\mathit{m},\mathit{k}\right)$ is passed to a decision device to determine the possible occupancy of the corresponding frequency band at the corresponding time interval. The window length in frequency direction is selected based on the expected minimum bandwidth of the PU signal or spectrum hole, and then the required time domain window length can be calculated from the target false alarm and missed detection probabilities. The basic approach would be to calculate (8) for a nonoverlapping set of windows. However, using a sliding window in frequency and/or time direction can also be done with relatively small addition to complexity. Timedomain sliding window helps to detect rapidly reappearing PU [4, 5] whereas sliding window in the frequency direction helps to locate spectrum gaps with unknown center frequencies. Due to the Gaussian distribution of Y(m, k), the probability distribution function (PDF) of $\tilde{\mathit{T}}\left(\mathit{m},\mathit{k}\right)$ becomes approximately Gaussian under ℋ_{0} and ℋ_{1}. [3].
is the leakage power from the adjacent PU transmitter to the sensing frequency band between frequencies f_{1} and f_{2}. H_{2}(f) is the channel frequency response from a primary transmitter the CR receiver. In (9), λ is the decision threshold value which is calculated using a wellknown analytical model from the noise variance estimate and target false alarm probability P_{FA}.
In principle, if there is a reliable estimate of the PU transmission power and reliable knowledge about its spectrum shape, then the above analysis could be used for improving the spectrum sensing at the frequencies affected by the spectrum leakage. However, this would be very challenging in practice due to the unpredictability of the PA characteristics, and the above model is used only for the purpose of performance analysis.
For different PU SNR values, different number of empty subbands, N_{gap}, will be detected due to the PU spectral leakage effects and statistical nature of the spectrum sensing process. The expression (9) can be used for evaluating the false alarm probability for different sensing bandwidths in different parts of the spectrum gap.
The spectrum sensing function identifies groups of L_{ f } subbands which are deemed to be available for secondary transmissions. In the following stage, the spectrum utilization function is employed to perform power and bit allocation to those subcarriers.
4 Spectrum utilization
After nonactive spectrum has been identified, spectrum utilization becomes an important consideration, when considering the overall efficiency of the CR system. The number of available nonactive subbands is the output of the sensing algorithm, along with information about the nonactive band edges.
The above solution is optimal only when the total power is greater than or equal to the power under the interference constraint. Mostly, in practice, this condition is not true and this is the motivation for the PI algorithm. Detailed discussion and its comparison to various other algorithms for spectrum utilization are available in [27].
In this study, the PI algorithm is found to be directly applicable in case of the developed greatly enhanced system model for the secondary usage scenario. PI algorithm has four stages: maximum power determination, power constraint, power budget distribution, and power level readjustment [27].
5 Simulation results
In our test scenario, the CR's spectrum sensing function has identified a potential spectral gap between two relatively strong PUs, as illustrated in Figure 2. We should also consider the possibility that there is another, relatively weak PU signal, using one of the WLAN channels 4…7, and fully or partly occupying the gap between channels 3 and 8. Thus, one purpose of spectrum sensing is to secure that there are no other PUs active in the considered gap. We assume that there are no additional signals within the spectral gap, but the spectrum sensing makes anyway false alarms. Especially close to the edges of the gap, the spectrum leakage from the PUs raises the false alarm probability. This effect depends on the power level (SNR) of the PUs. In our case study, the spectrum sensing and CR transmissions use a smaller subband spacing of 81.5 kHz, instead of the 325kHz subcarrier spacing of WLANs, in order to reduce the effects of frequency selective channels. Targeting at −5 dB SNR in spectrum sensing, false alarm probability of 0.1, and detection probability of 90%, the required sample complexity is around 250 complex samples. The time and frequency averaging lengths are chosen as 50 and 5, respectively. The spectral hole starts from the side lobes of WLAN 1 signal and ends at the side lobes of WLAN 2 spectrum. The available number of subbands/bandwidth of the spectrum is obtained after subbandbased energy detection, using FFT or AFB for spectrum analysis. Then, the initial SINR estimation and spectrum allocation is done based on the sensing results. Later on, the SINR estimates are updated during SU system operation to track the changing radio environment under frequencyselective fading channel conditions. It is assumed that the spectrum sensing is done in regular intervals during gaps in the CR transmission and this helps in detecting reappearing PU signals in the spectral gap.
