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1. Physics-inspired heuristics for soft MIMO detection in 5G new radio and beyond

   https://doi.org/10.1145/3447993.3448619  

   Kim, Minsung; Mandrà, Salvatore; Venturelli, Davide; Jamieson, Kyle (January 2021, Proceedings of the 27th Annual International Conference on Mobile Computing and Networking)

   null (Ed.)

   Overcoming the conventional trade-off between throughput and bit error rate (BER) performance, versus computational complexity is a long-term challenge for uplink Multiple-Input Multiple-Output (MIMO) detection in base station design for the cellular 5G New Radio roadmap, as well as in next generation wireless local area networks. In this work, we present ParaMax, a MIMO detector architecture that for the first time brings to bear physics-inspired parallel tempering algorithmic techniques [28, 50, 67] on this class of problems. ParaMax can achieve near optimal maximum-likelihood (ML) throughput performance in the Large MIMO regime, Massive MIMO systems where the base station has additional RF chains, to approach the number of base station antennas, in order to support even more parallel spatial streams. ParaMax is able to achieve a near ML-BER performance up to 160 × 160 and 80 × 80 Large MIMO for low-order modulations such as BPSK and QPSK, respectively, only requiring less than tens of processing elements. With respect to Massive MIMO systems, in 12 × 24 MIMO with 16-QAM at SNR 16 dB, ParaMax achieves 330 Mbits/s near-optimal system throughput with 4--8 processing elements per subcarrier, which is approximately 1.4× throughput than linear detector-based Massive MIMO systems. 

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2. Benchmarking Chain Strength: An Optimal Approach for Quantum Annealing

   https://doi.org/10.1109/QCE57702.2023.00052  

   Le, Thinh V.; Nguyen, Manh V.; Nguyen, Tu N.; Dinh, Thang N.; Djordjevic, Ivan; Zhang, Zhi-Li (September 2023, 2023 IEEE International Conference on Quantum Computing and Engineering (QCE))

   Quantum annealing (QA) is a promising optimization technique used to find global optimal solution of a combinatorial optimization problem by leveraging quantum fluctuations. In QA, the problem being solved is mapped onto the quantum processing unit (QPU) composed of qubits through a procedure called minor-embedding. The qubits are connected by a network of couplers, which determine the strength of the interactions between the qubits. The strength of the couplers that connect qubits within a chain is often referred to as the chain strength. The appropriate balance of chain strength is equally imperative in enabling the qubits to interact with one another in a way that is strong enough to obtain the optimal solution, but not excessively strong so as not to bias the original problem terms. To this end, we address the problem of identifying the optimal chain strength through the utilization of Path Integral Monte Carlo (PIMC) quantum simulation algorithm. The results indicate that our judicious choice of chain strength parameter facilitates enhancements in quantum annealer performance and solution quality, thereby paving the way for QA to compete with, or potentially outperform, classical optimization algorithms. 

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3. Quantum Annealing for Large MIMO Downlink Vector Perturbation Precoding

   https://doi.org/10.1109/ICC42927.2021.9500557  

   Kasi, Srikar; Singh, Abhishek Kumar; Venturelli, Davide; Jamieson, Kyle (June 2021, IEEE International Conference on Communications)

   null (Ed.)

   In a multi-user system with multiple antennas at the base station, precoding techniques in the downlink broadcast channel allow users to detect their respective data in a non-cooperative manner. Vector Perturbation Precoding (VPP) is a non-linear variant of transmit-side channel inversion that perturbs user data to achieve full diversity order. While promising, finding an optimal perturbation in VPP is known to be an NP-hard problem, demanding heavy computational support at the base station and limiting the feasibility of the approach to small MIMO systems. This work proposes a radically different processing architecture for the downlink VPP problem, one based on Quantum Annealing (QA), to enable the applicability of VPP to large MIMO systems. Our design reduces VPP to a quadratic polynomial form amenable to QA, then refines the problem coefficients to mitigate the adverse effects of QA hardware noise. We evaluate our proposed QA based VPP (QAVP) technique on a real Quantum Annealing device over a variety of design and machine parameter settings. With existing hardware, QAVP can achieve a BER of 10 −4 with 100µs compute time, for a 6 × 6 MIMO system using 64 QAM modulation at 32 dB SNR. 

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4. Towards quantum belief propagation for LDPC decoding in wireless networks

   https://doi.org/10.1145/3372224.3419207  

   Kasi, Srikar; Jamieson, Kyle (September 2020, Proceedings of the 26th Annual International Conference on Mobile Computing and Networking (ACM MobiCom))

   We present Quantum Belief Propagation (QBP), a Quantum Annealing (QA) based decoder design for Low Density Parity Check (LDPC) error control codes, which have found many useful applications in Wi-Fi, satellite communications, mobile cellular systems, and data storage systems. QBP reduces the LDPC decoding to a discrete optimization problem, then embeds that reduced design onto quantum annealing hardware. QBP's embedding design can support LDPC codes of block length up to 420 bits on real state-of-the-art QA hardware with 2,048 qubits. We evaluate performance on real quantum annealer hardware, performing sensitivity analyses on a variety of parameter settings. Our design achieves a bit error rate of 10--8 in 20 μs and a 1,500 byte frame error rate of 10--6 in 50 μs at SNR 9 dB over a Gaussian noise wireless channel. Further experiments measure performance over real-world wireless channels, requiring 30 μs to achieve a 1,500 byte 99.99% frame delivery rate at SNR 15-20 dB. QBP achieves a performance improvement over an FPGA based soft belief propagation LDPC decoder, by reaching a bit error rate of 10--8 and a frame error rate of 10--6 at an SNR 2.5--3.5 dB lower. In terms of limitations, QBP currently cannot realize practical protocol-sized (e.g., Wi-Fi, WiMax) LDPC codes on current QA processors. Our further studies in this work present future cost, throughput, and QA hardware trend considerations. 

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5. A Quantum Annealer-Enabled Decoder and Hardware Topology for NextG Wireless Polar Codes

   https://doi.org/10.1109/TWC.2023.3311204  

   Kasi, Srikar; Kaewell, John; Jamieson, Kyle (January 2023, IEEE Transactions on Wireless Communications)

   We present the Hybrid Polar Decoder (HyPD), a hybrid classical-quantum decoder design for Polar error correction codes, which are becoming widespread in today’s 5G and tomorrow’s 6G networks. HyPD employs CMOS processing for the Polar decoder’s binary tree traversal, and Quantum Annealing (QA) processing for the Quantum Polar Decoder (QPD)-a Maximum-Likelihood QA-based Polar decoder submodule. QPD’s design efficiently transforms a Polar decoder into a quadratic polynomial optimization form, then maps this polynomial on to the physical QA hardware via QPD-MAP, a customized problem mapping scheme tailored to QPD. We have experimentally evaluated HyPD on a state-of-the-art QA device with 5,627 qubits, for 5G-NR Polar codes with block length of 1,024 bits, in Rayleigh fading channels. Our results show that HyPD outperforms Successive Cancellation List decoders of list size eight by half an order of bit error rate magnitude, and achieves a 1,500-bytes frame delivery rate of 99.1%, at 1 dB signal-to-noise ratio. Further studies present QA compute time considerations. We also propose QPD-HW, a novel QA hardware topology tailored for the task of decoding Polar codes. QPD-HW is sparse, flexible to code rate and block length, and may be of potential interest to the designers of tomorrow’s 6G wireless networks. 

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