PAUL TILGHMANIt’s Really Big • 25.6 GHz total instantaneous bandwidth • 100MHz per channel x...

PAUL

PROGRAM MANAGER

TILGHMAN

DARPA/MTO

DISTRIBUTION A. Approved for public release: distribution unlimited.

The world’s first collaborative machine-intelligence competition to overcome spectrum scarcity.

DISTRIBUTION A. Approved for public release: distribution unlimited. 2

ERI Summit July 23-25 2018

Spectrum Collaboration ChallengeImplications for next-generation electronics

ORIGINS OF THE SPECTRUM GRAND CHALLENGE… COEXISTENCE

2030

1000 EB/mo

1980 2016

7 EB/mo

https://en.wikipedia.org/wiki/1899_Americahttp://www.marconicalling.com/

1926 - Today1899 - 1900

Centralized Human Decision Making

Pushing spectrum intelligence to the edge

Radios smart enough to collaboratively manage the entire ecosystem

Coexistence is the original, and enduring, Spectrum Grand Challenge


THE GAME:

• Five teams need to move data through a spectrum obstacle course

• The teams themselves are obstacles

• Spectrum obstacles such as incumbents and jammers increase difficulty

THE OBJECTIVE:

• The ensemble must devise a strategy maximize number of objectives met

• The faster an ensemble completes each objective the better their scored time

Incumbent

Team 1 Team 2 Team 3 Team 4 Team 5

collaborate

Collaborative Intelligent Radio Network Language Required OutcomesTeam 110 VOIP links

Team 2UAV surveillance

Team 3Sat. imagery

Team 4GPS Positions

Team 5Helmet Cam Video

ALLProtect Incumbent


SC2 COLLABORATIVE COMPETITION

It’s Really Big• 25.6 GHz total instantaneous bandwidth• 100MHz per channel x 256 x 256 channels• 420 Tb/s of digital RF data• 1.88 TB of scenario model data (30min)

Comprised of• 128 USRP X310• 16 ATCA-3671 hosting 64 FPGAs

Specifications• 128 2x2 MIMO Tx/Rx Ports• Phase Coherent• Bandwidth : 80 MHz BW• Tunable: 10 MHz to 6GHz• 4 tap PDP emulation (10ns resolution, 5us max delay, 1000Hz updates)


COLOSSEUM: THE WORLD’S LARGEST RF EMULATOR… THE ENVIRONMENT FOR ENSEMBLE SPECTRUM AI

Poor Group of 3 Better Group of 3 Best Group of 32014 competition: 3 Radios

2017 competition: 3 Networks, 15 Radios 2014• No collaboration• Best strategy was to constantly sense and avoid

2017• With collaboration• Networks discovered stable sharing arrangements• Higher spectrum utilization, less overhead


EARLY EVIDENCE OF COLLABORATIVE INTELLIGENT RADIO NETWORK (CIRN) VIABILITY


EARLY EVIDENCE OF COLLABORATIVE INTELLIGENT RADIO NETWORK (CIRN) VIABILITY

©Intel

©NVIDIA

©Xilinx

Standard Radio Node

Physical

Medium Access Control

Network

Radio Stack

SoftwareFirmware

AI Sensing & Control

Software

Collaborative Intelligent Radios (CIRNs) combine two computationally demanding disciplines: Software Defined Radio & Artificial Intelligence


COMPETITORS BUILD SOFTWARE & FIRMWARE, NOT HARDWARE

• No one-size-fits-all processing resource to meet needs… heterogeneous computing• No viable heterogeneous programming paradigm exists… manual decomposition of

design• Using FPGA is an “investment” – Very few teams made use of the FPGA in Phase 1

• Heterogeneous computation = latency in data movement• Tradeoff between real-time constraints and development time

Deterministic Timing

Programming Ease

Low Latency SIMD Best for

FPGA ✓ ✓ ✓- Real-time DSP

GPP ✓ Slow-time (seconds) control

GPU ✓ ✓ Offline DSP, AI classification / decision making


ELECTRONICS CHALLENGES FOR CIRN TECHNOLOGY

https://www.mathworks.com/

https://www.matroid.com/blog/post/the-hard-thing-about-deep-learning

https://www.popularmechanics.com/technology/a19863/googles-alphago-ai-wins-second-game-go/

vs


SC2 ROADMAP

SpectrumCollaborationChallenge.com

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GREGORY WRIGHT, NOKIA BELL LABS

The Mind-body problem in INTELLIGENT radio NETWORKS


Challenges to Realizing the Vision of Collaborative Intelligent Radio Networks


The “Mind-Body” problem of intelligent radio networks:We not only need to solve the problem of inferring, understanding and predicting the behavior of other radio networks, but also need to sense the radio environment accurately and swiftly control response to it.