It should be noticed that in the considered scenario, there is no way for the CR system to determine the useful received power level at the PU receiver. Therefore, we choose the interference threshold to be 6 dB below the thermal noise level, in order not to introduce significant performance loss in case the primary receiver is operating close to the sensitivity level (i.e., minimum received power level expected to be detectable). To determine the threshold value, we assume a simplified scenario, where the path losses of channels h_{0} and h_{1} are normalized to 1, i.e., the average power gains of channels h_{0} and h_{1}, denoted as G_{0} and G_{1}, are equal to one. Further, we assume that the average SNR of the CR receiver is 10 dB. Then, the interference threshold is −16 dB in reference to the total CR transmission power P_{ T }, or I_{th} = P_{ T }/40. More generally, relaxing the normalization of h_{0} and h_{1}, this can be expressed as I_{th} = G_{1}P_{ T }/(40G_{0}).
6 Conclusions
We have studied the effects of combined spectrum sensing and spectrum utilization for FBMCbased cognitive radios with realistic signal model under frequencyselective fading channel conditions. Firstly, the performance of energy detectionbased spectrum sensing technique was analyzed using both the FFT and filter bankbased spectrum analysis methods for both WLAN and FBMC signal models. Then, the utilization of dynamically identified spectral holes with spectrum allocation algorithms, subject to power and interference constraints, was investigated. Through this study, the effect of PU waveform's spectral containment on the CR transmission capacity was revealed. Here, we considered the choice between OFDM and FBMC primaries, together with the effect of spectral regrowth due to power amplifier nonlinearity.
In terms of the spectrum sensing performance, AFB has clear benefits due to much better spectral containment of the subbands. One important benefit of FBMC as a transmission technique in CR systems is that it can utilize narrow spectral gaps in an effective and flexible way, even in the presence of strong primaries at the adjacent spectral slots. This is due to the excellent spectral containment properties of the FBMC system. Additionally, an FBMC receiver can use the AFB for highperformance spectrum sensing with no additional complexity.
The utilization of the sensed spectrum can be optimized by using proper spectrum allocation algorithms. The PI algorithm has relatively low complexity, and it improves the capacity of the CR system as compared to the simple water fillingbased spectrum allocation. One of the main observations of this work was that the PI algorithm can be directly utilized with the developed highly enhanced and realistic CR system model. The system model accommodates frequencyselective channel models for all the associated transmission links between PUs and SUs, as well as arbitrary transmitted power spectra and receiver frequency responses. Because of the above features, a FBMCbased CR system achieves higher capacity in comparison with traditional WLANbased system. This increase in capacity can be attributed to the efficient use of the available spectrum and very small interference introduced to the primary transmissions at adjacent frequencies.
One of the important aims of this study was to understand the interdependence of the spectrum sensing and the spectrum utilization parts. It can be seen that increased false alarm probability has a direct effect on the available spectrum, and hence, it heavily influences the spectrum utilization. The PU power amplifier nonlinearity influences the sensed secondary spectrum introducing false alarms, hence lowering the CR system's spectrum utilization. It was demonstrated that, with heavy power amplifier nonlinearity, the FBMCbased primary is no better than the OFDM primary in what comes to the available capacity for secondary usage in the nearby frequencies.
In the numerical studies of this paper, we considered an ideal FBMC waveform for the CR, without considering the PA nonlinearity effects, since the focus is on the SU capacity and it dependency on the PU signal characteristics. Generally, good spectral containment is regarded as one of the key requirements for the CR transmitter. Also, the nonlinear PA model can be straightforwardly included in the developed interference models. Detailed evaluation of the performancecomplexity tradeoffs for the CR implementation, including the PA linearity requirements, is a rather complicated issue and is left as a topic for future studies.
Declarations
Acknowledgements
This work was partially supported by Tekniikan Edistämissäätiö (TES), GETA Graduate School, the European Commission under Project EMPhAtiC (FP7ICT201209318362), COST Action IC0902, Tekes (the Finnish Funding Agency for Technology and Innovation) under the project ENCOR in the Trial Program.
