A Likelihood-Based Algorithm for Blind Identification of QAM and PSK Signals

Zhu, Daimei; Mathews, V.J.; Detienne, David H.

doi:10.1109/twc.2018.2811802

Cited by 32 publications

(16 citation statements)

References 28 publications

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“…We set the simulation SNR to 20 dB. After each time interval, we can achieve 100 groups of random sparse channels [21], and then we use the proposed method to estimate these channels. Finally, we take the average to be the result as it is shown in Figure 5.…”

Section: Performance Of Sbl Blind Channel Estimationmentioning

confidence: 99%

“…This method does not take into account that the original constellation is unknown and the frequency-selective channel was slow-varying. In [21], a new MPSK modulation identification method based on likelihood function is proposed. This method identify modulation without knowledge of the symbol rate, carrier frequency and noise power in the received signals.…”

Section: Introductionmentioning

confidence: 99%

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Blind Modulation Identification of Underwater Acoustic MPSK Using Sparse Bayesian Learning and Expectation Maximization

et al. 2020

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This paper presents a likelihood-based algorithm for identifying different phase shift keying (PSK) modulations, i.e., BPSK, QPSK, and 8PSK. This algorithm selects the modulation type that maximizes a loglikelihood function that is based on the known original constellation associated with the constellation of the received signals for the candidate modulation types. However, there are two problems in non-cooperative underwater acoustic Multiple Phase Shift Keying (MPSK) modulation identification based on the likelihood method. One is the original constellation, which as prior information is unknown. The other is the underwater acoustic multipath channel makes the constellation distort seriously. In this paper, we solved these problems by combining sparse bayesian learning (SBL) with expectation maximization (EM). The specific steps are as follows. Firstly, blind channel equalization can be achieved by channel impulse response (CIR), which is estimated by sparse bayesian learning in single input multi output (SIMO) underwater acoustic channel. Subsequently, we used expectation maximization to compensate amplitude attenuation and phase offset, as the original constellation of MPSK is unknown. Finally, modulation can be successfully identified by the Quasi Hybrid Likelihood Ratio Test (QHLRT). The simulation results show that the channel estimation method based on SBL can eliminate the influence of channel effectively, and the EM algorithm can make the received constellation converge to the preset constellation in the case of unknown original transmit constellation, which effectively solves these two problems. We use the proposed SBL-EM-QHLRT method to achieve an identification rate of more than 95% in underwater acoustic multipath channels with Signal to Noise Ratio (SNR) higher than 15 dB, which provides a new idea for modulation identification of non-cooperative underwater acoustic MPSK.

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Section: Performance Of Sbl Blind Channel Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blind Modulation Identification of Underwater Acoustic MPSK Using Sparse Bayesian Learning and Expectation Maximization

et al. 2020

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“…The column permutation of the sub-block interleaver is given as follows: [0, 16,8,24,4,20,12,28,2,18,10,26,6,22,14,30,1,17,9,25,5,21,13,29,3,19,11,27,7,23,15,31]. The bit_collection block accepts input from three sub-block_interleaver and generates output depending on coding type.…”

Section: Rate Matchings and Rate De-matchingmentioning

confidence: 99%

Automated performance‐based design technique for an efficient LTE PDSCH implementation using SDSoC tool

