Abstract
Cognitive radios are expected to play a major role towards meeting the exploding traffic demand over wireless systems. A cognitive radio node senses the environment, analyzes the outdoor parameters, and then makes decisions for dynamic time-frequency-space resource allocation and management to improve the utilization of the radio spectrum. For efficient real-time process, the cognitive radio is usually combined with artificial intelligence and machine-learning techniques so that an adaptive and intelligent allocation is achieved. This paper firstly presents the cognitive radio networks, resources, objectives, constraints, and challenges. Then, it introduces artificial intelligence and machine-learning techniques and emphasizes the role of learning in cognitive radios. Then, a survey on the state-of-the-art of machine-learning techniques in cognitive radios is presented. The literature survey is organized based on different artificial intelligence techniques such as fuzzy logic, genetic algorithms, neural networks, game theory, reinforcement learning, support vector machine, case-based reasoning, entropy, Bayesian, Markov model, multi-agent systems, and artificial bee colony algorithm. This paper also discusses the cognitive radio implementation and the learning challenges foreseen in cognitive radio applications.
Highlights
1.1 Introduction According to Cisco Visual Networking Index, the global IP traffic will reach 168 exabytes per month by 2019 [1], and the number of devices will be three times the global population
The main contributions of this paper are as follows: (1) it provides a comprehensive study on learning approach and presents their application in Cognitive radio (CR) networks, evaluation, strengths, complexity, limitations, and challenges; (2) this paper presents different cognitive radio tasks, as well as the challenges that face cognitive radio implementations; (3) it evaluates the application of the learning techniques to cognitive radio tasks; and (4) categorizes learning approaches based on their implementations and their application in performing major cognitive radio tasks such as spectrum sensing and decision-making
Yang et al in [36] proposed a design of the cognitive engine based on genetic algorithms and radial basis function network (RBF) in order to adjust the parameters of the system so as to effectively adapt to the environment as it changes
Summary
1.1 Introduction According to Cisco Visual Networking Index, the global IP traffic will reach 168 exabytes per month by 2019 [1], and the number of devices will be three times the global population. The decision-making can be based on optimization algorithms; in order to reduce the complexity and achieve efficient real-time resource allocation, cognitive radio networks use machine learning and artificial intelligence. Task 2—After performing spectrum sensing and situation awareness, the CR network needs to use its reconfigurable capability to dynamically adjust operational and transmission parameters and policies to achieve the highest performance gain such as maximizing the utilization of the spectrum and throughput, reducing the energy and power consumption, and reducing the interference level while meeting users’ quality of service (QoS) requirements such as rate, bit error rate, and delay. In order to reduce the complexity and achieve efficient real-time resource allocation, cognitive radio networks use machine learning and artificial intelligence. This decision-making is based on models built using the CR learning capability, which is based on the environment information. Artificial intelligence and machine learning are introduced as well as a survey of the state-of-the-art achievements in applying learning techniques in cognitive radio networks
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