Adaptive digital self-interference cancellation based on fractional order LMS in LFMCW radar

Luo, Yongjiang; Luhao, Bi; Dong, Zhao

doi:10.23919/jsee.2021.000049

Cited by 10 publications

(3 citation statements)

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“…At present, the SI signal can be eliminated in several stages and various domains, as shown in Figure 1 . Typically, SIC is achieved through the cascaded antenna domain [ 8 ], analog domain [ 9 ], and digital domain [ 10 , 11 , 12 , 13 , 14 , 15 ]. The antenna domain SIC is passive in nature, and mainly uses antenna isolation or related beamforming algorithms to prevent RF reception path blockage.…”

Section: Introductionmentioning

confidence: 99%

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Digital Self-Interference Canceler with Joint Channel Estimator for Simultaneous Transmit and Receive System

Song,

Tang,

et al. 2024

Sensors

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Simultaneous transmit and receive wireless communications have been highlighted for their potential to double the spectral efficiency. However, it is necessary to mitigate self-interference (SI). Considering both the SI channel and remote transmission (RT) channel need to be estimated before equalizing the received signal, we propose two adaptive algorithms for linear and nonlinear self-interference cancellation (SIC), based on a multi-layered joint channel estimator structure. The proposed algorithms estimate the RT channel while performing SIC, and the multi-layered structure ensures improved performance across various interference-to-signal ratios. The M-estimate function enhances the robustness of the algorithm, allowing it to converge even when affected by impulsive noise. For nonlinear SIC, this paper introduces an adaptive algorithm based on generalized Hammerstein polynomial basis functions. The simulation results indicate that this approach achieves a better convergence speed and normalized mean squared difference compared to existing SIC methods, leading to a lower system bit error rate.

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Section: Introductionmentioning

confidence: 99%

“…Digital cancellation algorithms accounting for impulsive noise have been proposed in earlier research [ 10 , 17 ]. The work in [ 11 , 12 ] focused on linear SIC by employing the least mean square (LMS) adaptive filter. In addition, different linear SIC methods were compared in [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Digital Self-Interference Canceler with Joint Channel Estimator for Simultaneous Transmit and Receive System

Song,

Tang,

et al. 2024

Sensors

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“…Fractional calculus operators are also exploited to design novel recursive/adaptive algorithms as well as evolutionary/swarm computation heuristics for different optimization tasks involved in engineering and science applications. For example, fractional gradient descent/fractional least mean square algorithm was proposed for various applications including recommender systems [10], channel estimation [11], automatic identification system [12], power system optimization [13], economics [14], radar signal processing [15], system identification [16,17], Hammerstein output error identification [18], wireless sensor network [19], neural network optimization [20][21][22][23][24], chaotic time-series prediction [25,26], oscillator [27], vibration rejection [28], nonlinear AR-MAX identification [29] and parameter estimation of input nonlinear control autoregressive (IN-CAR) systems [29,30].…”

Section: Introduction 1literature Reviewmentioning

confidence: 99%

Novel Fractional Swarming with Key Term Separation for Input Nonlinear Control Autoregressive Systems

et al. 2022

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In recent decades, fractional order calculus has become an important mathematical tool for effectively solving complex problems through better modeling with the introduction of fractional differential/integral operators; fractional order swarming heuristics are also introduced and applied for better performance in different optimization tasks. This study investigates the nonlinear system identification problem of the input nonlinear control autoregressive (IN-CAR) model through the novel implementation of fractional order particle swarm optimization (FO-PSO) heuristics; further, the key term separation technique (KTST) is introduced in the FO-PSO to solve the over-parameterization issue involved in the parameter estimation of the IN-CAR model. The proposed KTST-based FO-PSO, i.e., KTST-FOPSO accurately estimates the parameters of an unknown IN-CAR system with robust performance in cases of different noise scenarios. The performance of the KTST-FOPSO is investigated exhaustively for different fractional orders as well as in comparison with the standard counterpart. The results of statistical indices through Monte Carlo simulations endorse the reliability and stability of the KTST-FOPSO for IN-CAR identification.

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Design of IIR Fractional Order Digital Integrators Using Iterative Convex Optimization Algorithm

Wei,

Chen,

Zhao

et al. 2024

Lecture Notes in Electrical Engineering

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Adaptive digital self-interference cancellation based on fractional order LMS in LFMCW radar

Cited by 10 publications

References 0 publications

Digital Self-Interference Canceler with Joint Channel Estimator for Simultaneous Transmit and Receive System

Digital Self-Interference Canceler with Joint Channel Estimator for Simultaneous Transmit and Receive System

Novel Fractional Swarming with Key Term Separation for Input Nonlinear Control Autoregressive Systems

Design of IIR Fractional Order Digital Integrators Using Iterative Convex Optimization Algorithm

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