Massive multiple-input multiple-output (MIMO) architecture is promising to be adopted in the 5G/6G communication systems to combat the severe attenuation of millimeter-wave and terahertz bands by implementing precoding and combining at the transmitter and receiver jointly. Nevertheless, such massive MIMO-based systems are highly susceptible to pilot spoofing attacks (PSAs) because of the openness of wireless channel and the public pilot sequences. These PSAs will not only render significant decrease in system capacity, but also make the transmitted information leak to the attackers. In this paper, a novel multi-cue detection and identification network (MCDI-net) is proposed to conduct spoofing detection and identify the attacked user simultaneously. The MCDI-net integrates a dual-branch convolutional neural network with squeeze-and-excitation (SE) attention modules to extract the complementary features from two distinct cues, which are in-phase/quadrature (I/Q) and statistical features derived from pilot correlation and projected average power. This multi-cue fusion approach can enhance the network’s ability to detect pilot spoofing and identify the attacked legitimate user accurately. Numerical results are presented to validate the superior performance of the MCDI-net framework in detection and identification accuracy compared to the existing ones.

For 5G/B5G communication systems, to cater the explosive demands in communication rates, extending communication band to millimeter wave (mmWave) seems to be an essential solution, and hence massive multiple-input-multiple-output (MIMO) architecture is usually adopted to combat the severe path-loss of mmWave signals. However, the traditional fully digital precoding manner is inapplicable owing to its high hardware cost and power consumption rendered by the individual requirements on radio frequency (RF) chains of each antenna. In this paper, for massive MIMO systems with multiple users, a novel hybrid precoding scheme is proposed based on the concept of equivalent channel. Specifically, for each user in the system, its analog combiner and analog precoder are designed jointly aiming to maximize the achievable rate with its equivalent channel. After the analog precoder/combiner phase-shifters for each user has been obtained, see them as a part of the channel and form a comprehensive channel of the multi-user system, and then the total baseband digital precoding at the BS is implemented with the block diagonalization method to delete the interference among users. Simulation results show that the proposed scheme can achieve higher spectral efficiency with low complexity compared to the existing schemes.

Parkinson's disease (PD) is one of the most common neurodegenerative disorders. The increasing demand for high-accuracy forecasts of disease progression has led to a surge in research employing multi-modality variables for prediction. In this review, we selected articles published from 2016 through June 2024, adhering strictly to our exclusion-inclusion criteria. These articles employed a minimum of two types of variables, including clinical, genetic, biomarker, and neuroimaging modalities. We conducted a comprehensive review and discussion on the application of multi-modality approaches in predicting PD progression. The predictive mechanisms, advantages, and shortcomings of relevant key modalities in predicting PD progression are discussed in the paper. The findings suggest that integrating multiple modalities resulted in more accurate predictions compared to those of fewer modalities in similar conditions. Furthermore, we identified some limitations in the existing field. Future studies that harness advancements in multi-modality variables and machine learning algorithms can mitigate these limitations and enhance predictive accuracy in PD progression.

With the development of information technology, a variety of mobile devices with large computing requirements and delay constraints are growing rapidly, such as Connected Autonomous Vehicles (CAVs). In addition to the energy consumption required for vehicle driving, the CAV also needs to support the normal operation of sensors and computing platforms, which brings huge energy consumption and affects the endurance of vehicles. In this paper, a Mobile Edge Computing (MEC) system is considered that includes three types of network nodes: connected Autonomous Vehicles, Roadside Units (RSUs), and a Base Station (BS). Considering the task duration of the CAV, tasks on the CAVs are partitioned into three sections, which are executed at CAVs, RSUs, and BS, respectively. It is challenging to correlate resource-limited CAVs with other types of network nodes with high performance and implement partial computation offloading between them to minimize the total energy consumption of CAVs and RSUs under the delay constraint of CAVs. Based on the classical orthogonal-frequency-division multiple-access (OFDMA), a joint computing and communication cooperation offloading protocol is proposed to minimize the total energy consumption of all CAVs and RSUs under delay constraints and is described as a constrained Mixed-Integer Nonlinear Program (MINLP) problem. To address this problem, a Reference Vector-Guided Evolutionary Algorithm for Multi-Objective Optimization based on Prior Experience (RVEAPE) is designed in this paper. RVEAPE jointly optimizes the task division ratio variables and resource allocation variables (computation resources, communication resources). Simulations demonstrate that RVEAPE significantly outperforms the GA, NSGA-II, and NSGA-II algorithms in terms of the total energy consumption of CAVs and RSUs in the whole system consisting of CAVs, RSUs, and a BS.Note to Practitioners—This paper is motivated by the use of multi-objective optimization algorithms to improve the efficiency and quality of CAVs in edge computing networks. Our goal is to reduce energy consumption by offloading the computation tasks of the CAV to be computed in the RSU and BS. To this end, this paper proposes the use of the RVEAPE algorithm to jointly optimize offloading decisions and resource allocation variables to achieve the lowest energy consumption. The proposed method is theoretically effective in reducing the energy consumption of the CAV under the time constraint. Experimental studies show that the RVEAPE algorithm can effectively offload the computational tasks of the CAV to the BS, and can thus reduce the total energy consumption of the CAV and the RSU. In future research, we will implement computation offloading in large-scale CAV scenarios and consider a binary offloading model.

In edge-enabled Internet of Things (IoT), computation offloading service is expected to offer users with better Quality of Experience (QoE) than traditional IoT. Unfortunately, the growing multiple tasks from users are occuring with the emergence of the IoT environment. Meanwhile, the current computation offloading with QoE is solved by deep reinforcement learning (DRL) with the issue of instability and slow convergence. Therefore, improving the QoE in edge-enabled IoT is still the ultimate challenge. In this article, to enhance the QoE, we propose a new QoE model to study the computation offloading. Specifically, the emerged QoE model can capture three influential elements: 1) service latency determined by local computing latency and transmission latency; 2) energy consumption according to local calculation and transmission consumption; and 3) task success rate based on the coding error probability. Moreover, we improve the deep deterministic policy gradients (DDPG) algorithm and propose a algorithm named the double-dueling-deterministic policy gradients (D3PG) based on the proposed model. Specifically, the actor network highly relies on the critic network, which makes the performance of the DDPG sensitive to the critic and thus leads to poor stability and slow convergence in the computation offloading process. To solve this, we redesign the critic network by using Double Q -learning and Dueling networks. Extensive experiments verify the better stability and faster convergence of our proposed algorithm than existing methods. In addition, experiments also indicate that our proposed algorithm can improve the QoE performance.