A brain tumor is a disorder caused by the growth of abnormal brain cells. The survival rate of a patient affected with a tumor is difficult to determine because they are infrequent and appear in various forms. These tumors can be identified through Magnetic Resonance (MRI) Images, which plays an essential role in determining the tumor site; however, manual detection is a time-consuming and challenging procedure that can cause some errors in results. The adoption of computer-assisted approaches is essential to help in overcoming these constraints. With the advancement of artificial intelligence, deep learning (DL) models are being used in medical imaging to diagnose brain tumors using MR images. In this study, a deep convolutional neural network (CNN) EfficientNet-B0 base model is fine-tuned with our proposed layers to efficiently classify and detect brain tumor images. The image enhancement techniques are used by applying various filters to enhance the quality of the images. Data augmentation methods are applied to increase the data samples for better training of our proposed model. The results show that the proposed fine-tuned state-of-the-art EfficientNet-B0 outperforms other CNN models by achieving the highest classification accuracy, precision, recall, and area under curve values surpassing other state-of-the-art models, with an overall accuracy of 98.87% in terms of classification and detection. Other DL algorithms such as VGG16, InceptionV3, Xception, ResNet50, and InceptionResNetV2 are used for comparative analysis.

We propose a deep learning based channel estimation technique for wireless energy transfer. Specifically, we develop a channel learning scheme using the deep autoencoder, which learns the channel state information (CSI) at the energy transmitter based on the harvested energy feedback from the energy receiver, in the sense of minimizing the mean square error (MSE) of the channel estimation. Numerical results demonstrate that the proposed scheme learns the CSI very well and significantly outperforms the conventional scheme in terms of the channel estimation MSE as well as the harvested energy.

Nonorthogonal multiple access (NOMA) and multiple-input multiple-output (MIMO) are two key enablers for 5G systems. In this article, considering the practical issue that successive interference cancellation (SIC) decoding is imperfect in the real-world NOMA system, we propose a novel scheme for the downlink of the MIMO-NOMA system based on deep learning. In this scheme, both precoding and SIC decoding of the MIMO-NOMA system are jointly optimized (or learned) in the sense of minimizing total mean square error of the users’ signals. To this end, we construct the precoder and SIC decoders using deep neural networks such that the transmitted signals intended to multiple users can be properly precoded at the transmitter based on the superposition coding technique and the received signals are accurately decodable at the users by the SIC decoding. Numerical results demonstrate the effectiveness and superior performance of the proposed scheme.

In this paper, we study rate-energy (R-E) tradeoffs for simultaneous wireless information and power transfer (SWIPT). In the existing literature, by invoking a simplistic and ideal assumption of linear energy harvesting, the R-E tradeoff performance was analyzed only for the four SWIPT schemes: the dynamic power splitting, type-I on-off power splitting (OPS), static power splitting, and time switching. Different from such works, in this work, we consider the realistic and practical scenario of nonlinear energy harvesting. Furthermore, to characterize the R-E tradeoff with nonlinear energy harvesting, we propose a new SWIPT scheme, the generalized OPS (GOPS). As a special case of the proposed GOPS, we also investigate an additional SWIPT scheme, the type-II OPS. Through the analysis based on the realistic nonlinear models reported in the literature, we derive new theoretical results on the R-E tradeoff, which are in sharp contrast to those in the existing literature obtained with linear energy harvesting. Furthermore, we provide various useful insights into the SWIPT system with nonlinear energy harvesting.

Beamforming with movable antenna (MA) array has recently attracted increasing attention as an enabling technology for next-generation wireless communications, e.g., 6G. In this paper, we consider a multicast scenario where a base station (BS) equipped with a linear MA array broadcasts common information to multiple users, each equipped with a single fixed-position antenna. Our objective is to jointly optimize the antenna position vector (APV) and antenna weight vector (AWV) by maximizing the minimum beamforming gain for the users, which is challenging to tackle analytically due to nonconvexity. To effectively and intelligently break through such challenge, we propose a novel deep learning (DL) model with three modules, namely, feature extractor, APV optimizer, and AWV optimizer. An effective training strategy for the proposed DL model is also developed in an unsupervised manner with a customized loss function. The superiority and effectiveness of the proposed scheme are confirmed through simulation results.

We investigate channel estimation for an intelligent reflecting surface (IRS) aided wireless communication system with a multi-antenna access point (AP), an multi-element IRS, and a set of multiple users, each with a single antenna. Our goal is to minimize mean square error (MSE) of estimating the channels of direct and cascaded links from the AP to each user via the IRS elements by jointly optimizing the training sequence of the AP and reflection pattern of the IRS. To solve this challenging problem, we first find the joint optimal structure and reflection pattern for the training sequence, and then, we convert the problem into a convex form to design the optimal training sequence efficiently. To reduce the computational complexity, we also propose a suboptimal training sequence in closed form. Numerical results show that the proposed scheme outperforms baseline schemes.