Essays about: "cell edge users"
Showing result 1 - 5 of 14 essays containing the words cell edge users.
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1. Performance of UE Relaying for 6G Networks
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Throughout the evolution of communication networks, users have consistently been demanding additional data and coverage. Future 6G networks seek to enable a seamless cyber-physical world through interconnected and integrated connectivity. READ MORE
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2. Link Adaptation in 5G Networks : Reinforcement Learning Framework based Approach
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Link Adaptation is a core feature introduced in gNodeB (gNB) for Adaptive Modulation and Coding (AMC) scheme in new generation cellular networks. The main purpose of this is to correct the estimated Signal-to-Interference-plus-Noise ratio (SINR) at gNB and select the appropriate Modulation and Coding Scheme (MCS) so the User Equipment (UE) can decode the data successfully. READ MORE
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3. Reinforcement Learning for Link Adaptation in 5G-NR Networks
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The Adaptive Modulation and Coding (AMC) scheme in the link adaptation is a core feature in the current cellular networks. In particular, based on Channel Quality Indicator (CQI) measurements that are computed from the Signal-to-Interference-plus-Noise Ratio (SINR) level of User Equipment (UE), the base station (e.g. READ MORE
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4. Hardware Power Optimization of Base Station Using Reinforcement Learning
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : 5G comes with requirements of much higher data rates for a digitally connected society where billions of new devices will be added in the coming years. From a RAN perspective, these demands will be served by an increasing number of eNB base stations. READ MORE
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5. Deep Reinforcement Learning for Downlink Power Control in Dense 5G Networks
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : This thesis examines the problem of downlink power allocation in dense 5Gnetworks, and attempts to develop a data-driven solution by employing deepreinforcement learning. We train and test multiple reinforcement learningagents using the deep Q-networks (DQN) algorithm, and the so-called Rainbowextensions of DQN. READ MORE
