相关论文: Simultaneous Wireless Information and Power Transf…

Federated Machine Learning (Fed ML) is a new distributed machine learning technique applied to collaboratively train a global model using clients local data without transmitting it. Nodes only send parameter updates (e.g., weight updates in…

Simultaneous wireless information and power transfer (SWIPT) is an appealing solution to balance the energy distribution in wireless networks and improve the energy-efficiency of the entire network. In this paper, we study the optimal…

The rapid growth of the so-called Internet of Things is expected to significantly expand and support the deployment of resource-limited devices. Therefore, intelligent scheduling protocols and technologies such as wireless power transfer,…

This letter proposes a new concept of integrated sensing and wireless power transfer (ISWPT), where radar sensing and wireless power transfer functions are integrated into one hardware platform. ISWPT provides several benefits from the…

The growing number of wireless edge devices has magnified challenges concerning energy, bandwidth, latency, and data heterogeneity. These challenges have become bottlenecks for distributed learning. To address these issues, this paper…

Federated learning (FL) is recognized as a key enabling technology to support distributed artificial intelligence (AI) services in future 6G. By supporting decentralized data training and collaborative model training among devices, FL…

Wireless power transfer (WPT) is an emerging paradigm that will enable using wireless to its full potential in future networks, not only to convey information but also to deliver energy. Such networks will enable trillions of future…

It is widely perceived that leveraging the success of modern machine learning techniques to mobile devices and wireless networks has the potential of enabling important new services. This, however, poses significant challenges, essentially…

This paper investigates the simultaneous wireless information and power transfer (SWIPT) for two-hop orthogonal frequency division multiplexing (OFDM) decode-and-forward (DF) relay communication system, where a relay harvests energy from…

Recently, a considerable amount of works have been made to tackle the communication burden in federated learning (FL) (e.g., model quantization, data sparsification, and model compression). However, the existing methods, that boost the…

We introduce a new and increasingly relevant setting for distributed optimization in machine learning, where the data defining the optimization are unevenly distributed over an extremely large number of nodes. The goal is to train a…

This paper considers the problem of simultaneous information and energy transmission (SIET), where the energy harvesting function is only known experimentally at sample points, e.g., due to nonlinearities and parameter uncertainties in…

This letter considers simultaneous wireless information and power transfer (SWIPT) for a multiple-input multiple-output (MIMO) relay system. The relay is powered by harvesting energy from the source via time switching (TS) and utilizes the…

In this paper, the deployment of federated learning (FL) is investigated in an energy harvesting wireless network in which the base station (BS) employs massive multiple-input multiple-output (MIMO) to serve a set of users powered by…

We consider a three-node decode-and-forward (DF) half-duplex relaying system, where the source first harvests RF energy from the relay, and then uses this energy to transmit information to the destination via the relay. We assume that the…

Considering ubiquitous connectivity and advanced information processing capability, huge amount of low-power IoT devices are deployed nowadays and the maintenance of those devices which includes firmware/software updates and recharging the…

Future wireless networks are expected to support diverse mobile services, including artificial intelligence (AI) services and ubiquitous data transmissions. Federated learning (FL), as a revolutionary learning approach, enables…

Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the training data local. This decentralized approach to train models provides…

This paper studies the simultaneous wireless information and power transfer (SWIPT) in a multiuser wireless system, in which distributed transmitters send independent messages to their respective receivers, and at the same time…

Federated Learning is a machine learning setting where the goal is to train a high-quality centralized model while training data remains distributed over a large number of clients each with unreliable and relatively slow network…