Optimization and Learning in Energy Efficient Cognitive Radio System

Energy efficiency and spectrum efficiency are two biggest concerns for wireless communication. The constrained power supply is always a bottleneck to the modern mobility communication system. Meanwhile, spectrum resource is extremely limited but seriously underutilized. Cognitive radio (CR) as a promising approach could alleviate the spectrum underutilization and increase the quality of service. In contrast to traditional wireless communication systems, a distinguishing feature of cognitive radio systems is that the cognitive radios, which are typically equipped with powerful computation machinery, are capable of sensing the spectrum environment and making intelligent decisions. Moreover, the cognitive radio systems differ from traditional wireless systems that they can adapt their operating parameters, i.e. transmission power, channel, modulation according to the surrounding radio environment to explore the opportunity. In this dissertation, the study is focused on the optimization and learning of energy efficiency in the cognitive radio system, which can be considered to better utilize both the energy and spectrum resources. Firstly, drowsy transmission, which produces optimized idle period patterns and selects the best sleep mode for each idle period between two packet transmissions through joint power management and transmission power control/rate selection, is introduced to cognitive radio transmitter. Both the optimal solution by dynamic programming and flexible solution by reinforcement learning are provided. Secondly, when cognitive radio system is benefited from the theoretically infinite but unsteady harvested energy, an innovative and flexible control framework mainly based on model predictive control is designed. The solution to combat the problems, such as the inaccurate model and myopic control policy introduced by MPC, is given. Last, after study the optimization problem for point-to-point communication, multi-objective reinforcement learning is applied to the cognitive radio network, an adaptable routing algorithm is proposed and implemented. Epidemic propagation is studied to further understand the learning process in the cognitive radio network

Machine learning algorithms for cognitive radio wireless networks

In this thesis new methods are presented for achieving spectrum sensing in cognitive radio wireless networks. In particular, supervised, semi-supervised and unsupervised machine learning based spectrum sensing algorithms are developed and various techniques to improve their performance are described. Spectrum sensing problem in multi-antenna cognitive radio networks is considered and a novel eigenvalue based feature is proposed which has the capability to enhance the performance of support vector machines algorithms for signal classification. Furthermore, spectrum sensing under multiple primary users condition is studied and a new re-formulation of the sensing task as a multiple class signal detection problem where each class embeds one or more states is presented. Moreover, the error correcting output codes based multi-class support vector machines algorithms is proposed and investigated for solving the multiple class signal detection problem using two different coding strategies. In addition, the performance of parametric classifiers for spectrum sensing under slow fading channel is studied. To address the attendant performance degradation problem, a Kalman filter based channel estimation technique is proposed for tracking the temporally correlated slow fading channel and updating the decision boundary of the classifiers in real time. Simulation studies are included to assess the performance of the proposed schemes. Finally, techniques for improving the quality of the learning features and improving the detection accuracy of sensing algorithms are studied and a novel beamforming based pre-processing technique is presented for feature realization in multi-antenna cognitive radio systems. Furthermore, using the beamformer derived features, new algorithms are developed for multiple hypothesis testing facilitating joint spatio-temporal spectrum sensing. The key performance metrics of the classifiers are evaluated to demonstrate the superiority of the proposed methods in comparison with previously proposed alternatives

Spectrum Sensing Algorithms for Cognitive Radio Applications

Future wireless communications systems are expected to be extremely dynamic, smart and capable to interact with the surrounding radio environment. To implement such advanced devices, cognitive radio (CR) is a promising paradigm, focusing on strategies for acquiring information and learning. The first task of a cognitive systems is spectrum sensing, that has been mainly studied in the context of opportunistic spectrum access, in which cognitive nodes must implement signal detection techniques to identify unused bands for transmission. In the present work, we study different spectrum sensing algorithms, focusing on their statistical description and evaluation of the detection performance. Moving from traditional sensing approaches we consider the presence of practical impairments, and analyze algorithm design. Far from the ambition of cover the broad spectrum of spectrum sensing, we aim at providing contributions to the main classes of sensing techniques. In particular, in the context of energy detection we studied the practical design of the test, considering the case in which the noise power is estimated at the receiver. This analysis allows to deepen the phenomenon of the SNR wall, providing the conditions for its existence and showing that presence of the SNR wall is determined by the accuracy of the noise power estimation process. In the context of the eigenvalue based detectors, that can be adopted by multiple sensors systems, we studied the practical situation in presence of unbalances in the noise power at the receivers. Then, we shift the focus from single band detectors to wideband sensing, proposing a new approach based on information theoretic criteria. This technique is blind and, requiring no threshold setting, can be adopted even if the statistical distribution of the observed data in not known exactly. In the last part of the thesis we analyze some simple cooperative localization techniques based on weighted centroid strategies

