Low Complexity Optimal Hard Decision Fusion under Neyman-Pearson Criterion

Decision fusion is a fundamental operation in many signal processing systems where multiple sensors collaborate to improve the accuracy and robustness of the decision being made. The decision of each individual binary decision maker (or sensor) is often error-prone due to various environment challenges. These challenges are mitigated to certain extent using the spatial diversity obtained by deploying the sensors over a geographically distributed area. Subsequently, the decisions from the individual sensors are collected and fused at a fusion center to obtain a global decision. One such recent application of decision fusion is cooperative spectrum sensing in cognitive radio networks (CRN). The secondary users (SUs) of the CRN are tasked to garner the much needed unutilized spectrum allocated to the primary users (PUs). It is important for the SUs to precisely detect the spectrum usage opportunities inorder to improve the spectral efficiency and also to restrict the interference caused to PUs in this process. However, these are two conflicting objectives. Tuning the system to low levels of interference to the primary network will result in higher missed spectrum utilization oppurtunities. Similarly, increasing the detection of spectral usage opportunities will lead to increased interference to the primary users. The fusion centers require optimal fusion rules that improve the spectral efficiency of the CRN and minimize the interference caused to the primary network. The spectrum sensing in this case is generally modeled as a binary hypothesis problem: ‘PU signal present’ and ‘PU signal absent’. The fusion rules are broadly classified into two categories, namely (i) non-randomized (ii) randomized. In a ‘non-randomized’ rule, the global decision generated is deterministic for all the combinations of the local observations received. And in a ‘randomized’ rule the global decision generated is random (0 or 1) with a certain probability distribution for some local observations. The design of the optimal randomized decision fusion is generally simple, however introduce randomness in the decision equations and are difficult to implement. Whereas vii the design of the optimal non-randomized hard decision fusion rule is difficult, and under the Neyman-Pearson (NP) criterion is known to be exponential in complexity. In this thesis, we develop low-complexity (i) optimal and (ii) near-optimal algorithms for two variants of non-randomized hard decision fusion problems under NP crierion (i) clairvoyant1 decision fusion and (ii) novel (semi-)blind decision fusion. In all the sub-categories considered therein, we present low-complexity algorithms and obtain receiver operating characteristics (ROCs) for different number of participating sensors (N) which was intractable with the existing approaches. We formulate a more generalized version of this problem called “Generalized Decision Fusion Problem (GDFP)” and relate it to the classical 0−1 Knapsack problem. Consequently we show that the GDFP has a worst case pseudo-polynomial time solution using dynamic programming approach. Additionaly, we show that the decision fusion problem exhibits semi-monotonic property in most practical cases. We propose to exploit this property to reduce the dimension of the feasible solution space. Subsequently, we apply dynamic programming to efficiently solve the problem with further reduction in complexity. Further, we show that though the non-randomized single-threshold likelihood ratio based test (non-rand-st LRT) is sub-optimal, its performance approaches the upper bound obtained by randomized LRT (rand LRT) with increase in N. This alleviates the need for employing the exponentially complex non-randomized optimal solution for N larger than a specific value. As a variant of GDFP, we propose novel (semi-)blind hard decision fusion rules that use the mean of the secondary user characteristics instead of their actual values. We show that these rules with slight (or no) additional system knowledge achieve better ROC than existing (semi-)blind alternatives. Finally, we present a branch and bound algorithm with novel termination to obtain 1A rule that has complete knowledge of the system viii a near-optimal solution as the proposed dynamic programming approach exhibits limitations for the GDFP that require high-precision computations. We validate the performance of the proposed branch and bound algorithm for a wide range of {high, low} precision and {monotonic, semi, non-monotonic} GDFPs. All the algorithms have been rigorously verified by simulations in Matla

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

Cooperative subcarrier sensing using antenna diversity based weighted virtual sub clustering

