Peihan Qi

Energy-Efficient Optimal Power Allocation for Fading Cognitive Radio Channels: Ergodic Capacity, Outage Capacity, and Minimum-Rate Capacity

Green communications is an inevitable trend for future communication network design, especially for a cognitive radio network. Power allocation strategies are of crucial importance for green cognitive radio networks. However, energy-efficient power allocation strategies in green cognitive radio networks have not been fully studied. Energy efficiency maximization problems are analyzed in delay-insensitive cognitive radio, delay-sensitive cognitive radio, and simultaneously delay-insensitive and delay-sensitive cognitive radio, where a secondary user coexists with a primary user and the channels are fading. Using fractional programming and convex optimization techniques, energy-efficient optimal power allocation strategies are proposed subject to constraints on the average interference power, along with the peak/average transmit power. It is shown that the secondary user can achieve energy efficiency gains under the average transmit power constraint, in contrast to the peak transmit power constraint. Simulation results show that the fading of the channel between the primary user transmitter and the secondary user receiver and the fading of the channel between the secondary user transmitter and the primary user receiver are favorable to the secondary user with respect to the energy efficiency maximization of the secondary user, whereas the fading of the channel between the secondary user transmitter and the secondary user receiver is unfavorable to the secondary user.

Maximum-Eigenvalue-Based Sensing and Power Recognition for Multiantenna Cognitive Radio System

Spectrum sensing, as a way for a secondary user (SU) to detect the on/off status of the primary user (PU), has been well studied in the area of cognitive radio (CR) for the past ten years. It is noticed that most exiting techniques only assumed that the PU has constant transmit power, although in practice, the PU could possibly operate under more than one discrete power levels, as can be seen from many practical standards, e.g., 802.11, Global System for Mobile Communications, and Long-Term Evolution. In this paper, we investigate this new multiple primary transmit power (MPTP) scenario and propose the corresponding detection/recognition framework based on multiple-antenna configuration at the SU. Although the primary target in MPTP is still to detect the on/off status of the PU, a secondary target appears in order to identify PUs transmit power level once the PU is “detected” in the first place. To achieve these two targets, an eigenvalue-based approach is applied due to its robustness and least requirement on the knowledge of channels. To obtain the closed-form threshold expressions and the performance analysis, we adopt nonasymptotic Gaussian and Gamma approximation for the distribution of the eigenvalues of the sample covariance matrix. Simulation results are provided to verify the proposed studies.

Predecision for Wideband Spectrum Sensing With Sub-Nyquist Sampling

Built on compressed sensing theories, sub-Nyquist spectrum sensing (SNSS) has emerged as a promising solution to the wideband spectrum sensing problem. However, most of the existing SNSS methods do not distinguish if primary users (PUs) are present or absent in the concerned spectrum band and directly pursue support recovery of the PUs. This may lead to a high false alarm rate and a waste of computational cost. To address the issue, we propose a predecision algorithm, referred to as the pairwise channel energy ratio (PCER) detector, to determine the presence or absence of PUs prior to signal support recovery. The proposed detector is based on the popular modulated wideband converter (MWC) framework for SNSS, which has several advantages over other SNSS approaches. The PCER test statistic is constructed from compressed samples obtained by the MWC. The decision threshold and the detection probability are derived in closed form following the Neyman–Pearson criterion. Numerical results are presented to verify the theoretical calculation. The proposed PCER detection method is shown to be able to detect the existence of PUs in a wide range of signal-to-noise ratio, while being robust to noise uncertainty and does not need the prior knowledge of the PU signals. Additionally, our results show that the use of the PCER detector leads to a significant improvement of the correct support recovery rate of the PU signals.

Residual correlation matrix detection based blind sub-Nyquist spectrum sensing for cognitive radio networks

Benefiting from compressed sensing theory, sub-Nyquist spectrum sensing (SNSS) has been considered as a promising way to deal with the implementation limitations of conventional wideband spectrum sensing in cognitive radio (CR) networks. However, in most existing SNSS methods, the prior knowledge of the monitored frequency bands is needed to determine the termination condition of the iteration process in implicit signal recovery stage, which may be difficult to acquire in practical CR scenarios. To address this dilemma, a blind SNSS algorithm for multicoset sub-Nyquist sampling framework, referred to as Residual corrElation mAtrix Detection (READ), is proposed to control the iteration process autonomously and appropriately. Simulation results show that, without any prior knowledge, the READ algorithm can precisely determine the support of a multiband signal contaminated by the background noise in a wide range of signal to noise ratio (SNR).

Measuring the Complexity of Chaotic Time Series by Fuzzy Entropy

A bridge called chaotic time series connects the chaos theory and real world. The properties of chaos make a natural relationship with security problem about information, such as sensitive dependence on initial conditions, unpredictable result for long and so on. With the rapid development of communication and the Internet technologies in information time, the security problem about information has become a hot focus in our daily life. Accordingly the implementation of chaotic theory is becoming widely used, which makes the complexity analysis of the chaotic time series become the highlights. The meaning of complexity is hard to make sense. When applied in high-security multiple-access communication systems, the high complexity makes the sequence obscure and hard to analysis. In this paper, an introduction of a new complexity measurement to evaluate the chaotic time series based on the Fuzzy Entropy is given. The results of simulations and analysis illustrates that, it is the FuzzyEn scheme that makes it possible to have a better understanding of the changing complexities of the sequences.

