8 research outputs found
An Optimal Application-Aware Resource Block Scheduling in LTE
In this paper, we introduce an approach for application-aware resource block
scheduling of elastic and inelastic adaptive real-time traffic in fourth
generation Long Term Evolution (LTE) systems. The users are assigned to
resource blocks. A transmission may use multiple resource blocks scheduled over
frequency and time. In our model, we use logarithmic and sigmoidal-like utility
functions to represent the users applications running on different user
equipments (UE)s. We present an optimal problem with utility proportional
fairness policy, where the fairness among users is in utility percentage (i.e
user satisfaction with the service) of the corresponding applications. Our
objective is to allocate the resources to the users with priority given to the
adaptive real-time application users. In addition, a minimum resource
allocation for users with elastic and inelastic traffic should be guaranteed.
Every user subscribing for the mobile service should have a minimum
quality-of-service (QoS) with a priority criterion. We prove that our
scheduling policy exists and achieves the maximum. Therefore the optimal
solution is tractable. We present a centralized scheduling algorithm to
allocate evolved NodeB (eNodeB) resources optimally with a priority criterion.
Finally, we present simulation results for the performance of our scheduling
algorithm and compare our results with conventional proportional fairness
approaches. The results show that the user satisfaction is higher with our
proposed method.Comment: 5 page
Position Estimation of Robotic Mobile Nodes in Wireless Testbed using GENI
We present a low complexity experimental RF-based indoor localization system
based on the collection and processing of WiFi RSSI signals and processing
using a RSS-based multi-lateration algorithm to determine a robotic mobile
node's location. We use a real indoor wireless testbed called w-iLab.t that is
deployed in Zwijnaarde, Ghent, Belgium. One of the unique attributes of this
testbed is that it provides tools and interfaces using Global Environment for
Network Innovations (GENI) project to easily create reproducible wireless
network experiments in a controlled environment. We provide a low complexity
algorithm to estimate the location of the mobile robots in the indoor
environment. In addition, we provide a comparison between some of our collected
measurements with their corresponding location estimation and the actual robot
location. The comparison shows an accuracy between 0.65 and 5 meters.Comment: (c) 2016 IEEE. Personal use of this material is permitted. Permission
from IEEE must be obtained for all other uses, in any current or future
media, including reprinting/republishing this material for advertising or
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this work in other work
Context-Aware Resource Allocation in Cellular Networks
We define and propose a resource allocation architecture for cellular
networks. The architecture combines content-aware, time-aware and
location-aware resource allocation for next generation broadband wireless
systems. The architecture ensures content-aware resource allocation by
prioritizing real-time applications users over delay-tolerant applications
users when allocating resources. It enables time-aware resource allocation via
traffic-dependent pricing that varies during different hours of day (e.g. peak
and off-peak traffic hours). Additionally, location-aware resource allocation
is integrable in this architecture by including carrier aggregation of various
frequency bands. The context-aware resource allocation is an optimal and
flexible architecture that can be easily implemented in practical cellular
networks. We highlight the advantages of the proposed network architecture with
a discussion on the future research directions for context-aware resource
allocation architecture. We also provide experimental results to illustrate a
general proof of concept for this new architecture.Comment: (c) 2015 IEEE. Personal use of this material is permitted. Permission
from IEEE must be obtained for all other uses, in any current or future
media, including reprinting/republishing this material for advertising or
promotional purposes, creating new collective works, for resale or
redistribution to servers or lists, or reuse of any copyrighted component of
this work in other work
Traffic-aware carrier allocation with aggregation for load balancing
We consider the resource allocation with aggregation of multiple bands including unlicensed band for heterogeneous traffic. While the mobile data traffic including high volume of video traffic is expected to increase significantly, an efficient management of radio resources from multiple bands is required to guarantee the quality of service (QoS) of different traffic types. In this context, we formulate an optimal resource allocation by using different utility functions for heterogeneous traffic and the two-step resource allocation algorithm including resource grouping has been proposed. Simulation results demonstrate that the proposed algorithm enhances the connection robustness and shows good performance in terms of higher utility value of inelastic traffic even at high traffic loads by steering elastic traffic to unlicensed band