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Related Experiment Video

Updated: Jul 1, 2026

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

Symmetric complex-valued RBF receiver for multiple-antenna-aided wireless systems.

Shenq Chen¹, Lajos Hanzo, S Tan

¹School of Electronics and Computer Science, University of Southampton, Southampton UK. sqc@ecs.soton.ac.uk

IEEE Transactions on Neural Networks

|September 10, 2008

Summary

This summary is machine-generated.

This study introduces a novel nonlinear beamforming detector for wireless systems, improving performance using complex-valued symmetric radial basis function networks. The method approaches optimal Bayesian detection even with imperfect channel data.

Related Experiment Videos

Last Updated: Jul 1, 2026

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

Area of Science:

Wireless Communications
Signal Processing
Machine Learning

Background:

Multiple-antenna systems and complex-valued quadrature phase shift-keying (QPSK) modulation are crucial in modern wireless communications.
Optimal Bayesian detection in these systems is computationally complex and sensitive to channel estimation errors.
Existing linear beamforming methods often fall short of theoretical performance limits.

Purpose of the Study:

To propose a nonlinear beamforming assisted detector for multiple-antenna wireless systems.
To develop a complex-valued symmetric radial basis function (SRBF)-network-based detector that approximates optimal Bayesian performance.
To enable adaptive nonlinear beamforming for both uplink and downlink scenarios.

Main Methods:

Exploiting the symmetry of the optimal Bayesian detection solution to design the SRBF-network-based detector.
Implementing adaptive nonlinear beamforming in the uplink using least squares channel estimation.
Employing a cluster-variation enhanced clustering algorithm for downlink adaptive nonlinear beamforming to identify SRBF center vectors.

Main Results:

The proposed SRBF-network-based detector achieves near-optimal Bayesian performance even with channel-impaired training data.
Efficient adaptive nonlinear beamforming is demonstrated for the uplink scenario.
A novel approach for downlink adaptive nonlinear beamforming is presented, overcoming previous challenges.

Conclusions:

The developed adaptive nonlinear beamforming solution significantly improves performance compared to linear minimum bit error rate beamforming benchmarks.
The SRBF-network-based detector offers a practical approach to achieving high performance in complex wireless systems.
The proposed methods provide a pathway for enhanced spectral efficiency and reliability in wireless communications.