The automatic recognition of the modulation format of a detected signal, the intermediate step between signal detection and demodulation, is a major task of an intelligent receiver, with various civilian and military applications. Obviously, with no knowledge of the transmitted data and many unknown parameters at the receiver, such as the signal power, carrier frequency and phase offsets, timing information and so on, blind identification of the modulation is a difficult task. This becomes even more challenging in real-world scenarios with multipath fading, frequency-selective and time-varying channels. With this in mind, the authors provide a comprehensive survey of different modulation recognition techniques in a systematic way. A unified notation is used to bring in together, under the same umbrella, the vast amount of results and classifiers, developed for different modulations. The two general classes of automatic modulation identification algorithms are discussed in detail, which rely on the likelihood function and features of the received signal, respectively. The contributions of numerous articles are summarised in compact forms. This helps the reader to see the main characteristics of each technique. However, in many cases, the results reported in the literature have been obtained under different conditions. So, we have also simulated some major techniques under the same conditions, which allows a fair comparison among different methodologies. Furthermore, new problems that have appeared as a result of emerging wireless technologies are outlined. Finally, open problems and possible directions for future research are briefly discussed.

Survey of automatic modulation classification techniques: classical approaches and new trends

In this paper, likelihood-based algorithms are explored for linear digital modulation classification. Hybrid likelihood ratio test (HLRT)- and quasi HLRT (QHLRT)- based algorithms are examined, with signal amplitude, phase, and noise power as unknown parameters. The algorithm complexity is first investigated, and findings show that the HLRT suffers from very high complexity, whereas the QHLRT provides a reasonable solution. An upper bound on the performance of QHLRT-based algorithms, which employ unbiased and normally distributed non-data aided estimates of the unknown parameters, is proposed. This is referred to as the QHLRT-Upper Bound (QHLRT-UB). Classification of binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals is presented as a case study. The Cramer-Rao Lower Bounds (CRBs) of non-data aided joint estimates of signal amplitude and phase, and noise power are derived for BPSK and QPSK signals, and further employed to obtain the QHLRT-UB. An upper bound on classification performance of any likelihood-based algorithms is also introduced. Method-of-moments (MoM) estimates of the unknown parameters are investigated and used to develop the QHLRT-based algorithm. Classification performance of this algorithm is compared with the upper bounds, as well as with the quasi Log-Likelihood Ratio (qLLR) and fourth-order cumulant based algorithms.

On the likelihood-based approach to modulation classification

Adaptive modulation and automatic modulation classification are highly demanded in software-defined radio (SDR) for both commercial and military applications. Various design options of automatic classifiers have attracted researchers in developing 3G and 4G wireless communication systems. There is an urgent need to investigate the different methods of coherent and noncoherent modulation estimations, discuss the challenges in cooperative and noncooperative communication environment, and understand the distinct requirements in real-time modulation classifications. This survey paper focuses on the automatic modulation classification methods based on likelihood functions, studies various classification solutions derived from likelihood ratio test, and discusses the detailed characteristics associated with all major algorithms.

Likelihood-Ratio Approaches to Automatic Modulation Classification

Signal identification, which initially found applications in electronic warfare and spectrum monitoring and surveillance, has been recently considered for commercial communications in the context of software defined and cognitive radios. In this article, I present a snapshot of the status of signal identification algorithms, starting from a general description of maximum likelihood (ML) and feature based (FB) approaches to a more detailed discussion of a practical methodology using cyclostationarity-based features. I discuss the cyclostationarity-based features of various signals and the criteria of decision for their identification, while considering classical problems of identifying single carrier linearly digitally (SCLD) modulated signals, as well as new challenges posed by the identification of orthogonal frequency division multiplexing (OFDM), SC frequency domain equalization (SC-FDE), and multiple-transmit antenna signals. I conclude the article with remarks on practical solutions to signal identification and open research issues.

Signal identification for emerging intelligent radios: classical problems and new challenges

In this paper, we investigate the specific emitter identification (SEI) problem, which distinguishes different emitters using features generated by the nonlinearity of the power amplifiers of emitters. SEI is performed by measuring the features representing the individual specifications of emitters and making a decision based on their differences. In this paper, the SEI problem is considered in both single-hop and relaying scenarios, and three algorithms based on the Hilbert spectrum are proposed. The first employs the entropy and the first- and second-order moments as identification features, which describe the uniformity of the Hilbert spectrum. The second uses the correlation coefficient as an identification feature, by evaluating the similarity between different Hilbert spectra. The third exploits Fisher’s discriminant ratio to obtain the identification features by selecting the Hilbert spectrum elements with strong class separability. When compared with the existing literature, we further consider the identification problem in a relaying scenario, in which the fingerprint of different emitters is contaminated by the relay’s fingerprints. Moreover, we explore the identification performance under various channel conditions, such as additive white Gaussian noise, non-Gaussian noise, and fading. Extensive simulation experiments are performed to evaluate the identification performance of the proposed algorithms, and results show their effectiveness in both single-hop and relaying scenarios, as well as under different channel conditions.

Specific Emitter Identification via Hilbert–Huang Transform in Single-Hop and Relaying Scenarios

Blind modulation classification (MC) is an intermediate step between signal detection and demodulation, with both military and civilian applications. MC is a challenging task, especially in a non-cooperative environment, as no prior information on the incoming signal is available at the receiver. In this paper, we investigate classification of linear digital modulations in slowly varying flat fading channels. With unknown channel amplitude, phase and noise power at the receive-side, we derive hybrid likelihood ratio test (HLRT) and quasi-HLRT (QHLRT) -based classifiers, and discuss their performance versus computational complexity. It is shown that the QHLRT algorithm provides a low computational complexity solution, yet yielding performance close to the HLRT algorithm.

Likelihood-Based Algorithms for Linear Digital Modulation Classification in Fading Channels

In this paper we investigate bounds on performance of joint parameter estimation and modulation classification. The Cramer-Rao Lower Bounds (CRLBs) of non-data aided joint estimates of signal amplitude and phase, and noise power are derived for binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals. In addition, an upper bound on performance of Quasi Hybrid Likelihood Ratio Test (QHLRT)-based modulation classifiers is proposed, for the case when unbiased and normally-distributed non-data aided estimates of unknown parameters are available. Results for this upper bound are presented for BPSK and QPSK classification, with signal amplitude and phase, and noise power as unknown parameters.

F. Hameed

Papers

On the likelihood-based approach to modulation classification

Likelihood-Based Algorithms for Linear Digital Modulation Classification in Fading Channels

On performance bounds for joint parameter estimation and modulation classification