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20212026
most citedIncreasing Energy Efficiency of Massive-MIMO Network via Base Stations Switching using Reinforcement Learning and Radio Environment Maps

31 citations · 82 across the 17 of their papers we have counts for

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5 papers · 1 filter

cs.NI2026

WIP: Energy-Efficient LLM-Based Serving Cluster Formulation in Cell-Free Massive MIMO

Marcin Hoffmann, Paweł Kryszkiewicz

One way to increase the Energy Efficiency (EE) of 6G wireless networks is to utilize existing network infrastructure more efficiently. This can be achieved by introducing User-Cent…

cs.NI2026

A Unified E2E Energy Efficiency Testing Framework for Open RAN

Marcin Hoffmann, Marcin Dryjański, Adrian Kliks +4

Energy efficiency (EE) is one of the key challenges for contemporary and future mobile networks, including within the Open Radio Access Network (O-RAN) architecture. However, there…

cs.NI2026

Large-scale wireless network management via Open-RAN Tandem Apps: Cell on/off switching use case

Paweł Kryszkiewicz, Łukasz Kułacz, Marcin Pakuła +5

With growing mobile-network complexity, management and optimization have become increasingly difficult. Centralized algorithms face high control-data overhead and computational loa…

cs.NI2026

Radio Environment Map for Energy-Efficient User-Centric Cell-Free M-MIMO Network

Marcin Hoffmann, Paweł Kryszkiewicz

This paper proposes a Radio Environment Map (REM) for energy-efficient (EE) serving cluster formulation in a user-centric cell-free network. By incorporating the location of the us…

cs.LG2026

Impact of Nonlinear Power Amplifier on Massive MIMO: Machine Learning Prediction Under Realistic Radio Channel

Marcin Hoffmann, Paweł Kryszkiewicz

M-MIMO is one of the crucial technologies for increasing spectral and energy efficiency of wireless networks. Most of the current works assume that M-MIMO arrays are equipped with…