8 research outputs found
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Semi-Static Cell Differentiation and Integration with Dynamic BBU-RRH Mapping in Cloud Radio Access Network
Abstract—In this paper, a Self-Organising Cloud Radio Access
Network is proposed, which dynamically adapt to varying network
capacity demands. A load prediction model is considered
for provisioning and allocation of Base Band Units (BBUs) and
Remote Radio Heads (RRHs). The density of active BBUs and
RRHs is scaled based on the concept of cell differentiation and
integration (CDI) aiming efficient resource utilisation without
sacrificing the overall QoS. A CDI algorithm is proposed in
which a semi-static CDI and dynamic BBU-RRH mapping for
load balancing are performed jointly. Network load balance is
formulated as a linear integer-based optimisation problem with
constraints.The semi-static part of CDI algorithm selects proper
BBUs and RRHs for activation/deactivation after a fixed CDI cycle,
and the dynamic part performs proper BBU to RRH mapping
for network load balancing aiming maximum Quality of Service
(QoS) with minimum possible handovers. A Discrete Particle
Swarm Optimisation (DPSO) is developed as an Evolutionary
Algorithm (EA) to solve network load balancing optimisation
problem. The performance of DPSO is tested based on two
problem scenarios and compared to Genetic Algorithm (GA) and
the Exhaustive Search (ES) algorithm. The DPSO is observed to
deliver optimum performance for small-scale networks and near
optimum performance for large-scale networks. The DPSO has
less complexity and is much faster than GA and ES algorithms.
Computational results of a CDI-enabled C-RAN demonstrate
significant throughput improvement compared to a fixed C-RAN,
i.e., an average throughput increase of 45.53% and 42.102%, and
an average blocked users reduction of 23.149%, and 20.903% is
experienced for Proportional Fair (PF) and Round Robin (RR)
schedulers, respectivel
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QoS-Aware dynamic RRH allocation in a Self-Optimised cloud radio access network with RRH proximity constraint
An inefficient utilisation of network resources in a
time-varying traffic environment often leads to load imbalances,
high call-blocking events and degraded Quality of Service
(QoS). This paper optimises the QoS of a Cloud Radio Access
Network (C-RAN) by investigating load balancing solutions.
The dynamic re-mapping ability of C-RAN is exploited to
configure the Remote Radio Heads (RRHs) to proper Base
Band Unit (BBU) sectors in a time-varying traffic environment.
RRH-sector configuration redistributes the network capacity
over a given geographical area. A Self-Optimised Cloud
Radio Access Network (SOCRAN) is considered to enhance
the network QoS by traffic load balancing with minimum
possible handovers in the network. QoS is formulated as an
optimisation problem by defining it as a weighted combination
of new key performance indicators (KPIs) for the number
of blocked users and handovers in the network subject to
RRH sectorisation constraint. A Genetic Algorithm (GA) and
Discrete Particle Swarm Optimisation (DPSO) are proposed
as evolutionary algorithms to solve the optimisation problem.
Computational results based on three benchmark problems
demonstrate that GA and DPSO deliver optimum performance
for small networks, whereas close-optimum is delivered for large
networks. The results of both GA and DPSO are compared to
Exhaustive Search (ES) and K-mean clustering algorithms. The
percentage of blocked users in a medium sized network scenario
is reduced from 10.523% to 0.421% and 0.409% by GA and
DPSO, respectively. Also in a vast network scenario, the blocked
users are reduced from 5.394% to 0.611% and 0.56% by GA
and DPSO, respectively. The DPSO outperforms GA regarding
execution, convergence, complexity, and achieving higher levels
of QoS with fewer iterations to minimise both handovers and
blocked users. Furthermore, a trade-off between two critical
parameters for the SOCRAN algorithm is presented, to achieve
performance benefits based on the type of hardware utilised for
C-RAN
Performance Optimization of Cloud Radio Access Networks
The exponential growth of cellular data traffic over the years imposes a hard challenge on the next cellular generations. The cloud radio access network (CRAN) is an emerging cellular architecture that is expected to face that challenge effectively. The main difference between the CRAN architecture and the conventional cellular architecture is that the baseband units (BBUs) are aggregated at a centralized baseband unit pool, hence, enabling statistical multiplexing gains. However, to acquire the several advantages offered by the CRAN architecture, efficient optimization algorithms and transmission techniques should be implemented to enhance the network performance. Hence, in this thesis, we consider jointly optimizing user association, resource allocation and power allocation in a two tier heterogeneous cloud radio access network (H-CRAN). Our objective is to utilize all the network resources in the most efficient way to maximize the network average throughput, while keeping some constraints such as the quality of service (QoS), interference protection to the devices associated with the Macro remote radio head (MRRH), and fronthaul capacity. In our system, we propose using coordinated multi-point (CoMP) transmissions to utilize any excess resources to maximize the network performance, in contrast to the literature, in which CoMP is usually used only to support edge users. We divide our joint problem into three sub-problems: user association, radio resource allocation, and power allocation. We propose matching game based low complexity algorithms to tackle the first two sub-problems. For the power allocation sub-problem, we propose a novel technique to convexify the non-convex original problem to obtain the optimal solution. Given the conducted simulations, our proposed algorithms proved to enhance the network average weighted sum rate significantly, compared to the state of the art algorithms in the literature.
The high computational complexity of the optimization techniques currently proposed in the literature prevents from totally reaping the benefits of the CRAN architecture. Learning based techniques are expected to replace the conventional optimization techniques due to their high performance and very low online computational complexity. In this thesis, we propose tackling the power allocation in CRAN via an unsupervised deep learning based approach. Different from the previous works, user association is considered in our optimization problem to reflect a real cellular scenario. Additionally, we propose a novel scheme that can enhance the deep learning based power allocation approaches, significantly. We provide intensive analysis to discuss the trade-offs faced when employing our deep learning based approach for power allocation. Simulation results prove that the proposed technique can obtain a very close to optimal performance with negligible computational complexity