KNN and SVM based Optimal Adaptive Modulation Selection in Spatial Modulation

Abstract

In this paper, link adaptation (LA) schemes based on adaptive modulation (AM), where the transmit parameters can be adapted to the varying channel conditions, are investigated for spatial modulation (SM). We propose machine learning (ML) based adaptive spatial modulation (ASM) as the optimal technique for modulation selection (MS) compared with the exhaustive search based ASM scheme. Specifically, two supervised machine learning classifiers (MLCs), K-nearest neighbors (KNN) and support vector machine (SVM), are proposed to design the MS in ASM and make the tradeoff between the system complexity and the bit error rate (BER) performance. The proposed classifiers are employed to solve the MS problem in ASM as a multi-class classification problem and obtain the best modulation selection candidate (MSC) that achieves the maximum minimum Euclidean distance. Simulation results show that, under the same spectral efficiency (SE), the proposed schemes provide considerable system complexity reduction and obtain a sub-optimal BER performance compared with the typical ASM scheme.