3,330 research outputs found
Next Generation M2M Cellular Networks: Challenges and Practical Considerations
In this article, we present the major challenges of future machine-to-machine
(M2M) cellular networks such as spectrum scarcity problem, support for
low-power, low-cost, and numerous number of devices. As being an integral part
of the future Internet-of-Things (IoT), the true vision of M2M communications
cannot be reached with conventional solutions that are typically cost
inefficient. Cognitive radio concept has emerged to significantly tackle the
spectrum under-utilization or scarcity problem. Heterogeneous network model is
another alternative to relax the number of covered users. To this extent, we
present a complete fundamental understanding and engineering knowledge of
cognitive radios, heterogeneous network model, and power and cost challenges in
the context of future M2M cellular networks
Optimized Performance Evaluation of LTE Hard Handover Algorithm with Average RSRP Constraint
Hard handover mechanism is adopted to be used in 3GPP Long Term Evolution
(3GPP LTE) in order to reduce the complexity of the LTE network architecture.
This mechanism comes with degradation in system throughput as well as a higher
system delay. This paper proposes a new handover algorithm known as LTE Hard
Handover Algorithm with Average Received Signal Reference Power (RSRP)
Constraint (LHHAARC) in order to minimize number of handovers and the system
delay as well as maximize the system throughput. An optimized system
performance of the LHHAARC is evaluated and compared with three well-known
handover algorithms via computer simulation. The simulation results show that
the LHHAARC outperforms three well-known handover algorithms by having less
number of average handovers per UE per second, shorter total system delay
whilst maintaining a higher total system throughput.Comment: 16 pages, 9 figures, International Journal of Wireless & Mobile
Networks (IJWMN
A Price Selective Centralized Algorithm for Resource Allocation with Carrier Aggregation in LTE Cellular Networks
In this paper, we consider a resource allocation with carrier aggregation
optimization problem in long term evolution (LTE) cellular networks. In our
proposed model, users are running elastic or inelastic traffic. Each user
equipment (UE) is assigned an application utility function based on the type of
its application. Our objective is to allocate multiple carriers resources
optimally among users in their coverage area while giving the user the ability
to select one of the carriers to be its primary carrier and the others to be
its secondary carriers. The UE's decision is based on the carrier price per
unit bandwidth. We present a price selective centralized resource allocation
with carrier aggregation algorithm to allocate multiple carriers resources
optimally among users while providing a minimum price for the allocated
resources. In addition, we analyze the convergence of the algorithm with
different carriers rates. Finally, we present simulation results for the
performance of the proposed algorithm.Comment: Submitted to IEE
Easy 4G/LTE IMSI Catchers for Non-Programmers
IMSI Catchers are tracking devices that break the privacy of the subscribers
of mobile access networks, with disruptive effects to both the communication
services and the trust and credibility of mobile network operators. Recently,
we verified that IMSI Catcher attacks are really practical for the
state-of-the-art 4G/LTE mobile systems too. Our IMSI Catcher device acquires
subscription identities (IMSIs) within an area or location within a few seconds
of operation and then denies access of subscribers to the commercial network.
Moreover, we demonstrate that these attack devices can be easily built and
operated using readily available tools and equipment, and without any
programming. We describe our experiments and procedures that are based on
commercially available hardware and unmodified open source software
An Application-Aware Spectrum Sharing Approach for Commercial Use of 3.5 GHz Spectrum
In this paper, we introduce an application-aware spectrum sharing approach
for sharing the Federal under-utilized 3.5 GHz spectrum with commercial users.
In our model, users are running elastic or inelastic traffic and each
application running on the user equipment (UE) is assigned a utility function
based on its type. Furthermore, each of the small cells users has a minimum
required target utility for its application. In order for users located under
the coverage area of the small cells' eNodeBs, with the 3.5 GHz band resources,
to meet their minimum required quality of experience (QoE), the network
operator makes a decision regarding the need for sharing the macro cell's
resources to obtain additional resources. Our objective is to provide each user
with a rate that satisfies its application's minimum required utility through
spectrum sharing approach and improve the overall QoE in the network. We
present an application-aware spectrum sharing algorithm that is based on
resource allocation with carrier aggregation to allocate macro cell permanent
resources and small cells' leased resources to UEs and allocate each user's
application an aggregated rate that can at minimum achieves the application's
minimum required utility. Finally, we present simulation results for the
performance of the proposed algorithm.Comment: Submitted to IEE
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