High order cumulants based spectrum sensing and power recognition in hybrid interweave-underlay spectrum access

In this paper, we propose a high order cumulants based spectrum sensing and power recognition (CSR) detector for hybrid interweave-underlay spectrum access, where the primary system is with multiple transmit power levels. Specifically, to detect the idle spectrum when primary user (PU) is absent, high order cumulants based spectrum sensing is performed in interweave model. When PU is detected, the working model is switched to underlay model, where detection of PUs transmit power level is performed to allow secondary user (SU) to adjust its power for fully exploring spectrum access opportunities without harmful interference to PU. Given a certain order and a certain lag, the test statistics of the proposed detector is derived by leveraging general likelihood ratio test. Since cumulants higher than second order are zero for Gaussian distributions, the proposed CSR detector can extract non-Gaussian signal from Gaussian noise even when the noise is colored. Additionally, the proposed detector does not require any prior knowledge about the noise variance, thus it is robust to noise uncertainty. Closedform results for threshold expression are derived, and numerical results are provided to evaluate the proposed detector.

Comparison results of stochastic resonance effects realised by coherent and non-coherent receivers under Gaussian noise

To boost the performance of binary pulse amplitude modulation at low signal-to-noise ratio, the parameter-tuned stochastic resonance (SR) is introduced into digital communications system. In this study, an analytical framework is developed for evaluating the system performance by approximating the probability density function of the Ornstein–Uhlebeck noise based on the central limit theorem. Expression for the bit error rate of the bistable SR system with coherent receiver is derived. Theoretical and numerical results are presented to verify the analysis that the noise can improve the performance of the SR system with non-coherent receiver. Also, it is shown that the performance of the bistable SR system with coherent receiver is superior to that with non-coherent receiver, and the background noise is not favourable to signal processing in the coherent receiver.

Random, Persistent, and Adaptive Spectrum Sensing Strategies for Multiband Spectrum Sensing in Cognitive Radio Networks With Secondary User Hardware Limitation

In this paper, we consider hardware limitation at the secondary user, which makes multiband (wideband) spectrum sensing more challenging. Under secondary user (SU) hardware limitation, the SU can only sense a small portion of the multiband spectrum for a given time period, which introduces a design issue of selecting subchannels to sense at a given time. A random spectrum sensing strategy (RSSS) is presented to select the subchannels to sense in a totally random fashion. With the Markov assumption of the primary user (PU) behavior, a persistent spectrum sensing strategy (PSSS) is proposed to take advantage of the PU traffic patterns in determining the channels to sense. Theoretical and simulation results show that RSSS and PSSS display different performance in different ranges of PU traffic parameters. We finally propose an adaptive spectrum sensing strategy (ASSS), which determines whether to use RSSS or PSSS for spectrum sensing at a given time based on the estimated PU traffic parameters. Numerical results under various system parameters are presented to evaluate the performance of RSSS, PSSS, and ASSS. The ASSS is shown to gain the advantages of both RSSS and PSSS in different ranges of PU traffic parameters and provide more available subchannels for SU.

Feasibly efficient cooperative spectrum sensing scheme based on Cholesky decomposition of the correlation matrix

Cooperative spectrum sensing, proposed to improve the performance of spectrum sensing in cognitive radio systems where there are multiple secondary users who can cooperatively detect the presence of one primary user, is receiving significant attention. However, few cooperative sensing algorithms take the correlation among the received primary user signals into account. A feasibly efficient cooperative spectrum sensing scheme based on Cholesky decomposition of the correlation matrix of the received signals is proposed. The ratio of the maximum eigenvalue to the minimum eigenvalue of the matrix obtained by Cholesky decomposition is used to construct the test statistic. Analytical approximations for the false alarm probability and decision threshold are derived using a moment matching method. The new scheme is in the category of blind cooperative spectrum sensing schemes requiring neither information about the primary user signal nor the channel nor the noise power. The new scheme can work better than the existing eigenvalue-based cooperative spectrum sensing methods in some conditions, and it has lower complexity.

Adaptive secondary-user selection without prior information for cooperative spectrum sensing in CRNs

Cooperative spectrum sensing has draw great attention due to its advantages in high sensing performance and overcoming the hidden primary user problem. However, large numbers of secondary users participating in cooperative spectrum sensing result in extensive overhead due to sensing reporting and sensing decision. Additionally, spatial correlation among secondary users can decrease the sensing performance. In this paper, we develop an adaptive user selection mechanism for cooperative spectrum sensing without any information about secondary user location or distance among different users. Additionally, to decrease the effect of spatial correlation to sensing performance, we presents an effective parameter to evaluate that correlation relationship. Numerical results show that the performance of cooperative spectrum sensing achieves improvement and the overhead can be decreased under our proposed user adaptive user selection mechanism.