Beyond this, need to understand whether there are general principles governing interacting radio networks. Examples:• Can we guarantee that a stable equilibrium is obtained, with no network

being starved of resources? • What is the minimum amount of information that a radio node needs to send

to signal its intentions? • Can physical level coherence be learned?

Learning the radio environment

What we have learned so far: Interacting agents can learn how to communicate over noisy channels


LEARNING the radio environment

Initial distribution

Gray coded 16-QAM

Separation after lowering variance

Splitting into pairs with minimal Hamming

distance


Decentralized Reinforcement Learning

Agents share a fixed-length preamble b. Transmitters are parameterized by neural network p(a|x). Each agent takes turns transmitting b to the other, receiving an echoed version back and using a policy gradient algorithm. Rewards are computed in a decentralized manner.

← Next timestep: switch roles →Variational Transmitter Architecture

Discovers (within 2dB) modulation in under 100ms with 20MHz channel: How can we extend this idea to other radio network parameters that we need to learn?


The Spirit is Willing but the Flesh is Weak

We can do a lot in principle -- but in practice, we are bogged down with implementation challenges

We are working in parallel on our idea of deep-network-defined radio (DNDR), the principles of online learning of resource allocation strategies with learned control strategies and learned information sharing, and trying to figure out robust ways of implementing all of this without a nightmare of programming and hours-long-compiles.

We need better analog radios

• We need more dynamic range. The SC2 Colosseum radios have 14 bit data convertors; commercial LTE systems typically use 15 bits plus cooperative power control (and allow no in-band interferers!) to meet less ambitious goals. To replace analog filters with digital ones we need at least another 5 to 6 bits of resolution.

• If we could make tunable, highly linear filters, we wouldn’t need such good ADCs. But we don’t know how.

• We believe there are a few promising approaches but we are not close to an answer.



To build a digital radio that can implement our vision, we need fast, flexible signal processing combined with massive interconnection bandwidth.

• General purpose CPUs are easy to program but are limited in the RF bandwidth and data throughput they can handle. Furthermore, high throughput may increase latency, restricting ability to handle rapidly varying channels. For example, our mostly software SC2 radio can only handle a quarter of the potentially usable bandwidth. And software implementations of WiFifalter on the strict latency requirements for acknowledgement signals.

• If we had our choice, we’d be using specialized communication SoC ICs combining programmable processors and hardware accelerators. But these have also been disappointing: We always run out of interconnection bandwidth long before the compute engines saturate.

• FPGAs ought to have enough compute power and interconnect bandwidth, but the design process is rightly feared. The lack of progress here is a black mark against our profession.



Examples:

General purpose CPU: WiFi requires responding to a frame with a acknowledgement within 10 or 16 μs (Short Interframe Space, or SIFS, specification). Measured on our SC2 computer clone, the network stack alone has about 15 μs latency. In this situation there is no time for doing any WiFi signal processing.

Specialized Programmable System-on-Chip: In an commercial OFDM-like system, when the channel was rapidly changing, allocating data to subcarriers (“scheduling”) would starve processing resources from the physical layer. Plenty of processing power was available, but the cache memories in the “network on chip” began to thrash, exposing the latency of the DRAM.

Measured data transfer throughput on SC2 computer



Chart courtesy of Chris Dick, Xilinx, Inc.

FPGAs: Raw performance is enough for challenging communication systems (approaching 1012 multiply-accumulates per second in last generation devices), and most importantly, we can usually get guaranteed worst-case behavior.

The cost of this guarantee is a much more complex design process, since we have to address running functional blocks in parallel and timing closure much earlier. Tools have improved but not as fast as our ambitions.


PAUL TILGHMANIt’s Really Big • 25.6 GHz total instantaneous bandwidth • 100MHz per channel x...

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