Project Title: Quantum and Quantum-Inspired Computing for Large-Scale NOMA-MIMO Wireless Networks

WINLAB Summer Internship 2025
Group Members: Alexander Markley^GR, Jeffrey Tang^UG
Advisors: Minsung Kim, Byungjun Kim

Project Objective

The project will explore non-traditional computing methods for "Non-Orthogonal Medium Access-based Multiple-Input Multiple-Output" (NOMA-MIMO) wireless systems. MIMO and NOMA are among the most promising techniques to increase wireless capacity by scaling up the number of serviced devices at a time. However, to do so, they require much more computationally demanding processing at the receiver. A proposed solution is to reduce MIMO Maximum Likelihood Detection (MLD) to Quadratic Unconstrained Binary Optimization(QUBO), which resembles a Hamiltonian. We, then, convert QUBO into the Ising form under the Ising model, and use an Ising solver for the best Ising configurations. Finally, the best candidate will be mapped to MIMO Detected Bits. This framework is called "ParaMax".

This project aims to construct a hardware implementation of ParaMax, using the Orbit MIMO racks as receivers and the overhead nodes as transmitters. In addition, we are comparing the Bit-Error-Rate (BER) of ParaMax to other conventional MIMO detectors and another experimental parallel probabilistic MIMO detector, FlexCore.

Weekly Progress

WEEK ONE

​Week 1 NOMA-MIMO Presentation

Progress:

• Familiarize ourselves with OrbitLab, ParaMax: quantum-inspired algorithm using simulated annealing and parallel tempering for MIMO ML detection, and other state-of-the-art quantum approaches for NOMA-MIMO

WEEK TWO

​Week 2 NOMA-MIMO Presentation

Progress:

• Completed tutorials for GNURadio, USRP2, X310; Reviewed wireless packet detection and synchronization; Reviewed ParaMax architecture

WEEK THREE

​Week 3 NOMA-MIMO Presentation

Progress:

• Obtained access to the Grid; Successful SISO transmission from nodes to MIMO rack

WEEK FOUR

​Week 4 NOMA-MIMO Presentation

Progress:

• Began looking at Packet Carrier Frequency Offset(CFO) correction and Channel Estimation in MATLAB; Investigating UHD integration and construction of GNURadio Out-Of-Tree (OOT) custom C++/Python blocks

WEEK FIVE

​Week 5 NOMA-MIMO Presentation

Progress:

• Ran into difficulties with Packet CFO correction, achieved a 37.5-50% channel estimation. Also, due to the ceiling nodes having no central node for synchronization, we were faced with distributed systems communication issues. This led to a shift from MIMO rack to MIMO rack transmission and receiving. We began investigating FlexCore and Multisphere detection as alternative classical parallelized detectors.

WEEK SIX

​Week 6 NOMA-MIMO Presentation

Progress:

• Began software-based implementation of FlexCore and continued GNURadio setup verification for MIMO rack to MIMO rack transmission

WEEK SEVEN

​Week 7 NOMA-MIMO Presentation

Progress:

• Adjusted settings for software testing of FlexCore and other conventional MIMO detectors (ZF, MMSE, FCSD, ML)
• Verifying time synchronization on the transmitter nodes

WEEK EIGHT

​Week 8 NOMA-MIMO Presentation

Progress:

• Ran into performance issues for FlexCore (not reaching near-ML performance), so we started unit testing on the FlexCore methods to ensure correctness and consistency with the details of the FlexCore paper
• Ran into issues with time synchronization, mainly consistency among multiple runs of data verification. We stopped using GNURadio due to a lack of PTP support and are looking into the C++ UHD API

WEEK NINE

​Week 9 NOMA-MIMO Presentation

Progress:

• Got 2x1 MISO to work for C++ UHD implementation
• FlexCore implementation is able to reach near-ML performance, but not fast enough (We expect FlexCore to reach FCSD performance at N_{pe} = 16, but we are obtaining it at N_{pe} = 32)
• Reading into g-MultiSphere to look at existing results on parallel MIMO detectors in NOMA-MIMO scenarios

WEEK TEN

​NOMA-MIMO Final Presentation

Summary:

• Get a 1x1 SISO to transmit time-synchronized
• Get a 2x1 MISO to transmit time-synchronized
• Successful CFO (Carrier Frequency Offset) Correction, Channel Estimation on the receiver
• Successful offline MIMO detection for conventional MIMO detectors and ParaMax
• Near theoretical BER performance of FlexCore

Acknowledgements

We want to thank Minsung Kim and Byungjun Kim for their guidance throughout the summer. We also thank Jennifer Shane, Ivan Seskar, and the WINLAB faculty and staff for their support.

References

1) Minsung Kim, Salvatore Mandrà, Davide Venturelli, and Kyle Jamieson. "Physics-inspired heuristics for soft MIMO detection in 5G new radio and beyond." In Proceedings of the 27th Annual International Conference on Mobile Computing and Networking (MobiCom '21), pages 42–55. Association for Computing Machinery, 2021.
2) Christopher Husmann, Georgios Georgis, Konstantinos Nikitopoulos, and Kyle Jamieson. "FlexCore: Massively Parallel and Flexible Processing for Large MIMO Access Points." In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI '17), pages 197–211. USENIX Association, March 2017.
3) Chathura Jayawardena and Konstantinos Nikitopoulos, “G-MultiSphere: Generalizing Massively Parallel Detection for Non-Orthogonal Signal Transmissions,” IEEE Transactions on Communications, vol. 68, no. 2, pp. 1227–1239, Feb. 2020.