Authors’ Affiliations
References
 Mitola J, Maguire JGQ: Cognitive radio: making software radios more personal. IEEE Pers Commun Mag 1999, 6(4):1318. 10.1109/98.788210View ArticleGoogle Scholar
 Zeng Y, Liang YC, Hoang AT, Zhang R: A review on spectrum sensing for cognitive radio: challenges and solutions. EURASIP J Adv Signal Process 2010, 2010: 115.View ArticleGoogle Scholar
 Yucek T, Arslan H: A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun Surv Tutorials 2009, 11(1):116130.View ArticleGoogle Scholar
 Dikmese S, Renfors M, Dincer H: FFT and filter bank based spectrum sensing for WLAN signals, in Proc (European Conference on Circuit Theory and Design (ECCTD’11). Linkoping, Sweden; 2011.Google Scholar
 Dikmese S, Renfors M: Optimized FFT and filter bank based spectrum sensing for bluetooth signal, in Proc (Wireless Communications and Networking Conference (WCNC’12). France, Paris; 2012.Google Scholar
 Goldsmith AJ, Varaiya PP: Capacity of fading channels with channel side information. IEEE Trans Inf Theory 1997, 43(6):19861992. 10.1109/18.641562MATHMathSciNetView ArticleGoogle Scholar
 Biglieri E, Proakis J, Shamai S: Fading channels: informationtheoretic and communications aspects. IEEE Trans Inf Theory 1998., 44(6):Google Scholar
 Gastpar M: On capacity under receivedsignal constraints, in Proc (42nd Annual Allerton Conference Communication. Control Comput, Monticello, USA; 2004.Google Scholar
 Ghasemi A, Sousa E: Fundamental limits of spectrumsharing in fading environments. IEEE Trans Wirel Commun 2007, 6(2):649658.View ArticleGoogle Scholar
 Musavian L, Aissa S: Ergodic and outage capacities of spectrumsharing systems in fading channels, in Proc (IEEE Global Telecommunications Conference (GLOBECOM’07), Washington. DC, USA; 2007.Google Scholar
 Kang X, Liang YC, Nallanathan A, Garg HK, Zhang R: Optimal power allocation for fading channels in cognitive radio networks: ergodic capacity and outage capacity. IEEE Trans Wirel Commun 2009, 8(2):940950.View ArticleGoogle Scholar
 Sahin A, Arslan H: Edge windowing for OFDM based systems. Commun Lett IEEE 2011, 15(11):12081211.View ArticleGoogle Scholar
 Brandes S, Cosovic I, Schnell M: Sidelobe suppression in OFDM systems by insertion of cancellation carriers’, in Proc (Vehicular Technology Conference, (VTC’05Fall). Dallas, USA; 2005.Google Scholar
 Loulou A, Renfors M: ‘Effective schemes for OFDM sidelobe control in fragmented spectrum use, in Proc (IEEE 24th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMR’13). United Kingdom, London; 2013.Google Scholar
 Boroujeny BF, Kempter R: Multicarrier communication techniques for spectrum sensing and communication in cognitive radios. IEEE Commun Mag (Spec Issue Cogn Radios Dynamic Spectr Access) 2008, 48(4):8085.Google Scholar
 Amini A, Kempter R, Lin L, FarhangBoroujeny B: Filter bank multitone: a candidate for physical layer of cognitive radio, in Proc (Software Defined Radio Technical Conference and Product Exhibition (SDR '05). Orange County, USA; 2005.Google Scholar
 Amini A, Kempter R, FarhangBoroujeny B: A comparison of alternative filterbank multicarrier methods in cognitive radios, in Proc (Software Defined Radio Technical Conference and Product Exhibition (SDR '06). Orlando, USA; 2006.Google Scholar
 Bellanger M, Ihalainen T, Renfors M: Filter bank based cognitive radio physical layer, in Proc. Future Network & Mobile Summit, Santander, Spain; 2009.Google Scholar
 Ihalainen T, Viholainen A: TH Stitz, M Renfors, Spectrum monitoring scheme for filter bank based cognitive radios, in Proc. Future Network & Mobile Summit, Florence, Italy; 2010.Google Scholar
 Srinivasan S, Dikmese S, Renfors M: Spectrum sensing and spectrum utilization model for OFDM and FBMC based cognitive radios, in Proc (Signal Processing Advances in Wireless Communications (SPAWC’12). Cesme, Turkey; 2012.Google Scholar
