Multi-Channel Preemptive Priority Model for Spectrum Mobility in Cognitive Radio Networks

Cognitive Radio techniques have been proposed for improving utilization of the spectrum by exploiting the unoccupied bands of the licensed spectrum. This paper proposes a preemptive multi-channel access model for prioritized cognitive radio networks using an iterative method of queuing theory to solve the spectrum scarcity problem. The proposed model formulates accurate closed form of an expected waiting time in the queue, an expected number of users in the queue, an expected waiting time in the system, and an expected number of users in the system. The results compared to the basic model (without preemptive priority) show that, the waiting time in queue and the waiting time in the system compared to the basic model will be improved by 92.99% and 33.15% respectively for class one secondary user. The results also show that, the waiting time in queue and the waiting time in the system will be improved by 43.25% and 15.42% respectively for class two secondary users. The proposed model investigates the desirable schedules of primary and secondary users

A Channel Ranking And Selection Scheme Based On Channel Occupancy And SNR For Cognitive Radio Systems

Wireless networks and information traffic have grown exponentially over the last decade. Consequently, an increase in demand for radio spectrum frequency bandwidth has resulted. Recent studies have shown that with the current fixed spectrum allocation (FSA), radio frequency band utilization ranges from 15% to 85%. Therefore, there are spectrum holes that are not utilized all the time by the licensed users, and, thus the radio spectrum is inefficiently exploited. To solve the problem of scarcity and inefficient utilization of the spectrum resources, dynamic spectrum access has been proposed as a solution to enable sharing and using available frequency channels. With dynamic spectrum allocation (DSA), unlicensed users can access and use licensed, available channels when primary users are not transmitting. Cognitive Radio technology is one of the next generation technologies that will allow efficient utilization of spectrum resources by enabling DSA. However, dynamic spectrum allocation by a cognitive radio system comes with the challenges of accurately detecting and selecting the best channel based on the channelâs availability and quality of service. Therefore, the spectrum sensing and analysis processes of a cognitive radio system are essential to make accurate decisions. Different spectrum sensing techniques and channel selection schemes have been proposed. However, these techniques only consider the spectrum occupancy rate for selecting the best channel, which can lead to erroneous decisions. Other communication parameters, such as the Signal-to-Noise Ratio (SNR) should also be taken into account. Therefore, the spectrum decision-making process of a cognitive radio system must use techniques that consider spectrum occupancy and channel quality metrics to rank channels and select the best option. This thesis aims to develop a utility function based on spectrum occupancy and SNR measurements to model and rank the sensed channels. An evolutionary algorithm-based SNR estimation technique was developed, which enables adaptively varying key parameters of the existing Eigenvalue-based blind SNR estimation technique. The performance of the improved technique is compared to the existing technique. Results show the evolutionary algorithm-based estimation performing better than the existing technique. The utility-based channel ranking technique was developed by first defining channel utility function that takes into account SNR and spectrum occupancy. Different mathematical functions were investigated to appropriately model the utility of SNR and spectrum occupancy rate. A ranking table is provided with the utility values of the sensed channels and compared with the usual occupancy rate based channel ranking. According to the results, utility-based channel ranking provides a better scope of making an informed decision by considering both channel occupancy rate and SNR. In addition, the efficiency of several noise cancellation techniques was investigated. These techniques can be employed to get rid of the impact of noise on the received or sensed signals during spectrum sensing process of a cognitive radio system. Performance evaluation of these techniques was done using simulations and the results show that the evolutionary algorithm-based noise cancellation techniques, particle swarm optimization and genetic algorithm perform better than the regular gradient descent based technique, which is the least-mean-square algorithm

A graph convolutional network-based deep reinforcement learning approach for resource allocation in a cognitive radio network

Cognitive radio (CR) is a critical technique to solve the conflict between the explosive growth of traffic and severe spectrum scarcity. Reasonable radio resource allocation with CR can effectively achieve spectrum sharing and co-channel interference (CCI) mitigation. In this paper, we propose a joint channel selection and power adaptation scheme for the underlay cognitive radio network (CRN), maximizing the data rate of all secondary users (SUs) while guaranteeing the quality of service (QoS) of primary users (PUs). To exploit the underlying topology of CRNs, we model the communication network as dynamic graphs, and the random walk is used to imitate the users’ movements. Considering the lack of accurate channel state information (CSI), we use the user distance distribution contained in the graph to estimate CSI. Moreover, the graph convolutional network (GCN) is employed to extract the crucial interference features. Further, an end-to-end learning model is designed to implement the following resource allocation task to avoid the split with mismatched features and tasks. Finally, the deep reinforcement learning (DRL) framework is adopted for model learning, to explore the optimal resource allocation strategy. The simulation results verify the feasibility and convergence of the proposed scheme, and prove that its performance is significantly improved

