MIMO Volterra Modeling for Nonlinear Communication Channels

Fernandes, Carlos Alexandre Rolim; Mota, J.C.M.; Favier, Gérad

doi:10.21528/lnlm-vol8-no2-art1

Cited by 18 publications

(11 citation statements)

References 81 publications

(138 reference statements)

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“…Table III summarizes the weather data we used in this study. We obtained one-minute temperature, humidity, and solar irradiance data for Berkeley from Lawrence Berkeley National Laboratoray [18] and for Los Angeles and Sacramento from SOLRMAP at Loyola Marymount University and Sacramento Municipal Utility District [19]. The Los Angeles and Sacramento data, having been quality controlled at the source, appeared to be quite complete and reliable and was used with only minor reformatting.…”

Section: Pv Generation Datamentioning

confidence: 99%

Effects of distributed PV generation on California’s distribution system, part 2: Economic analysis

2016

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Section: Pv Generation Datamentioning

confidence: 99%

Effects of distributed PV generation on California’s distribution system, part 2: Economic analysis

2016

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“…Polynomial AR (PAR) models are based on the Volterra series expansion (1), which has been applied successfully in areas such as biological systems [24], industrial plant control systems [25], communications [26] and seismology [27]. PAR processes differ from the other non-linear AR processes in that they are linear-inthe-parameters and thus many mathematical applications developed for linear models can be employed without much difficulty [28].…”

Section: Introductionmentioning

confidence: 99%

One‐day ahead wind speed/power prediction based on polynomial autoregressive model

Karakuş¹,

Kuruoğlu²,

Altınkaya³

2017

IET Renewable Power Generation

113

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Wind has been one of the popular renewable energy generation methods in the last decades. Foreknowledge of power to be generated from wind is crucial especially for planning and storing the power. It is evident in various experimental data that wind speed time series has non-linear characteristics. It has been reported in the literature that nonlinear prediction methods such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) perform better than linear autoregressive (AR) and AR moving average models. Polynomial AR (PAR) models, despite being non-linear, are simpler to implement when compared with other non-linear AR models due to their linear-in-the-parameters property. In this study, a PAR model is used for one-day ahead wind speed prediction by using the past hourly average wind speed measurements of Çeşme and Bandon and performance comparison studies between PAR and ANN-ANFIS models are performed. In addition, wind power data which was published for Global Energy Forecasting Competition 2012 has been used to make power predictions. Despite having lower number of model parameters, PAR models outperform all other models for both of the locations in speed predictions as well as in power predictions when the prediction horizon is longer than 12 h.

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“…Remark 3: Observe that this identification procedure involves the two loops: the outer loop of a) and b) as well as the inner loop of a.1) and a.2), with the three stages of alternating 'LS' estimation: (37), (38) and (40). The maximum number of the outer-loop iterations τ max = 1 or 2 is sufficient.…”

Section: B Identification Of Nonlinear Mimo Uplink Modelmentioning

confidence: 99%

Multiuser Detection for Nonlinear MIMO Uplink

Chen

Khalaf

et al. 2020

IEEE Trans. Commun.

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For the multiple-input multiple-output (MIMO) uplink employing high-order quadrature amplitude modulation (QAM) signaling and with nonlinear high power amplifiers (HPAs) at mobile users' transmitters, the existing multiuser detection methods can no longer be applied. We propose a novel nonlinear multiuser detection scheme for the nonlinear MIMO uplink. Specifically, we adopt an effective B-spline parameterization of the nonlinear transmit HPAs and derive an efficient and accurate algorithm to identify the nonlinear MIMO uplink channel, including the nonlinear B-spline model of the nonlinear transmit HPAs and the estimate of the linear MIMO channel matrix. Moreover, as the direct result of this nonlinear MIMO channel identification, the B-spline inverse model of nonlinear transmit HPAs can readily be identified. The nonlinear multiuser detection can be effectively implemented by the zeroforcing linear detection based on the estimated linear MIMO channel and followed by compensating the nonlinear distortion of the nonlinear transmit HPAs based on the estimated B-spline inverse model. An extensive simulation investigation is performed to demonstrate the effectiveness of our proposed nonlinear multiuser detection scheme for nonlinear MIMO uplink with high-order QAM signaling.

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MIMO Volterra Modeling for Nonlinear Communication Channels

Cited by 18 publications

References 81 publications

Effects of distributed PV generation on California’s distribution system, part 2: Economic analysis

Effects of distributed PV generation on California’s distribution system, part 2: Economic analysis

One‐day ahead wind speed/power prediction based on polynomial autoregressive model

Multiuser Detection for Nonlinear MIMO Uplink

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