用于MU-MIMO中向量扰动预编码的哈密顿蒙特卡洛方法：基于连续松弛
Hamiltonian Monte Carlo for Vector Perturbation Precoding in MU-MIMO via Continuous Relaxation
- School of Social Informatics, Mukogawa Women’s University（武库川女子大学社会信息学部）
- Faculty of Information Science and Technology, Hokkaido University（北海道大学信息科学研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出将MU-MIMO中向量扰动预编码的整数搜索松弛为t分布连续混合，利用哈密顿蒙特卡洛高效求解，复杂度O(N^2)，性能接近超球近似极限。
AI中文摘要：
多用户多输入多输出（MU-MIMO）是一种通过多天线提高无线容量的关键技术。在MU-MIMO下行链路预编码中，向量扰动（VP）是一种具有代表性的非线性方法，可实现高性能。然而，其对整数扰动向量的搜索归结为最近向量问题，其复杂度随用户数量的增加而迅速增长。我们提出了一种方法，将整数扰动的离散结构松弛为$t$-分布的连续混合，从而能够通过基于梯度的哈密顿蒙特卡洛（HMC）进行高效搜索。复杂度分析和数值实验证明了所提方法的有效性。其搜索复杂度随用户数N按O(N^2)规模增长。在符号错误率为10^-3时，其性能与超球近似基准（该基准近似VP的性能极限）相差在2.4 dB以内。本文将VP扰动搜索重新表述为概率推断问题，为在连续空间中处理高维离散搜索提供了一种通用框架。
英文摘要：
Multi-user multiple-input multiple-output (MU-MIMO) is a key technology that improves wireless capacity through multiple antennas. In MU-MIMO downlink precoding, vector perturbation (VP) is a representative nonlinear method that achieves high performance. However, its search for the integer perturbation vector reduces to a closest vector problem, whose complexity grows rapidly as the number of users increases. We propose a method that relaxes the discrete structure of the integer perturbation into a continuous mixture of $t$-distributions, enabling efficient search via gradient-based Hamiltonian Monte Carlo (HMC). Complexity analysis and numerical experiments demonstrate the effectiveness of the proposed method. Its search complexity scales as O(N^2) in the number of users N. At a symbol error rate of 10^-3, it performs within 2.4 dB of a hypersphere approximation benchmark, which approximates the performance limit of VP. This paper reframes the VP perturbation search as a probabilistic inference problem, providing a general formulation for handling high-dimensional discrete search in a continuous space.
