A Q-learning-based downlink scheduling in 5G systems
Therefore, in this study, we propose a 5G downlink scheduling mechanism, named the Q-learning based Scheduling and Resource Allocation Scheme (QSRAS), which deploys Q-learning
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Therefore, in this study, we propose a 5G downlink scheduling mechanism, named the Q-learning based Scheduling and Resource Allocation Scheme (QSRAS), which deploys Q-learning
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Considering that downlink secure beamforming, uplink transmission power, and uplink receivers are tightly coupled in both the objective function and the constraints, we propose a two-loop...
The proposed 5G base station throughput simulation is depicted uplink data rate (Mbps), downlink data rate (Mbps), ping value (ms) and jitter value (ms), respectively.
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This reference simulation shows how to measure the physical downlink shared channel (PDSCH) throughput of a 5G New Radio (NR) link, as defined by the
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This paper, therefore, thoroughly investigates the network impact of enabling DL-256QAM on FR2, focusing on downlink throughput, retransmission rate, and modulation utilization.
The 5G NR Throughput Formula The theoretical peak throughput for 5G NR downlink is defined in TS 38.306 clause 4.1.2:
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In this article, we will explore various power control techniques employed in 5G networks, highlighting their significance and impact on overall network performance. The 5G network
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Low Latency: Fifth-generation technology minimizes network latency to as little as 1 millisecond (ms), enabling nearly immediate communication
In summary, 5G optimizes downlink resource allocation for multi-antenna transmissions by leveraging MIMO technology, CSI feedback, precoding, beamforming, dynamic allocation, and
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Aiming at the problem of mobile data traffic surge in 5G networks, this paper proposes an effective solution combining massive multiple-input multiple-output techniques with Ultra-Dense
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