相关论文: Learning-Based Robust Resource allocation for D2D …

This paper studies robust resource allocation algorithm design for a multiuser multiple-input single-output (MISO) cognitive radio (CR) downlink communication network. We focus on a secondary system which provides unicast secure wireless…

Direct device-to-device (D2D) communication has been proposed as a possible enabler for vehicle-to-vehicle (V2V) applications, where the incurred intra-cell interference and the stringent latency and reliability requirements are challenging…

We consider the resource allocation problem in cellular networks which support Device-to-Device Communications (D2D). For systems that enable D2D via only orthogonal resource sharing, we propose and analyze two resource allocation policies…

Proactive wireless caching and D2D communication have emerged as promising techniques for enhancing users' quality of service and network performance. In this paper, we propose a new architecture for D2D caching with inter-cluster…

Cell-free networks outperform cellular networks in many aspects, yet their efficiency is affected by imperfect channel state information (CSI). In order to address this issue, this work presents a robust resource allocation framework…

This paper introduces a robust resource allocation framework for the downlink of cell-free massive multi-input multi-output (CF-mMIMO) networks to address the effects caused by imperfect channel state information (CSI). In particular, the…

We consider a cache-aided wireless device-to-device (D2D) network under the constraint of one-shot delivery, where the placement phase is orchestrated by a central server. We assume that the devices' caches are filled with uncoded data, and…

This paper proposes a mobile pipeline computing concept in a Device-to-Device (D2D) communication setup and studies related issues, where D2D is likely based on millimeter-wave (mmWave) in the 5G mobile communication. The proposed…

The mounting content sharing among users has resulted in a considerable rise in wireless data traffic, pressurizing the cellular networks to undergo a suitable upheaval. A competent technology of the fifth-generation networks (5G) for…

Device-to-device (D2D) communication underlay cellular networks is a promising technique to improve spectrum efficiency. In this situation, D2D transmission may cause severe interference to both the cellular and other D2D links, which…

Device-to-device (D2D) communication enables direct communication between proximate devices thereby improving the overall spectrum utilization and offloading traffic from cellular networks. This paper develops a new spatial model for D2D…

Semi-decentralized federated learning blends the conventional device to-server (D2S) interaction structure of federated model training with localized device-to-device (D2D) communications. We study this architecture over practical edge…

Device-to-Device (D2D) communication has been recognized as a promising technique to offload the traffic for the evolved Node B (eNB). However, the D2D transmission as an underlay causes severe interference to both the cellular and other…

Device-to-device (D2D) communication is a promising approach to optimize the utilization of air interface resources in 5G networks, since it allows decentralized opportunistic short-range communication. For D2D to be useful, mobile nodes…

Channel allocation is the task of assigning channels to users such that some objective (e.g., sum-rate) is maximized. In centralized networks such as cellular networks, this task is carried by the base station which gathers the channel…

Motivated by increasing popularity of delay sensitive applications, we investigate the queue stability in device-to-device (D2D) relay-assisted cellular networks. In contrast to prior works on D2D relay-assisted cellular networks, we…

This paper proposes a paradigm of uncertainty injection for training deep learning model to solve robust optimization problems. The majority of existing studies on deep learning focus on the model learning capability, while assuming the…

This paper proposes an novel knowledge-driven approach for resource allocation in device-to-device (D2D) networks using a graph neural network (GNN) architecture. To meet the millisecond-level timeliness and scalability required for the…

With the proliferation of computation-extensive and latency-critical applications in the 5G and beyond networks, mobile-edge computing (MEC) or fog computing, which provides cloud-like computation and/or storage capabilities at the network…

Device-to-Device (D2D) communication as a promising technology in 5G cellular networks provides the communication of the users in the vicinity and thereby decreases end-to-end delay and power consumption. In addition to the aforementioned…