M. Khan

Component and parameterised power model for cloud radio access network

The fifth generation cellular network performance is measured by its spectral and energy efficiency (EE). Densifying the network might provide a solution. However, this will lead to tremendous network infrastructure and increased power consumption (PC). Cloud radio access network (C-RAN) has emerged as a solution to curtail the PC and deliver spectrum sharing in a cost-effective and energy-efficient way. Since the PC is an important key success for the upcoming generations, and in order to study and evaluate the power reductions achievable by C-RAN, a reliable power model (PM) is required. This study proposes a component, linear and parameterised PMs to explore the individual components relevant for PC analysis, particularly for C-RAN architecture. The models quantify the EE by capturing the power consumed by individual components, and the amount of power reduced in the network. The model shows a dramatic reduction in the cooling PC (87.4%), whereas the total PC is reduced to about (33.3%) compared with macro base stations deployment. Finally, accuracy comparison of the component and parameterised models has been presented.

Modelling the energy efficiency of Heterogeneous Cloud Radio Access Networks

Cellular network performance is measured by its spectral and energy efficiency, with power consumption an important key to success for the upcoming heterogeneous (HetNet) communication system generations. The proposed models presented in this paper, component, linear, and parameterized power models, analyse and evaluate the power consumption participants in both Cloud Radio Access Network (C-RAN) and the State of the art (SotA) Base stations i.e.(Macro, Pico, Femto) when all participants interface in the area of interest, collectively called Heterogeneous-Cloud Radio Access Network (H-CRAN), in a proposal to enhance both energy and spectral efficiencies. Results showed that a C-RAN network reduced the cooling power consumption to about 87%, which reduced the overall power consumption compared to MBS deployment. Finally, H-CRAN cooling and total power consumption is evaluated by integrating C-RAN and the SotA BSs power consumption models.

QoS-Aware Dynamic RRH Allocation in a Self-Optimized Cloud Radio Access Network With RRH Proximity Constraint

An inefficient utilization 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 optimizes 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 sectors in a time-varying traffic environment. RRH-sector configuration redistributes the network capacity over a given geographical area. A self-optimized 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 optimization problem by defining it as a weighted combination of new key performance indicators for the number of blocked users and handovers in the network subject to RRH sectorization constraint. A genetic algorithm (GA) and discrete particle swarm optimization (DPSO) are proposed as evolutionary algorithms to solve the optimization 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 and

Quality of Service aware dynamic BBU-RRH mapping in Cloud Radio Access Network

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Network Performance Evaluation of M2M With Self Organizing Cluster Head to Sink Mapping

-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 minimize both handovers and blocked users. Furthermore, a tradeoff between two critical parameters for the SOCRAN algorithm is presented, to achieve performance benefits based on the type of hardware utilized for C-RAN.

Semistatic Cell Differentiation and Integration With Dynamic BBU-RRH Mapping in Cloud Radio Access Network

Cloud Radio Access Network (C-RAN) is considered to be the candidate for enabling the next generation mobile communication networks (5G). C-RAN has the ability to dynamically adjust the logical connections between the BBUs and RRHs according to traffic conditions. This paper explores the capacity routing ability of C-RAN to enable load balancing in the network. A self organised C-RAN is proposed and formulated as an optimisation problem, which aims to balance network traffic by reducing the number of blocked calls in the network and improving the QoS. The QoS in this paper is represented by a KPI, which is the inverse of blocked calls. A Genetic Algorithm is proposed for optimisation. A scenario of 19 RRHs distributed over a geographical area is considered. The RRHs are managed by 2 BBUs in the pool. The RRHs are divided into 6 sectors with each BBU handling 3 sectors. The initial BBU-RRH configuration causes 80 blocked calls in the entire network with a QoS evaluation value of 0.0125. After performing the algorithm, it is observed that the network is balanced by reconfiguring the BBU-RRH connections, finding the optimum solution, reducing the blocked calls to zero and improving the QoS of the network i.e., QoS=1.

