Ashutosh Dutta - Bridgewater NJ, US Subir Das - Kendall Park NJ, US Donald R. Lukacs - Red Bank NJ, US Tao Zhang - Fort Lee NJ, US David C. Shrader - New York NY, US Raquel Morera Sempere - Morristown NJ, US Stephanie Demers - Westfield NJ, US Bryan J. Lyles - Bedminster NJ, US James Alfieri - Palmer Township PA, US
International Classification:
H04Q 7/20
US Classification:
455436, 455445, 4554565, 370331
Abstract:
In a telecommunications network in which a mobile handset is capable of communicating in both an IP domain and a non-IP domain, the handoff of an existing communications session between the mobile handset and a fixed user is facilitated by a handoff controller implemented in a service control point. The handoff controller implements different handoff methods depending on the status of the fixed user and the transition of the mobile user. Additionally 802. 21 Media Independent Handover Function in conjunction with SIP is used to facilitate handover between IP and Non-IP points in the system.
Seamless Handoff Across Heterogeneous Access Networks Using A Handoff Controller In A Service Control Point
Ashutosh Dutta - Bridgewater NJ, US Subir Das - Kendall Park NJ, US Donald R. Lukacs - Red Bank NJ, US Tao Zhang - Fort Lee NJ, US David C. Shrader - New York NY, US Raquel Morera Sempere - Morristown NJ, US Stephanie Demers - Westfield NJ, US Bryan J. Lyles - Bedminster NJ, US James Alfieri - Palmer Township PA, US
In a telecommunications network in which a mobile handset is capable of communicating in both an IP domain and a non-IP domain, the handoff of an existing communications session between the mobile handset and a fixed user is facilitated by a handoff controller implemented in a service control point. The handoff controller implements different handoff methods depending on the status of the fixed user and the transition of the mobile user. Additionally 802. 21 Media Independent Handover Function in conjunction with SIP is used to facilitate handover between IP and Non-IP points in the system.
System And Method For Adaptive Seamless Mobility Of Multimedia Communication Sessions
Darek Smyk - Piscataway NJ, US Jacek Korycki - Monmouth Junction NJ, US David Shrader - New York NY, US James Alfieri - Palmer Township PA, US
International Classification:
H04Q 7/20
US Classification:
455436000
Abstract:
A network-based Adaptive Seamless Mobility Controller provides a view not only of the capabilities of the specific device in use by the user but also the capabilities of the access network serving each user involved in the session. When the user equipment identifies the opportunity to enhance the communication through adaption of the session to include, for example, a video connection in addition to a voice connection, by utilizing a different access network and corresponding device, the network-based Adaptive Seamless Mobility Controller determines the end-to-end capabilities required for the session and coordinates the adaptation of the session characteristics in addition to providing seamless handover across domains.
- Stockholm, SE Anders ENGSTRÖM - Lerum, SE Anders P. LARSSON - Mölndal, SE David SHRADER - Wilton Manors FL, US
International Classification:
H04M 15/00 H04W 4/24
Abstract:
The disclosure pertains to the field of Credit Control. More particularly the disclosure relates to methods of pre-emptive credit control, as well as to corresponding network elements and to a computer program. According to one aspect the disclosure relates to a method performed in a network element comprising receiving, from an online charging system, at least one pre-emptive credit control directive for a subscriber, the credit control directive applying to at least one rating group; storing the at least one pre-emptive credit control directive in the network element and applying the at least one pre-emptive credit control directive at content or service delivery start, prior to any service or content received from, or addressing, the subscriber and belonging to the at least one rating group being forwarded from the network element.
