A Coalitional Model Predictive Control Approach for Heterogeneous Cellular Networks

Heterogeneous cellular networks (HetNets) are large-scale systems that comprise numerous base stations interacting with a significant number of users of diverse types. Finding a trade-off between energy consumption and quality of service is one of the major challenges in these networks. To deal with this issue, a coalitional model predictive control (MPC) approach is proposed for a HetNet powered by renewable power sources, and compared in simulation with the traditional best-signal level mechanism and the centralized MPC method. Furthermore, other key performance indicators associated with grid consumption such as the number of served users and transmission rates are also evaluated

Energy Efficiency of Hybrid-Power HetNets: A Population-like Games Approach

In this paper, a distributed control scheme based on population games is proposed. The controller is in charge of dealing with the energy consumption problem in a Heterogeneous Cellular Network (HetNet) powered by hybrid energy sources (grid and renewable energy) while guaranteeing appropriate quality of service (QoS) level at the same time. Unlike the conventional approach in population games, it considers both atomicity and non-anonymity. Simulation results show that the proposed population-games approach reduces grid consumption by up to about 12% compared to the traditional best-signal level association policy.U.S. Air Force Office of Scientific Research FA9550-17-1-0259Ministerio de Cultura y Deporte DPI2016-76493-C3-3-RMinisterio de Economía y Empresa DPI2017-86918-

A Coalitional Model Predictive Control for the Energy Efficiency of Next-Generation Cellular Networks

<p>Next-generation cellular networks are large-scale systems composed of numerous base stations interacting with many diverse users. One of the main challenges with these networks is their high energy consumption due to the expected number of connected devices. We handle this issue with a coalitional Model Predictive Control (MPC) technique for the case of next-generation cellular networks powered by renewable energy sources. The proposed coalitional MPC approach is applied to two simulated scenarios and compared with other control methods: the traditional best-signal level mechanism, a heuristic algorithm, and decentralized and centralized MPC schemes. The success of the coalitional strategy is considered from an energy efficiency perspective, which means reducing on-grid consumption and improving network performance (e.g., number of users served and transmission rates).</p&gt

A Coalitional Model Predictive Control for the Energy Efficiency of Next-Generation Cellular Networks

Next-generation cellular networks are large-scale systems composed of numerous base stations interacting with many diverse users. One of the main challenges with these networks is their high energy consumption due to the expected number of connected devices. We handle this issue with a coalitional Model Predictive Control (MPC) technique for the case of next-generation cellular networks powered by renewable energy sources. The proposed coalitional MPC approach is applied to two simulated scenarios and compared with other control methods: the traditional best-signal level mechanism, a heuristic algorithm, and decentralized and centralized MPC schemes. The success of the coalitional strategy is considered from an energy efficiency perspective, which means reducing on-grid consumption and improving network performance (e.g., number of users served and transmission rates)

A Coalitional Model Predictive Control Approach for Heterogeneous Cellular Networks*

2020 European Control Conference (ECC) May 12-15, 2020. Saint Petersburg, RussiaHeterogeneous cellular networks (HetNets) are large-scale systems that comprise numerous base stations interacting with a significant number of users of diverse types. Finding a trade-off between energy consumption and quality of service is one of the major challenges in these networks. To deal with this issue, a coalitional model predictive control (MPC) approach is proposed for a HetNet powered by renewable power sources, and compared in simulation with the traditional best-signal level mechanism and the centralized MPC method. Furthermore, other key performance indicators associated with grid consumption such as the number of served users and transmission rates are also evaluated

A Coalitional Model Predictive Control for the Energy Efficiency of Next-Generation Cellular Networks

Nº de artículo: 6546Next-generation cellular networks are large-scale systems composed of numerous base stations interacting with many diverse users. One of the main challenges with these networks is their high energy consumption due to the expected number of connected devices. We handle this issue with a coalitional Model Predictive Control (MPC) technique for the case of next-generation cellular networks powered by renewable energy sources. The proposed coalitional MPC approach is applied to two simulated scenarios and compared with other control methods: the traditional best-signal level mechanism, a heuristic algorithm, and decentralized and centralized MPC schemes. The success of the coalitional strategy is considered from an energy efficiency perspective, which means reducing on-grid consumption and improving network performance (e.g., number of users served and transmission rates).Ministerio de Ciencia e Innovación y Universidades de España FPU18 / 04476Ministerio de Economía (Proyecto C3PO) DPI2017-86918-RJunta de Andalucía (Proyecto GESVIP) US-1265917Unión Europea H2020 (Proyecto DENiM) 958339Consejo Europeo de Investigación (Advanced Grant OCONTSOLAR) 789051Ministerio de Ciencia, Tecnología e Innovación de Colombia (MINCIENCIAS) 65797/825 - 201

