Çok girişli çok çikişli indis modülasyonu sistemlerinde makine öğrenmesi uygulamalari

Despite the practical challenges, multiple-input multiple-output (MIMO) systems are broadly utilized in modern communication systems due to the wide range of advantages provided by the use of spatial domain. To further improve the performance of MIMO systems, index modulation (IM) is applied using the spatial domain indices. IM can offer significant improvements in MIMO systems by transmitting information both with symbols and transmit antenna indices. Therefore, it is an emerging research topic in wireless communication. However, new challenges are introduced in MIMO-IM systems. For instance, due to the increased complexity in the transmission scheme, the receiver complexity is a serious practical concern. Moreover, poor channel conditions can cause significant performance degradation in MIMO-IM systems. Finally, although the IM literature is expanded in the recent years, the practical applications and use cases are not advancing in the same pace. In this thesis, we propose system models that present machine learning-based solutions to these challenges. Firstly, we propose a hybrid receiver to the quadrature permutation matrix modulation (QPMM) system that combines neural network (NN) outputs with analytical algorithms to provide a trade-off between receiver complexity and detection performance. The proposed NN-based detector is compared with the well known maximum likelihood detector and its low-complexity alternative. Secondly, a MIMO-IM system model that utilizes movabel antennas (MAs) is presented. To the best of our knowledge, this is the study that introduces the idea of integrating MAs to the MIMO-IM systems to improve communication quality. An Euclidean distance-based algorithm is proposed to select the best MA positions. Then, a low-complexity greedy algorithm is presented for better practical implementations. Finally, a genetic algorithm (GA) model is discussed for optimizing the MA positions. Finally, we present a semantic communication system that enhances semantic information transmission by transmitting semantically more information-dense bits through robust index bits of a MIMO-IM system. We compare semantic information loss with respect to errors in different bits of the transmitted sequence. Moreover, since the input data type to the presented system is image, we compared the image reconstruction metrics that are broadly utilized in the literature as well, such as peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).