An advance multiresolution wavelet based approach for wideband spectrum sensing for cognitive radio system is proposed in this paper. Prime focus is made on the coarse detection part for interweaved system, in which unoccupied spectrum can be used efficiently by the cognitive users. Quick and immediate shifting over the sensed vacant channel is extremely vital and is a challenging task. To overcome this issue, fast and efficient spectrum sensing technique is proposed for cognitive radios by improvising the Discrete Wavelet Packet Transform (DWPT) for multiresolution interweaved systems. This proposed scheme not only increases the system speed but also reduces complexity. Simulation results are used to analyse the system performance and numerical analysis for computing system complexity.

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Advanced Multiresolution Wavelet Based Wideband Spectrum Sensing Technique for Cognitive Radio

Noise uncertainty can severely deteriorate a primary user (PU) detector’s sensing performance, so robustness against the noise uncertainty is of fundamental significance in cognitive radio networks. In this paper, we investigate robust detection schemes in the presence of noise uncertainty for multiple-input multiple-output (MIMO) based cognitive radio wireless sensor networks (CR-WSN) with multiple spectrum sensing scenarios. In practice, it is very seldom that accurate statistical characterization of noise is known a priori. In this context, we propose the generalized likelihood ratio test (GLRT) paradigm and estimator-correlator based optimal detectors to sense the unoccupied primary bands in the presence of noise uncertainty. First, we compute the estimator-correlator based detector (ECD) and generalized likelihood detector (GLD) for known noise uncertainty statistics. Next, a composite hypothesis based detector (CHD) is proposed using the GLRT framework for unknown noise uncertainty statistics. The proposed detection schemes provide robustness against uncertainty in the available noise power estimates using a finite number of observations (i.e., fast spectrum sensing) and particularly in critical areas of low Signal-to-Noise Ratio (SNR). Theoretical analysis is performed based on statistical theory. Closed-form expressions for detection and false-alarm probability, and analytic decision threshold are derived to demonstrate spectrum sensing performance using receiver operating characteristic (ROC) curves. Simulation results validate that proposed PU detection schemes are robust and achieve an improved sensing performance when there is noise uncertainty and corroborate our theoretical findings.

Optimal Spectrum Sensing in MIMO-Based Cognitive Radio Wireless Sensor Network (CR-WSN) Using GLRT With Noise Uncertainty at Low SNR

The article deals with a computer-supported design of optimal and robust proportional-integral-derivative controllers with two degrees of freedom (2DoF PID) for a double integrator plus dead-time (DIPDT) process model. The particular design steps are discussed in terms of intelligent use of all available information extracted from a database of control tracking and disturbance rejection step responses, assessed by means of speed and shape-related performance measures of the process input and output signals, and denoted as a performance portrait (PP). In the first step, the performance portrait method (PPM) is used as a verifier, for whether the pilot analytical design of the parallel 2DoF PID controller did not omit practically interesting settings and shows that the optimality analysis can easily be extended to the series 2DoF PID controller. This is important as an explicit observer of equivalent input disturbances based on steady-state input values of ultra-local DIPDT models, while the parallel PID controller, allowing faster transient responses, needs an additional low-pass filter when reconstructed equivalent disturbances are required. Next, the design efficiency and conciseness in analyzing the effects of different loop parameters on changing the optimal processes are illustrated by an iterative use of PPM, enabled by the visualization of the dependence between the closed-loop performance and the shapes of the control signals. The main contributions of the paper are the introduction of PPM as an intelligent method for controller tuning that mimics an expert with sufficient experience to select the most appropriate solution based on a database of known solutions. In doing so, the analysis in this paper reveals new, previously undiscovered dimensions of PID control design.

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Performance Portrait Method: An Intelligent PID Controller Design Based on a Database of Relevant Systems Behaviors

In cognitive radio, the spectrum is being used in those modern applications that can monitor spectrum, i.e., alternative users (SUs), could use the spectrum that the main user (PU) does not actually control. Any stakeholders have found out that the approved spectrum is not in operation all the time/locations. To maximize spectrum usage, SUs are enabled to get access to frequency bands already allocated to PUs while they are not using it, under the condition that the SUs do not interact with the PU. To avoid interference to the PU, the SU CR faces a difficult task, i.e., detecting the existence of the PU signal in low-SNR (negative dB) area. This is due to signal attenuation due to multipath and shadowing that can occur before the signal hits the SU. For sensing, the energy detectors are commonly used, but they suffer from the noise uncertainty issue in such noise conditions. The SNR wall is the operating threshold at which image identification is no longer possible. This ambiguity is due to how imprecise the calculations of noise variance are over time, and due to the changing noise variance. In this article, a short overview is given for researchers in the field of cognitive radio technology (CRNs).

Blind Spectrum Sensing Algorithms in CRNs: A Brief Overview

Cognitive radio (CR) is the trending domain in addressing the inadequate bands for communication, and spectrum sensing is the hectic challenge need to be addressed extensively. In the conventional ...