 Ringset V, Rustad H, Schaich F, Vandermot J, Najar M: Performance of a filterbank multicarrier (FBMC) physical layer in the WiMAX context, in Proc. Future Network & Mobile Summit, Florence, Italy; 2010.Google Scholar
 Dikmese S, Srinivasan S, Renfors M: FFT and filter bank based spectrum sensing and spectrum utilization for cognitive radios, in Proc (International Symposium on Communications, Control, and Signal Processing (ISCCSP’12). Rome, Italy; 2012.Google Scholar
 Bansal G, Hossain MJ, Bhargava VK: Adaptive power loading for OFDMbased cognitive radio systems, in Proc (IEEE International Conference on Communications (ICC '07). Glasgow, UK; 2007.Google Scholar
 Bansal G, Hossain MJ, Bhargava VK: Optimal and suboptimal power allocation schemes for OFDMbased cognitive radio systems. IEEE Trans Wirel Commun 2008, 7(11):47104718.View ArticleGoogle Scholar
 Qin T, Leung C: Fair adaptive resource allocation for multiuser OFDM cognitive radio systems, in Proc (2nd International Conference on Communications and Networking in China (ChinaCom '07). Shanghai, China; 2007.Google Scholar
 Zhang Y: Resource allocation for OFDMbased cognitive radio systems. University of British Columbia, Vancouver, Canada; 2008. Dec, Ph.D. dissertationGoogle Scholar
 Shaat M, Bader F: Computationally efficient power allocation algorithm in multicarrierbased cognitive radio networks: OFDM and FBMC systems. EURASIP J Adv Signal Process 2010., 13: doi:Article ID 528378Google Scholar
 Shaat M, Bader F: Power allocation with interference constraint in multicarrier based cognitive radio systems, in Proc (7th International Workshop on MultiCarrier Systems and Solutions (MCSS '09). Herrsching, Germany; 2009.Google Scholar
 Jang J, Lee KB: Transmit power adaptation for multiuser OFDM systems. IEEE J Selected Areas Commun 2003, 21(2):171178. 10.1109/JSAC.2002.807348View ArticleGoogle Scholar
 Kivanc D, Li G, Liu H: Computationally efficient bandwidth allocation and power control for OFDMA. IEEE Trans Wirel Commun 2003, 2(6):11501158. 10.1109/TWC.2003.819016View ArticleGoogle Scholar
 Shen Z, Andrews JG, Evans BL: Optimal power allocation in multiuser OFDM systems, in Proc (IEEE Global Telecommunications Conference (GLOBECOM '03). San Francisco, USA; 2003.Google Scholar
 Wong CY, Cheng RS, Letaief KB, Murch RD: Multiuser OFDM with adaptive subcarrier, bit, and power allocation. IEEE J Selected Areas Commun 1999, 17(10):17471758. 10.1109/49.793310View ArticleGoogle Scholar
 Rapp C: Effects of the HPAnonlinearity on 4DPSK OFDM signal for a digital sound broadcasting system. In Proc Conf. Rec. ECSC’91. Luettich, Germany; 1991.Google Scholar
 Cioffi J: Digital Communication: Signal Processing. Standford, California, USA; 2000.Google Scholar
 Lu Q, Peng T, Wang W: Efficient multiuser waterfilling algorithm under interference temperature constraints in OFDMAbased cognitive radio networks. In Proc IEEE International Symposium Microwave, Antenna, Propagation, and EMC Technologies for Wireless Communications. MAPE’07, Hangzhou, China; 2007.Google Scholar
 Stavroulaki V, Tsagkaris K, Demestichas P, Gebert J, Mueck M, Schmidt A, Ferrus R, Sallent O, Filo M, Mouton C, Rakotoharison L: Cognitive control channels: from concept to identification of implementation options. IEEE Commun Mag 2012, 50(7):96108.View ArticleGoogle Scholar
 Eltholth AA, Mekhail AR, Elshirbini A, Dessouki MI, Abdelfattah AI: Modeling the effect of clipping and power amplifier nonlinearities on OFDM systems. Ubiquitous Comput Commun J 2009, 3(1):5459.Google Scholar
 PHYDYAS: Physical layer for dynamic spectrum access and cognitive radio. . Accessed 5 June 2013 http://www.ictphydyas.org/
 Yucek T, Arslan H: Spectrum characterization for opportunistic cognitive radio systems, in Proc. IEEE Military Communication Conference, Washington, D.C., USA; 2006.Google Scholar
 Jain R, Channel Models: A Tutorial (2007). . Accessed 5 July 2013 http://www.cse.wustl.edu/~jain/cse57408/ftp/channel_model_tutorial.pdf
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