Resource management with adaptive capacity in C-RAN

This work was supported in part by the Spanish ministry of science through the projectRTI2018-099880-B-C32, with ERFD funds, and the Grant FPI-UPC provided by theUPC. It has been done under COST CA15104 IRACON EU project.Efficient computational resource management in 5G Cloud Radio Access Network (CRAN) environments is a challenging problem because it has to account simultaneously for throughput, latency, power efficiency, and optimization tradeoffs. This work proposes the use of a modified and improved version of the realistic Vienna Scenario that was defined in COST action IC1004, to test two different scale C-RAN deployments. First, a large-scale analysis with 628 Macro-cells (Mcells) and 221 Small-cells (Scells) is used to test different algorithms oriented to optimize the network deployment by minimizing delays, balancing the load among the Base Band Unit (BBU) pools, or clustering the Remote Radio Heads (RRH) efficiently to maximize the multiplexing gain. After planning, real-time resource allocation strategies with Quality of Service (QoS) constraints should be optimized as well. To do so, a realistic small-scale scenario for the metropolitan area is defined by modeling the individual time-variant traffic patterns of 7000 users (UEs) connected to different services. The distribution of resources among UEs and BBUs is optimized by algorithms, based on a realistic calculation of the UEs Signal to Interference and Noise Ratios (SINRs), that account for the required computational capacity per cell, the QoS constraints and the service priorities. However, the assumption of a fixed computational capacity at the BBU pools may result in underutilized or oversubscribed resources, thus affecting the overall QoS. As resources are virtualized at the BBU pools, they could be dynamically instantiated according to the required computational capacity (RCC). For this reason, a new strategy for Dynamic Resource Management with Adaptive Computational capacity (DRM-AC) using machine learning (ML) techniques is proposed. Three ML algorithms have been tested to select the best predicting approach: support vector machine (SVM), time-delay neural network (TDNN), and long short-term memory (LSTM). DRM-AC reduces the average of unused resources by 96 %, but there is still QoS degradation when RCC is higher than the predicted computational capacity (PCC). For this reason, two new strategies are proposed and tested: DRM-AC with pre-filtering (DRM-AC-PF) and DRM-AC with error shifting (DRM-AC-ES), reducing the average of unsatisfied resources by 99.9 % and 98 % compared to the DRM-AC, respectively

Machine learning adaptive computational capacity prediction for dynamic resource management in C-RAN

Efficient computational resource management in 5G Cloud Radio Access Network (C-RAN)environments is a challenging problem because it has to account simultaneously for throughput, latency,power efficiency, and optimization tradeoffs. The assumption of a fixed computational capacity at thebaseband unit (BBU) pools may result in underutilized or oversubscribed resources, thus affecting the overallQuality of Service (QoS). As resources are virtualized at the BBU pools, they could be dynamically instan-tiated according to the required computational capacity (RCC). In this paper, a new strategy for DynamicResource Management with Adaptive Computational capacity (DRM-AC) using machine learning (ML)techniques is proposed. Three ML algorithms have been tested to select the best predicting approach: supportvector machine (SVM), time-delay neural network (TDNN), and long short-term memory (LSTM). DRM-AC reduces the average of unused resources by 96 %, but there is still QoS degradation when RCC is higherthan the predicted computational capacity (PCC). To further improve, two new strategies are proposed andtested in a realistic scenario: DRM-AC with pre-filtering (DRM-AC-PF) and DRM-AC with error shifting(DRM-AC-ES), reducing the average of unsatisfied resources by 98 % and 99.9 % compared to the DRM-AC, respectivelyThis work was supported in part by the Spanish ministry of science through the project CRIN-5G (RTI2018-099880-B-C32) withERDF (European Regional Development Fund) and in part by the UPC through COST CA15104 IRACON EU Project and theFPI-UPC-2018 Grant.Peer ReviewedPostprint (published version

Diagnóstico y tratamiento del cáncer de mama HER2+: Guía de Práctica Clínica de la Sociedad Peruana de Cancerología

