RINGS: Intelligent and Resilient Virtualization of Massive MIMO Physical Layer

Yale University, New Haven CT

Investigators

Abstract

NextG network systems rely on virtualization to move away from specialized, dedicated equipment to cloud and edge datacenters, to reduce cost and accelerate innovation. So far such virtualization efforts have met with very limited success, making inroads largely with 4G/LTE small cells. This is because 5G and beyond employ compute-intensive technologies such as massive multiple-input, multiple output (MIMO) and low-density parity-check (LDPC) code to deliver the unprecedented network performance. Massive MIMO not only demands massive computational power itself, but also proportionally increases that of LDPC. Not surprisingly, existing commercial massive MIMO solutions all rely on specialized, dedicated hardware such as FPGA and application-specific integrated circuits. Massive MIMO remains the largest barrier toward virtualized mobile networks. The goal of the proposed project is to overcome this technical barrier and virtualize the massive MIMO physical layer for NextG network systems. In doing so, the project will expedite the adoption of massive MIMO, resulting in more capable, more efficient, and more cost-effective mobile networks. Through our ongoing collaborations with industry leaders, the project will timely transfer technologies into practice. It also provides a meeting ground for software systems and wireless communication research and creates timely content for teaching Computer Science majors about the wireless physical layer. The project will provide a platform to engage undergraduate students and high-school students in computing research, especially women and underrepresented minorities. The project targets the following scientific contributions. (1) Design and implementation of the massive MIMO physical layer that scales up on a many-core server efficiently and intelligently. We will combine the efficiency of static and elasticity of dynamic task scheduling and devise latency-driven, automated schemes for optimal resource provisioning. (2) Distributed design and implementation that can utilize compute resources integrated over a local-area network, beyond a single server. We will leverage programmable switches to minimize the impact of the network and to utilize commodity servers as well as accelerators to achieve scale, cost effectiveness and energy efficiency beyond the reach of a single many-core server. (3) Elastic design and implementation that match up to dynamics in mobile network workload, simultaneously achieving high resilience, low cost for the network operator, and high utilization for the cloud provider, in a multi-tenant environment. We will explore serverless computing and develop it further to better meet the stringent latency requirement of the massive MIMO physical layer. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.