Hybrid Mixture Model Based on a Hybrid Optimization for Spectrum Sensing to Improve the Performance of MIMO–OFDM Systems

In the actual industrial production process, the method of adaptively tuning proportional–integral–derivative (PID) parameters online by neural network can adapt to different characteristics of different controlled objects better than the controller with PID. However, the commonly used microcontroller unit (MCU) cannot meet the application scenarios of real time and high reliability. Therefore, in this paper, a closed-loop motion control system based on BP neural network (BPNN) PID controller by using a Xilinx field programmable gate array (FPGA) solution is proposed. In the design of the controller, it is divided into several sub-modules according to the modular design idea. The forward propagation module is used to complete the forward propagation operation from the input layer to the output layer. The PID module implements the mapping of PID arithmetic to register transfer level (RTL) and is responsible for completing the output of control amount. The main state machine module generates enable signals that control the sequential execution of each sub-module. The error backpropagation and weight update module completes the update of the weights of each layer of the network. The peripheral modules of the control system are divided into two main parts. The speed measurement module completes the acquisition of the output pulse signal of the encoder and the measurement of the motor speed. The pulse width modulation (PWM) signal generation module generates PWM waves with different duty cycles to control the rotation speed of the motor. A co-simulation of Modelsim and Simulink is used to simulate and verify the system, and a test analysis is also performed on the development platform. The results show that the proposed system can realize the self-tuning of PID control parameters, and also has the characteristics of reliable performance, high real-time performance, and strong anti-interference. Compared with MCU, the convergence speed is far more than three orders of magnitude, which proves its superiority.

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A Design of FPGA-Based Neural Network PID Controller for Motion Control System

Cumulant-based blind cooperative spectrum sensing method for cognitive radio

This paper proposes an optimal resource allocation method. The method is to maximize the Energy Efficiency (EE) for an Energy Harvesting (EH) enabled underlay Cognitive Radio (CR) network. First, we assumed the Secondary Users (SUs) can harvest energy from the surrounding Radio Frequency (RF) signals. Then, we modelled the EE maximisation problem as a joint time and power optimization model. Next, the optimal EH time allocation factor can be calculated. After that the optimal power allocation strategy can be obtain by the fractional programming and Lagrange multiplier method. Finally simulation results show that the proposed iterative method can be better performance advantages compared with the exhaustive method and genetic algorithm. And the EE of this system model is significantly improved compared to the EE model without considering EH.

Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks

With the rapid development of technologies such as wireless communications and the Internet of Things (IoT), the proliferation of IoT devices will intensify the competition for spectrum resources. The introduction of cognitive radio technology in IoT can minimize the shortage of spectrum resources. However, the open environment of cognitive IoT may involve free-riding problems. Due to the selfishness of the participants, there are usually a large number of free-riders in the system who opportunistically gain more rewards by stealing the spectrum sensing results from other participants and accessing the spectrum without spectrum sensing. However, this behavior seriously affects the fault tolerance of the system and the motivation of the participants, resulting in degrading the system’s performance. Based on the energy-harvesting cognitive IoT model, this paper considers the free-riding problem of Secondary Users (SUs). Since free-riders can harvest more energy in spectrum sensing time slots, the application of energy harvesting technology will exacerbate the free-riding behavior of selfish SUs in Cooperative Spectrum Sensing (CSS). In order to prevent the low detection performance of the system due to the free-riding behavior of too many SUs, a penalty mechanism is established to stimulate SUs to sense the spectrum normally during the sensing process. In the system model with multiple primary users (PUs) and multiple SUs, each SU considers whether to free-ride and which PU’s spectrum to sense and access in order to maximize its own interests. To address this issue, a two-layer game-based cooperative spectrum sensing and access method is proposed to improve spectrum utilization. Simulation results show that compared with traditional methods, the average throughput of the proposed TL-CSAG algorithm increased by 26.3% and the proposed method makes the SUs allocation more fair.

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Free-Rider Games for Cooperative Spectrum Sensing and Access in CIoT Networks

The paper studies the secrecy communication threatened by a single eavesdropper in Energy Harvesting (EH)-based cognitive radio networks, where both the Secure User (SU) and the jammer harvest, store, and utilize RF energy from the Primary Transmitter (PT). Our main goal is to optimize the time slots for energy harvesting and wireless communication for both the secure user as well as the jammer to maximize the long-term performance of secrecy communication. A multi-agent Deep Reinforcement Learning (DRL) method is proposed for solving the optimization of resource allocation and performance. Specifically, each sub-channel from the Secure Transmitter (ST) to the Secure Receiver (SR) link, along with the jammer to the eavesdropper link, is regarded as an agent, which is responsible for exploring optimal power allocation strategy while a time allocation network is established to obtain optimal EH time allocation strategy. Every agent dynamically interacts with the wireless communication environment. Simulation results demonstrate that the proposed DRL-based resource allocation method outperforms the existing schemes in terms of secrecy rate, convergence speed, and the average number of transition steps.

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Ruiquan Lin

Papers

A Design of FPGA-Based Neural Network PID Controller for Motion Control System

Cumulant-based blind cooperative spectrum sensing method for cognitive radio

Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks

Free-Rider Games for Cooperative Spectrum Sensing and Access in CIoT Networks

Deep Reinforcement Learning for Physical Layer Security Enhancement in Energy Harvesting Based Cognitive Radio Networks