Eladawy

Mahmoud

Said

et al. 2019

Int J Communication

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System on a chip (SoC) creates massive design challenges for SoC-based designers. The design challenges start from functional, architectural verification complexity and finally meeting performance constraints. In addition, heterogeneity of components and tools introduces long design cycles. The Software-Defined System-on-Chip (SDSoC) developed by Xilinx is used to create custom SoC on a heterogeneous FPGA-CPU platform. The SDSoC tool provides fast, flexible, and short design cycle to develop heterogeneous FPGA-CPU platform. The objective of this paper is to introduce a new automated design technique to build a SoC on a heterogeneous FPGA-CPU platform that meets design requirements using SDSoC tool. In this paper, the typical SDSoC design flow is introduced. In addition, a new automated SDSoC design technique is developed to design SoC on a heterogeneous FPGA-CPU platform on the basis of performance metrics such as area, power, and latency. Design of physical downlink shared channel (PDSCH) in long-term evolution (LTE) is presented as a case study. This paper provides the implementation of the transmitter and the receiver of the PDSCH in LTE using SDSoC tool and selects a platform that meets performance metrics constraints.After refining all C/C++ functions, the SDSoC design flow is executed. 7 The SDSoC design flow is shown in Figure 2. First, all C/C++ functions are compiled, Next, the implementation of the C/C++ functions is partitioned into software implementation functions or hardware-accelerated functions depending on the designer selection.The Vivado tool (Xilinx Inc) and Software Development Kit (SDK) tool are the elements of hardware and software system design, respectively. The Vivado tool includes an HLS tool used for creating the hardware system components by transforming the C/C++ code to an RTL implementation. The SDK tool is a software design suite that includes driver support, C/C++ compiler library, and tools for debugging and profiling. Finally, for integration, the necessary communication blocks between hardware and software are generated automatically by the SDSoC tool, and SoC platform is created by SDSoC tool.The SDSoC tool generates the embedded FPGA SoC platform as shown in Figure 3. This platform allows executing parts of the C/C++ code on the ARM processor as software functions and the other parts of the C/C++ codes on the FPGA as hardware-accelerated functions. The embedded FPGA platform consists of three parts: First, the processing system includes dual-core ARM cortex-A9 processor hardcore processor. Second, interfacing logic includes ACP port interconnect and data movers. Third, hardware logic includes HLS generated block. | Proposed automated performance-based design techniqueThis section illustrates in detail the proposed automated performance-based design technique using SDSoC tool. The objective of this technique is to determine platforms that achieve performance metrics and to select the platform that achieves the best performance. Figure 4 shows the proposed automated performanc...

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“…Traditional adaptive beamformers mostly focus on second-order circular signals with stationary observations, such as minimum variance distortionless response (MVDR) beamformer and linearly constrained minimum variance (LCMV) beamformer [6][7][8][9][10]. The second-order noncircular and nonstationary signals frequently appear in the fields of radio communication and satellite communication, such as unbalanced quaternary phase shift keying (UQPSK), amplitude modulated (AM), amplitude-shift keying (ASK), and binary phase-shift keying (BPSK) signals [11][12][13]. However, these beamformers become suboptimal when they encounter noncircular signals.…”

Section: Introductionmentioning

confidence: 99%

Robust widely linear beamforming using estimation of extended covariance matrix and steering vector

Meng

Zhou

Gazor

2020

J Wireless Com Network

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The distribution of the received signals in many array processing applications is noncircular. Although optimal widely linear beamformer (WLB) can provide the best performance for noncircular received signals, its performance degrades severely under model mismatches in practical applications. As a remedy, we propose a robust WLB by using precise reconstruction of extended interference-plus-noise covariance matrix (EINCM) and low-complexity estimation of extended desired signal steering vector (EDSSV). We propose to first determine the steering vectors, powers, and noncircularity coefficients of all signals and the noise power. In contrast to the previous reconstruction methods using the integration over a wide angular sector, we reconstruct the interference-plus-noise covariance matrix (INCM) and the pseudo INCM accurately according to their definitions. By using INCM and pseudo INCM, we can precisely reconstruct the EINCM. We propose to estimate the EDSSV by intersecting two extended subspaces, which are respectively formed by eigendecomposing the extended sample covariance matrix and the extended desired signal covariance matrix. Unlike the convex optimization methods, the proposed EDSSV estimation does not require any optimization programming and yields a solution with closed expression in low computational complexity. Simulation results show that the proposed robust WLB provides near optimal performance under several model mismatch cases.

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A Likelihood-Based Algorithm for Blind Identification of QAM and PSK Signals

Cited by 32 publications

References 28 publications

Blind Modulation Identification of Underwater Acoustic MPSK Using Sparse Bayesian Learning and Expectation Maximization

Blind Modulation Identification of Underwater Acoustic MPSK Using Sparse Bayesian Learning and Expectation Maximization

Automated performance‐based design technique for an efficient LTE PDSCH implementation using SDSoC tool

Robust widely linear beamforming using estimation of extended covariance matrix and steering vector

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