Δ-MILP: Deep Space Network Scheduling via Mixed-Integer Linear Programming
This paper introduces-MILP, a powerful variant of the mixed-integer linear programming
(MILP) optimization framework to solve NASA's Deep Space Network (DSN) scheduling …
(MILP) optimization framework to solve NASA's Deep Space Network (DSN) scheduling …
Personalized resource allocation in wireless networks: an ai-enabled and big data-driven multi-objective optimization
The design and optimization of wireless networks have mostly been based on strong
mathematical and theoretical modeling. Nonetheless, as novel applications emerge in the …
mathematical and theoretical modeling. Nonetheless, as novel applications emerge in the …
Graph-based user scheduling algorithms for LEO-MIMO non-terrestrial networks
In this paper, we study the user scheduling prob-lem in a Low Earth Orbit (LEO) Multi-User
Multiple-Input-Multiple-Output (MIMO) system. We propose an iterative graph-based …
Multiple-Input-Multiple-Output (MIMO) system. We propose an iterative graph-based …
Multimode high-altitude platform stations for next-generation wireless networks: Selection mechanism, benefits, and potential challenges
The high-altitude platform station (HAPS) concept has recently received notable attention
from both industry and academia to support future wireless networks. A HAPS can be …
from both industry and academia to support future wireless networks. A HAPS can be …
Multi-mode high altitude platform stations (HAPS) for next generation wireless networks
The high altitude platform station (HAPS) concept has recently received notable attention
from both industry and academia to support future wireless networks. A HAPS can be …
from both industry and academia to support future wireless networks. A HAPS can be …
Resource Allocation in THz-NOMA-Enabled HAP Systems: A Deep Reinforcement Learning Approach
Aerial access networks have been considered as a key-enabling concept for addressing
emerging challenges of future 6G wireless networks. In this paper, we focus on using the …
emerging challenges of future 6G wireless networks. In this paper, we focus on using the …
Deep reinforcement learning for joint trajectory planning, transmission scheduling, and access control in UAV-assisted wireless sensor networks
Unmanned aerial vehicles (UAVs) can be used to relay sensing information and
computational workloads from ground users (GUs) to a remote base station (RBS) for further …
computational workloads from ground users (GUs) to a remote base station (RBS) for further …
Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing
The latest Satellite Communication (SatCom) missions are characterized by a fully
reconfigurable on-board software-defined payload, capable of adapting radio resources to …
reconfigurable on-board software-defined payload, capable of adapting radio resources to …
Two-Tier User Association and Resource Allocation Design for Integrated Satellite-Terrestrial Networks
This paper presents a study of an integrated satellite-terrestrial network, where Low-Earth-
Orbit (LEO) satellites are used to provide the backhaul link between base stations (BSs) and …
Orbit (LEO) satellites are used to provide the backhaul link between base stations (BSs) and …
[PDF][PDF] Simultaneous beam selection and users scheduling evaluation in a virtual world with reinforcement learning
The ifth generation of mobile networks evolved to serve applications with distinct
requirements, which results in a high management complexity due to simultaneous real‑time …
requirements, which results in a high management complexity due to simultaneous real‑time …