Throughput optimization of wireless LANs by surrogate model based cognitive decision making

Large scale growth of wireless networks and the scarcity of the electromagnetic spectrum are imposing more interference to the wireless terminals which jeopardize the Quality of Service offered to the end users. In order to address this kind of performance degradation, this paper proposes a novel experimentally verified cognitive decision engine which aims at optimizing the throughput of IEEE 802.11 links in presence of homogeneous IEEE 802.11 interference. The decision engine is based on a surrogate model that takes the current state of the wireless network as input and makes a prediction of the throughput. The prediction enables the decision engine to find the optimal configuration of the controllable parameters of the network. The decision engine was applied in a realistic interference scenario where utilization of the cognitive decision engine outperformed the case where the decision engine was not deployed by a worst case improvement of more than 100%

Surrogate modeling based cognitive decision engine for optimization of WLAN performance

Due to the rapid growth of wireless networks and the dearth of the electromagnetic spectrum, more interference is imposed to the wireless terminals which constrains their performance. In order to mitigate such performance degradation, this paper proposes a novel experimentally verified surrogate model based cognitive decision engine which aims at performance optimization of IEEE 802.11 links. The surrogate model takes the current state and configuration of the network as input and makes a prediction of the QoS parameter that would assist the decision engine to steer the network towards the optimal configuration. The decision engine was applied in two realistic interference scenarios where in both cases, utilization of the cognitive decision engine significantly outperformed the case where the decision engine was not deployed

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

An Architecture for Coexistence with Multiple Users in Frequency Hopping Cognitive Radio Networks

The radio frequency (RF) spectrum is a limited resource. Spectrum allotment disputes stem from this scarcity as many radio devices are con confined to a fixed frequency or frequency sequence. One alternative is to incorporate cognition within a configurable radio platform, therefore enabling the radio to adapt to dynamic RF spectrum environments. In this way, the radio is able to actively observe the RF spectrum, orient itself to the current RF environment, decide on a mode of operation, and act accordingly, thereby sharing the spectrum and operating in more flexible manner. This research presents a novel framework for incorporating several techniques for the purpose of adapting radio operation to the current RF spectrum environment. Specifically, this research makes six contributions to the field of cognitive radio: (1) the framework for a new hybrid hardware/software middleware architecture, (2) a framework for testing and evaluating clustering algorithms in the context of cognitive radio networks, (3) a new RF spectrum map representation technique, (4) a new RF spectrum map merging technique, (5) a new method for generating a random key-based adaptive frequency-hopping waveform, and (6) initial integration testing toward implementing the proposed system on a field-programmable gate array (FPGA)

COGNITIVE RADIO SOLUTION FOR IEEE 802.22

Current wireless systems suffer severe radio spectrum underutilization due to a number of problematic issues, including wasteful static spectrum allocations; fixed radio functionalities and architectures; and limited cooperation between network nodes. A significant number of research efforts aim to find alternative solutions to improve spectrum utilization. Cognitive radio based on software radio technology is one such novel approach, and the impending IEEE 802.22 air interface standard is the first based on such an approach. This standard aims to provide wireless services in wireless regional area network using TV spectrum white spaces. The cognitive radio devices employed feature two fundamental capabilities, namely supporting multiple modulations and data-rates based on wireless channel conditions and sensing a wireless spectrum. Spectrum sensing is a critical functionality with high computational complexity. Although the standard does not specify a spectrum sensing method, the sensing operation has inherent timing and accuracy constraints.This work proposes a framework for developing a cognitive radio system based on a small form factor software radio platform with limited memory resources and processing capabilities. The cognitive radio systems feature adaptive behavior based on wireless channel conditions and are compliant with the IEEE 802.22 sensing constraints. The resource limitations on implementation platforms post a variety of challenges to transceiver configurability and spectrum sensing. Overcoming these fundamental features on small form factors paves the way for portable cognitive radio devices and extends the range of cognitive radio applications.Several techniques are proposed to overcome resource limitation on a small form factor software radio platform based on a hybrid processing architecture comprised of a digital signal processor and a field programmable gate array. Hardware reuse and task partitioning over a number of processing devices are among the techniques used to realize a configurable radio transceiver that supports several communication modes, including modulations and data rates. In particular, these techniques are applied to build configurable modulation architecture and a configurable synchronization. A mode-switching architecture based on circular buffers is proposed to facilitate a reliable transitioning between different communication modes.The feasibility of efficient spectrum sensing based on a compressive sampling technique called "Fast Fourier Sampling" is examined. The configuration parameters are analyzed mathematically, and performance is evaluated using computer simulations for local spectrum sensing applications. The work proposed herein features a cooperative Fast Fourier sampling scheme to extend the narrowband and wideband sensing performance of this compressive sensing technique.The précis of this dissertation establishes the foundation of efficient cognitive radio implementation on small form factor software radio of hybrid processing architecture