Introduction. In Peru, breast cancer represents the most common type of cancer in women and the sixth most lethal type of cancer in the general population. Overexpression of the epidermal growth factor receptor (HER2 +) occurs in 20% to 30% of breast cancers, and is associated with more aggressive tumors, with greater recurrence and greater mortality. Objective. Prepare a set of evidence-based recommendations for the diagnosis and treatment of HER2 + breast cancer, in order to help reduce mortality, disease progression and improve quality of life. Methods. A panel of clinical specialists and methodologists was formed, who identified relevant clinical questions about the diagnosis and treatment of HER2 + breast cancer. A systematic search for CPGs was carried out in Medline (PubMed), and in developing and compiling agencies. For the formulation of recommendations, the panel of specialists discussed the evidence and elements of the context of implementation of the recommendation, following the methodology proposed by the Ministry of Health of Peru. Results. Nine clinical questions were prioritized. A total of 25 clinical recommendations were made. Conclusions. An evidence-based CPG was developed through a systematic, rigorous and transparent process developed by a multidisciplinary team.Introducción. En Perú, el cáncer de mama representa el tipo de cáncer más frecuente en mujeres y el sexto tipo de cáncer más letal en la población general. La sobreexpresión del receptor del factor de crecimiento epidérmico (HER2+) ocurre en 20% a 30% de los cánceres de mama, y se asocia con tumores más agresivos, con mayor recurrencia y mayor mortalidad. Objetivo. Elaborar un conjunto de recomendaciones basadas en evidencias para el diagnóstico y tratamiento del cáncer de mama HER2+, con la finalidad de contribuir a reducir la mortalidad, progresión de la enfermedad y mejorar la calidad de vida. Métodos. Se conformó un panel de especialistas clínicos y metodólogos, quienes identificaron preguntas clínicas relevantes sobre el diagnóstico y tratamiento del cáncer de mama HER2+. Se desarrolló una búsqueda sistemática de GPC en Medline (PubMed), y en organismos elaboradores y recopiladores. Para la formulación de recomendaciones, el panel de especialistas discutió la evidencia y elementos del contexto de implementación de la recomendación, siguiendo la metodología propuesta por el Ministerio de Salud del Perú. Resultados. Se priorizó nueve preguntas clínicas. Se formuló un total de 25 recomendaciones clínicas. Conclusiones. Se elaboró una GPC basada en evidencias, a través de un proceso sistemático, riguroso y transparente desarrollado por un equipo multidisciplinario.&nbsp

Resource management with adaptive capacity in C-RAN

This work was supported in part by the Spanish ministry of science through the projectRTI2018-099880-B-C32, with ERFD funds, and the Grant FPI-UPC provided by theUPC. It has been done under COST CA15104 IRACON EU project.Efficient computational resource management in 5G Cloud Radio Access Network (CRAN) environments is a challenging problem because it has to account simultaneously for throughput, latency, power efficiency, and optimization tradeoffs. This work proposes the use of a modified and improved version of the realistic Vienna Scenario that was defined in COST action IC1004, to test two different scale C-RAN deployments. First, a large-scale analysis with 628 Macro-cells (Mcells) and 221 Small-cells (Scells) is used to test different algorithms oriented to optimize the network deployment by minimizing delays, balancing the load among the Base Band Unit (BBU) pools, or clustering the Remote Radio Heads (RRH) efficiently to maximize the multiplexing gain. After planning, real-time resource allocation strategies with Quality of Service (QoS) constraints should be optimized as well. To do so, a realistic small-scale scenario for the metropolitan area is defined by modeling the individual time-variant traffic patterns of 7000 users (UEs) connected to different services. The distribution of resources among UEs and BBUs is optimized by algorithms, based on a realistic calculation of the UEs Signal to Interference and Noise Ratios (SINRs), that account for the required computational capacity per cell, the QoS constraints and the service priorities. However, the assumption of a fixed computational capacity at the BBU pools may result in underutilized or oversubscribed resources, thus affecting the overall QoS. As resources are virtualized at the BBU pools, they could be dynamically instantiated according to the required computational capacity (RCC). For this reason, a new strategy for Dynamic Resource Management with Adaptive Computational capacity (DRM-AC) using machine learning (ML) techniques is proposed. Three ML algorithms have been tested to select the best predicting approach: support vector machine (SVM), time-delay neural network (TDNN), and long short-term memory (LSTM). DRM-AC reduces the average of unused resources by 96 %, but there is still QoS degradation when RCC is higher than the predicted computational capacity (PCC). For this reason, two new strategies are proposed and tested: DRM-AC with pre-filtering (DRM-AC-PF) and DRM-AC with error shifting (DRM-AC-ES), reducing the average of unsatisfied resources by 99.9 % and 98 % compared to the DRM-AC, respectively

