相关论文: Deep Q-Network for Optimizing NOMA-Aided Resource …

Grant-free non-orthogonal multiple access (GF-NOMA) is a potential multiple access framework for short-packet internet-of-things (IoT) networks to enhance connectivity. However, the resource allocation problem in GF-NOMA is challenging due…

Grant-free non-orthogonal multiple access (GF-NOMA) is a potential technique to support massive Ultra-Reliable and Low-Latency Communication (mURLLC) service. However, the dynamic resource configuration in GF-NOMA systems is challenging due…

This letter investigates the power control and channel assignment problem in device-to-device (D2D) communications underlaying a non-orthogonal multiple access (NOMA) cellular network. With the successive interference cancellation decoding…

This correspondence considers the resource allocation problem in wireless interference channel (IC) under link outage constraints. Since the optimization problem is non-convex in nature, existing approaches to find the optimal power…

Multi-access edge computing (MEC) is seen as a vital component of forthcoming 6G wireless networks, aiming to support emerging applications that demand high service reliability and low latency. However, ensuring the ultra-reliable and…

In recent years, Non-Orthogonal Multiple Access (NOMA) system has emerged as a promising candidate for multiple access frameworks due to the evolution of deep machine learning, trying to incorporate deep machine learning into the NOMA…

Achieving significant performance gains both in terms of system throughput and massive connectivity, non-orthogonal multiple access (NOMA) has been considered as a very promising candidate for future wireless communications technologies. It…

In this paper, an operating system scheduling algorithm based on Double DQN (Double Deep Q network) is proposed, and its performance under different task types and system loads is verified by experiments. Compared with the traditional…

Non-orthogonal multiple access (NOMA) is a key technology to enable massive machine type communications (mMTC) in 5G networks and beyond. In this paper, NOMA is applied to improve the random access efficiency in high-density…

NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based technology that offers a range of flexible configurations for massive IoT radio access from groups of devices with heterogeneous requirements. A configuration specifies…

Grant-free non-orthogonal multiple access (NOMA) has been regarded as a key-enabler technology for ultra-reliable and low-latency communications (URLLC). In this paper, we analyse the performance of NOMA with short packet communications for…

The combination of energy harvesting (EH), cognitive radio (CR), and non-orthogonal multiple access (NOMA) is a promising solution to improve energy efficiency and spectral efficiency of the upcoming beyond fifth generation network (B5G),…

Remote state estimation, where many sensors send their measurements of distributed dynamic plants to a remote estimator over shared wireless resources, is essential for mission-critical applications of Industry 4.0. Most of the existing…

This study addresses the challenge of optimal power allocation in stochastic wireless networks by employing a Deep Reinforcement Learning (DRL) framework. Specifically, we design a Deep Q-Network (DQN) agent capable of learning adaptive…

In recent years, long-term evolution (LTE) and 5G NR (5th Generation New Radio) technologies have showed great potential to utilize Machine Learning (ML) algorithms in optimizing their operations, both thanks to the availability of…

In this paper, we apply the Non-Orthogonal Multiple Access (NOMA) technique to improve the massive channel access of a wireless IoT network where solar-powered Unmanned Aerial Vehicles (UAVs) relay data from IoT devices to remote servers.…

In this paper, a novel Deep Q-Network (DQN) based scheduling method to optimize delay time and fairness among entanglement requests in quantum repeater networks is proposed. The scheduling of requests determines which pairs of end nodes…

Future wireless networks will need to improve adaptive resource allocation and decision-making to handle the increasing number of intelligent devices. Unmanned aerial vehicles (UAVs) are being explored for their potential in real-time…

In this paper, a deep learning (DL) framework for the optimization of the resource allocation in multi-channel cellular systems with device-to-device (D2D) communication is proposed. Thereby, the channel assignment and discrete transmit…

In this paper, we generate a transmit power pool (PP) for Internet of things (IoT) networks with semi-grant-free non-orthogonal multiple access (SGF-NOMA) via multi-agent deep reinforcement learning (MA-DRL) to enable open loop power…