Exact and approximate log-likelihood ratio of M-ary QAM with two-time dimensions

Lee, Ji-hoon; Chung, Hae

doi:10.1016/j.icte.2018.08.004

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…This approximation is tight at high SNR and is commonly used in practical communications systems for conventional modulation schemes such as QAM [24]. It has been shown in [24] and in [25] that the log-sum approximation does not lead to any significant performance loss, even at low SNR. Subsequently, we obtain…”

Section: ) Probabilistic Demappermentioning

confidence: 97%

Design of Digital Communications for Strong Phase Noise Channels

Bicaïs

Doré

2020

IEEE Open J. Veh. Technol.

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To meet the requirements of beyond 5G networks, the significant amount of unused spectrum in sub-TeraHertz frequencies is contemplated for high-rate wireless communications. Yet, the performance of sub-TeraHertz systems is severely degraded by strong oscillator phase noise. We investigate in this paper the design of digital communications robust to phase noise. This problem is addressed in three steps: the characterization of the phase noise channel, the design of the optimum receiver, and the optimization of the modulation scheme. This paper proposes a joint performance and implementation optimization. First, we address the design of the demodulation scheme for phase noise channels and propose the polar metric, a soft-decision rule for symbol detection. It is shown that performance gains are achieved for coded and uncoded systems with valuable complexity reductions of the receiver. Second, we investigate the optimization of the modulation scheme for phase noise. We demonstrate that using a constellation defined upon a lattice in the amplitude-phase domain leads to significant performance gains and a low-complexity implementation. Thereupon, we propose the Polar-QAM scheme with efficient binary labeling and demodulation. Numerical simulation results show that the proposed modulation and demodulation schemes offer valuable solutions to achieve high-rate communications on systems strongly impaired by phase noise.

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Section: ) Probabilistic Demappermentioning

confidence: 97%

Design of Digital Communications for Strong Phase Noise Channels

Bicaïs

Doré

2020

IEEE Open J. Veh. Technol.

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“…This research created a system for comparing the bit errors of different M-ary modulation techniques, namely PSK and QAM transmission methods [1,2,3,4]. Both PSK and QAM modulation techniques are somewhat similar to each other [4,5].…”

Section: Introductionmentioning

confidence: 99%

Development of optimization model of the M-QAM and MPSK Modulation in AWGN channel

Lao¹

2019

IJATCSE

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M-QAM and MPSK are important modulation techniques. They are used to modulate systems. M-QAM is a popular scheme in high rate and high bandwidth efficiency systems while QAM is a combination of amplitude and phase modulation. Mathematically the M-QAM is described by the combined amplitude and phase modulation results. MPSK stands for Mary Phase Shift Keying and is a digital modulation process which conveys data by changing the modulating phase of the constant frequency. AWGN or Additive White Gaussian Noise is a basic noise model used to Mimic random processes that occur in nature. This research creates an optimization model for the M-QAM and MPSK Modulation in the AWGN Channel using MATLAB Simulink. Comparison of the two techniques was also performed.

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“…The next step is the similarity model testing, which divides the log-likelihood value of the test results with the log-likelihood value of the model. This method is called the log-likelihood ratio or exact log-likelihood ratio method [18].…”

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confidence: 99%

Anomaly detection on flight route using similarity and grouping approach based-on automatic dependent surveillance-broadcast

Pusadan

Buliali

Ginardi

2019

Int. J. Adv. Intell. Informatics

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Flight anomaly detection is used to determine the abnormal state data on the flight route. This study focused on two groups: general aviation habits (C1)and anomalies (C2). Groups C1 and C2 are obtained through similarity test with references. The methods used are: 1) normalizing the training data form, 2) forming the training segment 3) calculating the log-likelihood value and determining the maximum log-likelihood (C1) and minimum log-likelihood (C2) values, 4) determining the percentage of data based on criteria C1 and C2 by grouping SVM, KNN, and K-means and 5) Testing with log-likelihood ratio. The results achieved in each segment are Log-likelihood value in C1Latitude is -15.97 and C1Longitude is -16.97. On the other hand, Log-likelihood value in C2Latitude is -19.3 (maximum) and -20.3 (minimum), and log-likelihood value in C2Longitude is -21.2 (maximum) and -24.8 (minimum). The largest percentage value in C1 is 96%, while the largest in C2 is 10%. Thus, the highest potential anomaly data is 10%, and the smallest is 3%. Also, there are performance tests based on F-measure to get accuracy and precision.

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Exact and approximate log-likelihood ratio of M-ary QAM with two-time dimensions

Cited by 3 publications

References 14 publications

Design of Digital Communications for Strong Phase Noise Channels

Design of Digital Communications for Strong Phase Noise Channels

Development of optimization model of the M-QAM and MPSK Modulation in AWGN channel

Anomaly detection on flight route using similarity and grouping approach based-on automatic dependent surveillance-broadcast

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