A Robust Hierarchical Digital Modulation Classification Technique: using Linear Approximations

Baarrij, S.M.; Nasir, Fazal E; Masood, Sarfaraz

doi:10.1109/isspit.2006.270861

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…Unfortunately, we still need to resolve the need in (8) for s n which is the very signal Rx n is trying to demodulate. To solve this problem, we have to modify (8) and/or (9) in order for them to be useful. We propose to have an initial ''guess'' on s n by preliminarily demodulating the received signal using the standard MLD on y n via (3) without using (2).…”

Section: Breaking the ''Catch-22''mentioning

confidence: 99%

“…The works in [6]- [8] relate ELINT to radar. For potential COMINT application, [9] and [10] present good exposure to communication modulation classification and contain other references related to that area.…”

Section: Introductionmentioning

confidence: 99%

“…At this SIR, the three estimates improve as a function of increasing SNR (although do not converge to the actual parameter values at SNR = 15dB). The set of estimates for S 4 IR = 4.56 dB is a good example where there is a mixture of estimation performance which(9) does not easily convey. Two of the estimates improve as SNR is increased while the estimate β 1,4 actually gets worse as SNR is increased.…”

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confidence: 99%

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Signals of Interest Recovery With Multiple Receivers Using Reference-Based Successive Interference Cancellation for Signal Collection Applications

Romero

Rios

2014

IEEE Access

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In this paper, we introduce a novel but intuitive scheme to recover multiple signals of interest (SoI) from multiple emitters in signal collection applications such as signal intelligence, electronic intelligence, and communications intelligence. We consider a case where the SoIs form a heavy interference environment. The scheme, which is referred to as reference-based successive interference cancellation (RSIC), involves a combination of strategic receiver placement and signal processing techniques. The scheme works by placing a network of cooperative receivers where each receiver catches its own SoI (despite multiple interferences). The first receiver demodulates the initial SoI (called a reference signal) and forwards it to the second receiver. The second receiver collects a received signal containing the second SoI but is interfered with by the initial SoI, which is a problem called co-channel interference in cellular communications. Unfortunately, the amplitude scaling of the interference is unknown in the second receiver and therefore has to be estimated via least squares error. It turns out that the estimation requires a priori knowledge of the second SoI, which is the very signal it tries to demodulate, thereby yielding a Catch-22 problem. We propose using an initial guess on the second SoI to form an amplitude estimate such that the interference is subtracted (cancelled) from the collected measurement at the second receiver. The procedure is applied to a third receiver (or multiple receivers) until the last of the desired SoI is separated from all of the co-channel interferences. The RSIC scheme performs well. Using quaternary phase shift keying as example modulation, we present major symbol error rate (SER) performance improvements with the use of RSIC over the highly degraded SER of receivers that are heavily interfered and do not employ any cancellation technique.

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Section: Breaking the ''Catch-22''mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Signals of Interest Recovery With Multiple Receivers Using Reference-Based Successive Interference Cancellation for Signal Collection Applications

Romero

Rios

2014

IEEE Access

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“…Outra alternativa apresentadaé a classificação hierárquica das modulações, que oferece uma complexidade inferior, favorecendo-os para implementações em tempo real. Nestes trabalhos, alguns algoritmos utilizados para a discriminação de PSK (Phase Shift Keying) e FSK (Frequency Shift Keying) são sequência de zero-crossing [6], o desvio padrão da frequência instantânea [7], [8] ou a curtose da amplitude [9]. Em [10], a curtose da frequência instantâneá e utilizada para separação de níveis de FSK.…”

Section: Introductionunclassified

Um método para discriminação entre PSK e FSK utilizando estatísticas de ordem superior

Facenda¹,

Silva²

2017

Anais De XXXV Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Resumo-Este artigo apresenta um novo algoritmo para discriminação entre modulações digitais em fase e em frequência. Dado um sinal modulado em PSK (Phase-Shift Keying) ou FSK (Frequency-Shift Keying), com quaisquer números de níveis, o algoritmoé capaz de identificar a qual classe (PSK ou FSK) o sinal pertence. O método proposto consiste no uso de estatísticas de ordem superior da frequência instantânea do sinal recebido em banda base como atributos utilizados por um classificador SVM, apresentando um bom compromisso entre complexidade, conhecimento a priori e desempenho em comparação com os métodos existentes.Palavras-Chave-Identificação de modulação digital, estatísticas de ordem superior, PSK, FSK, support vector machine.

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“…Especially in the non-cooperative communication systems, modulation classification plays a more significant role which can identify the modulation type of a modulated signal corrupted by noise. Many methods have been used in the modulation classification, such as spectrum, moments, zero crossings and Bayes decision theory [5]- [16].…”

Section: Introductionmentioning

confidence: 99%

Correlation-Index Blind Classification for MFSK with Unreconstructed Compressive Samplings

Tong¹,

Li²

2015

jcm

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To improve the speed of traditional signal sampling and processing, which is based on the Nyqusit-sampling theorem, a new theorem called Compressive Sensing (CS) is presented. Most work in CS has focused on reconstructing the high-bandwidth signals from nonuniform low-rate samples. However, the signal reconstruction process of CS needs large amount of computation. In this paper, under the frame of CS, a method called Correlation-Index Blind Classification (CIBC) using the Correlation-Index to realize MFSK signal classification is presented, which exploits the compressive measurements without reconstructing the original signal or knowing any parameter. CIBC processes the compressive measurements directly, avoiding the high computation of reconstruction. What's more, compared with the traditional Peak-Index, the promotion of computing speed is significant, and the performance decreases very little. The simulation results show that CIBC has good performance and low computation complexity. It's an effective method to improve the real-time performance of communication system.

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A Robust Hierarchical Digital Modulation Classification Technique: using Linear Approximations

Cited by 5 publications

References 11 publications

Signals of Interest Recovery With Multiple Receivers Using Reference-Based Successive Interference Cancellation for Signal Collection Applications

Signals of Interest Recovery With Multiple Receivers Using Reference-Based Successive Interference Cancellation for Signal Collection Applications

Um método para discriminação entre PSK e FSK utilizando estatísticas de ordem superior

Correlation-Index Blind Classification for MFSK with Unreconstructed Compressive Samplings

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