A data-aided technique for cartier frequency offset estimation with continuous phase modulation （CPM） in burst- mode transmission is presented. The proposed technique first exploits a special pilot sequence, or training sequence, to form a sinusoidal waveform. Then, an improved dichotomous search frequency offset estimator is introduced to determine the frequency offset using the sinusoid. Theoretical analysis and simulation results indicate that our estimator is noteworthy in the following aspects. First, the estimator can operate independently of timing recovery. Second, it has relatively low outlier, i.e., the minimum signal-to-noise ratio （SNR） required to guarantee estimation accuracy. Finally, the most important property is that our estimator is complexity-reduced compared to the existing dichotomous search methods： it eliminates the need for fast Fourier transform （FFT） and modulation removal, and exhibits faster convergence rate without accuracy degradation.
A cognitive radio（CR） network with energy harvesting（EH） is considered to improve both spectrum efficiency and energy efficiency. A hidden Markov model（HMM） is used to characterize the imperfect spectrum sensing process. In order to maximize the whole satisfaction degree（WSD） of the cognitive radio network, a tradeoff between the average throughput of the secondary user（SU） and the interference to the primary user（PU） is analyzed. We formulate the satisfaction degree optimization problem as a mixed integer nonlinear programming（MINLP） problem. The satisfaction degree optimization problem is solved by using differential evolution（DE） algorithm. The proposed optimization problem allows the network to adaptively achieve the optimal solution based on its required quality of service（Qos）. Numerical results are given to verify our analysis.
Spectrum sensing is an essential component to realize the cognitive radio, and the requirement for real-time spectrum sensing in the case of lacking prior information, fading channel, and noise uncertainty, indeed poses a major challenge to the classical spectrum sensing algorithms. Based on the stochastic properties of scalar transformation of power spectral density（PSD）, a novel spectrum sensing algorithm, referred to as the power spectral density split cancellation method（PSC）, is proposed in this paper. The PSC makes use of a scalar value as a test statistic, which is the ratio of each subband power to the full band power. Besides, by exploiting the asymptotic normality and independence of Fourier transform,the distribution of the ratio and the mathematical expressions for the probabilities of false alarm and detection in different channel models are derived. Further, the exact closed-form expression of decision threshold is calculated in accordance with Neyman–Pearson criterion. Analytical and simulation results show that the PSC is invulnerable to noise uncertainty,and can achive excellent detection performance without prior knowledge in additive white Gaussian noise and flat slow fading channels. In addition, the PSC benefits from a low computational cost, which can be completed in microseconds.
Wideband spectrum sensing has drawn much attention in recent years since it provides more opportunities to the secondary users. However, wideband spectrum sensing requires a long time and a complex mechanism at the sensing terminal. A two-stage wideband spectrum sensing scheme is considered to proceed spectrum sensing with low time consumption and high performance to tackle this predicament. In this scheme, a novel multitaper spectrum sensing （MSS） method is proposed to mitigate the poor performance of energy detection （ED） in the low signal-to-noise ratio （SNR） region. The closed-form expression of the decision threshold is derived based on the Neyman-Pearson criterion and the probability of detection in the Rayleigh fading channel is analyzed. An optimization problem is formulated to maximize the probability of detection of the proposed two-stage scheme and the average sensing time of the two-stage scheme is analyzed. Numerical results validate the efficiency of MSS and show that the two-stage spectrum sensing scheme enjoys higher performance in the low SNR region and lower time cost in the high SNR region than the single-stage scheme.