Load balancing by dynamic BBU-RRH mapping in a self-optimised Cloud Radio Access Network

In this paper, a machine-to-machine (M2M) networks are arranged hierarchically to support an energy-efficient routing protocol for data transmission from terminal nodes to a sink node via cluster heads in a wireless sensor network (WSN) at perio network congestion caused by heavy M2M traffic is tackled using the load balancing solutions to maintain high levels of network performance. First, a multilevel clustering multiple sinks with IPv6 protocol over low wireless personal area networks is promoted to prolong network lifetime. Second, the enhanced network performance is achieved through non-linear integer-based optimization. A self-organizing cluster head to sink algorithm (SOCHSA) is proposed, hosting discrete particle swarm optimization (DPSO) and genetic algorithm (GA) as evolutionary algorithms to solve the network performance optimization problem. Network Performance is measured based on key performance indicators for load fairness and average residual network energy. The SOCHSA algorithm is tested by two benchmark problems with two and three sinks. DPSO and GA are compared with the exhaustive search algorithm to analyze their performances for each benchmark problem. Both algorithms achieve optimum network performance evaluation values of 108.059 and 108.1686 in the benchmark problems P1 and P2, respectively. Using three sinks under the same simulation settings, the average residual energy is improved by 2% when compared with two sinks. Computational results prove that DPSO outperforms GA regarding complexity and convergence, thus being best suited for a proactive Internet of Things network. The proposed mechanism satisfies different network performance requirements of M2M traffic by instant identification and dynamic rerouting.

Energy-efficient M2M routing protocol based on Tiny-SDCWN with 6LoWPAN

In this paper, a self-organizing cloud radio access network (C-RAN) 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 quality of service (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 optimization problem with constraints. The semistatic 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 QoS with minimum possible handovers. A discrete particle swarm optimization (DPSO) is developed as an evolutionary algorithm to solve network load balancing optimization 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 and round Robin schedulers, respectively.

Power Model for Heterogeneous Cloud Radio Access Networks

In this paper, a load-balancing scheme is investigated for C-RAN to optimise its performance subject to physical resource limitation and users Quality of Service (QoS) demands constraints. The Remote Radio Heads (RRHs) are allocated to the Base Band Units (BBUs) dynamically for load balancing and efficient resource utilisation. Dynamic BBU-RRH mapping is formulated as a linear integer-based constrained optimisation problem. An Estimation Distribution Discrete Particle Swarm Optimisation (EDDPSO) algorithm is developed for optimisation and to achieve a balanced network. Computational results based on two benchmark problems demonstrate that EDDPSO delivers optimum performance for small-scale networks. However, a close-optimum is achieved for large-scale networks which is 99.06% of the optimum value. The EDDPSO algorithm is fast and less complex compared to Exhaustive Search (ES) algorithm.

Reducing energy consumption by dynamic resource allocation in C-RAN

In this paper Tiny-SDCWN, a Tiny Software Defined Clustering Wireless Networking solution for wireless networking is proposed. The concept of Tiny-SDCWN proposed has structured and hierarchical management, which provides a solution to some inherent problems in WSN management and in particular, prolongs the network lifetime. The sensor field is divided into quarters with different levels of cluster heads (CHs), these being chosen in a purposeful manner along with two beneficially located Tiny-SDCWN controllers. In addition, the hierarchical structure is a Machine-to-Machine (M2M) energy efficient routing protocol of Multi-Level Clustering with Multiple Sinks (MLCMS), which are hosts to the controllers using IPv6 over Low Power Wireless Personal Area Networks (6LoWPAN). The MLCMS is used to enhance the lifetime of the network through a special network structure, which involves injecting a routing protocol into the controller. The performance of the MLCMS protocol is evaluated with and without using the Tiny-SDCWN controller. This modification performs 27% better than the traditional network in relation to energy usages all the decision are made by the controller and eliminate the broadcasting between the sensor nodes‥ It is contended that the proposed solution, whilst most importantly being effective in terms of energy usage, also introduces flexibility into the management of the network.