Deep learning models for scheduling of potentially-interfering communication pairs, in
device-to-device (D2D) settings, require large training samples in the order of hundreds to …

In this paper, we propose two novel geometric machine learning (G-ML) methods for the
wireless link scheduling problem in device-to-device (D2D) networks. In dynamic D2D …

In a cellular vehicle-to-everything (C-V2X) enabled computation-offloading network,
vehicular users may deliver computation tasks to a cellular base station (BS) through vehicle …

We study the problem of channel uncertainty on wireless transmissions from different users
with mutual interference. Specifically, the channel gains from the transmitters to the receivers …

This article studies the problem of Sum-secure Degrees of Freedom (SDoF) of the multiple-
input–multiple-output (MIMO) interference channel with local output feedback, so as to build …

As an essential technology of the fifth generation communication (5G), Vehicle-to-Everything
(V2X) has attracted wide attention lately. A well-designed power allocation scheme can …

The joint resource block (RB) allocation and power optimization problem is studied to
maximize the sum-rate of the vehicle-to-vehicle (V2V) links in the device-to-device (D2D) …

In a vehicular communication network, how to efficiently utilize the limited network resources
to meet the stringent transmission quality of service (QoS) requirements in the dynamically …

Abstract Vehicular Ad-Hoc Network (VANET) is a promising technology that enables road-
side vehicle-assisted communication between users and infrastructures. The network is …

This paper studies the robust resource allocation to improve the robustness against the
channel state information (CSI) uncertainties for energy harvesting (EH)-aided device-to …