No abstract

Abstract-Fifth generation mobile systems (5G) target an Average Area Spectral Efficiency (AASE) over hundred Gbps/km 2 /user for future mobile systems with an Energy Dissipation (ED) per unit area similar to the current ED levels. Heterogeneous networks (HetNets) with high density of deployed small cells are currently adopted to aid in achieving the target ED and AASE by 5G. Limited spectrum availability requires efforts to manage the spectrum utilization in such dense deployments. Development of new network architectures and Radio Resources Management (RRM) schemes is important to address such challenges. The objective of this work is to propose a new architecture that consists of a Decision Support System (DSS) and a data collection system to dynamically manage and control the spectrum allocation process. The DSS generates spectrum allocation patterns using non-parametric estimation and statistical analysis for the collected data. A new RRM model using a Plan, Do, Control and Act (PDCA) cycle is proposed as a new self optimization module in the self organizing network framework. The PDCA model utilizes the new architecture and the allocation patterns to dynamically predict future spectrum allocation. Results show improvement in the AASE achieved using the PDCA model compared to conventional spectrum allocation.

Mobile data traffic is growing in an unprecedented rate. Mobile service providers and other businesses relying on mobile traffic require talented calibers to hire with proper skills to operate and manage their networks. Broadband wireless networks and big data systems are two important technologies that current STEM students need to learn, comprehend and master to satisfy the market needs. Design and implementation of an academic big-data system and broadband wireless testbed for instruction and research purposes is a difficult task. In this work, challenges facing the design and implementation of a mobile networks and big-data lab are evaluated. This work aims at providing a comprehensive reporting about an experience gained from designing and implementing an academic lab of big-data system used for broadband wireless networks traffic analysis and management. Challenges facing the project team during the implementation are discussed and probable solutions are described. Lessons learned from different project milestones are detailed to highlight the advantages and disadvantages of different project paths adopted by the project team. Finally, recommendations to other teams willing to create similar labs are presented.

Sirena Hardy thrives on the ever-changing world of information technology and the various ways technology has advanced our society. She has acquired over 10 years of information technology experience in the areas of software consulting and implementation; software training and application support. She gained valuable insight and knowledge during her time traveling around the country providing software training as well as assisting various colleges with the implementation of an enterprise resource planning system. Currently she is providing human resource management system software training to the public school districts of North Carolina and assisting with the statewide implementation of a new applicant tracking solution. She holds a MS in Information Science from North Carolina Central University and is currently pursuing a MS in Networking Technology at East Carolina University.c American Society for Engineering Education, 2016 Broadband Wireless Networking in the Era of Big Data AbstractOrganizations accumulate huge amounts of data from various systems but more often than not the data is stored but not organized or analyzed by the organizations. When certain characteristics define this data such as volume refers to a large quantity of data received and stored; velocity refers to a high speed of receiving data from different data streams; variety involves the ever-changing data formats from new services, and new data types that are being captured; and finally that this data is valuable. Any data characterized by the aforementioned characteristics is articulated as big data and the systems managing such data is referred to as Big Data Systems (BDSs). Mobile service providers (MSPs) in their efforts to provide more efficient heterogeneous networks (HetNets) deal daily with data characterized by the same features. The successful implementation of a BDS involves having the required infrastructure in place to process the data. There are three key areas involved with a big data infrastructure which includes data acquisition, data organization, and data analysis. Since big data involves higher velocity, volume, and variety an organization must have the ability to capture this data. MSPs need to employ a system to actually extract and analyze network utilization big data to determine if it brings value to them and their customers. This work discusses the design, implementation and utilization aspects of a Hadoop system that can help MSPs to delve deep into their big data stores to analyze the potential of adding value to the organization. A Hadoop system would allow an entity to organize and process their big data. A system architecture for the BDS supporting the HetNet operations will be proposed together with the recommendations of an analytics framework. The BDS architecture together with the analytics framework aims at helping the MSPs in forecasting the network traffic. The results of the traffic big data analytics and the network load forecasting can be used to adjust different network operating parameters. Thes...

Satisfying the demand for higher data rate and ensuring the quality of service requirements are gaining more research interest. Investigating the problem of resource management for mobile systems is crucial in solving this issue. This paper aims at optimizing the down-link physical resources allocation in LTE-A multi cell systems. This constitutes the allocation of the physical resources to optimize the spectral and power eciency under dierent down-link frequency partition congurations. The allocation objective is to increase the mobile service providers return on investment by increasing revenues and decreasing operational costs, while maintaining the minimum quality of service (QoS) requirements. A new scheme employing a utility-based