Multiuser detection for nonlinear MIMO uplink
Multiuser detection for nonlinear MIMO uplink
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.
Multi-input multi-output (MIMO), complex-valued B-spline neural network, multiuser detection, nonlinear MIMO channel, nonlinear high power amplifier, uplink
207-219
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Ng, Soon
e19a63b0-0f12-4591-ab5f-554820d5f78c
Khalaf, Emad
7ecc0272-46ff-4e63-a37d-7e4703bff43b
Morfeq, Ali
134e2ca2-6d7b-42ae-90cf-e2aaf981a4b9
Alotaibi, Naif
3ede69be-ac73-43e0-8890-61ca5be9a2c2
15 January 2020
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Ng, Soon
e19a63b0-0f12-4591-ab5f-554820d5f78c
Khalaf, Emad
7ecc0272-46ff-4e63-a37d-7e4703bff43b
Morfeq, Ali
134e2ca2-6d7b-42ae-90cf-e2aaf981a4b9
Alotaibi, Naif
3ede69be-ac73-43e0-8890-61ca5be9a2c2
Chen, Sheng, Ng, Soon, Khalaf, Emad, Morfeq, Ali and Alotaibi, Naif
(2020)
Multiuser detection for nonlinear MIMO uplink.
IEEE Transactions on Communications, 68 (1), , [8886598].
(doi:10.1109/TCOMM.2019.2949991).
Abstract
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.
Text
nl-mimo-d
- Accepted Manuscript
More information
Accepted/In Press date: 29 October 2019
e-pub ahead of print date: 29 October 2019
Published date: 15 January 2020
Keywords:
Multi-input multi-output (MIMO), complex-valued B-spline neural network, multiuser detection, nonlinear MIMO channel, nonlinear high power amplifier, uplink
Identifiers
Local EPrints ID: 436794
URI: http://eprints.soton.ac.uk/id/eprint/436794
ISSN: 0090-6778
PURE UUID: 4d5de96b-2201-423c-b117-077b12d18910
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Date deposited: 09 Jan 2020 17:31
Last modified: 17 Mar 2024 02:46
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Contributors
Author:
Sheng Chen
Author:
Soon Ng
Author:
Emad Khalaf
Author:
Ali Morfeq
Author:
Naif Alotaibi
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