Byzantine Attack and Defense in Cognitive Radio Networks: A Survey

The Byzantine attack in cooperative spectrum sensing (CSS), also known as the spectrum sensing data falsification (SSDF) attack in the literature, is one of the key adversaries to the success of cognitive radio networks (CRNs). In the past couple of years, the research on the Byzantine attack and defense strategies has gained worldwide increasing attention. In this paper, we provide a comprehensive survey and tutorial on the recent advances in the Byzantine attack and defense for CSS in CRNs. Specifically, we first briefly present the preliminaries of CSS for general readers, including signal detection techniques, hypothesis testing, and data fusion. Second, we analyze the spear and shield relation between Byzantine attack and defense from three aspects: the vulnerability of CSS to attack, the obstacles in CSS to defense, and the games between attack and defense. Then, we propose a taxonomy of the existing Byzantine attack behaviors and elaborate on the corresponding attack parameters, which determine where, who, how, and when to launch attacks. Next, from the perspectives of homogeneous or heterogeneous scenarios, we classify the existing defense algorithms, and provide an in-depth tutorial on the state-of-the-art Byzantine defense schemes, commonly known as robust or secure CSS in the literature. Furthermore, we highlight the unsolved research challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral

Cognitive Radio Connectivity for Railway Transportation Networks

Reliable wireless networks for high speed trains require a significant amount of data communications for enabling safety features such as train collision avoidance and railway management. Cognitive radio integrates heterogeneous wireless networks that will be deployed in order to achieve intelligent communications in future railway systems. One of the primary technical challenges in achieving reliable communications for railways is the handling of high mobility environments involving trains, which includes significant Doppler shifts in the transmission as well as severe fading scenarios that makes it difficult to estimate wireless spectrum utilization. This thesis has two primary contributions: (1) The creation of a Heterogeneous Cooperative Spectrum Sensing (CSS) prototype system, and (2) the derivation of a Long Term Evolution for Railways (LTE-R) system performance analysis. The Heterogeneous CSS prototype system was implemented using Software-Defined Radios (SDRs) possessing different radio configurations. Both soft and hard-data fusion schemes were used in order to compare the signal source detection performance in real-time fading scenarios. For future smart railways, one proposed solution for enabling greater connectivity is to access underutilized spectrum as a secondary user via the dynamic spectrum access (DSA) paradigm. Since it will be challenging to obtain an accurate estimate of incumbent users via a single-sensor system within a real-world fading environment, the proposed cooperative spectrum sensing approach is employed instead since it can mitigate the effects of multipath and shadowing by utilizing the spatial and temporal diversity of a multiple radio network. Regarding the LTE-R contribution of this thesis, the performance analysis of high speed trains (HSTs) in tunnel environments would provide valuable insights with respect to the smart railway systems operating in high mobility scenarios in drastically impaired channels

Collaborative spectrum sensing optimisation algorithms for cognitive radio networks

The main challenge for a cognitive radio is to detect the existence of primary users reliably in order to minimise the interference to licensed communications. Hence, spectrum sensing is a most important requirement of a cognitive radio. However, due to the channel uncertainties, local observations are not reliable and collaboration among users is required. Selection of fusion rule at a common receiver has a direct impact on the overall spectrum sensing performance. In this paper, optimisation of collaborative spectrum sensing in terms of optimum decision fusion is studied for hard and soft decision combining. It is concluded that for optimum fusion, the fusion centre must incorporate signal-to-noise ratio values of cognitive users and the channel conditions. A genetic algorithm-based weighted optimisation strategy is presented for the case of soft decision combining. Numerical results show that the proposed optimised collaborative spectrum sensing schemes give better spectrum sensing performance

