Cooperative Modulation Classification Research Articles

A Robust Cooperative Modulation Classification Scheme with Intra sensor Fusion for the Time correlated Flat Fading Channels

Networks with distributed sensors, e.g. cognitive radio networks or wireless sensor networks enable large-scale deployments of cooperative automatic modulation classification (AMC). Existing cooperative AMC schemes with centralised fusion offer considerable performance increase in comparison to single sensor reception. Previous studies were generally focused on AMC scenarios in which multipath channel is assumed to be static during a signal reception. However, in practical mobile environments, time-correlated multipath channels occur, which induce large negative influence on the existing cooperative AMC solutions. In this paper, we propose two novel cooperative AMC schemes with the additional intra-sensor fusion, and show that these offer significant performance improvements over the existing ones under given conditions.

Distributed Hybrid Two-Stage Multi-Sensor Fusion for Cooperative Modulation Classification in Large-Scale Wireless Sensor Networks.

Recent studies showed that the performance of the modulation classification (MC) is considerably improved by using multiple sensors deployed in a cooperative manner. Such cooperative MC solutions are based on the centralized fusion of independent features or decisions made at sensors. Essentially, the cooperative MC employs multiple uncorrelated observations of the unknown signal to gather more complete information, compared to the single sensor reception, which is used in the fusion process to refine the MC decision. However, the non-cooperative nature of MC inherently induces large loss in cooperative MC performance due to the unreliable measure of quality for the MC results obtained at individual sensors (which causes the partial information loss while performing centralized fusion). In this paper, the distributed two-stage fusion concept for the cooperative MC using multiple sensors is proposed. It is shown that the proposed distributed fusion, which combines feature (cumulant) fusion and decision fusion, facilitate preservation of information during the fusion process and thus considerably improve the MC performance. The clustered architecture is employed, with the influence of mismatched references restricted to the intra-cluster data fusion in the first stage. The adopted distributed concept represents a flexible and scalable solution that is suitable for implementation of large-scale networks.

Cooperative Cumulants-Based Modulation Classification in Distributed Networks

Automatic modulation classification of unknown wireless signals has been studied as a key technology in several cognitive radio and military applications. However, few studies have examined the performance of cooperative classification, in which spatially scattered nodes share their data to generate an overall decision of the unknown modulation scheme. In this paper, we propose a centralized feature-level cooperative classification framework using maximum likelihood (ML) combining algorithm. Each node estimates a set of features, based on high-order cumulants, that are combined at a fusion center using ML to produce a final decision. We present new theoretical models for ML combining performance, as well as computer simulations, under both additive white Gaussian noise and flat Rayleigh fading channels. In addition, a new hierarchical framework for cooperative modulation classification of larger sets of modulation schemes is presented. Finally, the performance of the cooperative framework with correlated signals is studied. Analysis and simulations validate the theoretical models and prove the efficiency of the proposed cooperative framework under different scenarios.

Cooperative Modulation Classification for Multipath Fading Channels via Expectation-Maximization

In this paper, we investigate the cooperative modulation classification problem under multipath scenarios with blind channel information. Multipath channels cause severe degradation on the modulation classification performance, which has not yet been thoroughly solved in the existing literature. To address this issue, a likelihood-based classifier using the expectation-maximization algorithm is proposed, which is capable of finding the maximum likelihood estimates of unknown parameters in a tractable way. Furthermore, to evaluate the upper bound performance of the proposed algorithm, the Cramer–Rao lower bounds of the joint estimates of unknown parameters are derived. Extensive simulations show that the classification performance of the proposed algorithm with good initialization scheme is close to the performance upper bound in the high signal-to-noise ratio region. The results also demonstrate that the proposed algorithm provides significant performance improvement in the multipath channels compared with conventional approaches.

Joint cumulant estimate correction and decision for cooperative modulation classification by using multiple sensors

In this paper, several data fusion and soft decision fusion methods are proposed for cooperative automatic modulation classification (AMC) by using multiple sensors. A well-known AMC using fourth-order cumulant is considered. Also, a novel joint cumulant estimate correction is proposed. The AMC performance of proposed fusion methods with and without joint estimate correction is evaluated through the extensive Monte Carlo trials for different multipath fading channels, sensor spatial distributions, and application scenarios, and compared to those of the existing methods. Numerical results have confirmed huge performance improvement achievable with here proposed methods, especially in the non-idealized application scenarios.

Cooperative modulation classification with data fusion for multipath fading channels

Data fusion methods are proposed for cooperative modulation classification by using multiple sensors in multipath fading channels. Performances of these methods are evaluated, and compared with those of the existing optimal decision fusion method, through Monte Carlo simulations for a realistic application scenario, and evident gains are confirmed.