Processing Distribution and Architecture Tradeoff for Large Intelligent Surface Implementation

TL;DR

This paper analyzes the hardware and architectural trade-offs in implementing large intelligent surfaces for wireless communication, focusing on distributed processing and hierarchical architectures to enhance scalability and efficiency.

Contribution

It provides a detailed algorithm-architecture codesign approach and case studies for uplink detection in discrete LIS systems, offering practical guidelines for implementation.

Findings

Distributed processing improves scalability.

Hierarchical architectures balance data-rate and complexity.

Concrete implementation guidelines are provided.

Abstract

The Large Intelligent Surface (LIS) concept has emerged recently as a new paradigm for wireless communication, remote sensing and positioning. It consists of a continuous radiating surface placed relatively close to the users, which is able to communicate with users by independent transmission and reception (replacing base stations). Despite of its potential, there are a lot of challenges from an implementation point of view, with the interconnection data-rate and computational complexity being the most relevant. Distributed processing techniques and hierarchical architectures are expected to play a vital role addressing this while ensuring scalability. In this paper we perform algorithm-architecture codesign and analyze the hardware requirements and architecture trade-offs for a discrete LIS to perform uplink detection. By doing this, we expect to give concrete case studies and guidelines for efficient implementation of LIS systems.