High dimensional mimo detection improves with deterministic smoothed map method
Deterministic Smoothed MAP Detection for High-Dimensional MIMO Systems
Information Theory
Summary
Detecting signals in wireless systems with many antennas is difficult and usually involves guesswork or slow calculations. The authors developed a new method that turns the problem into a smooth and continuous optimization task, making it easier to solve deterministically. Their approach can improve existing detectors or work on its own, delivering better accuracy and efficiency, especially as the system size grows. It also produces more reliable probabilities for each possible transmitted symbol, helping with error correction.
What this means in practice
- •For wireless communication engineers: Improve signal detection accuracy and speed in large-scale MIMO systems by integrating the smoothed MAP method as a refinement or stand-alone detector.
- •For telecommunication system integrators: Enhance error correction performance in coded multi-user MIMO transmissions by using the soft-output smoothed MAP probabilities for better decoding.
Authors
Vitor Tucci Ramos, Stephane Senecal, Sheng Yang
Abstract
We propose a deterministic smoothed maximum a posteriori (SMAP) framework for high-dimensional multiple-input multiple-output (MIMO) detection. The discrete constellation prior is approximated by an i.i.d. Gaussian mixture, yielding a differentiable MAP objective that retains the constellation structure while enabling continuous optimization. Unlike sampling-based approaches, SMAP formulates detection as a deterministic optimization problem and can be initialized by the output of an arbitrary detector, allowing it to operate either as a standalone detector or as a refinement stage. For hard detection, we introduce SMAP-KR, which complements the continuous solution with a local $K_R$-neighbor search evaluated according to the original maximum-likelihood metric. Annealed continuation improves robustness for higher-order constellations, while a two-neighbor approximation reduces the cost of evaluating the mixture prior. We further develop a soft-output SMAP method in which a local Gaussian approximation based on the curvature of the smoothed posterior provides symbol probabilities and bit log-likelihood ratios without posterior sampling or explicit counterhypothesis lists. Numerical results for critically loaded MU-MIMO systems show that SMAP-KR provides an increasingly favorable performance-complexity tradeoff as the system dimension grows and can also effectively refine the output of existing detectors. For coded transmission, soft SMAP provides substantial block-error-rate gains over both LMMSE- and K-best-based soft detection.