Two-Dimensional Cartesian Memory Polynomial Model for Nonlinearity and I/Q Imperfection Compensation in Concurrent Dual-Band Transmitters

Lajnef, Souhir; Boulejfen, Noureddine; Abdelhafiz, Abubaker; Ghannouchi, Fadhel M.

doi:10.1109/tcsii.2015.2482678

Cited by 21 publications

(13 citation statements)

References 9 publications

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“…For example, we have considered two 32-APSK constellation designs that are specified for the DVB-S2X standard [22]: one has three rings with 4, 12, and 16 points on each ring, and the other has four rings with 4, 8, 4, and 16 points on each ring. Our numerical investigation shows that the CD receiver is optimal (i.e., ρ * = 1) for the (4,12,16) design of 32-APSK, while the splitting receiver achieves a significant performance gain for the (4,8,4,16) design of 32-APSK and, interestingly, the gain is more profound at larger P or lower SER. Therefore, the performance analysis and characterization of the splitting receiver for practical modulation can only be done in a case-by-case basis.…”

Section: Ser Performancementioning

confidence: 88%

“…A coherent receiver is based on coherent detection (CD) [3], where the received RF-band signal is converted to a baseband signal with in-phase (I) and quadrature (Q) components by using a down-conversion circuit, which is then digitized through an analog-to-digital converter (ADC). The coherent receiver design is rather mature and has been adopted in most of the wireless communication standards, and the current research mostly focuses on two directions: I/Q imbalance compensation due to the hardware imperfection [4] and low-power design for both cellular and wireless sensor networks [5]. For the latter, low-resolution or even one-bit ADCs have been considered for massive MIMO systems [6], [7], since highresolution ADCs are power-hungry for portable devices.…”

Section: Introductionmentioning

confidence: 99%

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On the Performance of Splitting Receiver With Joint Coherent and Non-Coherent Processing

Wang

Liu

Zhou

et al. 2020

IEEE Trans. Signal Process.

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In this paper, we revisit a recently proposed receiver design, named the splitting receiver, which jointly uses coherent and non-coherent processing for signal detection. By considering an improved signal model for the splitting receiver as compared to the original study in the literature, we conduct a performance analysis on the achievable data rate under Gaussian signaling and obtain a fundamentally different result on the performance gain of the splitting receiver over traditional receiver designs that use either coherent or noncoherent processing alone. Specifically, the original study ignored the antenna noise and concluded on a 50% gain in achievable data rate in the high signal-to-noise ratio (SNR) regime. In contrast, we include the antenna noise in the signal model and show that the splitting receiver improves the achievable data rate by a constant gap in the high SNR regime. This represents an important correction of the theoretical understanding on the performance of the splitting receiver. In addition, we examine the maximum-likelihood detection and derive a low-complexity detection rule for the splitting receiver for practical modulation schemes. Our numerical results give further insights into the conditions under which the splitting receiver achieves significant gains in terms of either achievable data rate or detection error probability.

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Section: Ser Performancementioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

On the Performance of Splitting Receiver With Joint Coherent and Non-Coherent Processing

Wang

Liu

Zhou

et al. 2020

IEEE Trans. Signal Process.

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“…1 [11]. The inverse function is then extracted by exchanging the input signal x(n) by the gain normalised output signal y(n)/G, where G is the small signal gain of the PA [15].…”

Section: Digital Predistortionmentioning

confidence: 99%

Robust digital predistorter for RF power amplifier linearisation

Manai

Chenini

Harguem

et al. 2020

IET Microwaves, Antennas & Propagation

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In this study, the authors propose a new numerically stable digital predistorter for the linearisation of RF Power amplifiers. The proposed predistorter is based on the parameterised Gegenbauer polynomials that can be optimised for maximum predistorter efficiency and stability under different input signal distributions. The robustness and the efficiency of the proposed predistorter are experimentally demonstrated and compared to the ones of previously published polynomial model‐based predistorters. The obtained results revealed exceptional numerical stability regardless of the input signal statistics, making the proposed predistorter suitable for the linearisation of multimode and broadband non‐linear wireless transmitters.

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“…The neural network models used for PA behavioral modeling mainly include bidirectional long short-term memory (BiLSTM) neural network [9], radial basis function (RBF) neural network [10] and time delay neural network (TDNN) [8]. However, there are few DPD models to jointly compensate I/Q imbalance and PA nonlinearities [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

Complex-Valued Pipelined Chebyshev Functional Link Recurrent Neural Network for Joint Compensation of Wideband Transmitter Distortions and Impairments

Cai

Yao

et al. 2020

IEEE Access

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In this paper, a novel digital predistortion (DPD) model based on complex-valued pipelined Chebyshev functional link recurrent neural network (CPCFLRNN) for joint compensation of wideband transmitter distortions and impairments is proposed. The functional link neural network (FLNN) model has attracted much attention from scholars, and many improved models using this structure, such as Chebyshev FLNN, have been applied in the DPD of power amplifiers (PAs). However, these existing neural network models cannot deal with complex-valued input signals simultaneously, and the real-valued model structure will introduce cumbersome training algorithm and result in a long training time. The pipelined recurrent neural network (PRNN) has been successfully applied to nonlinear signal prediction because of its excellent ability for dealing with nonlinear nonstationary signals. Therefore, the PRNN model containing Chebyshev structure is extended to complex domain for the first time to obtain the CPCFLRNN model for DPD application. Considering the strong correlation of in-phase and quadrature phase (I/Q) components of the transmitter signal, the real time recurrent learning (RTRL) algorithm based on fully complex activation function is selected and extended to complex domain to obtain the complex-valued RTRL (CRTRL) algorithm for CPCFLRNN model training. A GaN PA was employed to verify the effectiveness of the proposed models. And the input signal is a 30MHz LTE signals which consists of I/Q imbalance and dc offsets. The experimental results show that the proposed CPCFLRNN model have more accurate modeling effect and better linearization performance compared with the conventional DPD models. INDEX TERMS Digital predistortion (DPD), complex-valued pipelined Chebyshev functional link recurrent neural network (CPCFLRNN), I/Q imbalance, dc offset, power amplifier (PA).

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Two-Dimensional Cartesian Memory Polynomial Model for Nonlinearity and I/Q Imperfection Compensation in Concurrent Dual-Band Transmitters

Cited by 21 publications

References 9 publications

On the Performance of Splitting Receiver With Joint Coherent and Non-Coherent Processing

On the Performance of Splitting Receiver With Joint Coherent and Non-Coherent Processing

Robust digital predistorter for RF power amplifier linearisation

Complex-Valued Pipelined Chebyshev Functional Link Recurrent Neural Network for Joint Compensation of Wideband Transmitter Distortions and Impairments

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