A Novel Machine Learning Aided Antenna Selection Scheme for MIMO Internet of Things

Abstract

In this article, we propose a multi-label convolution neural network (MLCNN)-aided transmit antenna selection (AS) scheme for end-to-end multiple-input multiple-output (MIMO) Internet of Things (IoT) communication systems in correlated channel conditions. In contrast to the conventional single-label multi-class classification ML schemes, we opt for using the concept of multi-label in the proposed MLCNN-aided transmit AS MIMO IoT system, which may greatly reduce the length of training labels in the case of multi-antenna selection. Additionally, applying multi-label concept may significantly improve the prediction accuracy of the trained MLCNN model under correlated large-scale MIMO channel conditions with less training data. The corresponding simulation results verified that the proposed MLCNN-aided AS scheme may be capable of achieving near-optimal capacity performance in real time, and the performance is relatively insensitive to the effects of imperfect CSI.

Application of Machine Learning in Electromagnetics: Mini-Review

“…A multilevel CNN was used in a MIMO Internet of Things (IoT) system to select transmit antennae [74]. For modern MIMO communication transmit antennae selection, a learn-to-select (L2S) approach was implemented by Diamantaras et al [75] where achieving the optimal uniform linear array of antennas was expensive, both from the design and cost of materials perspectives.…”

“…However, SVM, Naive bayes, and KNN showed increased performance in selecting transmit antennas in untrusted relay networks conserving standard channel-state information (CSI) secrecy with decreased computational complexity compared to the exhaustive search in MIMO systems (Figure 5) [72,73]. A multilevel CNN was used in a MIMO Internet of Things (IoT) system to select transmit antennae [74]. For modern MIMO communication transmit antennae selection, a learn-to-select (L2S) approach was implemented by Diamantaras et al [75] where achieving the optimal uniform linear array of antennas was expensive, both from the design and cost of materials perspectives.…”

Design and Implementation of Quad-Element Super-Wideband MIMO Antenna for IoT Applications

“…2(f), in step-6, an L-shaped slit is engraved on the radiating patch (step-6) to remove 2.4 GHz (Bluetooth) interfering signals. The effective length of the L-shaped slit (SL) is evaluated as [32] = ( 1 + 2 + 3 + 3 ) ≈ 0.25 (6) The S11 response of the sickle-shaped antenna element design steps (-4, -5, and -6) is shown in Fig. 3(b).…”

“…In complex IoT modules, a low-profile, compact-sized, super-wideband (SWB) antenna could be the best option as it covers multiple wireless standards and technologies, which reduces the overall module size/weight [6][7]. Recently, several antenna designs have been investigated for the SWB [8][9][10][11][12][13].…”

Efficient Isolation Modelling for Two-Port Mimo Antenna by Gaussian Process Regression

“…There is a necessity to have a method which can save time and accurately predict the isolation values of MIMO antenna based on learning. In available literature, machine learning technique has effectively addressed various problems of MIMO antenna like antenna selection [13,14], optimum power allocation [15], antenna selection and power control [16], channel estimation [17], and fast beamforming [18]. Effectiveness of the GPR has been proved in existing works of literature to model, nonlinear problems of antenna [19].…”