Distributed Learning-Based Resource Allocation for Self-Organizing C-V2X Communication in Cellular Networks
In this paper, we investigate a resource allocation problem for a Cellular Vehicle to
Everything (C-V2X) network to improve energy efficiency of the system. To address this …
Everything (C-V2X) network to improve energy efficiency of the system. To address this …
Deep reinforcement learning based resource allocation for D2D communications underlay cellular networks
S Yu, JW Lee - Sensors, 2022 - mdpi.com
In this paper, a resource allocation (RA) scheme based on deep reinforcement learning
(DRL) is designed for device-to-device (D2D) communications underlay cellular networks …
(DRL) is designed for device-to-device (D2D) communications underlay cellular networks …
Vehicular network spectrum allocation using hybrid NOMA and multi-agent reinforcement learning
The recent years have seen a proven impact of the reinforcement learning use in many
applications which showed tremendous success in solving many decision-making …
applications which showed tremendous success in solving many decision-making …
Simultaneous data rate and transmission power adaptation in V2V communications: A deep reinforcement learning approach
J Aznar-Poveda, AJ Garcia-Sanchez… - IEEE …, 2021 - ieeexplore.ieee.org
In Vehicle-to-Vehicle (V2V) communications, channel load is key to ensuring the appropriate
operation of safety applications as well as driver-assistance systems. As the number of …
operation of safety applications as well as driver-assistance systems. As the number of …
Deep learning based power allocation for workload driven full-duplex D2D-aided underlaying networks
C Du, Z Zhang, X Wang, J An - IEEE Transactions on Vehicular …, 2020 - ieeexplore.ieee.org
Both Device-to-device (D2D) and full-duplex (FD) have been widely recognized as spectrum
efficient techniques in the fifth-generation (5G) networks. By combining them, the FD-D2D …
efficient techniques in the fifth-generation (5G) networks. By combining them, the FD-D2D …
Video semantics based resource allocation algorithm for spectrum multiplexing scenarios in vehicular networks
Due to the time-varying scenarios and multiple requirements in vehicular networks, it is
difficult to guarantee the accuracy of video semantic understanding within the scarce …
difficult to guarantee the accuracy of video semantic understanding within the scarce …
Multi-agent reinforcement learning based joint uplink–downlink subcarrier assignment and power allocation for D2D underlay networks
This paper investigates the joint uplink–downlink resource allocation in time-varying device-
to-device (D2D) underlay wireless cellular networks. Specifically, we formulate the joint …
to-device (D2D) underlay wireless cellular networks. Specifically, we formulate the joint …
Deep Reinforcement Learning-Based Resource Allocation for Cellular Vehicular Network Mode 3 with Underlay Approach
J Fu, X Qin, Y Huang, L Tang, Y Liu - Sensors, 2022 - mdpi.com
Vehicle-to-vehicle (V2V) communication has attracted increasing attention since it can
improve road safety and traffic efficiency. In the underlay approach of mode 3, the V2V links …
improve road safety and traffic efficiency. In the underlay approach of mode 3, the V2V links …
Deep reinforcement learning based user association and resource allocation for D2D-enabled wireless networks
With the ultra-dense deployment of small-cell base stations (SBSs), it is common today to
find a user locates within the coverage area of several SBSs. In this paper, we investigate …
find a user locates within the coverage area of several SBSs. In this paper, we investigate …
Dynamic spectrum access and sharing through actor-critic deep reinforcement learning
When primary users of the spectrum use frequency channels intermittently, secondary users
can selectively transmit without interfering with the primary users. The secondary users …
can selectively transmit without interfering with the primary users. The secondary users …