An MRL-Based Design Solution for RIS-Assisted MU-MIMO Wireless System under Time-Varying Channels

Utilizing Deep Reinforcement Learning (DRL) for Reconfigurable Intelligent Surface (RIS) assisted wireless communication has been extensively researched. However, existing DRL methods either act as a simple optimizer or only solve problems with concurrent Channel State Information (CSI) represented in the training data set. Consequently, solutions for RIS-assisted wireless communication systems under time-varying environments are relatively unexplored. However, communication problems should be considered with realistic assumptions; for instance, in scenarios where the channel is time-varying, the policy obtained by reinforcement learning should be applicable for situations where CSI is not well represented in the training data set. In this paper, we apply Meta-Reinforcement Learning (MRL) to the joint optimization problem of active beamforming at the Base Station (BS) and phase shift at the RIS, motivated by MRL's ability to extend the DRL concept of solving one Markov Decision Problem (MDP) to multiple MDPs. We provide simulation results to compare the average sum rate of the proposed approach with those of selected forerunners in the literature. Our approach improves the sum rate by more than 60% under time-varying CSI assumption while maintaining the advantages of typical DRL-based solutions. Our study's results emphasize the possibility of utilizing MRL-based designs in RIS-assisted wireless communication systems while considering realistic environment assumptions.Comment: To be published in proceedings of the 2023 IEEE Conference on Global Communications (GLOBECOM

Algorithms for time -frequency distributions: Suppressing cross terms and enhancing resolution.

The importance of Time-Frequency Distributions (TFDs) has been widely recognized in many fields after years of intensive research. However, its usage is shadowed by the Uncertainty Principle and the conviction that ideal TFDs are impossible to obtain in general cases. It is realized that for different situations, specific time-frequency analysis tools can be devised to be superbly effective. The key to successful design of such a tool is to understand and match the construction mechanism of TFDs and the nature of the signal. In this dissertation, several approaches of constructing TFDs are studied. The major contributions include three new TFD algorithms with emphasis on different aspects of TFDs. The first (in Chapter 3), an adaptive kernel design procedure, aims at minimizing the Renyi entropy on the TF plane. The second algorithm (in Chapter 4), an algorithm for positive TFDs, forces a positive TFD to satisfy the time and frequency marginals and to be closest to the Wigner distribution at the same time. For the positive TFD algorithm, the concept of collective marginal error is also introduced. The last algorithm (in Chapter 5) is to construct the high-concentration TFDs introduced in this thesis for the first time, in the framework of considering the concentration issue explicitly with the construction of TFDs. Typical test signals are provided in each case to demonstrate the effectiveness and, sometimes, the limitations of the three new algorithms. A quantitative indication of performance, the Renyi entropy measure is also provided to validate the claims. Besides the major contributions, basic issues of the mechanism of constructing TFDs and the limitation on energy concentration also obtain proper attention in Chapter 2. In addition, a sampling theory is developed for the Wigner distribution, out of the consideration of applying TFDs to real-world problems. Finally, I conclude the dissertation by pointing out that a comprehensive way to match a suitable TF analysis tool with the signal to be analyzed will be an important research direction in the future.Ph.D.Applied SciencesElectrical engineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/132236/2/9959852.pd

Clipping Noise Compensation with Neural Networks in OFDM Systems

The application of deep learning (DL) to solve physical layer issues has emerged as a prominent topic. In this paper, the mitigation of clipping effects for orthogonal frequency division multiplexing (OFDM) systems with the help of a Neural Network (NN) is investigated. Unlike conventional clipping recovery algorithms, which involve costly iterative procedures, the DL-based method learns to directly reconstruct the clipped part of the signal while the unclipped part is protected. Furthermore, an interpretation of the learned weight matrices of the neural network is presented. It is observed that parts of the network, in effect, implement transformations very similar to the (Inverse) Discrete Fourier Transform (DFT/IDFT) to provide information in both the time and frequency domains. The simulation results show that the proposed method outperforms existing algorithms for recovering clipped OFDM signals in terms of both mean square error (MSE) and Bit Error Rate (BER)

DoA Estimation for FMCW Radar by 3D-CNN

A method of direction-of-arrival (DoA) estimation for FMCW (Frequency Modulated Continuous Wave) radar is presented. In addition to MUSIC, which is the popular high-resolution DoA estimation algorithm, deep learning has recently emerged as a very promising alternative. It is proposed in this paper to use a 3D convolutional neural network (CNN) for DoA estimation. The 3D-CNN extracts from the radar data cube spectrum features of the region of interest (RoI) centered on the potential positions of the targets, thereby capturing the spectrum phase shift information, which corresponds to DoA, along the antenna axis. Finally, the results of simulations and experiments are provided to demonstrate the superior performance, as well as the limitations, of the proposed 3D-CNN