Optimal MIMO detection has been one of the most challenging and computationally inefficient tasks in wireless systems. We show that the new analog computing techniques like Coherent Ising Machines (CIM) and Oscillator-based Ising Machines (OIM) are promising candidates for performing near-optimal MIMO detection. We illustrate a fundamental problem with using classical, optical or quantum mechanical Ising Machines for MIMO detection: the error floor problem, which is a major bottleneck to practical deployments of Ising machine-based MIMO detectors. We propose a novel regularized Ising formulation for MIMO detection that mitigates the error floor and further evolves it into an algorithm that achieves near-optimal MIMO detection. Massive MIMO systems, that have a much larger number of antennas at the Access point (AP) than at the users, allow linear detectors to be near-optimal. However, the simplified detection in these systems comes at the cost of overall throughput, which can be improved by supporting more users. We show that our methods allow us to add more transmitter antennas/users and increase the overall throughput of the cell by several folds. We further show that our methods allow us to operate using more aggressive modulation and coding schemes and hence achieve much higher throughput. We demonstrate that, for a $16\times16$ large MIMO system, our methods achieve around 2.5$\times$ more throughput in mid-SNR regime ($\approx 12 dB$) and 2$\times$ more throughput in high-SNR regime( $>$ 20dB) than MMSE.
翻译:最优化的MIMO检测是无线系统中最具挑战性和计算效率最低的任务之一。 我们表明,Coherent Ising 机器(CIM)和Oscilator Ising 机器(OIM)等新型模拟计算技术是有望进行接近最佳的IMIMO检测的候选技术。 我们举例说明了使用古典、光学或量子机械Ising 机器进行MIMO检测的根本问题:错误地面问题,这是在实际部署以Ising 机器为基础的MIMO探测器时遇到的一个重大瓶颈。 我们提议了一种新型的IMO检测常规化配方,以缓解错误的地板,并进一步演变成一种能够实现接近最佳的MIMIMO检测的算法。 大规模MIMO系统在接入点(AP)的天线性天线比用户多得多,使得线性探测器接近最佳。 然而,这些系统中简化的检测成本是美元的总体通量,可以通过支持更多的用户来改进。 我们指出,我们的方法可以进一步增加IMIMO的发送器/用户数量,并进一步将其演变成一种算法。 我们通过12MIMO的深度的系统,从而实现一个更高级的操作。