Orthogonal AMP
By Junjie Ma, Li Ping. IEEE Access, 2016
454 citations recorded by the source corpus.
Approximate message passing (AMP) is a low-cost iterative signal recovery algorithm for linear system models. When the system transform matrix has independent identically distributed (IID) Gaussian entries, the performance of AMP can be asymptotically characterized by a simple scalar recursion called state evolution (SE). However, SE may become unreliable for other matrix ensembles, especially for ill-conditioned ones. This imposes limits on the applications of AMP. In this paper, we propose an orthogonal AMP (OAMP) algorithm based on de-correlated linear estimation (LE) and divergence-free non-linear estimation (NLE). The Onsager term in standard AMP vanishes as a result of the divergence-free constraint on NLE. We develop an SE procedure for OAMP and show numerically that the SE for OAMP is accurate for general unitarily-invariant matrices, including IID Gaussian matrices and partial orthogonal matrices. We further derive optimized options for OAMP and show that the corresponding SE fixed point coincides with the optimal performance obtained via the replica method. Our numerical results demonstrate that OAMP can be advantageous over AMP, especially for ill-conditioned matrices.
Subfields: Computational Mechanics, Computer Networks and Communications, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/59f0a6eb5802649db2ea036e2fd52aeb7cc35c47
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In Computational Mechanics
- Philip Schniter
- Sundeep Rangan
- Geoffrey Y. Li
- Alyson K. Fletcher
- Shuguang Cui
- Merouane Debbah
- Khaled Ben Letaief
- Chao-Kai Wen
In Computer Networks and Communications
- H. Vincent Poor
- Geoffrey Y. Li
- Khaled Ben Letaief
- Philip Schniter
- Merouane Debbah
- Shuguang Cui
- Sundeep Rangan
- Dusist Niyato
In Electrical and Electronic Engineering
- Geoffrey Y. Li
- Merouane Debbah
- Rui Zhang
- H. Vincent Poor
- Khaled Ben Letaief
- Shi Jin
- Chao-Kai Wen
- Shuguang Cui
Citations in the corpus
- Learning to Optimize: A Primer and A Benchmark (2021, unknown): “• In the noisy setting of 20dB SNR, the performance of LAMP degrades severely while LISTA-CPSS and ALISTA still perform robustly.”
- Model-Driven Deep Learning for MIMO Detection (2020, methodology): “From [42], the optimal one is where ˆ W t is the LMMSE matrix given by where v 2 t is expressed in (16), and R ˆ n d ˆ n d is the covariance matrix of the equivalent noise ˆ n d in signal detector, which includes the contribution of the channel estimation error ΔH and original additive noise n d ,…”
- A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications (2023, background): “In contrast to the existing state-of-the-art baselines, such as the turbo-OAMP with i.i.d. Bernoulli Gaussian prior [170], sub-array OMP [76] and variational Bayesian inference (VBI) with the Dirichlet process [171], the proposed turbo-OAMP algorithm achieved better NMSE performance with…”
- Vector Approximate Message Passing (2019, methodology): “Furthermore, OAMP-LMMSE differs from VAMP in its reliance on matrix inversion (see, e.g., the comments in the Conclusion of [20]).”