A Techno-Economic Analysis of New Market Models for 5G+ Spectrum Management

While 5G has become a reality in several places around the world, some countries are still in the process of assigning frequency bands and deploying networks. In this context, there is a significant opportunity to explore new market models for the management and utilization of the radio spectrum. Access to the radio spectrum results in diverse competition schemes, where market behavior varies based on the regulator-defined access scheme and the competitive strategies of different actors. To thoroughly analyze potential competition scenarios, this work introduces a model that enhances the comprehension of market variables, emphasizing behaviors influenced by relationships. The model’s development leverages the potential of artificial intelligence and historical data from Colombia’s mobile telecommunications market. Intelligent spectrum sensing, based on Software Defined Radio, augments the model’s construction, utilizing lightweight AI algorithms to acquire real data on spectrum occupancy. In this way, the model provides novel insights into market dynamics, enabling the formulation of informed decision-making policies for regulatory bodies. Additionally, the application of causal machine learning (CausalML) helps understand the underlying causes of market behaviors, facilitating the design of guiding policies to maximize spectrum usage and foster competition. This approach demonstrates how AI-driven approaches and a deeper understanding of market dynamics can lead to effective 5G spectrum management, fostering a more competitive and efficient wireless communication landscape

Atomicity and non-anonymity in population-like games for the energy efficiency of hybrid-power HetNets

In this paper, the user–base station association problem is addressed to reduce grid consumption in heterogeneous cellular networks powered by hybrid energy sources (grid and renewable energy). This paper proposes a novel distributed control scheme inspired by population games and designed considering both atomicity and non-anonymity , i.e., describing the individual decisions of each agent. The controller performance is considered from an energy-efficiency perspective, which requires the guarantee of appropriate quality-of-service levels according to renewable energy availability. The efficiency of the proposed scheme is compared with other heuristic and optimal alternatives in two simulation scenarios. Simulation results show that the proposed approach inspired by population games reduces grid consumption by 12% when compared to the traditional best-signal-level association policy.This work was supported in part by the Colombian funding entity “Departamento Administrativo de Ciencia, Tecnolog´ıa e Innovacion - COLCIENCIAS” ´ for the Ph.D. scholarship number 6172. Also by DEOCS project (ref. DPI2016-76493-C3-3-R) from the Spanish Ministry of Culture and Sports and AGAUR - Agencia de Gesti ` o d’Ajuts Universitaris i de Recerca of ´ the Generalitat de Catalunya. Second author acknowledges U.S. Air Force Office of Scientific Research under grant number FA9550-17-1-0259. The work of C. Ocampo-Martinez is partially supported by the project DEOCS (Ref. DPI2016-76493-C3-3-R) from the Spanish MINECO/FEDER. Financial support by the Spanish MINECO project DPI2017-86918-R is gratefully acknowledged.Peer reviewe

Resource-Efficient Spectrum-Based Traffic Classification on Constrained Devices

Traffic Classification (TC) systems are designed to identify the applications generating network traffic. Recent advancements in TC leverage Deep Learning (DL) techniques, surpassing traditional methods in complex scenarios, including those with encrypted traffic. Notably, state-of-the-art DL-based TC systems have been developed for wireless networks using Physical Layer (L1) packets. This approach overcomes the common limitation in TC research that assumes traffic flows within a wired network under a single network management domain. Despite their benefits, DL-based TC systems often demand significant computational resources, typically available only in cloud environments. Consequently, deploying models at the edge is often infeasible due to their resource-intensive nature, given their original training and optimization for high-resource environments. The inherent challenge lies in adapting these systems for edge computing scenarios, including deployment at access points. In this paper, we propose a novel methodology that exploits expert knowledge in combination with recent advances in Multi-Task Learning (MTL) and Deep Neural Network (DNN) optimization to allow spectrum-based TC systems to run on constrained devices. This paper propose a well-defined and innovative methodology for resource-efficient, spectrum-based TC to address this issue, combining MTL with DNN optimization techniques. Performance evaluations on an NVIDIA Jetson TX2 demonstrate that our most optimized MTL model, handling four TC tasks, can reduce memory requirements by a factor of 2.65x and improve execution time by 3.6x compared to sequential execution of four Single-Task Learning (STL) models in a server-grade configuration, with minimal accuracy impact (less than a 0.5% drop) and energy efficiency of 0.97 millijoules per sample at inference. Compared to other edge platforms such as the Raspberry Pi model 3B+ (RPI3B+) with a low-power Artificial Intelligence (AI)-accelerator such as the Coral Tensor Processing Unit (TPU), the NVIDIA Jetson achieves a 12-fold improvement in energy efficiency with no impact on accuracy.These are the first available results to provide a benchmark for different performance metrics (memory, computing, energy) over heterogeneous constrained devices for this type of TC system