A Real Time Radio Spectrum Scanning Technique Based On The Bayesian Model And Its Comparison With The Frequentist Technique

The proliferation of mobile devices led to an exponential demand for wireless radio spectrum resources. The current fixed spectrum assignment has caused some portions of the radio spectrum to be heavily used whereas others to be scarcely used. This has resulted in underutilization of spectrum resources, and, hence has demanded the need for solutions to address the spectrum scarcity problem. Cognitive radio was proposed as one of the solutions. One of the techniques involved in cognitive radio is the dynamic spectrum access technique. This technique requires the identification of free channels in order to allow secondary users to exploit the spectrum resources. The process of identification of free channels is known as radio spectrum scanning, which is performed by sensing a particular channel in the radio spectrum to determine the presence or absence of a signal. In most of existing studies, the frequentist technique using energy detection with fixed threshold was used to scan the radio spectrum. However, this method comes with a major drawbacks. First, energy detection is unable to distinguish between signals and noise and suffer for high false detection rates. Second, energy detection has high false alarm probability. Finally, frequentist techniques are subject to uncertainty and do not provide real time monitoring/sensing. Therefore, the goal of this thesis is to develop a more efficient scanning technique that deals with uncertainty and scans the radio spectrum in real time and determines its occupancy levels. An enhanced spectrum scanning approach is developed using an efficient spectrum sensing technique: an uncertainty handling Bayesian model along with a Bayesian inferential approach. Two Bayesian models are developed: 1) a simplified model, and 2) an improved model to incorporate the Bayesian inferential approach to estimate the spectrum occupancy level. The performance evaluation of the proposed technique has been done using simulations as well as real experiments. For this purpose, two metrics were used: probability of detection and probability of false alarm. Furthermore, the efficiency of the proposed technique was compared to the efficiency of the frequentist technique, which uses only a spectrum sensing technique to identify the occupancy of the spectrum channels. As expected significant improvements in the spectrum occupancy measurements have been observed with the proposed Bayesian inference method

Peak to average power ratio based spatial spectrum sensing for cognitive radio systems

The recent convergence of wireless standards for incorporation of spatial dimension in wireless systems has made spatial spectrum sensing based on Peak to Average Power Ratio (PAPR) of the received signal, a promising approach. This added dimension is principally exploited for stream multiplexing, user multiplexing and spatial diversity. Considering such a wireless environment for primary users, we propose an algorithm for spectrum sensing by secondary users which are also equipped with multiple antennas. The proposed spatial spectrum sensing algorithm is based on the PAPR of the spatially received signals. Simulation results show the improved performance once the information regarding spatial diversity of the primary users is incorporated in the proposed algorithm. Moreover, through simulations a better performance is achieved by using different diversity schemes and different parameters like sensing time and scanning interval

A Sensing Error Aware MAC Protocol for Cognitive Radio Networks

Cognitive radios (CR) are intelligent radio devices that can sense the radio environment and adapt to changes in the radio environment. Spectrum sensing and spectrum access are the two key CR functions. In this paper, we present a spectrum sensing error aware MAC protocol for a CR network collocated with multiple primary networks. We explicitly consider both types of sensing errors in the CR MAC design, since such errors are inevitable for practical spectrum sensors and more important, such errors could have significant impact on the performance of the CR MAC protocol. Two spectrum sensing polices are presented, with which secondary users collaboratively sense the licensed channels. The sensing policies are then incorporated into p-Persistent CSMA to coordinate opportunistic spectrum access for CR network users. We present an analysis of the interference and throughput performance of the proposed CR MAC, and find the analysis highly accurate in our simulation studies. The proposed sensing error aware CR MAC protocol outperforms two existing approaches with considerable margins in our simulations, which justify the importance of considering spectrum sensing errors in CR MAC design.Comment: 21 page, technical repor