Hamiltonian Monte Carlo speeds up wireless signal vector search
Hamiltonian Monte Carlo for Vector Perturbation Precoding in MU-MIMO via Continuous Relaxation
Information TheoryNetworking and Internet Architecture
Summary
Wireless systems send data to multiple users at once using multiple antennas, but picking the best way to do this can be very complex and slow. The authors found a way to turn this complex search into a smoother problem that computers can solve faster using a technique called Hamiltonian Monte Carlo. This method makes finding good signal adjustments quicker as more users join, with only a small drop in accuracy compared to the best possible methods. Their approach treats the problem like guessing the most likely signal settings in a continuous space instead of checking every option one by one.
What this means in practice
- •For wireless communication engineers: Improve MU-MIMO downlink precoding efficiency by using HMC to find integer perturbations faster in signal processing.
- •For signal processing software developers: Implement gradient-based continuous relaxation methods to enable faster search algorithms in multiuser MIMO systems.
Authors
Junichiro Hagiwara, Toshihiko Nishimura, Yasutaka Ogawa, Takeo Ohgane
Abstract
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.