Analog computational structures are evolving, inspired by quantum compute
We are investigating the use of analog computational structures inspired by quantum computation, in particular Parallel Tempering methods, the Coherent Ising Machine, Optical Ising Machine, to solve computationally hard problems in wireless networks.
In this paper, we explore an enhanced CIM model, and propose a novel Ising formulation, which together are shown to be the first Ising solver that provides significant gains in the BER performance of large and massive MIMO systems, like 16x16 and 16x32, and sustain its performance gain even at 256-QAM modulation. We further perform a spectral efficiency analysis and show that, for a 16x16 MIMO with Adaptive Modulation and Coding, our method can provide substantial throughput gains over MMSE, achieving 2x throughput for SNR <=25 dB, and up to 1.5x throughput for SNR >= 30 dB.
Optimal MIMO detection is one of the most computationally challenging tasks in wireless systems. We show that the quantum-inspired computing approach based on Coherent Ising Machines~(CIMs) is a promising candidate for performing near-optimal MIMO detection. We propose a novel regularized Ising formulation for MIMO detection that mitigates a common error floor issue in the direct approach adopted in the existing literature on MIMO detection using Quantum Annealing. We evaluate our methods using a simplified, quantum-inspired model and show that our methods can achieve a near-optimal performance for several Large MIMO systems, like 16x16, 20x20, and 24x24 MIMO with BPSK modulation.
Optimal MIMO detection is one of the most computationally challenging tasks in wireless systems. We show that new analog computing approaches, such as Coherent Ising Machines (CIMs), are promising candidates for performing near-optimal MIMO detection. We propose a novel regularized Ising formulation for MIMO detection that mitigates a common error floor issue in the naive approach and evolve it into a regularized, Ising-based tree search algorithm that achieves near-optimal performance. By means of numerical simulation using the Rayleigh fading channel model, we show that in principle, a MIMO detector based on a high-speed Ising machine (such as a CIM implementation optimized for latency) would allow a higher transmitter antennas (users)-to-receiver antennas ratio and thus increase the overall throughput of the cell by a factor of two or more for massive MIMO systems. Our methods create an opportunity to operate wireless systems using more aggressive modulation and coding schemes and hence achieve high spectral efficiency: for a 16×16 MIMO system, we estimate around 2.5× more throughput in the mid-SNR regime (≈12 dB) and 2× more throughput in the high-SNR regime (>20 dB) as compared to the industry standard, a Minimum-Mean Square Error (MMSE) linear decoder.
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