Automatic modulation classification (AMC) is an important function of radio surveillance systems in order to identify unknown signals. Many previous works on AMC have utilized signal cyclostationarity, particularly spectral correlation density (SCD), but many of them fail to address several implementation issues, such as the assumption of perfect knowledge of the symbol rate. In this paper, we discuss several practical issues, e.g. cyclic frequency mismatch, which may affect the SCD, and propose compensation techniques to overcome those issues. We also propose a novel feature extraction technique from the SCD, which utilizes the SCD of not only the original received signal, but also the squared received signal. A symbol rate estimation technique which complements the feature extraction is also proposed. Finally, the classification performance of the system is evaluated through Monte Carlo simulations using a wide variety of modulated signals, and simulation results show that the proposed technique can estimate the symbol rate and classify modulation with a probability of above 0.9 down to SNRs of 5 dB.

Conventional localization techniques such as triangulation and multilateration are not reliable in non-line-of-sight (NLOS) environments such as dense urban areas. Although fingerprint-based localization techniques have been proposed to solve this problem, we may face difficulties because we do not know the parameters of the illegal radio when creating the fingerprint database. This paper proposes a novel technique to localize illegal radios in an urban environment by interpolating the channel impulse responses stored as fingerprints in a database. The proposed interpolation technique consists of interpolation in the bandwidth (delay), frequency and spatial domains. A localization algorithm that minimizes the squared error criterion is employed in this paper, and the proposed technique is evaluated through Monte Carlo simulations using location fingerprints obtained from ray-tracing simulations. Results show that utilizing an interpolated fingerprint database is advantageous in such scenarios.

Location information is essential to varieties of applications. It is one of the most important context to be detected by wireless distributed sensors, which is a key technology in Internet-of-Things. Fingerprint-based methods, which compare location unique fingerprints collected beforehand with the fingerprint measured from the target, have attracted much attention recently in both of academia and industry. They have been successfully used for many location-based applications. From the viewpoint of practical applications, in this paper, four different typical approaches of fingerprint-based radio emitter localization system are introduced with four different representative applications: localization of LTE smart phone used for anti-cheating in exams, indoor localization of Wi-Fi terminals, localized light control in BEMS using location information of occupants, and illegal radio localization in outdoor environments. Based on the different practical application scenarios, different solutions, which are designed to enhance the localization performance, are discussed in detail. To the best of the authors' knowledge, this is the first paper to give a guideline for readers about fingerprint-based localization system in terms of fingerprint selection, hardware architecture design and algorithm enhancement.

Automatic modulation classification (AMC) involves extracting a set of unique features from the received signal. Accuracy and uniqueness of the features along with the appropriate classification algorithm determine the overall performance of AMC systems. Accuracy of any modulation feature is usually limited by the blindness of the signal information such as carrier frequency, symbol rate etc. Most papers do not sufficiently consider these impairments and so do not directly target practical applications. The AMC system proposed herein is trained with probable input signals, and the appropriate decision tree should be chosen to achieve robust classification. Six unique features are used to classify eight analog and digital modulation schemes which are widely used by low frequency mobile emergency radios around the globe. The Proposed algorithm improves the classification performance of AMC especially for the low SNR regime.