Dish Wireless
Principal Architect
Ranzure Networks 2016 - 2018
Director- Business Development and Products
Jio 2016 - 2018
Director, Product Management and Development
Cisco Feb 2013 - Sep 2016
Senior Solution Architect, Mobility Engineering
Intucell 2012 - 2013
Senior Manager Products and Performance , Son Engineering and Deployment
Education:
La Martiniere College, Lucknow 1985 - 1993
University Institute of Chemical Technology (Formerly Udct)
Bachelor of Engineering, Bachelors, Electronics Engineering
Skills:
Lte Wireless Gsm 3G Rf Umts Wcdma Wireless Networking Optimization Rf Engineering Business Development Team Leadership Communications Audits Das Muti Vendor Experience Strategic Planning Process Improvement Sales Proposal Writing Project Manager Proposal Evaluation Sow Developement Innovation Customer Relations Special Events Planning and Optimization High End Network Audits Performance and Optimization Telecom Network Design Son Auditing Iot Mobility 5G Sdn Nfv Telco Cloud Big Data Analytics Mongodb Business Intelligence Cloud Ran
IBM India - Pune Area, India since Jan 2011
Business Analyst - Financial Market
IBM India - Pune Sep 2010 - Dec 2010
Data Specialist
Infosys Technologies Ltd - Pune Jul 2004 - Aug 2010
Technical Lead
- ENGLEWOOD CO, US Kazi Bashir - Lewisville TX, US Ash Khamas - Goffstown NM, US Ashish Bansal - Frisco TX, US Siddhartha Chenumolu - Broadlands VA, US
International Classification:
H04W 28/08 H04W 48/18
Abstract:
Embodiments are directed towards systems and methods for user plane function (UPF) and network slice load balancing within a 5G network. Example embodiments include systems and methods for load balancing based on current UPF load and thresholds that depend on UPF capacity; UPF load balancing using predicted throughput of new UE on the network based on network data analytics; UPF load balancing based on special considerations for low latency traffic; UPF load balancing supporting multiple slices, maintaining several load-thresholds for each UPF and each slice depending on the UPF and network slice capacity; and UPF load balancing using predicted central processing unit (CPU) utilization and/or predicted memory utilization of new UE on the network based on network data analytics.
User Plane Function (Upf) Load Balancing Supporting Multiple Slices
- Englewood CO, US Kazi Bashir - Lewisville TX, US Ash Khamas - Goffstown NH, US Ashish Bansal - Frisco TX, US Siddhartha Chenumolu - Broadlands VA, US
International Classification:
H04W 28/08 H04W 48/18
Abstract:
Embodiments are directed towards systems and methods for user plane function (UPF) and network slice load balancing within a 5G network. Example embodiments include systems and methods for load balancing based on current UPF load and thresholds that depend on UPF capacity; UPF load balancing using predicted throughput of new UE on the network based on network data analytics; UPF load balancing based on special considerations for low latency traffic; UPF load balancing supporting multiple slices, maintaining several load-thresholds for each UPF and each slice depending on the UPF and network slice capacity; and UPF load balancing using predicted central processing unit (CPU) utilization and/or predicted memory utilization of new UE on the network based on network data analytics.
User Plane Function (Upf) Load Balancing Based On Central Processing Unit (Cpu) And Memory Utilization Of The User Equipment (Ue) In The Upf
- ENGLEWOOD CO, US Kazi Bashir - Lewisville TX, US Ash Khamas - Goffstown NM, US Ashish Bansal - Frisco TX, US Siddhartha Chenumolu - Broadlands VA, US
International Classification:
H04W 28/08 H04L 47/127 H04L 47/125 H04L 47/215
Abstract:
Embodiments are directed towards embodiments are directed toward systems and methods for user plane function (UPF) and network slice load balancing within a 5G network. Example embodiments include systems and methods for load balancing based on current UPF load and thresholds that depend on UPF capacity; UPF load balancing using predicted throughput of new UE on the network based on network data analytics; UPF load balancing based on special considerations for low latency traffic; UPF load balancing supporting multiple slices, maintaining several load-thresholds for each UPF and each slice depending on the UPF and network slice capacity; and UPF load balancing using predicted central processing unit (CPU) utilization and/or predicted memory utilization of new UE on the network based on network data analytics.
User Plane Function (Upf) Load Balancing Based On Special Considerations For Low Latency Traffic
- Englewood CO, US Kazi Bashir - Lewisville TX, US Ash Khamas - Goffstown NH, US Ashish Bansal - Frisco TX, US Siddhartha Chenumolu - Broadlands VA, US
International Classification:
H04W 28/08
Abstract:
Embodiments are directed towards systems and methods for user plane function (UPF) and network slice load balancing within a 5G network. Example embodiments include systems and methods for load balancing based on current UPF load and thresholds that depend on UPF capacity; UPF load balancing using predicted throughput of new UE on the network based on network data analytics; UPF load balancing based on special considerations for low latency traffic; UPF load balancing supporting multiple slices, maintaining several load-thresholds for each UPF and each slice depending on the UPF and network slice capacity; and UPF load balancing using predicted central processing unit (CPU) utilization and/or predicted memory utilization of new UE on the network based on network data analytics.
