Cognitive Radio Networks

Transcription

1 Cognitive Radio Networks Parag Naik & Anindya Saha The SDR Company

2 Current Network Challenges Standards churn Frequent network upgrades Major 3GPP LTE release every 18 months Higher investment and lower ROI Cycles of monetizing Telecom infrastructure are shrinking due to fast pace of standardization. Spectrum Scarcity Static Allocation of Spectrum Spectrally inefficient schemes More licensed bands : Co existence Issues and fragmented bands Networks designed for cell edge performance Static nature of cell boundaries/edge definitions Cannot take proactive measures to minimize interference without sacrificing bandwidth. Wide Dynamic Range of Traffic Tweets to Videos Wide ranging applications and data requirements 2

3 Architectural limitations of Current RANs Static provisioning of Networks Networks designed for cell edge penalizing average case Longer than necessary GI s, Frame designs for worst case Doppler and fading and interference 80% of users are away from the edge Upto 15% of spectrum wasted for the average case Virtualization of implementation not interface X Ran style initiative only address Base station => Open COTs hardware only for enodeb Complex Custom hardware for handsets Expensive design and certification cycles

4 Architectural limitations of Current Radios Lack of Digitization at the edge and physical Layer Lack of Visible/Observability Metrics No avenues of exploiting Machine Learning at RAN layer 1 & 2 Closed modem architectures throttle innovation Bring SDN to layer 1 Need for a radically new innovative, dynamic and open architecture for RAN

5 RAN of the future Cognitive RAN with the ability to auto tune Ability to take local optimal decisions based on a boot strap or a template Move 3GPP to the cloud! Virtualize the interface between the UE and the Network Two models for virtualization Static OpenGL like API that is done for Graphics OR Definition of Java Like VM for Radio Interface Hardware now implements the Virtual Machine or the API Every g defines a new VM Open architectures 3GPP defines the VM, not a modulation standard Linux like lightly licensed model ending domination of a few players Digitized architectures amenable for ML algorithms on COTS hardware

6 Saankhya Cognitive RAN Learns constantly and updates real time Senses current environment, Learns from past data Hyper personalized waveforms for devices that enter the network Optimal performance, all the time for all users Design modulation schemes on the fly based on stated cost functions Applies Deep Learning to figure the optimal use of resources Virtualize the interfaces between components of the RAN Virtual Machine on the lines of Java /OpenGL for each end point in the network The VM or RTS can be static or dynamic New open architecture Use COTS hardware that implement the Virtual Machines Radio Adaptation Environ Transition to new operating ment parameters Sensing Realtime wideband monitoring Reasoning Determine best response strategy R c e 6

7 Components of a Cognitive RAN Design PHY and MAC on the fly Based on current condition and capabilities of network end points Optimize a pre set cost function ( spectrum usage or low power for IoT ) Optimization search space not limited to existing standards Option to map to nearest operational standard/modulation Allows Real time upgrade Code update, Not just parameter update For Base station and UE Big Data Cloud Compute Server Device Capability Model Collect data, sense, analyze, design Big Data Analytics, Optimization Capability description by Java like virtual machine Standardize on the Virtual Machine, not the modulation scheme! Flexible ASSP and Accelerators to support PHY primitives Allows code download and execution Virtual Machine Interface (VM) Flexible PHY Baseband RF Platfom 1 Virtual Machine Interface (VM) Flexible PHY Baseband RF platform 2 Virtual Machine Interface (VM) Flexible PHY Baseband RF platform 3 Configurable Wideband RF Programmable SDR Baseband or a ASIC implementing the VM Modulation switching: OFDM, Single Carrier, Custom Modulation 7

8 Dynamic Design of PHY MAC Layers Device Capabilities OPTIMIZATION COGNITIVE RADIO Bytecode DYNAMIC repository DESIGN of PHY MAC

9 Market Enablers Ride Moore s law 28nm and 14nm silicon enables complex software defined modems Advances in Big Data Analytics already used by operators Costs of managing and deploying edge networks SON 2.0! Spectrum scarcity and traffic explosion With IoT Billions of devices getting on the network Spectrum needs vary, from a few kilobits to broadband Single scalable platform that detects and provisions nodes on the fly

10 Benefits to the Operators Spectrum Efficiency Spectrum is finite and needs to be fairly allocated Scarcity due to static allocation All else same dynamic technique improves spectral efficiency 15 20% Network Usage Model TDM vs concurrent use Operate in interference dominated regimes Greedy secondary user could swamp the network Cost Benefits Standardized open modem architecture reduces the cost of managing and deploying edge networks and UE Certification costs almost nil Second order benefits : Enables more OEMs to design modems => More innovation on the lines of SDN Scalability With 5G and IoT, Billions of devices getting on the network Spectrum needs vary, from a few kilobits to broadband Single scalable platform that detects and provisions nodes on the fly

11 Thank You Contact: