A Spatial Array for Spectrally Agile Wireless Processing

TL;DR

This paper introduces a reconfigurable spatial array architecture optimized for wireless processing tasks, demonstrating potential to match specialized cores in efficiency and enabling scalable, agile next-generation wireless systems.

Contribution

It proposes a novel reconfigurable spatial array architecture tailored for wireless kernels, evaluated against specialized cores to enhance scalability and spectral agility.

Findings

Reconfigurable array approaches specialized core efficiency in certain conditions.

Design synthesized in 32 nm process shows promising latency and power metrics.

Conditions identified where general-purpose architectures match specialized core performance.

Abstract

Massive MIMO is a cornerstone of next-generation wireless communication, offering significant gains in capacity, reliability, and energy efficiency. However, to meet emerging demands such as high-frequency operation, wide bandwidths, co-existence, integrated sensing, and resilience to dynamic interference, future systems must exhibit both scalability and spectral agility. These requirements place increasing pressure on the underlying processing hardware to be both efficient and reconfigurable. This paper proposes a custom-designed spatial array architecture that serves as a reconfigurable, general-purpose core optimized for a class of wireless kernels that commonly arise in diverse communications and sensing tasks. The proposed spatial array is evaluated against specialized cores for each kernel using High-Level Synthesis (HLS). Both the reconfigurable and specialized designs are synthesized in a 32 nm process to assess latency, throughput, area, and power in realistic processes. The results identify conditions under which general-purpose systolic architectures can approach the efficiency of specialized cores, thereby paving the way toward more scalable and agile systems.