Advances and emerging challenges in cognitive internet-of-things

The evolution of IoT devices and their adoption in new generation intelligent systems has generated a huge demand for wireless bandwidth. This bandwidth problem is further exacerbated by another characteristics of IoT applications, i.e. IoT devices are usually deployed in massive number, thus leading to an awkward scenario that many bandwidth-hungry devices are chasing after the very limited wireless bandwidth within a small geographic area. As such, cognitive radio has received much attention of there search community as an important means for addressing the bandwidth needs of IoT applications. When enabling IoT devices with cognitive functionalities including spectrum sensing, dynamic spectrum accessing, circumstantial perceiving and self-learning, one will also need to fully study other critical issues such as standardization, privacy protection and heterogeneous coexistence. In this paper, we investigate the structural frameworks and potential applications of cognitive IoT. We further discuss the spectrum-based functionalities and heterogeneity for cognitive IoT. Security and privacy issues involved in cognitive IoT are also investigated. Finally, we present the key challenges and future direction of research on cognitiveradio-based IoT networks

Location privacy preservation in secure crowdsourcing-based cooperative spectrum sensing

Spectrum sensing is one of the most essential components of cognitive radio since it detects whether the spectrum is available or not. However, spectrum sensing accuracy is often degraded due to path loss, interference, and shadowing. Cooperative spectrum sensing (CSS) is one of the proposed solutions to overcome these challenges. It is a key function for dynamic spectrum access that can increase largely the reliability in cognitive radio networks. In fact, several users cooperate to detect the availability of a wireless channel by exploiting spatial diversity. However, cooperative sensing is also facing some series of security threats. In this paper, we focus on two major problems. The first problem is the localization preservation of the secondary users. In fact, malicious users can exploit spatial diversity to localize a secondary user by linking his location-dependent sensing report to his physical position. The existing solutions present a high level of complexity which decreases the performance of the systems. The second problem is the data injection attack, in which malicious CR users may affect the decisions taken by the cognitive users by providing false information, introducing spectrum sensing data falsification (SSDF). In fact, they can submit false sensing reports containing power measurements much larger (or smaller) than the true value to inflate (or deflate) the final average, in which case the fusion center may falsely determine that the channel is busy (or vacant) which increases the false alarm and miss detection probabilities. In this paper, we propose a novel scheme to overcome these problems: iterative per cluster malicious detection (IPCMD). It utilizes applied cryptographic techniques to allow the fusion center (FC) to securely obtain the aggregated result from various secondary users without learning each individual report. IPCMD combines the aggregated sensing reports with their reputation scores during data fusion. The proposed scheme is based on a new algorithm for key generation which can significantly reduce the key management complexity and consequently increase the system performance. Therefore, it can enable secure cooperative spectrum sensing and improve the secondary user location privacy.Ooreedoo, Doha, QatarScopu

Attacking Spectrum Sensing With Adversarial Deep Learning in Cognitive Radio-Enabled Internet of Things

Cognitive radio-based Internet of Things (CR-IoT) network provides a solution for IoT devices to efficiently utilize spectrum resources. Spectrum sensing is a critical problem in CR-IoT network, which has been investigated extensively based on deep learning (DL). Despite the unique advantages of DL in spectrum sensing, the black-box and unexplained properties of deep neural networks may lead to many security risks. This article considers the fusion of traditional interference methods and data poisoning which is an attack method on the training data of a machine learning tool. We propose a new adversarial attack for reducing the sensing accuracy in DL-based spectrum sensing systems. We introduce a novel design of jamming waveform whose interference capability is reinforced by data poisoning. Simulation results show that significant performance enhancement and higher mobility can be achieved compared with traditional white-box attack methods

Rogue Signal Threat on Trust-based Cooperative Spectrum Sensing in Cognitive Radio Networks

