Shuo Shi

A joint optimization of energy harvesting and spectrum sensing for wireless sensor networks under Middleton Class A noise

With the growing popularity of wireless sensor networks, the environment in which the network is located becomes more undesirable. In addition, the problems of spectrum scarcity and the short sensor lifetime have become increasingly prominent. In this article, we incorporate the two technologies of cognitive radio and energy harvesting to solve the above problems of wireless sensor networks under impulsive noise. First, we use a Middleton Class A noise model to imitate the practical environment and the fractional lower order moments detector is employed to perform spectrum sensing for the sensors of wireless sensor networks, which are performing as the second users. Second, a new time-slots structure is proposed for the self-powered second user and the analytical expression of the second user’s average throughput is derived. Finally, we maximize the second user’s average throughput by a joint optimization of the sensing duration and data transmission duration while giving the primary user sufficient protection. Simulation shows that a much better performance can be achieved by fractional lower order moment detector than the traditional energy detector. Moreover, our optimization of the time-slots allocation is feasible and the maximum second user’s average throughput can be obtained.

A Modified AODV Protocol Based on Nodes Velocity

MANET has been widely used in many fields with the development of wireless communication technology. The AODV routing protocol which is known as a well-designed protocol of MANET has received widespread attention. However, high node velocity and frequent changes of network topology pose a challenge to the classic AODV protocol. Considering the stability of link, this paper proposes an algorithm to quantify the change frequency of network topology at first. Then a modified AODV protocol based on node velocity which is named RAODV is introduced in detail for high dynamic network topology. RAODV can build a more stable link according to the node velocity and reduce the normalized overhead of routing and average end-to-end delay by prolonging routing’s survival time.

Extension of 2FSK Signal Detection Utilizing Duffing Oscillator

Based on the sensitivity of the chaotic system to the initial value and the characteristics of the noise immunity, this paper presents a method to detect the FSK signal of closed carrier frequency under the low signal-to-noise ratio based on the Duffing oscillator, and then give the principle of FSK signal and its modulation. Furthermore, a method to solve the problem that frequency overlapping occurred between two closed frequencies FSK signal is proposed. Based on the theoretical analysis, the simulation model is established by using MATLAB and Simulink. The simulation results show that the model can solve the frequency overlapping of the FSK signal effectively; meanwhile, it has good detection precision and anti-noise performance.

A Relevance Vector Machine Based Probability Prediction Method of Channel Available Time

The uncertainty of spectrum resources will seriously affect the prediction results of cognitive radio, and then affect the communication channel allocation and spectrum access. Therefore, it is very important to judge, analyze and estimate the state change of the spectrum resources. This paper introduces the RVM (Relevance Vector Machine) theory and put forward the probability interval prediction method of channel state duration. Based on the traditional machine learning, RVM is integrated with the Bayesian inference framework, and it can give the estimate value of the prediction error and give the prediction interval, which can cover the real value well.

A Robust FLOM Based Spectrum Sensing Scheme under Middleton Class A Noise in IoT

Accessibility to remote users in dynamic environment, high spectrum utilization, and no spectrum purchase make Cognitive Radio (CR) a feasible solution of wireless communications in the Internet of Things (IoT). Reliable spectrum sensing becomes the prerequisite for the establishment of communication between IoT-capable objects. Considering the application environment, spectrum sensing not only has to cope with man-made impulsive noises but also needs to overcome noise fluctuations. In this paper, we study the Fractional Lower Order Moments (FLOM) based spectrum sensing method under Middleton Class A noise and incorporate a Noise Power Estimation (NPE) module into the sensing system to deal with the issue of noise uncertainty. Moreover, the NPE process does not need noise-only samples. The analytical expressions of the probabilities of detection and the probability of false alarm are derived. The impact on sensing performance of the parameters of the NPE module is also analyzed. The theoretical analysis and simulation results show that our proposed sensing method achieves a satisfactory performance at low SNR.

Frequency Detection of Weak Signal in Narrowband Noise Based on Duffing Oscillator

Duffing oscillator is used to detect weak signal in strong noise because traditional linear methods cannot work correctly in this situation. Normal Duffing oscillator is used under broadband noise because it is immune to broadband noise. But it is not suitable in narrowband noise because zero expectation of noise is damaged in narrowband noise. In this paper, the difference influence to Duffing oscillator between broadband noise and narrowband noise is analyzed and the resistance of Duffing oscillator to narrowband noise is proved. Then a new frequency detection method based on higher initial driving force amplitude and duration of cycle state is developed. Finally, the appropriate initial amplitude needed in this method is confirmed and the method is verified that it can detect frequency in narrowband noise by simulation.