The idea of cooperation and the clustering amongst cognitive radios (CRs) has recently been focus of attention of research community, owing to its potential to improve performance of spectrum sensing (SS) schemes. This focus has led to the paradigm of cluster based cooperative spectrum sensing (CBCSS). In perspective of high date rate 4th generation wireless systems, which are characterized by orthogonal frequency division multiplexing (OFDM) and spatial diversity, there is a need to devise effective SS strategies. A novel CBCSS scheme is proposed for OFDM subcarrier detection in order to enable the non-contiguous OFDM (NC-OFDM) at the physical layer of CRs for efficient utilization of spectrum holes. Proposed scheme is based on the energy detection in MIMO CR network, using equal gain combiner as diversity combining technique, hard combining (AND, OR and Majority) rule as data fusion technique and antenna diversity based weighted clustering as virtual sub clustering algorithm. Results of proposed CBCSS are compared with conventional CBCSS scheme for AND, OR and Majority data fusion rules. Moreover the effects of antenna diversity, cooperation and cooperating clusters are also discussed

A Message Passing Approach for Decision Fusion in Adversarial Multi-Sensor Networks

We consider a simple, yet widely studied, set-up in which a Fusion Center (FC) is asked to make a binary decision about a sequence of system states by relying on the possibly corrupted decisions provided by byzantine nodes, i.e. nodes which deliberately alter the result of the local decision to induce an error at the fusion center. When independent states are considered, the optimum fusion rule over a batch of observations has already been derived, however its complexity prevents its use in conjunction with large observation windows. In this paper, we propose a near-optimal algorithm based on message passing that greatly reduces the computational burden of the optimum fusion rule. In addition, the proposed algorithm retains very good performance also in the case of dependent system states. By first focusing on the case of small observation windows, we use numerical simulations to show that the proposed scheme introduces a negligible increase of the decision error probability compared to the optimum fusion rule. We then analyse the performance of the new scheme when the FC make its decision by relying on long observation windows. We do so by considering both the case of independent and Markovian system states and show that the obtained performance are superior to those obtained with prior suboptimal schemes. As an additional result, we confirm the previous finding that, in some cases, it is preferable for the byzantine nodes to minimise the mutual information between the sequence system states and the reports submitted to the FC, rather than always flipping the local decision

COMPARISON PARADOX, COMPARATIVE SITUATION AND INTER-PARADIGMATICY: A METHODOLOGICAL REFLECTION ON CROSS-CULTURAL PHILOSOPHICAL COMPARISON [abstract]

It is commonly believed that philosophical comparison depends on having some common measure or standard between and above the compared parts. The paper is to show that the foregoing common belief is incorrect and therewith to inquire into the possibility of cross-cultural philosophical comparison. First, the comparison paradox will be expounded. It is a theoretical difficulty for the philosophical tendency represented by Platos theory of Ideas to justify comparative activities. Further, the connection of the comparative paradox with the obstacles met by cross-cultural philosophical comparisons will be demonstrated. It will be shown that to attribute the difficulty of cross-cultural comparisons to incommensurability of traditions is irrelevant and misleading. It is to be argued that the original possibility of comparison depends on the comparative situation, i.e., the mechanism of meaning-production that functions in a non-universalistic and anonymous way. A philosophical paradigm does facilitate the attendance of such a situation, but it is also possible for the situation to emerge between paradigms in a gamesome way. Accordingly, the genuine comparison at issue will not originate primarily and merely on the level of concepts and propositions, but can only be achieved through inter-paradigmatic conditions, where we have the sharp awareness of a paradigms boundary from which we can attempt to achieve situational communication with another paradigm. In light of this, the perspective of a philosophical comparison differs not only from the traditional or universalistic one, but also from Gadamers hermeneutics, such as the doctrine of fusion of horizons. The new perspective finds an illustration in Heideggers relations with Daoism

Hand classification of fMRI ICA noise components

We present a practical "how-to" guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets