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20082026
most citedOptimal Power Allocation for Fading Channels in Cognitive Radio Networks: Ergodic Capacity and Outage Capacity

644 citations · 1.7k across the 110 of their papers we have counts for

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Showing 2023Show all

7 papers · 1 filter

eess.SP2023

Intelligent Resource Allocation for UAV-Based Cognitive NOMA Networks: An Active Inference Approach

Felix Obite, Ali Krayani, Atm S. Alam +3

Future wireless networks will need to improve adaptive resource allocation and decision-making to handle the increasing number of intelligent devices. Unmanned aerial vehicles (UAV…

cs.IT2023★ 1 cited

RIS-aided Near-Field MIMO Communications: Codebook and Beam Training Design

Suyu Lv, Yuanwei Liu, Xiaodong Xu +2

Downlink reconfigurable intelligent surface (RIS)-assisted multi-input-multi-output (MIMO) systems are considered with far-field, near-field, and hybrid-far-near-field channels. Ac…

eess.SP2023★ 1 cited

Active Inference for Sum Rate Maximization in UAV-Assisted Cognitive NOMA Networks

Felix Obite, Ali Krayani, Atm S. Alam +3

Given the surge in wireless data traffic driven by the emerging Internet of Things (IoT), unmanned aerial vehicles (UAVs), cognitive radio (CR), and non-orthogonal multiple access…

cs.NI2023

Joint Computing Offloading and Resource Allocation for Classification Intelligent Tasks in MEC Systems

Yuanpeng Zheng, Tiankui Zhang, Jonathan Loo +2

Mobile edge computing (MEC) enables low-latency and high-bandwidth applications by bringing computation and data storage closer to end-users. Intelligent computing is an important…

cs.IT2023

Destination Scheduling for Secure Pinhole-Based Power-Line Communication

Chinmoy Kundu, Ankit Dubey, Andrea M. Tonello +2

We propose an optimal destination scheduling scheme to improve the physical layer security (PLS) of a power-line communication (PLC) based Internet-of-Things system in the presence…

cs.LG2023

Adaptive Federated Pruning in Hierarchical Wireless Networks

Xiaonan Liu, Shiqiang Wang, Yansha Deng +1

Federated Learning (FL) is a promising privacy-preserving distributed learning framework where a server aggregates models updated by multiple devices without accessing their privat…