User Plane Function (Upf) Load Balancing Based On Current Upf Load And Thresholds That Depend On Upf Capacity
- Englewood CO, US Kazi Bashir - Lewisville TX, US Ash Khamas - Goffstown NH, US Ashish Bansal - Frisco TX, US Siddhartha Chenumolu - Broadlands VA, US
International Classification:
H04W 28/08 H04W 72/12
Abstract:
Embodiments are directed towards systems and methods for user plane function (UPF) and network slice load balancing within a 5G network. Example embodiments include systems and methods for load balancing based on current UPF load and thresholds that depend on UPF capacity; UPF load balancing using predicted throughput of new UE on the network based on network data analytics; UPF load balancing based on special considerations for low latency traffic; UPF load balancing supporting multiple slices, maintaining several load-thresholds for each UPF and each slice depending on the UPF and network slice capacity; and UPF load balancing using predicted central processing unit (CPU) utilization and/or predicted memory utilization of new UE on the network based on network data analytics.
Method And System For Aggregate Maximum Bit Rate (Ambr) Management
Systems and methods of scheduling bit rates for first-class traffic for a first and second class session for a device; honoring for the device each first class session with the bit rate equal to a session aggregate maximum bit rate (AMBR) by a scheduler by assigning a threshold for an aggregate bit rate for traffic for each first-class session; if a total of the aggregate bit rate of traffic for each first class session is below the threshold assigned, then assigning each first class session the session AMBR for first-class session traffic for the device; if the total of the aggregate bit rate of traffic for each first class session is above the threshold assigned, then proportionality scheduling packets of the traffic for the first class session of the device to limit the aggregate rate of the first-class traffic below the threshold.
State Pooling For Stateful Re-Homing In A Disaggregated Radio Access Network
- Englewood CO, US Ashish Bansal - Frisco TX, US Dhaval Mehta - Aldie VA, US Manjari Asawa - Cupertino CA, US
International Classification:
H04W 40/02
Abstract:
Techniques described herein provide pooling of radio access network (RAN) resources in a disaggregated RAN in a manner that enables dynamic re-homing of RAN functions with minimal disruption to stateful communications. The RAN is a hierarchical arrangement of RAN units, each having an associated state at any given time. A RAN state pooling system (RSPS) is in communication with a pool of the RAN units via a high-speed communication channel. The RSPS is configured to maintain an updated repository of the state information for the RAN units in its serviced pool. In the event of re-homing from a first RAN unit to a second RAN unit in the pool (e.g., due to the first RAN unit failing, becoming overloaded, etc.), the RSPS provides the second RAN unit with the updated state information for the first RAN unit to facilitate a stateful re-homing.
Interfrequency And Inter-Technology Neighbor Planning On A Self-Organizing Network
In an example, a self-organizing network (SON) provides automated interfrequency load balancing for a base station such as a NodeB. The NodeB may provide a plurality of carriers, such as in a plurality of UARFCN frequencies, and the SON may provide configuration directives for increasing efficiency. For example, when one carrier becomes loaded, the SON may update neighbor associations to take advantage of relatively unloaded frequency carriers. A plurality of scenarios S may be provided, and a policy P may be defined for each. When the NodeB encounters a scenario S, SON may send configuration directives to implement policy P. Similar concept and policy could be applied in conjunction with INTER Technology Neighbor Definitions between LTE and UMTS and UMTS and GSM. Example if GSM Frequency Neighbors needs to be replaced with different Frequency Neighbors from UMTS based on Load or RF conditions.
Guru Gobind Singh Indraprastha University - B.Tech
Ashish Bansal
Work:
HCL Technologies - Software Engineer (10-2011)
Education:
Thapar University - BE Computer Sciences
Ashish Bansal
Work:
TCS - ASE (9)
Education:
BVU - Computer Application
Ashish Bansal
Work:
ORACLE India (2011)
Education:
JIET - BTech ECE
Ashish Bansal
Education:
Iipm - Noida
Tagline:
A person who has good thoughts cannot ever be ugly. You can have a wonky nose and a crooked mouth and a double chin and stick-out teeth, but if you have good thoughts they will shine out of your face like sunbeams and you will always look lovely.
Ashish Bansal
Work:
Advocate (2011)
Tagline:
If you avoid DIFFICULTIES you avoid your GROWTH !!!
Ashish Bansal
Work:
Amrit Group - Finance Manager
Education:
ICAI - CA
Ashish Bansal
Education:
Institute of Chartered Accountants of India - Commerce