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

2. 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

3. 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

4. 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”

5. 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

6. 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: 6

7. 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

8. 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

9. 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)

10. 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)

11. 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

12. 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

13. 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