arXiv subjects

Petteri Kela

Publications and source records attributed to Petteri Kela.

3 recordsLinked to original sources

arXiv2026

Enhancing User Throughput in Multi-panel mmWave Radio Access Networks for Beam-based MU-MIMO Using a DRL Method

Millimeter-wave (mmWave) communication systems, particularly those leveraging multi-user multiple-input and multiple-output (MU-MIMO) with hybrid beamforming, face challenges in optimizing user throughput and minimizing latency due to the high complexity of dynamic beam selection and management. This paper introduces a deep reinforcement learning (DRL) approach for enhancing user throughput in multi-panel mmWave radio access networks in a practical network setup. Our DRL-based formulation utilizes an adaptive beam management strategy that models the interaction between the communication agent and its environment as a Markov decision process (MDP), optimizing beam selection based on real-time observations. The proposed framework exploits spatial domain (SD) characteristics by incorporating the cross-correlation between the beams in different antenna panels, the measured reference signal received power (RSRP), and the beam usage statistics to dynamically adjust beamforming decisions. As a result, the spectral efficiency is improved and end-to-end latency is reduced. The numerical results demonstrate an increase in throughput of up to 16% and a reduction in latency by factors 3-7x compared to baseline (legacy beam management).

cs.IT↗

arXiv2025

From Simulation to Practice: Generalizable Deep Reinforcement Learning for Cellular Schedulers

Efficient radio packet scheduling remains one of the most challenging tasks in cellular networks, and while heuristic methods exist, practical deep learning-based schedulers that are 3GPP-compliant and capable of real-time operation in 5G and beyond are still missing. To address this, we first take a critical look at previous deep scheduler efforts. Secondly, we enhance State-of-the-Art (SoTA) deep Reinforcement Learning (RL) algorithms and adapt them to train our deep scheduler. In particular, we propose a novel combination of training techniques for Proximal Policy Optimization (PPO) and a new Distributional Soft Actor-Critic Discrete (DSACD) algorithm, which outperformed other variants tested. These improvements were achieved while maintaining minimal actor network complexity, making them suitable for real-time computing environments. Furthermore, entropy learning in SACD was fine-tuned to accommodate resource allocation action spaces of varying sizes. Our proposed deep schedulers exhibited strong generalization across different bandwidths, number of Multi-User MIMO (MU-MIMO) layers, and traffic models. Ultimately, we show that our pre-trained deep schedulers outperform their heuristic rivals in realistic and standard-compliant 5G system-level simulations.

eess.SP↗

arXiv2017

High-Efficiency Device Positioning and Location-Aware Communications in Dense 5G Networks

In this article, the prospects and enabling technologies for high-efficiency device positioning and location-aware communications in emerging 5G networks are reviewed. We will first describe some key technical enablers and demonstrate by means of realistic ray-tracing and map based evaluations that positioning accuracies below one meter can be achieved by properly fusing direction and delay related measurements on the network side, even when tracking moving devices. We will then discuss the possibilities and opportunities that such high-efficiency positioning capabilities can offer, not only for location-based services in general, but also for the radio access network itself. In particular, we will demonstrate that geometric location-based beamforming schemes become technically feasible, which can offer substantially reduced reference symbol overhead compared to classical full channel state information (CSI)-based beamforming. At the same time, substantial power savings can be realized in future wideband 5G networks where acquiring full CSI calls for wideband reference signals while location estimation and tracking can, in turn, be accomplished with narrowband pilots.

cs.IT↗