Combined Soft Hard Cooperative Spectrum Sensing in Cognitive Radio Networks

Providing some techniques to enhance the performance of spectrum sensing in cognitive radio systems while accounting for the cost and bandwidth limitations in practical scenarios is the main objective of this thesis. We focus on an essential element of cooperative spectrum sensing (CSS) which is the data fusion that combines the sensing results to make the final decision. Exploiting the advantage of the superior performance of the soft schemes and the low bandwidth of the hard schemes by incorporating them in cluster based CSS networks is achieved in two different ways. First, a soft-hard combination is employed to propose a hierarchical cluster based spectrum sensing algorithm. The proposed algorithm maximizes the detection performances while satisfying the probability of false alarm constraint. Simulation results of the proposed algorithm are presented and compared with existing algorithms over the Nakagami fading channel. Moreover, the results show that the proposed algorithm outperforms the existing algorithms. In the second part, a low complexity soft-hard combination scheme is suggested by utilizing both one-bit and two-bit schemes to balance between the required bandwidth and the detection performance by taking into account that different clusters undergo different conditions. The scheme allocates a reliability factor proportional to the detection rate to each cluster to combine the results at the Fusion center (FC) by extracting the results of the reliable clusters. Numerical results obtained have shown that a superior detection performance and a minimum overhead can be achieved simultaneously by combining one bit and two schemes at the intra-cluster level while assigning a reliability factor at the inter-cluster level

Cooperative Spectrum Sensing based on 1-bit Quantization in Cognitive Radio Networks

The wireless frequency spectrum is a very valuable resource in the field of communications. Over the years, different bands of the spectrum were licensed to various communications systems and standards. As a result, most of the easily accessible parts of it ended up being theoretically occupied. This made it somewhat difficult to accommodate new wireless technologies, especially with the rise of communications concepts such as the Machine to Machine (M2M) communications and the Internet of Things (IoT). It was necessary to find ways to make better use of wireless spectrum. Cognitive Radio is one concept that came into the light to tackle the problem of spectrum utilization. Various technical reports stated that the spectrum is in fact under-utilized. Many frequency bands are not heavily used over time, and some bands have low activity. Cognitive Radio (CR) Networks aim to exploit and opportunistically share the already licensed spectrum. The objective is to enable various kinds of communications while preserving the licensed parties' right to access the spectrum without interference. Cognitive radio networks have more than one approach to spectrum sharing. In interweave spectrum sharing scheme, cognitive radio devices look for opportunities in the spectrum, in frequency and over time. Therefore, and to find these opportunities, they employ what is known as spectrum sensing. In a spectrum sensing phase, the CR device scans certain parts of the spectrum to find the voids or white spaces in it. After that it exploits these voids to perform its data transmission, thus avoiding any interference with the licensed users. Spectrum sensing has various classifications and approaches. In this thesis, we will present a general review of the main spectrum sensing categories. Furthermore, we will discuss some of the techniques employed in each category including their respective advantages and disadvantages, in addition to some of the research work associated with them. Our focus will be on cooperative spectrum sensing, which is a popular research topic. In cooperative spectrum sensing, multiple CR devices collaborate in the spectrum sensing operation to enhance the performance in terms of detection accuracy. We will investigate the soft-information decision fusion approach in cooperative sensing. In this approach, the CR devices forward their spectrum sensing data to a central node, commonly known as a Fusion Center. At the fusion center, this data is combined to achieve a higher level of accuracy in determining the occupied parts and the empty parts of the spectrum while considering Rayleigh fading channels. Furthermore, we will address the issue of high power consumption due to the sampling process of a wide-band of frequencies at the Nyquist rate. We will apply the 1-bit Quantization technique in our work to tackle this issue. The simulation results show that the detection accuracy of a 1-bit quantized system is equivalent to a non-quantized system with only 2 dB less in Signal-to-Noise Ratio (SNR). Finally, we will shed some light on multiple antenna spectrum sensing, and compare its performance to the cooperative sensing

Cluster-based cooperative subcarrier sensing using antenna diversity-based weighted data fusion

Cooperative spectrum sensing (CSS) is used in cognitive radio (CR) networks to improve the spectrum sensing performance in shadow fading environments. Moreover, clustering in CR networks is used to reduce reporting time and bandwidth overhead during CSS. Thus, cluster-based cooperative spectrum sensing (CBCSS) has manifested satisfactory spectrum sensing results in harsh environments under processing constraints. On the other hand, the antenna diversity of multiple input multiple output CR systems can be exploited to further improve the spectrum sensing performance. This paper presents the CBCSS performance in a CR network which is comprised of single- as well as multiple-antenna CR systems. We give theoretical analysis of CBCSS for orthogonal frequency division multiplexing signal sensing and propose a novel fusion scheme at the fusion center which takes into account the receiver antenna diversity of the CRs present in the network. We introduce the concept of weighted data fusion in which the sensing results of different CRs are weighted proportional to the number of receiving antennas they are equipped with. Thus, the receiver diversity is used to the advantage of improving spectrum sensing performance in a CR cluster. Simulation results show that the proposed scheme outperforms the conventional CBCSS scheme