Konstantinos Nikitopoulos Enabling Extreme Resource Sharing in Future Wireless Communication Systems Konstantinos Nikitopoulos 5G INNOVATION CENTRE

Research Background Signal Processing for Wireless Communication Systems Target: Pragmatic, Energy/Latency Efficient Communication Systems able to both scale achievable throughput support and numbers of connected devices. Focus: both on theoretical aspects and the “implementability” of promising concepts and theoretical ideas and as well as on their “coherent integration” “In theory, theory and practice are the same. In practice, they are not.” Albert Einstein

Research Approach Identification of (new) performance bottlenecks High level “Proof of Concept” using research platforms (WARPs, USRPs, etc) Validation on testbeds Development of algorithmic solutions at a functional level Development and testing of “Hardware-Based” Algorithmic Solutions

The need for Extreme Resource Sharing According to CISCOs Global Mobile Data Traffic Forecast Almost half a billion (429 million) mobile devices and connections were added in 2016 Globally, there were 325 million wearable devices (a sub-segment of the machine-to-machine [M2M] category) in 2016 There will be 11.6 billion mobile-connected devices by 2021, including M2M modules—exceeding the world’s projected population at that time (7.8 billion) We need to support these devices despite the actual limited “resources”

New Research Questions How can we efficiently scale capacity with the number of users? How can we efficiently translate this capacity into actual throughput? How can we efficiently and fairly distribute this capacity to a massive number of users? 5

MIMO Spatial Multiplexing for Scaling Capacity Challenge: “Efficiently” demultiplex the mutually (and intentionally) interfering information streams Linear Detectors (e.g., ZF, MMSE) are low latency and complexity but highly suboptimal Maximum-Likelihood (ML) Detection maximizes throughput but it is highly complex ~4.2x109 possible combinations for a 16-QAM modulated 8x8 MIMO system! This is a fundamental problem that has been solved for sparse interference matrices (e.g., in channel decoding) Photo credit: www.cultofmac.com 6

The Need for Parallelization CPU speed has plateaued [Danowitz, ACM Queue ’10] Massively Parallel PHY Algorithms are required Transistors to stop shrinking by 2021 106 1012 100 Far beyond the capabilities of traditional processors Final International Technology Roadmap for Semiconductors report Simulation on channel traces at 21.5 dB; 20MHZ (256 subcarriers); 64-QAM

A Parallelization Attempt We could achieve minimum latency by performing the Euclidean distance calculations for each x on a separate PE in parallel. 1010 1020 100 1 PEs 2 PEs 8 PEs 37 PEs 6 8 10 12 Simulation on channel traces at 21.5 dB; 20MHZ (256 subcarriers); 64-QAM C. Husmann, G. Georgis, K. Nikitopoulos and K. Jamieson, “FlexCore: Massively Parallel and Flexible Processing for Large MIMO Systems” NSDI 2017

The “Signalling” Barrier In a Random Access Channel (RACH) as used in LTE/LTE-A, to transmit 100 bytes of data from a user to the Base Station (BS), the signaling procedure requires approximately 59 and 136 bytes of overhead in the uplink and downlink, respectively. Challenge: Substantially reduce access overhead, while increasing transmission reliability and security Potential Solution: Joint, Grant-Free, Medium Access and Data Transmission (Joint coding of header and payload to reduce the overhead and delay related to signaling)

Non-Decoded Packet Combination and Rateless Coding for High-Reliability Header Payload ID of the device New Challenge: The “signature” information of the transmitted packet should be reliably identified, even if the payload information is not successfully decoded order of the packet For short packets the header overhead can be significant Even if data is not decoded but the signature information is, the received decoded signals can be efficiently combined (e.g., in a rateless manner)

Joint Medium Access and Data Transmission with Space-Time Super Modulation No headers are needed for transmitting the ID of a user (i.e., the signature packet) Higher throughput and higher transmission reliability can be achieved K. Nikitopoulos, F. Mehran and H. Jafarkhani, “Space-Time Super-Modulation and its Application to Joint Medium Access and Rateless Transmission,” IEEE Globecom 2016 K. Nikitopoulos, F. Mehran and H. Jafarkhani, “Space-Time Super-Modulation: Concept, Design Rules, and its Application to Joint Medium Access and Rateless Transmission,” under revision IEEE Trans. on Wireless Communications

Space-Time Super Modulation Evaluation Significant throughput gains compared to systems with headers Conventionally Modulated Bits (CMB) Super Modulated Bits (SMB) One low-rate and highly reliable channel can be transmitted together with the traditional space-time encoded information (STSM, with rateless coding and L=200, and 9 ID bits) STSM can provide nearly optimal rates

Conclusions The need for Massive Connectivity introduces new challenges as well as new opportunities in the field of PHY design design, including: PHY layer massively parallel algorithmic architectures appropriate for multi-core implementation architectures Detection/Decoding algorithms for non-orthogonal multiple access techniques New PHY approaches that can enable and grant-free multiple access with high reliability