A Heterogeneous Multi-agent Deep Reinforcement Learning Framework for Wireless Power Allocation under Realistic Urban Mobility Models
Globecom, pp. 2204–2209
Abstract
Deep reinforcement learning (DRL) is a powerful method for Dynamic Power Allocation (DPA) due to its adaptability to changing wireless environments. Crafting proficient DRL agents necessitates a well-structured problem representation, learning scheme, and agent interaction framework. Additionally, the environment, as the second pivotal component in DRL, must mirror realistic channel dynamics faithfully. This research tackles these often-neglected aspects. We introduce Het-DRL, a heterogeneous multi-agent DRL framework with a competitive interaction paradigm for DPA, moving away from conventional collaborative and centralized learning schemes. The independent learning scheme eliminates information exchange among agents, empowering autonomous decision-making. Moreover, we present our CELLUCARLA wireless simulator, an extension of the CARLA (Car Learning to Act) simulator with a cellular network layer, enabling realistic urban mobility modeling. We leverage CELLUCARLA to evaluate the proposed Het-DRL, highlighting its effectiveness for DPA in Orthogonal Frequency-Division Multiplexing (OFDM) systems, with up to 153% sum rate relative to the Weighted Minimum Mean Square Error (WMMSE) algorithm.
Authors 5
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Affiliation as printed
RWTH Aachen University,Chair for Distributed Signal Processing,Aachen,Germany
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Affiliation as printed
RWTH Aachen University,Chair for Distributed Signal Processing,Aachen,Germany
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Affiliation as printed
RWTH Aachen University,Chair for Distributed Signal Processing,Aachen,Germany
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Affiliation as printed
RWTH Aachen University,Chair for Distributed Signal Processing,Aachen,Germany
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Affiliation as printed
RWTH Aachen University,Chair for Distributed Signal Processing,Aachen,Germany
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