Node And Method For Service Usage Reporting And Quota Establishment
- Stockholm, SE Lars LÖVSÉN - Göteborg, SE David SHRADER - Wilton Manors FL, US Jiehong YANG - Mölndal, SE
Assignee:
Telefonaktiebolaget L M Ericsson (Publ) - Stockholm
International Classification:
H04L 12/24 H04W 4/24 H04L 12/14
Abstract:
Example embodiments presented herein are directed towards a Packet Domain Network Gateway (PGW) (), and corresponding methods therein, for service usage reporting and quota management in a Policy and Charging Control (PCC) based network. Example embodiments presented herein are also directed towards an Online Charging System (OCS) () for receiving service usage reporting and quota management in a PCC based network. Quota reporting and quota management is performed on a per-rating group basis.
Advanced Service-Aware Policy And Charging Control Methods, Network Nodes, And Computer Programs
Fabian Castro Castro - Madrid, ES Susana Fernandez Alonso - Madrid, ES David Shrader - Wilton Manors FL, US
International Classification:
H04L 12/14 H04L 12/26
Abstract:
In a method carried out in a telecommunication network, a policy decision function provides an instruction to a policy enforcement function and/or a traffic detection function. Within the instruction, some service instances of a service are identified by the order according to which the service instances are started. The instruction also indicates: (i) a service instance level reporting rule according to which the policy enforcement function and/or traffic detection function should report, or should not report, information to the policy decision function after occurrence of an event relating to any one of the identified service instances; and/or (ii) a service instance level enforcement rule that the policy enforcement function and/or traffic detection function should enforce on a received packet relating to any one of the identified service instances. The invention also relates to network nodes and computer programs.
Reed Group
Senior Software Developer at Reed Group, Inc
Information Graphics Systems Dec 2000 - Nov 2001
Senior Developer
Emobileforce Apr 2000 - Oct 2000
Senior Developer
Infopro Incorporated Mar 1996 - Apr 2000
Project Manager and Senior Software Engineer
Ellsworth Associates Oct 1992 - Mar 1996
Software Developer
Education:
The Computer Learning Center, Springfield, Virginia 1989 - 1990
The George Washington University 1988 - 1989
Skills:
Ssis Microsoft Sql Server Database Design Agile Methodologies Databases Scrum Sql User Interface Design Desktop Application Development Vba Visual Basic T Sql Microsoft Access Xml Visual Studio Etl Data Warehousing Software Documentation Software Development Business Intelligence Visio Web Applications Agile Project Management .Net Sdlc Sharepoint Data Modeling Software Project Management C# Tfs Asp.net Ssrs Requirements Analysis Integration Database Administration Software Development Life Cycle Transact Sql
Verizon
Dmts
Ericsson
Solution Architect and Technology Consultant at Ericsson
Ericsson Dec 2015 - Sep 2016
Solution Architect and Technology Consultant
Ericsson Jan 2012 - Nov 2015
Principal Solution Architect
Telcordia Technologies Jan 2010 - Jan 2012
Principal Solution Architect
Education:
Nova Southeastern University 2002 - 2014
Doctorates, Doctor of Philosophy, Computer Information Systems, Philosophy
Stanford University 1985 - 1986
Master of Science, Masters, Computer Science
Rice University 1981 - 1985
Bachelors, Bachelor of Arts, Bachelor of Science, Electrical Engineering, Computer Science
Penfield High School
Skills:
3Gpp Ss7 Diameter Charging Systems Pcrf Ip Multimedia Subsystem Sip Application Servers Intelligent Networks Inap Camel Mbms Lte Gprs Epc Ssp Mobile Communications Solution Architecture Telecom Infrastructure Network Architecture System Architecture Requirements Analysis Technology Integration Technical Training Software Development Data Analysis
Certifications:
Machine Learning Deep Learning Specialization (Neural Networks and Deep Learning, Improving Deep Neural Networks, Structuring Machine Learning Projects, Convolutional Neural Networks, Sequence Models) Google Cloud Platform Big Data and Machine Learning Fundamentals Information Service Engineering Practical Reinforcement Learning Machine Learning With Big Data Production Machine Learning Systems Aws Fundamentals: Going Cloud-Native Aws Fundamentals: Building Serverless Applications