Cognitive Radio Networks (CRNs) are a next generation network that is expected to solve the wireless spectrum shortage problem, which is the shrinking of available wireless spectrum resources needed to facilitate future wireless applications. The first CRN standard, the IEEE 802.22, addresses this particular problem by allowing CRNs to share geographically unused TV spectrum to mitigate the spectrum shortage. Equipped with reasoning and learning engines, cognitive radios operate autonomously to locate unused channels to maximize its own bandwidth and Quality-of-Service (QoS). However, their increased capabilities over traditional radios introduce a new dimension of security threats. In an NSF 2009 workshop, the FCC raised the question, “What authentication mechanisms are needed to support cooperative cognitive radio networks? Are reputation-based schemes useful supplements to conventional Public Key Infrastructure (PKI) authentication protocols?” Reputation-based schemes in cognitive radio networks are a popular technique for performing robust and accurate spectrum sensing without any inter-communication with licensed networks, but the question remains on how effective they are at satisfying the FCC security requirements. Our work demonstrates that trust-based Cooperative Spectrum Sensing (CSS) protocols are vulnerable to rogue signals, which creates the illusion of inside attackers and raises the concern that such schemes are overly sensitive Intrusion Detection Systems (IDS). The erosion of the sensor reputations in trust-based CSS protocols makes CRNs vulnerable to future attacks. To counter this new threat, we introduce community detection and cluster analytics to detect and negate the impact of rogue signals on sensor reputations

Machine Learning-Enabled Resource Allocation for Underlay Cognitive Radio Networks

Due to the rapid growth of new wireless communication services and applications, much attention has been directed to frequency spectrum resources and the way they are regulated. Considering that the radio spectrum is a natural limited resource, supporting the ever increasing demands for higher capacity and higher data rates for diverse sets of users, services and applications is a challenging task which requires innovative technologies capable of providing new ways of efficiently exploiting the available radio spectrum. Consequently, dynamic spectrum access (DSA) has been proposed as a replacement for static spectrum allocation policies. The DSA is implemented in three modes including interweave, overlay and underlay mode [1]. The key enabling technology for DSA is cognitive radio (CR), which is among the core prominent technologies for the next generation of wireless communication systems. Unlike conventional radio which is restricted to only operate in designated spectrum bands, a CR has the capability to operate in different spectrum bands owing to its ability in sensing, understanding its wireless environment, learning from past experiences and proactively changing the transmission parameters as needed. These features for CR are provided by an intelligent software package called the cognitive engine (CE). In general, the CE manages radio resources to accomplish cognitive functionalities and allocates and adapts the radio resources to optimize the performance of the network. Cognitive functionality of the CE can be achieved by leveraging machine learning techniques. Therefore, this thesis explores the application of two machine learning techniques in enabling the cognition capability of CE. The two considered machine learning techniques are neural network-based supervised learning and reinforcement learning. Specifically, this thesis develops resource allocation algorithms that leverage the use of machine learning techniques to find the solution to the resource allocation problem for heterogeneous underlay cognitive radio networks (CRNs). The proposed algorithms are evaluated under extensive simulation runs. The first resource allocation algorithm uses a neural network-based learning paradigm to present a fully autonomous and distributed underlay DSA scheme where each CR operates based on predicting its transmission effect on a primary network (PN). The scheme is based on a CE with an artificial neural network that predicts the adaptive modulation and coding configuration for the primary link nearest to a transmitting CR, without exchanging information between primary and secondary networks. By managing the effect of the secondary network (SN) on the primary network, the presented technique maintains the relative average throughput change in the primary network within a prescribed maximum value, while also finding transmit settings for the CRs that result in throughput as large as allowed by the primary network interference limit. The second resource allocation algorithm uses reinforcement learning and aims at distributively maximizing the average quality of experience (QoE) across transmission of CRs with different types of traffic while satisfying a primary network interference constraint. To best satisfy the QoE requirements of the delay-sensitive type of traffics, a cross-layer resource allocation algorithm is derived and its performance is compared against a physical-layer algorithm in terms of meeting end-to-end traffic delay constraints. Moreover, to accelerate the learning performance of the presented algorithms, the idea of transfer learning is integrated. The philosophy behind transfer learning is to allow well-established and expert cognitive agents (i.e. base stations or mobile stations in the context of wireless communications) to teach newly activated and naive agents. Exchange of learned information is used to improve the learning performance of a distributed CR network. This thesis further identifies the best practices to transfer knowledge between CRs so as to reduce the communication overhead. The investigations in this thesis propose a novel technique which is able to accurately predict the modulation scheme and channel coding rate used in a primary link without the need to exchange information between the two networks (e.g. access to feedback channels), while succeeding in the main goal of determining the transmit power of the CRs such that the interference they create remains below the maximum threshold that the primary network can sustain with minimal effect on the average throughput. The investigations in this thesis also provide a physical-layer as well as a cross-layer machine learning-based algorithms to address the challenge of resource allocation in underlay cognitive radio networks, resulting in better learning performance and reduced communication overhead