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Títol: Resource Management with adaptive capacity in C-RAN

Estudiants que han llegit aquest projecte:

Director/a: RUIZ BOQUÉ, SÍLVIA

Departament: TSC

Títol: Resource Management with adaptive capacity in C-RAN

Data inici oferta: 16-01-2020     Data finalització oferta: 16-09-2020


Estudis d'assignació del projecte:
    MU MASTEAM 2015
Tipus: Individual
 
Lloc de realització: EETAC
 
Paraules clau:
C-RAN, 5G, Machine Learning, Resource Allocation
 
Descripció del contingut i pla d'activitats:
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.
 
Overview (resum en anglès):
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.

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