Joint Channel and Nonlinearity Estimation for Memoryless Nonlinear Systems
System nonlinearity due to hardware impairments has always been a challenging issue. Distortion cancellation and iterative detection based receivers such as the Bussgang Noise Cancelling (BNC) receiver are used to detect the original data in the presence of strong nonlinear (NL) effects. However, th...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10843673/ |
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| author | Zahra Mokhtari Rui Dinis Sha Hu Dzevdan Kapetanovic |
| author_facet | Zahra Mokhtari Rui Dinis Sha Hu Dzevdan Kapetanovic |
| author_sort | Zahra Mokhtari |
| collection | DOAJ |
| description | System nonlinearity due to hardware impairments has always been a challenging issue. Distortion cancellation and iterative detection based receivers such as the Bussgang Noise Cancelling (BNC) receiver are used to detect the original data in the presence of strong nonlinear (NL) effects. However, these receivers require knowledge of the system nonlinearity which is usually unknown in practical systems. Bussgang decomposition and its general form denoted Generalized Bussgang decomposition (GBD), have been commonly used to model system nonlinearity. In GBD the nonlinearity output is decomposed as the sum of uncorrelated terms of increased orders and provides spectral characteristics of the useful and distortion terms. In this paper we consider nonlinearity at the transmitter side and model it with GBD. We aim to estimate the scalar weights in the GBD to later use them at the BNC receiver. However, knowledge of the channel is required to make a reliable estimate of the NL parameters. On the other hand the pilots for channel estimation are affected by the system nonlinearity, which can preclude reliable channel estimation. Therefore, in this paper we propose a joint channel and NL parameter estimation technique by designing appropriate training signals for each estimation phase (i.e. channel estimation and NL parameter estimation). We also derive a closed form expression for the average power of residual distortion in GBD with estimated parameters to see how well this model can characterize the nonlinearity. The results show that the proposed estimation technique has good accuracy and enables quasi-ideal performance for a BNC receiver. |
| format | Article |
| id | doaj-art-0f418ef4f1214031b908025f52bf1117 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0f418ef4f1214031b908025f52bf11172025-01-25T00:01:44ZengIEEEIEEE Access2169-35362025-01-0113131431315510.1109/ACCESS.2025.353081710843673Joint Channel and Nonlinearity Estimation for Memoryless Nonlinear SystemsZahra Mokhtari0https://orcid.org/0000-0002-3037-9555Rui Dinis1https://orcid.org/0000-0002-8520-7267Sha Hu2https://orcid.org/0000-0001-8495-3906Dzevdan Kapetanovic3https://orcid.org/0000-0002-8219-320XInstituto de Telecomunicações, Lisbon, PortugalInstituto de Telecomunicações, FCT-UNL, Caparica, PortugalLund Research Center, Huawei Technologies Sweden, Lund, AB, SwedenLund Research Center, Huawei Technologies Sweden, Lund, AB, SwedenSystem nonlinearity due to hardware impairments has always been a challenging issue. Distortion cancellation and iterative detection based receivers such as the Bussgang Noise Cancelling (BNC) receiver are used to detect the original data in the presence of strong nonlinear (NL) effects. However, these receivers require knowledge of the system nonlinearity which is usually unknown in practical systems. Bussgang decomposition and its general form denoted Generalized Bussgang decomposition (GBD), have been commonly used to model system nonlinearity. In GBD the nonlinearity output is decomposed as the sum of uncorrelated terms of increased orders and provides spectral characteristics of the useful and distortion terms. In this paper we consider nonlinearity at the transmitter side and model it with GBD. We aim to estimate the scalar weights in the GBD to later use them at the BNC receiver. However, knowledge of the channel is required to make a reliable estimate of the NL parameters. On the other hand the pilots for channel estimation are affected by the system nonlinearity, which can preclude reliable channel estimation. Therefore, in this paper we propose a joint channel and NL parameter estimation technique by designing appropriate training signals for each estimation phase (i.e. channel estimation and NL parameter estimation). We also derive a closed form expression for the average power of residual distortion in GBD with estimated parameters to see how well this model can characterize the nonlinearity. The results show that the proposed estimation technique has good accuracy and enables quasi-ideal performance for a BNC receiver.https://ieeexplore.ieee.org/document/10843673/Generalized Bussgang decompositionnonlinear effectsnonlinear parameter estimationOFDM |
| spellingShingle | Zahra Mokhtari Rui Dinis Sha Hu Dzevdan Kapetanovic Joint Channel and Nonlinearity Estimation for Memoryless Nonlinear Systems IEEE Access Generalized Bussgang decomposition nonlinear effects nonlinear parameter estimation OFDM |
| title | Joint Channel and Nonlinearity Estimation for Memoryless Nonlinear Systems |
| title_full | Joint Channel and Nonlinearity Estimation for Memoryless Nonlinear Systems |
| title_fullStr | Joint Channel and Nonlinearity Estimation for Memoryless Nonlinear Systems |
| title_full_unstemmed | Joint Channel and Nonlinearity Estimation for Memoryless Nonlinear Systems |
| title_short | Joint Channel and Nonlinearity Estimation for Memoryless Nonlinear Systems |
| title_sort | joint channel and nonlinearity estimation for memoryless nonlinear systems |
| topic | Generalized Bussgang decomposition nonlinear effects nonlinear parameter estimation OFDM |
| url | https://ieeexplore.ieee.org/document/10843673/ |
| work_keys_str_mv | AT zahramokhtari jointchannelandnonlinearityestimationformemorylessnonlinearsystems AT ruidinis jointchannelandnonlinearityestimationformemorylessnonlinearsystems AT shahu jointchannelandnonlinearityestimationformemorylessnonlinearsystems AT dzevdankapetanovic jointchannelandnonlinearityestimationformemorylessnonlinearsystems |