Deep Reinforcement Learning-based Power Control and Bandwidth Allocation Policy for Weighted Cost Minimization in Wireless Networks
H Ke, H Wang, H Sun - Applied Intelligence, 2023 - Springer
Mobile edge computing (MEC) can dispatch its powerful servers close by to assist with the
computation workloads that intelligent wireless terminals have offloaded. The MEC server's …
computation workloads that intelligent wireless terminals have offloaded. The MEC server's …
Dynamic beam pattern and bandwidth allocation based on multi-agent deep reinforcement learning for beam hopping satellite systems
Due to the non-uniform geographic distribution and time-varying characteristics of the
ground traffic request, how to make full use of the limited beam resources to serve users …
ground traffic request, how to make full use of the limited beam resources to serve users …
Multi-Tier Deep Reinforcement Learning for Non-Terrestrial Networks
To provide global coverage and ubiquitous wireless services, non-terrestrial networks
(NTNs) composed of space-tier, air-tier, and ground-tier stations, have been regarded as a …
(NTNs) composed of space-tier, air-tier, and ground-tier stations, have been regarded as a …
An overview of intelligent wireless communications using deep reinforcement learning
Future wireless communication networks tend to be intelligentized to accomplish the
missions that cannot be preprogrammed. In the new intelligent communication systems …
missions that cannot be preprogrammed. In the new intelligent communication systems …
The emergence of wireless MAC protocols with multi-agent reinforcement learning
In this paper, we propose a new framework, exploiting the multi-agent deep deterministic
policy gradient (MADDPG) algorithm, to enable a base station (BS) and user equipment …
policy gradient (MADDPG) algorithm, to enable a base station (BS) and user equipment …
Improving performance by a dynamic adaptive success-collision backoff algorithm for contention-based vehicular network
G Wu, P Xu - IEEE Access, 2017 - ieeexplore.ieee.org
The distributed coordination function (DCF) is the core of the IEEE 802.11 standard and is
applied routinely to contention-based vehicular networks. Many existing backoff algorithms …
applied routinely to contention-based vehicular networks. Many existing backoff algorithms …
[HTML][HTML] Deep reinforcement learning evolution algorithm for dynamic antenna control in multi-cell configuration HAPS system
S Yang, M Bouazizi, T Ohtsuki, Y Shibata… - Future Internet, 2023 - mdpi.com
In this paper, we propose a novel Deep Reinforcement Learning Evolution Algorithm
(DRLEA) method to control the antenna parameters of the High-Altitude Platform Station …
(DRLEA) method to control the antenna parameters of the High-Altitude Platform Station …
[HTML][HTML] Next-Hop Relay Selection for Ad Hoc Network-Assisted Train-to-Train Communications in the CBTC System
In the communication-based train control (CBTC) system, traditional modes such as LTE or
WLAN in train-to-train (T2T) communication face the problem of a complex and costly …
WLAN in train-to-train (T2T) communication face the problem of a complex and costly …
Placement optimization of aerial base stations with deep reinforcement learning
Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations (ABSs) to assist
terrestrial infrastructure for keeping wireless connectivity in various emergency scenarios. To …
terrestrial infrastructure for keeping wireless connectivity in various emergency scenarios. To …
Joint MCS adaptation and RB allocation in cellular networks based on deep reinforcement learning with stable matching
Joint modulation-coding scheme (MCS) adaptation and resource block (RB) allocation is an
effective approach to guarantee different quality of service (QoS) requirements of all UEs …
effective approach to guarantee different quality of service (QoS) requirements of all UEs …