Moreover, the closed-form optimal solution of the semantic extraction factor is derived.
In this paper, the problem of semantic information extraction for resource constrained text data transmission is studied.
In this article, we propose a hierarchical SemCom-enabled vehicular metaverses framework consisting of the global metaverse, local metaverses, SemCom module, and resource pool.
Precoding design for the downlink of multiuser multiple-input multiple-output (MU-MIMO) systems is a fundamental problem.
In conventional distributed learning over a network, multiple agents collaboratively build a common machine learning model.
To facilitate the deployment of digital twins in Metaverse, the paradigm with semantic awareness has been proposed as a means for enabling accurate and task-oriented information extraction with inherent intelligence.
The proposed SD-CsiNet projects the raw CSI onto a self-information matrix in the newly-defined self-information domain, extracts both temporal and spatial features of the self-information matrix, and then couples these two features for effective compression.
Then, we design a novel physics-inspired spatial channel gradient network (SCGNet), which represents the derivative process of channel varying as a special neural network and can obtain the gradients at any relative displacement needed for the ODE solving.
Beamforming with large-scale antenna arrays has been widely used in recent years, which is acknowledged as an important part in 5G and incoming 6G.
In this survey, we first introduce several popular DL algorithms such as federated learning (FL), multi-agent Reinforcement Learning (MARL), distributed inference, and split learning, and present a comprehensive overview of their applications for UAV swarms, such as trajectory design, power control, wireless resource allocation, user assignment, perception, and satellite communications.
The results showed better performance for the proposed TF-Net in terms of precision, IoU, GFLOPS, model size, and FPS compared to the YOLOv5s.
Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines.
The precoding and combining matrices are trainable parameters in such a system, whereas the MIMO channel is implicit.
In such a split ML system, the precoding and combining matrices are regarded as trainable parameters, while MIMO channel matrix is regarded as unknown (implicit) parameters.
Furthermore, we define an optimization problem to reduce this upper bound and the total privacy leakage.
Then, substituting the expressions of the beamforming matrices of the BS and the users, the original sum-rate maximization problem can be transformed into a problem that only needs to optimize the phase shifts of the IRS.
Communication systems to date primarily aim at reliably communicating bit sequences.
Through exploring the intrinsic correlation among a set of historical CSI instances randomly obtained in a certain communication environment, channel prediction can significantly increase CSI accuracy and save signaling overhead.
In this paper, we consider the problem of sensing the environment within a wireless cellular framework.
This paper investigates the use of the reconfigurable dual-functional surface to guarantee the full-space secure transmission in non-orthogonal multiple access (NOMA) networks.
This emerging technology relies on sharing ground truth labeled data between Unmanned Aerial Vehicle (UAV) swarms to train a high-quality automatic image recognition model.
In this paper, a joint model split and neural architecture search (JMSNAS) framework is proposed to automatically generate and deploy a DNN model over a mobile edge network.
With the tremendous advances in the architecture and scale of convolutional neural networks (CNNs) over the past few decades, they can easily reach or even exceed the performance of humans in certain tasks.
In this paper, a novel framework for proactive caching is proposed.
In this paper, the problem of minimizing the weighted sum of age of information (AoI) and total energy consumption of Internet of Things (IoT) devices is studied.
The problem is formulated as an optimization problem whose goal is to maximize the reliability of the VR network by selecting the appropriate VAPs to be turned on and controlling the user association with SBSs.
We investigate the joint design of digital beamforming matrix at the BS and analog beamforming matrices at the RISs, by leveraging the recent advances in deep reinforcement learning (DRL) to combat the propagation loss.
To meet the traffic and illumination demands of the ground users while minimizing the energy consumption of the UAVs, one must optimize UAV deployment, phase shift of RISs, user association and RIS association.
Information Theory Information Theory
Transformer, first applied to the field of natural language processing, is a type of deep neural network mainly based on the self-attention mechanism.
The power of deep neural networks is to be unleashed for analyzing a large volume of data (e. g. ImageNet), but the architecture search is often executed on another smaller dataset (e. g. CIFAR-10) to finish it in a feasible time.
This paper investigates the problem of model aggregation in federated learning systems aided by multiple reconfigurable intelligent surfaces (RISs).
Information Theory Signal Processing Information Theory
To solve this problem, a dual method is proposed, where the dual problem is obtained as a semidefinite programming problem.
Wireless communication in the TeraHertz band (0. 1--10 THz) is envisioned as one of the key enabling technologies for the future six generation (6G) wireless communication systems.
Quantized neural networks with low-bit weights and activations are attractive for developing AI accelerators.
In this paper, the problem of delay minimization for federated learning (FL) over wireless communication networks is investigated.
In this paper, we employ Cooperative Rate-Splitting (CRS) technique to enhance the Secrecy Sum Rate (SSR) for the Multiple Input Single Output (MISO) Broadcast Channel (BC), consisting of two legitimate users and one eavesdropper, with perfect Channel State Information (CSI) available at all nodes.
To achieve an extremely fast NAS while preserving the high accuracy, we propose to identify the vital blocks and make them the priority in the architecture search.
This paper investigates the problem of secure energy efficiency maximization for a reconfigurable intelligent surface (RIS) assisted uplink wireless communication system, where an unmanned aerial vehicle (UAV) equipped with an RIS works as a mobile relay between the base station (BS) and a group of users.
In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficients matrix of the RISs.
To this end, we propose a hierarchical trinity search framework to simultaneously discover efficient architectures for all components (i. e. backbone, neck, and head) of object detector in an end-to-end manner.
In this paper, a machine learning based deployment framework of unmanned aerial vehicles (UAVs) is studied.
Experiments on benchmarks demonstrate that images compressed by using the proposed method can also be well recognized by subsequent visual recognition and detection models.
This letter investigates a channel assignment problem in uplink wireless communication systems.
Weakly-supervised object detection attempts to limit the amount of supervision by dispensing the need for bounding boxes, but still assumes image-level labels on the entire training set.
Ranked #23 on Weakly Supervised Object Detection on PASCAL VOC 2012 test (using extra training data)
Using GRUs and CNNs, the UAVs can model the long-term historical illumination distribution and predict the future illumination distribution.
To solve this problem, an iterative algorithm is proposed where, at every step, closed-form solutions for time allocation, bandwidth allocation, power control, computation frequency, and learning accuracy are derived.
This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm.
This problem is formulated as an optimization problem whose goal is to minimize the total transmit power while meeting the illumination and communication requirements of users.
Architectures in the population that share parameters within one SuperNet in the latest generation will be tuned over the training dataset with a few epochs.
Learning portable neural networks is very essential for computer vision for the purpose that pre-trained heavy deep models can be well applied on edge devices such as mobile phones and micro sensors.