Spectrum Sensing Security in Cognitive Radio Networks

This thesis explores the use of unsupervised machine learning for spectrum sensing in cognitive radio (CR) networks from a security perspective. CR is an enabling technology for dynamic spectrum access (DSA) because of a CR's ability to reconfigure itself in a smart way. CR can adapt and use unoccupied spectrum with the help of spectrum sensing and DSA. DSA is an efficient way to dynamically allocate white spaces (unutilized spectrum) to other CR users in order to tackle the spectrum scarcity problem and improve spectral efficiency. So far various techniques have been developed to efficiently detect and classify signals in a DSA environment. Neural network techniques, especially those using unsupervised learning have some key advantages over other methods mainly because of the fact that minimal preconfiguration is required to sense the spectrum. However, recent results have shown some possible security vulnerabilities, which can be exploited by adversarial users to gain unrestricted access to spectrum by fooling signal classifiers. It is very important to address these new classes of security threats and challenges in order to make CR a long-term commercially viable concept. This thesis identifies some key security vulnerabilities when unsupervised machine learning is used for spectrum sensing and also proposes mitigation techniques to counter the security threats. The simulation work demonstrates the ability of malicious user to manipulate signals in such a way to confuse signal classifier. The signal classifier is forced by the malicious user to draw incorrect decision boundaries by presenting signal features which are akin to a primary user. Hence, a malicious user is able to classify itself as a primary user and thus gains unrivaled access to the spectrum. First, performance of various classification algorithms are evaluated. K-means and weighted classification algorithms are selected because of their robustness against proposed attacks as compared to other classification algorithm. Second, connection attack, point cluster attack, and random noise attack are shown to have an adverse effect on classification algorithms. In the end, some mitigation techniques are proposed to counter the effect of these attacks

Towards realisation of spectrum sharing of cognitive radio networks

Cognitive radio networks (CRN) have emerged as a promising solution to spectrum shortcoming, thanks to Professor Mitola who coined Cognitive Radios. To enable efficient communications, CRNs need to avoid interference to both Primary (licensee) Users (PUs), and among themselves (called self-coexistence). In this thesis, we focus on self-coexistence issues. Very briefly, the problems are categorised into intentional and unintentional interference. Firstly, unintentional interference includes: 1) CRNs administration; 2) Overcrowded CRNs Situation; 3) Missed spectrum detection; 4) Inter-cell Interference (ICI); and 5) Inability to model Secondary Users’ (SUs) activity. In intentional interference there is Primary User Emulation Attack (PUEA). To administer CRN operations (Prob. 1), in our first contribution, we proposed CogMnet, which aims to manage the spectrum sharing of centralised networks. CogMnet divides the country into locations. It then dedicates a real-time database for each location to record CRNs’ utilisations in real time, where each database includes three storage units: Networks locations storage unit; Real-time storage unit; and Historical storage unit. To tackle Prob. 2, our second contribution is CRNAC, a network admission control algorithm that aims to calculate the maximum number of CRNs allowed in any location. CRNAC has been tested and evaluated using MATLAB. To prevent research problems 3, 4, and to tackle research problem (5), our third contribution is RCNC, a new design for an infrastructure-based CRN core. The architecture of RCNC consists of two engines: Monitor and Coordinator Engine (MNCE) and Modified Cognitive Engine (MCE). Comprehensive simulation scenarios using ICS Designer (by ATDI) have validated some of RCNC’s components. In the last contribution, to deter PUEA (the intentional interference type), we developed a PUEA Deterrent (PUED) algorithm capable of detecting PUEAs commission details. PUED must be implemented by a PUEA Identifier Component in the MNCE in RCNC after every spectrum handing off. Therefore, PUED works like a CCTV system. According to criminology, robust CCTV systems have shown a significant prevention of clear visible theft, reducing crime rates by 80%. Therefore, we believe that our algorithm will do the same. Extensive simulations using a Vienna simulator showed the effectiveness of the PUED algorithm in terms of improving CRNs’ performance