Analytical evaluation of adaptive-modulation-based opportunistic cognitive radio in Nakagami-m fading channels

The performance of adaptive modulation for cognitive radio with opportunistic access is analyzed by considering the effects of spectrum sensing, primary user (PU) traffic, and time delay for Nakagami- m fading channels. Both the adaptive continuous rate scheme and the adaptive discrete rate scheme are considered. Numerical examples are presented to quantify the effects of spectrum sensing, PU traffic, and time delay for different system parameters

Development of Adaptive Sensing Algorithm for Minimizing Energy and Bandwidth Consumption in Cooperative Spectrum Sensing Technology

Optimized consumption of energy and bandwidth is crucial for efficient utilization of the limited electromagnetic spectrum for telecommunication purposes. Cognitive radio is one of the dynamic spectrum management applications with numerous benefits related to the management of available spectrum. But it has the challenge of high energy and bandwidth usage when the cooperative scheme of spectrum sensing is applied for accurate sensing. In this paper, an adaptive spectrum sensing algorithm was developed to minimize energy and bandwidth consumption in cognitive radio spectrum sensing while ensuring accurate spectrum sensing. The adaptive algorithm was developed based on the signal-to-noise ratio conditions of the channel. Results reveal that the energy and bandwidth usage by the cooperative spectrum sensing can be significantly reduced without negatively affecting the performance and detection of the cognitive radio in varying noisy condition

Affine Projection Algorithm Based Decision Fusion for Cooperative Spectrum Sensing In Cognitive Radio Networks

Spectrum sensing is a main function in cognitive radio networks to detect the spectrum holes or unused spectrum. Cooperative spectrum sensing schemes are recently suggested and they provide fast and accurate results. In this paper, we suggested a new adaptive and cooperative spectrum sensing technique based on the affine projection algorithm (APA). In this method, each secondary user (SU) takes a binary decision by its local sensing of the spectrum using energy detector. Local decisions are then forward to the fusion center (FC), where definitive decision is taken on the status of the spectrum using adaptive filters. In our suggested technique, APA updates the weights of the adaptive filter by using the current and the 1 delayed input signal vectors. Simulation results indicate that the suggested approach provides faster convergence speed and less steady state mean square error than the existing methods that are based on the normalized least mean square (NLMS) or the so-called kernel least mean square (KLMS) algorithm

Cyclostationary Algorithm for Signal Analysis in Cognitive 4G Networks with Spectral Sensing and Resource Allocation

Cognitive Radio (CR) effectively involved in the management of spectrum to perform improved data transmission. CR system actively engaged in the data sensing, learning and dynamic adjustment of radio spectrum parameters with management of unused spectrum in the signal. The spectrum sensing is indispensable in the CR for the management of Primary Users (PUs) and Secondary users (SUs) without any interference. Spectrum sensing is considered as the effective adaptive signal processing model to evaluate the computational complexity model for the signal transmission through Matched filtering, Waveform and Cyclostationary based Energy sensing model. Cyclostationary based model is effective for the energy based sensing model based on unique characteristics with estimation of available channel in the spectrum to extract the received signal in the PU signal. Cyclostationary based model uses the spectrum availability without any periodic property to extract the noise features. This paper developed a Adaptive Cross Score Cyclostationary (ACSCS) to evaluate the spectrum sensing in the CR network. The developed ACSCS model uses the computational complexity with estimation of Signal-to-Interference-and-Noise Ratio (SINR) elimination of cost function. ACSCS model uses the Adaptive Least square Spectral Self-Coherence Restoral (SCORE) with the Adaptive Cross Score (ACS) to overcome the issues in CR. With the derived ACSCS algorithm minimizes the computational complexity based on cost function compared with the ACS algorithm. To minimize the computational complexity pipeline triangular array based Gram-Schmidt Orthogonalization (GSO) structure for the optimization of network. The simulation performance analysis with the ACSCS scheme uses the Rician Multipath Fading channel to estimate detection probability to sense the Receiver Operating Characteristics, detection probability and probability of false alarm using Maximum Likelihood (ML) detector. The ACSC model uses the Square-law combining (SLC) with the moment generation function in the multipath fading channel for the channel sensing with reduced computational complexity. The simulation analysis expressed that ACSC scheme achieves the maximal detection probability value of 1. The analysis expressed that proposed ACSC scheme achieves the improved channel estimation in the 4G communication environment

An Adaptive Spectrum-Sensing Algorithm for Cognitive Radio Networks based on the Sample Covariance Matrix

A novel adaptive threshold spectrum sensing technique based on the covariance matrix of received signal samples is proposed. The adaptive threshold in terms of signal to noise ratio (SNR) and spectrum utilisation ratio of primary user is derived. It considers both the probability of detection and the probability false alarm to minimise the overall decision error probability. The energy- based spectrum sensing scheme shows high vulnerability under noise uncertainty and low SNR. The existing covariance-based spectrum sensing technique overcomes the noise uncertainty problem but its performance deteriorates under low SNR. The proposed covariance-based scheme effectively addresses the low SNR problem. The superior performance of this scheme over the existing covariance-based detection method is confirmed by the simulation results in terms of probability of detection, probability of error, and requirement of samples for reliable detection of spectrum

Design and Optimal Configuration of Full-Duplex MAC Protocol for Cognitive Radio Networks Considering Self-Interference

In this paper, we propose an adaptive Medium Access Control (MAC) protocol for full-duplex (FD) cognitive radio networks in which FD secondary users (SUs) perform channel contention followed by concurrent spectrum sensing and transmission, and transmission only with maximum power in two different stages (called the FD sensing and transmission stages, respectively) in each contention and access cycle. The proposed FD cognitive MAC (FDC-MAC) protocol does not require synchronization among SUs and it efficiently utilizes the spectrum and mitigates the self-interference in the FD transceiver. We then develop a mathematical model to analyze the throughput performance of the FDC-MAC protocol where both half-duplex (HD) transmission (HDTx) and FD transmission (FDTx) modes are considered in the transmission stage. Then, we study the FDC-MAC configuration optimization through adaptively controlling the spectrum sensing duration and transmit power level in the FD sensing stage where we prove that there exists optimal sensing time and transmit power to achieve the maximum throughput and we develop an algorithm to configure the proposed FDC-MAC protocol. Extensive numerical results are presented to illustrate the characteristic of the optimal FDC-MAC configuration and the impacts of protocol parameters and the self-interference cancellation quality on the throughput performance. Moreover, we demonstrate the significant throughput gains of the FDC-MAC protocol with respect to existing half-duplex MAC (HD MAC) and single-stage FD MAC protocols.Comment: To Appear, IEEE Access, 201