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  • Downlink Power Control for Cell-Free Massive MIMO With Deep . . .
    Recently, model-free power control approaches have been developed to achieve the near-optimal performance of cell-free (CF) massive multiple-input multiple-output (MIMO) with affordable computational complexity In particular, deep reinforcement learning (DRL) is one of such promising techniques for realizing effective power control In this paper, we propose a model-free method adopting the
  • Deep reinforcement learning based joint cooperation clustering and . . .
    In recent times, various power control and clustering approaches have been proposed to enhance overall performance for cell-free massive multiple-input multiple-output (CF-mMIMO) networks With the emergence of deep reinforcement learning (DRL), significant progress has been made in the field of network optimization as DRL holds great promise for improving network performance and efficiency
  • Enhancing the Uplink of Cell-Free Massive MIMO Through Prioritized . . .
    Effective power control is key to solving the inter-user interference problem that degrades performance in cell-free massive multiple-input multiple-output (MIMO) systems Motivated by its ability to operate online and model-free, without relying on training datasets, we leverage deep reinforcement learning (DRL) for uplink power control, aiming to maximize the guaranteed rate We propose a
  • Dynamic Power Allocation for Cell-Free Massive MIMO: Deep Reinforcement . . .
    Deep reinforcement learning (DRL) is one such method We explore two DRL power allocation methods, namely the deep Q-network (DQN) and the deep deterministic policy gradient (DDPG) The objective is to maximize the sum-spectral efficiency (SE) in CF massive MIMO, operating in the microwave domain
  • Learning-Based Downlink Power Allocation in Cell-Free Massive MIMO . . .
    This paper considers a cell-free massive multiple-input multiple-output (MIMO) system that consists of a large number of geographically distributed access points (APs) serving multiple users via coherent joint transmission The downlink performance of the system is evaluated, with maximum ratio and regularized zero-forcing precoding, under two optimization objectives for power allocation: sum
  • Deep Learning Based Power Control for Cell-Free Massive MIMO with MRT . . .
    Cell-Free Massive MIMO with MRT (Maximum-Ratio Transmission) has the advantage of decentralized beam-forming with the smallest front-haul overhead Its downlink power control plays a dual role of fair power distribution among users and interference mitigation
  • Power Allocation in Cell-Free Massive MIMO: A Deep Learning Method
    A deep neural network (DNN) for power allocation in cell-free massive MIMO The input is large-scale fading coefficient and the output is power allocation coefficient
  • Multi-Agent Reinforcement Learning-Based Joint Precoding and Phase . . .
    Cell-free (CF) massive multiple-input multiple-output (mMIMO) is a promising technique for achieving high spectral efficiency (SE) using multiple distributed access points (APs) However, harsh propagation environments often lead to significant communication performance degradation due to high penetration loss To overcome this issue, we introduce the reconfigurable intelligent surface (RIS
  • A Dynamic Power Allocation Approach for Downlink Cell-Free Massive MIMO . . .
    Cell-free (CF) massive multiple-input multiple-output (MIMO) is regarded as a potentially transformative paradigm in 6G Internet of Things (IoT) networks The efficient downlink power allocation is crucial in CF massive MIMO, especially in the presence of IoT device mobility In this study, we propose a novel approach based on a dynamically connected graph neural network (DCGNN) to optimize
  • Accelerated Deep Reinforcement Learning for Uplink Power Control in a . . .
    We investigate the deep reinforcement learning (DRL) framework for uplink power control in a cell-free massive multiple-input, multiple-output (MIMO) network Although DRL does not require prior sets of training data as opposed to supervised or unsupervised machine learning approaches, existing methods suffer from substantial convergence time, which is prohibitive in a highly dynamic or large





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