Cell-free massive multiple-input multiple-output (CF mMIMO) networks, in which multiple antennas simultaneously serve multiple user equipments (UEs), offer significant spectral efficiency (SE) gains. However, their energy efficiency (EE) performance still requires further investigation. Traditional approaches to radio resource allocation in CF mMIMO systems focus on solving the two-dimensional UE-antenna precoding problem. In this work, we propose a novel resource allocation framework that addresses the four-dimensional UE-antennafrequency-time precoding allocation for EE maximization. Considering the frequency-domain fast fading variations and timedomain traffic dynamics, we develop algorithms for EE maximization. Our heuristic delayed scheduling algorithm enhances EE by up to 10% compared to the algorithm designed for sum SE maximization. Furthermore, we demonstrate that EE performance is highly sensitive to system load, achieving a 7.4% higher EE at 69% system load compared to full load under the simulation settings. Finally, we analyze the impact of UE load on IP packet delay, establishing a relationship between the maximum UE load and packet delay budget.

Navigating mobile robots from remote servers offers numerous benefits in industrial settings, such as centralized decision-making and powerful hardware availability. However, the QoS of outsourced controllers is heavily influenced by the communication infrastructure state. Allowing robot applications to gain insights and control these states has the potential to significantly enhance mission performance. Programmable 5G O-RAN standardized near real-time network controllers allow application domain entities to access network-specific metrics and control parameters. We propose a solution where multiple robots are connected via 5G O-RAN to a common robot application framework capable of forwarding network performance metrics and managing network resource usage. This new component allows mobile robot applications to optimize their behavior based on network conditions. The system is evaluated in a simulated environment, where necessary navigation computations can be performed either on the mobile robot's local hardware or on more powerful remote hardware. Results demonstrate enhanced mission performance in the case of network-aware navigation and further improvements when the agent's QoS is prioritized.

We propose a deep Q-Learning (DQN) and heuristic-based radio resource scheduling (RRS) algorithms, which determine serving access points (APs), for maximum ratio transmission (MRT) precoder in the cell-free massive multiple-input multiple-output (CF mMIMO) networks. We show that, DQN-based algorithm yields up to 6% higher sum spectral efficiency (SE) than the heuristic and the reinforcement learning based algorithms for MRT precoder, but the heuristic algorithm yields 1000 times shorter computation time than the DQN-based algorithm. Thus, the low complexity MRT heuristic algorithms are the trade off between the performance and the cost.

Communication by jointly precoded transmission from many distributed access points (APs), called cell-free massive multiple-input multiple-output (CF mMIMO), is a promising concept for beyond 5G systems. One of the challenging aspects of CF mMIMO is the efficient management of the radio resources. We propose both reinforcement learning (RL)-based and heuristic precoding aware radio resource scheduling (RRS) algorithms aiming at maximizing sum spectral efficiency (SE). The proposed algorithms allocate resources for Maximum Ratio Transmission (MRT), Zero-Forcing (ZF), Regularised Zero-Forcing (RZF), and Optimized Zero Forcing (OZF) precoders. For the resource allocation, both the set of serving APs and the physical resource blocks are considered. In high noise scenarios, the proposed RL-based RRS algorithm combined with the MRT precoder shows 2.4 times higher sum SE than the standard Round Robin scheduler. Moreover, we demonstrate that the proposed heuristic algorithms offer similar sum SE while significantly reducing the complexity compared to the RL-based solution. We also show that the RZF and OZF precodings, which are superior to the ZF precoding in noisy environments, result overall in more transmitted power. Therefore, assuming the same radio resource schedule and precoding strategy in the neighboring cells, it will result in more inter-cell interference and an overall reduced performance.