Performance Analysis of Spatiotemporal 2-D Polar Codes for Massive MIMO with MMSE Receivers

2025-07-26

AI Summary

To address performance degradation caused by short blocklengths in 6G ultra-reliable low-latency communication (URLLC), this paper proposes a spatio-temporal two-dimensional polar coding scheme tailored for massive MIMO systems. The method establishes, for the first time, a joint spatio-temporal polarization theoretical framework: leveraging the near-deterministic signal-to-interference-plus-noise ratio (SINR) of MMSE receivers under large-scale antenna arrays, it models the spatial domain as parallel Gaussian subchannels and achieves two-dimensional channel polarization via Gaussian approximation. Theoretically, the scheme attains channel capacity under finite blocklength and high spatial degrees of freedom. Simulation results demonstrate that, compared to conventional time-domain polar codes, the proposed scheme significantly improves reliability at the same latency, or drastically reduces end-to-end latency while guaranteeing a target block error rate—thereby breaking the fundamental low-latency–high-reliability trade-off bottleneck of polar codes in URLLC scenarios.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

With the evolution from 5G to 6G, ultra-reliable low-latency communication (URLLC) faces increasingly stringent performance requirements. Lower latency constraints demand shorter channel coding lengths, which can severely degrade decoding performance. The massive multiple-input multiple-output (MIMO) system is considered a crucial technology to address this challenge due to its abundant spatial degrees of freedom (DoF). While polar codes are theoretically capacity-achieving in the limit of infinite code length, their practical applicability is limited by significant decoding latency. In this paper, we establish a unified theoretical framework and propose a novel spatiotemporal two-dimensional (2-D) polar coding scheme for massive MIMO systems employing minimum mean square error (MMSE) receivers. The polar transform is jointly applied over both spatial and temporal dimensions to fully exploit the large spatial DoF. By leveraging the near-deterministic signal-to-interference-plus-noise ratio (SINR) property of MMSE detection, the spatial domain is modeled as a set of parallel Gaussian sub-channels. Within this framework, we perform a theoretical analysis of the 2-D polarization behavior using the Gaussian approximation method, and the capacity-achieving property of the proposed scheme is proved under finite blocklength constraints and large spatial DoF. Simulation results further demonstrate that, compared to traditional time-domain polar codes, the proposed 2-D scheme can significantly reduce latency while guaranteeing reliability, or alternatively improve reliability under the same latency constraint -- offering a capacity-achieving and latency-efficient channel coding solution for massive MIMO systems in future 6G URLLC scenarios.

Problem

• Reducing decoding latency in massive MIMO systems
• Improving reliability under low-latency constraints
• Achieving capacity with finite blocklength polar codes

Innovation

• Spatiotemporal 2-D polar coding scheme
• MMSE receivers exploit spatial DoF
• Gaussian approximation for 2-D polarization