arXiv subjects

Alvaro Valcarce Rial

Publications and source records attributed to Alvaro Valcarce Rial.

5 recordsLinked to original sources

arXiv2026

MU-MIMO Uplink Timely Throughput Maximization for Extended Reality Applications

In this work, we study the cross-layer timely throughput maximization for extended reality (XR) applications through uplink multi-user MIMO (MU-MIMO) scheduling. Timely scheduling opportunities are characterized by the peak age of information (PAoI)-metric and are incorporated into a network-side optimization problem as constraints modeling user satisfaction. The problem being NP-hard, we resort to a signaling-free, weighted proportional fair-based iterative heuristic algorithm, where the weights are derived with respect to the PAoI metric. Extensive numerical simulation results demonstrate that the proposed algorithm consistently outperforms existing baselines in terms of XR capacity without sacrificing the overall system throughput.

cs.IT↗

arXiv2024

Joint Resource-Power Allocation and UE Rank Selection in Multi-User MIMO Systems with Linear Transceivers

Next-generation wireless networks aim to deliver data speeds much faster than 5G. This requires base stations with lots of antennas and a large operating bandwidth. These advanced base stations are expected to serve several multiantenna user-equipment (UEs) simultaneously on the same time-frequency resources on both the uplink and the downlink. The UE data rates are affected by the following three main factors: UE rank, which refers to the number of data layers used by each UE, UE frequency allocation, which refers to the assignment of slices of the overall frequency band to use for each UE in an orthogonal frequency-division multiplexing (OFDM) system, and UE power allocation/control, which refers to the allocation of power by the base station for data transmission to each UE on the downlink or the power used by each UE to send data on the uplink. Since multiple UEs are to be simultaneously served, the type of precoder used for downlink transmission and the type of receiver used for uplink reception predominantly influence these three aforementioned factors and the resulting overall UE throughput. This paper addresses the problem of jointly selecting these three parameters specifically when zero-forcing (ZF) precoders are used for downlink transmission and linear minimum mean square error (LMMSE) receivers are employed for uplink reception.

cs.IT↗

arXiv2024

PDCCH Scheduling via Maximum Independent Set

In 5G, the Physical Downlink Control CHannel (PDCCH) carries crucial information enabling the User Equipment (UE) to connect in UL and DL. UEs are unaware of the frequency location at which PDCCH is encoded, hence they need to perform blind decoding over a limited set of possible candidates. We address the problem faced by the gNodeB of selecting PDCCH candidates for each UE to optimize data transmission. We formulate it as a Maximum Weighted Independent Set (MWIS) problem, that is known to be an NP-hard problem and cannot even be approximated. A solution method called Weight-to-Degree Ratio (WDR) Greedy emerges as a strong contender for practical implementations due to its favorable performance-to-complexity trade-off and theoretical performance guarantees.

cs.IT↗

arXiv2023

Towards Mobility Management with Multi-Objective Bayesian Optimization

One of the consequences of network densification is more frequent handovers (HO). HO failures have a direct impact on the quality of service and are undesirable, especially in scenarios with strict latency, reliability, and robustness constraints. In traditional networks, HO-related parameters are usually tuned by the network operator, and automated techniques are still based on past experience. In this paper, we propose an approach for optimizing HO thresholds using Bayesian Optimization (BO). We formulate a multi-objective optimization problem for selecting the HO thresholds that minimize HOs too early and too late in indoor factory scenarios, and we use multi-objective BO (MOBO) for finding the optimal values. Our results show that MOBO reaches Pareto optimal solutions with few samples and ensures service continuation through safe exploration of new data points.

cs.NI↗

arXiv2021

Bayesian Optimization for Radio Resource Management: Open Loop Power Control

We provide the reader with an accessible yet rigorous introduction to Bayesian optimisation with Gaussian processes (BOGP) for the purpose of solving a wide variety of radio resource management (RRM) problems. We believe that BOGP is a powerful tool that has been somewhat overlooked in RRM research, although it elegantly addresses pressing requirements for fast convergence, safe exploration, and interpretability. BOGP also provides a natural way to exploit prior knowledge during optimization. After explaining the nuts and bolts of BOGP, we delve into more advanced topics, such as the choice of the acquisition function and the optimization of dynamic performance functions. Finally, we put the theory into practice for the RRM problem of uplink open-loop power control (OLPC) in 5G cellular networks, for which BOGP is able to converge to almost optimal solutions in tens of iterations without significant performance drops during exploration.

cs.IT↗