3,898 research outputs found
Optimal channel allocation with dynamic power control in cellular networks
Techniques for channel allocation in cellular networks have been an area of
intense research interest for many years. An efficient channel allocation
scheme can significantly reduce call-blocking and calldropping probabilities.
Another important issue is to effectively manage the power requirements for
communication. An efficient power control strategy leads to reduced power
consumption and improved signal quality. In this paper, we present a novel
integer linear program (ILP) formulation that jointly optimizes channel
allocation and power control for incoming calls, based on the
carrier-to-interference ratio (CIR). In our approach we use a hybrid channel
assignment scheme, where an incoming call is admitted only if a suitable
channel is found such that the CIR of all ongoing calls on that channel, as
well as that of the new call, will be above a specified value. Our formulation
also guarantees that the overall power requirement for the selected channel
will be minimized as much as possible and that no ongoing calls will be dropped
as a result of admitting the new call. We have run simulations on a benchmark
49 cell environment with 70 channels to investigate the effect of different
parameters such as the desired CIR. The results indicate that our approach
leads to significant improvements over existing techniques.Comment: 11 page
A Wised Routing Protocols for Leo Satellite Networks
This Study proposes a routing strategy of combining a packet scheduling with
congestion control policy that applied for LEO satellite network with high
speed and multiple traffic. It not only ensures the QoS of different traffic,
but also can avoid low priority traffic to be "starve" due to their weak
resource competitiveness, thus it guarantees the throughput and performance of
the network. In the end, we set up a LEO satellite network simulation platform
in OPNET to verify the effectiveness of the proposed algorithm.Comment: The 10th Asian Control Conference (ASCC), Universiti Teknologi
Malaysia, Malaysi
Resource allocation in mobile edge cloud computing for data-intensive applications
Rapid advancement in the mobile telecommunications industry has motivated the development of mobile applications in a wide range of social and scientific domains. However, mobile computing (MC) platforms still have several constraints, such as limited computation resources, short battery life and high sensitivity to network capabilities. In order to overcome the limitations of mobile computing and benefit from the huge advancement in mobile telecommunications and the rapid revolution of distributed resources, mobile-aware computing models, such as mobile cloud computing (MCC) and mobile edge computing (MEC) have been proposed. The main problem is to decide on an application execution plan while satisfying quality of service (QoS) requirements and the current status of system networking and device energy. However, the role of application data in offloading optimisation has not been studied thoroughly, particularly with respect to how data size and distribution impact application offloading. This problem can be referred to as data-intensive mobile application offloading optimisation. To address this problem, this thesis presents novel optimisation frameworks, techniques and algorithms for mobile application resource allocation in mobile-aware computing environments. These frameworks and techniques are proposed to provide optimised solutions to schedule data intensive mobile applications. Experimental results show the ability of the proposed tools in optimising the scheduling and the execution of data intensive applications on various computing environments to meet application QoS requirements. Furthermore, the results clearly stated the significant contribution of the data size parameter on scheduling the execution of mobile applications. In addition, the thesis provides an analytical investigation of mobile-aware computing environments for a certain mobile application type. The investigation provides performance analysis to help users decide on target computation resources based on application structure, input data, and mobile network status
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