Space Time Adaptive Processing for Radio Frequency Identification Smart Antenna Systems

Smart antenna has been recognized as a promising technology to improve wireless communications. Interference suppression, power consumption reduction and high data rates support are some of its advantages. Radio frequency identification (RFID) demands power efficiency, better reading ranges, tracking tag signals and interference mitigation. In this paper, a smart antenna processing for RFID systems is implemented. Space Time adaptive processing (STAP) with digital down converters, IQ beamformer and complex least mean square (LMS) algorithm has been designed and validated on Xilinx z7020 field programmable gate array (FPGA) platform. The system exhibits good performance in term of bit error rate (BER) when signal to noise plus interference ratio (SNIR) is over 1 dB with signal to noise ratio (SNR) of 7 dB environments

Flexible radio access network optimization with cell coordination

This paper focuses on Beyond fifth generation (B5G) non-linear data modeling and decision-making tools to optimize cost reduction versus coverage-QoS tradeoff. Especially, the distribution of active Remote Radio Heads or Units (RRHs) needed, according to traffic demands, is improved. The proposed optimization platform is based on a multi-objective optimization model, which is designed to reduce the network cost while maintaining the coverage-QoS. Capacity constraints, User Equipments (UEs), and different slices are considered to test the results under realistic conditions. Results at 3.6 and 28 GHz are presented by analyzing and comparing several Cloud Radio Access Network (C-RAN) split options in a heterogeneous deployment with Macro-RRHs (MRRHs) and Small-RRHs (SRRHs). Results show cost reductions from 30% to 70% depending on the scenario. Moreover, the proposed algorithm aggregates the possibility to consider the coordination between cells in order to improve the cost reduction. The results considering cooperation has been presented at both frequency bands with a fully centralized C-RAN (split option 8).This work has been done under COST20120- INTERACT (Intelligence-Enabling Radio Communications for Seamless Inclusive Interactions) EU project. It was supported by the Grant RTI2018-099880-B-C32 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, and the Grant FPIUPCPeer ReviewedPostprint (published version

Energy and Cost Footprint Reduction for 5G and Beyond with Flexible Radio Access Network

Publisher Copyright: AuthorThis paper focuses on Beyond fifth generation (B5G) non-linear data modeling and decisionmaking tools to optimize cost reduction versus coverage-QoS trade-off, in other words, the number of active Remote Radio Heads or Units (RRHs) needed according to traffic demands. The cost and energy optimization are analytically expressed by modeling the complex relationships between input and output system parameters using realistic scenarios and traffic profiles for low, medium, and high traffic environments. The optimization tool is based on a multi-objective integer linear programming model, designed to reduce the network cost while maintaining a good coverage-QoS and accounting for capacity constraints, User Equipments (UEs), and different slices. Results at 3.6 and 28 GHz are presented by analyzing and comparing several Cloud Radio Access Network (C-RAN) split options in a heterogeneous deployment with Macro-RRHs (MRRHs) and Small-RRHs (SRRHs). Cost reductions ranging from 30 % to 70 % have been obtained depending on the scenario. This proposal allows mobile network operators (MNOs) to achieve further optimization, while providing better network diagnostics.Peer reviewe

Machine-learning based traffic forecasting for resource management in C-RAN

The assumption of a fixed computational capacityat the Baseband Unit (BBU) pools in a Cloud Radio Access Network (C-RAN) deployment results in underutilized resourcesor unsatisfied users depending on traffic requirements. In thispaper a new strategy to predict the required resources based on Machine Learning techniques is proposed and analysed. SupportVector Machine (SVM), Time-Delay Neural Network (TDNN),and Long Short-Term Memory (LSTM) have been tested andcompared to select the best predicting approach. Instead of usinga regular synthetic scenario a realistic dense cell deployment overVienna city is used to validate the results. Authors show that theproposed solution reduces the unused resources average by 96 %This work has been done under COST CA15104 IRACONEU project. It was supported in part by the Spanish ministryof science through the project RTI2018-099880-B-C32, with ERFD funds, and the Grant FPI-UPC provided by the UPC.Peer ReviewedPostprint (published version