Non-Linear Precoding via Dirty Paper Coding for Near-Field Downlink MISO Communications

2025-10-15

AI Summary

In 6G near-field communications, severe multi-user interference and limited sum-rate performance—caused by high power consumption of linear precoding (e.g., zero-forcing) under large-scale antenna arrays and terahertz frequencies—pose critical challenges. To address this, this paper pioneers the application of dirty paper coding (DPC) to near-field MISO downlink systems, establishing a nonlinear precoding framework. Leveraging a near-field beam-focusing channel model, we jointly optimize DPC codeword ordering and power allocation, deriving the optimal power solution that maximizes sum rate. Simulation results under typical near-field configurations demonstrate that the proposed DPC-based scheme significantly outperforms zero-forcing precoding in sum rate, with particularly pronounced gains in scenarios featuring small inter-user spacing and high user density. This validates both the effectiveness and practical viability of DPC for near-field wireless communications.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Applications

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements

Abstract

In 6G systems, extremely large-scale antenna arrays operating at terahertz frequencies extend the near-field region to typical user distances from the base station, enabling near-field communication (NFC) with fine spatial resolution through beamfocusing. Existing multiuser NFC systems predominantly employ linear precoding techniques such as zero-forcing (ZF), which suffer from performance degradation due to the high transmit power required to suppress interference. This paper proposes a nonlinear precoding framework based on Dirty Paper Coding (DPC), which pre-cancels known interference to maximize the sum-rate performance. We formulate and solve the corresponding sum-rate maximization problems, deriving optimal power allocation strategies for both DPC and ZF schemes. Extensive simulations demonstrate that DPC achieves substantial sum-rate gains over ZF across various near-field configurations, with the most pronounced improvements observed for closely spaced users.

Problem

• Proposes nonlinear precoding for near-field MISO communications
• Solves sum-rate maximization with optimal power allocation
• Demonstrates DPC outperforms ZF, especially for closely spaced users

Innovation

• Nonlinear precoding using Dirty Paper Coding
• Pre-cancels known interference for sum-rate maximization
• Optimal power allocation for near-field MISO communications