EMF-aware cell selection in heterogeneous cellular networks

The growing concern on the exposure of users to the electromagnetic field (EMF) has recently brought new challenges to the mobile research community. In this letter, we propose a novel cell association framework for heterogeneous cellular networks (HetNets), which aims to balance the load amongst heterogeneous cells so as to improve the resource usage and to increase the user satisfaction in terms of both data rate and EMF exposure. We model the cell selection problem as a General Assignment Problem (GAP) and we present two heuristic algorithms, which solve it with limited complexity. Our analysis shows that the proposed solutions lead to notable improvements with respect to legacy association schemes.This papers reports work undertaken in the context of the project LEXNET. LEXNET is a project supported by the European Commission in the 7th Framework Programme (Grant Agreement n. 318273)

Assessing the WiFi offloading benefit on both service performance and EMF exposure in urban areas

In this paper we assess the benefit of WiFi offloading over dense urban scenarios in terms of both Quality of Service (QoS) and Electromagnetic Field (EMF) exposure. This study relies on results obtained with two complementary simulation platforms: a two-tier dynamic system-level simulator and a 3D coverage-based simulator. Outputs are usual service coverage key performance indicators, handover probability statistics, as well as common and innovative metrics for EMF exposure characterization that jointly take into account the contributions from the base-station and the User-Equipment (UE) transmissions. The main outcome is that, for elastic services, the best QoS and minimum global EMF exposure are jointly achieved with maximum WiFi offloading.This paper reports work undertaken in the context of the FP7 project LEXNET (GA nº 318273). Ramón Agüero also acknowledges the Spanish Government for the project “Connectivity as a Service: Access for the Internet of the Future”, COSAIF (TEC2012-38574-C02-02)

Wireless networks and EMF-paving the way for low-EMF networks of the future: the LEXNET project

While, according to the World Health Organization, no adverse health effects of radio-frequency (RF) electromagnetic fields (EMFs) have been established to date, EMF exposure from wireless communication networks is nonetheless often cited as a major cause of public concern and is frequently given considerable media coverage. This article presents the results of a new survey on RF-EMF exposure risk perception together with a comprehensive overview of the EMF footprint of existing and emerging networks. On the basis of these findings, we then put forward the rationale for EMF-aware networking. Subsequently, we highlight the gaps in existing systems, which impede EMF-aware networking, and outline the key concepts of the recently launched European Union (EU) Seventh Framework Programme (FP7) Integrated Project Low-EMF Exposure Future Networks (LEXNET): a new, all-encompassing, population-based metric of exposure and ways it can be used for low-EMF, quality of service (QoS)-aware network optimization.This paper reports work undertaken in the context of the project LEXNET. LEXNET is a project supported by the European Commission in the 7th Framework Programme (GA n°318273). For further information, please visit www.lexnet-project.e

Recent Trend in Electromagnetic Radiation and Compliance Assessments for 5G Communication

The deployment of the 5G networks will feature high proliferation of radio base station (RBS) in order to meet the increasing demand for bandwidth and also to provide wider coverage that will support more mobile users and the internet-of-things (IoT). The radio frequency (RF) waves from the large-scale deployment of the RBS and mobile devices will raise concerns on the level of electromagnetic (EM) radiation exposure to the public. Hence, in this paper, we provide an overview of the exposure limits, discuss some of the effects of the EM emission, reduction techniques and compliance assessment for the 5G communication systems. We discuss the open issues and give future directions

Permutation based load balancing technique for long term evolution advanced heterogeneous networks

Traffic congestion has been one of the major performance limiting factors of heterogeneous networks (HetNets). There have been several load balancing schemes put up to solve this by balancing load among base stations (BSs), but they appear to be unfeasible due to the complexity required and other unsatisfactory performance aspects. Cell range extension (CRE) has been a promising technique to overcome this challenge. In this paper, a permutation based CRE technique is proposed to find the best possible formation of bias for BSs to achieve load balance among BSs. In comparison to the baseline scheme, results depict that the suggested method attains superior performance in terms of network load balancing and average throughput. The complexity of the suggested algorithm is considerably reduced in comparison to the proposed permutation based CRE method it is further modified from

A comparison of reinforcement learning algorithms in fairness-oriented OFDMA schedulers

Due to large-scale control problems in 5G access networks, the complexity of radioresource management is expected to increase significantly. Reinforcement learning is seen as apromising solution that can enable intelligent decision-making and reduce the complexity of differentoptimization problems for radio resource management. The packet scheduler is an importantentity of radio resource management that allocates users’ data packets in the frequency domainaccording to the implemented scheduling rule. In this context, by making use of reinforcementlearning, we could actually determine, in each state, the most suitable scheduling rule to be employedthat could improve the quality of service provisioning. In this paper, we propose a reinforcementlearning-based framework to solve scheduling problems with the main focus on meeting the userfairness requirements. This framework makes use of feed forward neural networks to map momentarystates to proper parameterization decisions for the proportional fair scheduler. The simulation resultsshow that our reinforcement learning framework outperforms the conventional adaptive schedulersoriented on fairness objective. Discussions are also raised to determine the best reinforcement learningalgorithm to be implemented in the proposed framework based on various scheduler settings