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An intelligent reflecting surface (IRS) is a programmable device that can be used to control electromagnetic waves propagation by changing the electric and magnetic properties of its surface. Therefore, IRS is considered a smart technology for the sixth generation (6G) of communication networks. In addition, machine learning (ML) techniques are now widely adopted in wireless communication as the computation power of devices has increased. As it is an emerging topic, we provide a comprehensive overview of the state-of-the-art on ML, especially on deep learning (DL)-based IRS-enhanced communication. We focus on their operating principles, channel estimation (CE), and the applications of machine learning to IRS-enhanced wireless networks. In addition, we systematically survey existing designs for IRS-enhanced wireless networks. Furthermore, we identify major issues and research opportunities associated with the integration of IRS and other emerging technologies for applications to next-generation wireless communication.

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In this paper, we propose the adaptive modulation (AM) model based on machine learning for a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system. Since 5G new radio (NR) system can be used in a large variety of Internet of Things fields than the conventional systems, the AM scheme that adjusts data rate and reliability according to channel condition can be effectively utilized. The conventional AM schemes are implemented by defining modulation schemes to be used according to each channel condition as table in advance. However, since the rule-based AM cannot analyze the communication performance according to the correlation of channels between antennas and the number of transmission modes is exponentially increased according to the number of available modulation schemes and antennas, it is not suitable for 5G NR system. The learning of the proposed AM model is based on the generated training signal by the extracted features from the received signal and assigned label through the performance analysis for signal detection. We focus on the application of deep neural network for AM and cover the precedence method of principal component analysis to improve the performance of the model. The simulations on the classification of optimal transmission mode for the MIMO-OFDM signal demonstrate that the proposed model supports the adaptability according to the condition of complex MIMO channel. INDEX TERMS MIMO-OFDM, adaptive modulation, machine learning, deep neural network, principal component analysis.

In this paper, a novel efficient algorithm for MIMO detection in MIMO-OFDM systems has been proposed. This paper also shows that the complexity can be greatly reduced by applying the threshold in the breadth-first tree search algorithm step to eliminating paths. That is, this algorithm derives thresholds based on the LR-aided nonlinear algorithm and performs a simple tree search detection algorithm. This algorithm requires a lower computational complexity than the conventional QRD-M detection algorithm and achieves the same bit error performance. After the novel detection algorithm is proposed, this paper applies this algorithm to the MIMO-OFDM system for performance comparison in terms of error performance and complexity. In many cases of MIMO-OFDM receivers, the proposed method will be an excellent option for implementation.

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