Beamforming is considered one of the most important techniques for designing advanced multiple-input and multiple-output (MIMO) systems. Among these design criterions, sum rate maximization (SRM) under a total power constraint is a challenge issue due to its nonconvexity. Existing techniques for the SRM problem only obtain suboptimal solutions but require huge amount of computation due to their complex matrix operation and iterations. Unlike these conventional methods, we propose a deep learning based fast beamforming design method without complex matrix operation and iterations. Specifically, we first derive a heuristic solution structure of the downlink beamforming through the virtual equivalent uplink channel based on optimum MMSE receiver which separates the problem into power allocation and virtual uplink beamforming (VUB) design. Next, beamforming prediction network (BPNet) is designed to perform the joint optimization of power allocation and VUB. Moreover, the BPNet is trained offline using two-step training strategy. Simulation results demonstrate that our proposed method is fast while obtains the comparable performance to the state-of-the-art method.

In the downlink transmission scenario, power allocation and beamforming design at the transmitter are essential when using multiple antenna arrays. This paper considers a multiple input-multiple output broadcast channel to maximize the weighted sum-rate under the total power constraint. The classical weighted minimum mean-square error (WMMSE) algorithm can obtain suboptimal solutions but involves high computational complexity. To reduce this complexity, we propose a fast beamforming design method using unsupervised learning, which trains the deep neural network (DNN) offline and provides real-time service online only with simple neural network operations. The training process is based on an end-to-end method without labeled samples avoiding the complicated process of obtaining labels. Moreover, we use the ’APoZ’-based pruning algorithm to compress the network volume, which further reduces the computational complexity and volume of the DNN, making it more suitable for low computation-capacity devices. Finally, experimental results demonstrate that the proposed method improves computational speed significantly with performance close to the WMMSE algorithm.

With the rapid growth of computation demands from mobile applications, mobile-edge computing (MEC) provides a new method to meet requirement of high data rate and high computation capability. By offloading the latency-critical or computation-intensive tasks to the edge server, mobile devices (MDs) could save energy consumption and extend battery life. However, unlike cloud servers, resource bottlenecks in MEC servers limit the scalability of offloading. Hence, computation offloading and resource allocation need to be optimized. Toward this end, we consider a multi-access MEC servers system in which Orthogonal Frequency-Division Multiplexing Access (OFDMA) is used as the transmission mechanism for uplink. In order to minimize energy consumption of MDs, we propose a joint optimization strategy for computation offloading, subcarrier allocation, and computing resource allocation, which is a mixed integer non-linear programming (MINLP) problem. First, we design a bound improving branch-and-bound (BnB) algorithm to find the global optimal solution. Then, we present a combinational algorithm to obtain the suboptimal solution for practical application. Simulation results reveal that the combinational algorithm performs very closely to the BnB algorithm in energy saving, but it has a better performance in average algorithm time. Furthermore, our proposed solutions outperform other benchmark schemes.

Automatic modulation recognition (AMR) plays an important role in various communications systems. It has the ability of adaptive modulation and can adapt to various complex environments. Automatic modulation recognition is also widely used in orthogonal frequency division multiplexing (OFDM) systems. However, because the recognition accuracy of traditional methods to extract the features of OFDM signals is very limited. In order to solve these problems, many deep learning based AMR methods have been proposed to improve the recognition performance. However, most of these AMR methods neglect the harmful effect by carrier phase offset (PO) which often appears in realistic communications systems. Hence it is required to consider the PO effect for designing the OFDM system. Unlike conventional methods, we propose a convolutional neural network (CNN) based AMR method for considering PO in the OFDM system. The proposed method is used to eliminate the PO to achieve the high classification accuracy. Experiment results are provided to confirm the proposed method when comparing to conventional methods.

In millimeter-wave and terahertz band communication systems, precise beam prediction is crucial for optimizing network performance and enhancing signal transmission efficiency. Traditional beam prediction methods have primarily relied on single-modal data, which often fails to capture the comprehensive environmental information necessary for optimal accuracy. In contrast, multi-modal data-based approaches offer a more promising solution by leveraging the strengths of diverse data sources. However, many existing fusion methods are static, inadequately accounting for variations in information content across different modalities, which can hinder the full utilization of each modality’s advantages.To address these limitations, this paper proposes an advanced multi-modal beam prediction method that integrates multipath-like data augmentation (MLDA), cross-modal feature enhancement (CMFE), and an uncertainty-aware dynamic fusion mechanism. Our approach combines image and radar data to predict beam indices, dynamically adjusting the weights of different modalities to accommodate varying information densities. The proposed method employs ResNet34 for feature extraction from the multi-modal data, followed by a cross-modal feature enhancement module that aggregates complementary information from the image and radar data. Finally, the dynamic fusion mechanism integrates the predictions from the single-modal data. Experimental results demonstrate that our method significantly improves the accuracy and robustness of beam prediction, achieving an overall accuracy of 89.72%. The performance of the proposed method is further validated through comparisons with various existing methods and comprehensive ablation studies, highlighting its superiority in multi-modal assisted beam prediction scenarios.

Millimeter wave (mmWave) systems need beam management to establish and maintain reliable links. This complex and time-consuming process seriously affects communication efficiency. Benefiting from data-driven technology in deep learning, the beam can be predicted from the waveform without coordination between transceivers. By passively listening enough waveforms that are transmitted from the base station (BS) to other receivers, the BS can predict which beam is suitable for transmitting in the downlink. However, training such a waveform learning neural network usually requires a large number of labeled training samples. This is a huge challenge, because it is difficult for the receiver to get the precise signal parameters from the transmitter in advance in the non-cooperative mmWave system. As a result, the limited samples may cause overfitting and seriously restrict the performance. Although the data augmentation technology can improve the performance under limited samples, existing data augmentation methods are mostly based on strong prior knowledge which cannot further exploit the potential characteristics of data in the real environment. This paper proposes a mixed regularization training method for training the beam prediction neural network under limited training samples. Specifically, data augmentation is implemented in the data pre-processing procedure with prior knowledge and then the signal splicing strategy is proposed in the training procedure. In order to mine the time correlation characteristics of signals, the cyclic time shift (CTS) based data augmentation method is proposed in the data augmentation step. The simulation results show that our proposed deep regularized waveform learning method needs less training samples under the same performance. Moreover, the proposed method can achieve best performance compared with existing data augmentation methods.