Massive multiple-input multiple-output (M-MIMO) architecture is the workhorse of modern communication systems. Currently, two fundamental bottlenecks, namely, power consumption and receiver saturation, limit the full potential achievement of this technology. These bottlenecks are intricately linked with the analog-to-digital converter (ADC) used in each radio frequency (RF) chain. The power consumption in MIMO systems grows exponentially with the ADC's bit budget while ADC saturation causes permanent loss of information. This motivates the need for a solution that can simultaneously tackle the above-mentioned bottlenecks while offering advantages over existing alternatives such as low-resolution ADCs. Taking a radically different approach to this problem, we propose $\lambda$-MIMO architecture which uses modulo ADCs ($M_\lambda$-ADC) instead of a conventional ADC. Our work is inspired by the Unlimited Sampling Framework. $M_\lambda$-ADC in the RF chain folds high dynamic range signals into low dynamic range modulo samples, thus alleviating the ADC saturation problem. At the same time, digitization of modulo signal results in high resolution quantization. In the novel $\lambda$-MIMO context, we discuss baseband signal reconstruction, detection and uplink achievable sum-rate performance. The key takeaways of our work include, (a) leveraging higher signal-to-quantization noise ratio (SQNR), (b) detection and average uplink sum-rate performances comparable to a conventional, infinite-resolution ADC when using a $1$-$2$ bit $M_\lambda$-ADC. This enables higher order modulation schemes e.g. $1024$ QAM that seemed previously impossible, (c) superior trade-off between energy efficiency and bit budget, thus resulting in higher power efficiency. Numerical simulations and modulo ADC based hardware experiments corroborate our theory and reinforce the clear benefits of $\lambda$-MIMO approach.
翻译:M-MIMO(M-MIMO)结构是现代通信系统的工作马匹。目前,有两个基本瓶颈,即电耗和接收机机的饱和率,限制了这一技术的完全潜在成就。这些瓶颈与每个无线电频率链中使用的模拟数字转换器(ADC)有着错综复杂的联系。MIMO系统中的电力消耗随着ADC的比特预算而成倍增长,而ADC饱和度则造成信息永久损失。这促使需要找到一种解决方案,既能同时解决上述瓶颈,同时又能为低分辨率ADC等现有替代品提供优势。对该问题采取完全不同的方法,我们建议用Mulda-数字转换器(ADC)而不是常规ADC的模拟转换器。我们的工作受到“无限制的炼油框架”的启发。 $M&LAM-ADC(ADC)方法将高动态范围信号转换成低动态范围样本,从而缓解了ADC的比值数据机率升级的比值 。我们提议用美元-MON-M(A-MER-M)预算的高级检测结果。