Self and Turbo Iterations for MIMO Receivers and Large-Scale Systems

Santos, Irene; Murillo-Fuentes, Juan José

doi:10.1109/lwc.2019.2907941

Cited by 27 publications

(29 citation statements)

References 16 publications

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“…where E {x|r t , τ t } is the MMSE estimate of x d with the equivalent AWGN channel (21), and has the same expression with (22). Compared with the MMSE estimator (22) in the OAMP detector, the divergence-free estimator (28) considers the contribution of the linear estimator and learnable parameters (φ t , ξ t ). The MMSE estimator (22) can be interpreted as a special case of (28) by setting φ t = 1 and ξ t = 0.…”

Section: B Oamp-net2 Detectormentioning

confidence: 99%

“…The parameters (φ t , ξ t ) in the nonlinear estimator η t (·) play important roles in constructing an appropriate divergence-free estimator, which has been discussed in [38]. In precise, the divergence-free estimator (28) can be applied in the OAMP detector, but the φ t and ξ t are related to the prior distribution of the original signal and difficult to calculate. Therefore, MMSE estimator (22) is considered for simplicity in the OAMP detector.…”

Section: ) Learnable Variablesmentioning

confidence: 99%

“…From (28), the OAMP-Net2 can decouple the joint posterior probability P(x|y d ,Ĥ) into a series of marginal posterior probability P(x j |r t , τ t ). The marginal posterior probability is used to produce the soft output log-likelihood ratios (LLR), which is given by…”

Section: Soft-input and Soft-outputmentioning

confidence: 99%

See 2 more Smart Citations

Model-Driven Deep Learning for MIMO Detection

Wen

Jin

et al. 2020

IEEE Trans. Signal Process.

334

164

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In this paper, we investigate the model-driven deep learning (DL) for joint MIMO channel estimation and signal detection (JCESD), where signal detection considers channel estimation error and channel statistics while channel estimation is refined by detected data and takes the signal detection error into consideration. In particular, the MIMO signal detector is specially designed by unfolding an iterative algorithm and adding some trainable parameters. Since the number of trainable parameters is much fewer than the data-driven DL based signal detector, the model-driven DL based MIMO signal detector can be rapidly trained with a much smaller data set. Furthermore, the proposed signal detector can be extended to soft-input soft-output detection easily. Based on numerical results, the model-driven DL based JCESD scheme significantly improves the performance of the corresponding traditional iterative detector and the signal detector exhibits superior robustness to signal-to-noise ratio (SNR) and channel correlation mismatches. Recently, it has been applied in physical layer communications [10]-[12], such as channel estimation [13]-[15], CSI feedback [16], signal detection [17]-[25], and channel coding [26], [27]. In particular, a five-layer fully connected deep neural network (DNN) is embedded into an orthogonal frequency-division multiplexing (OFDM) system for joint channel estimation and signal detection (JCESD) by treating the receiver as a black box and without exploiting domain knowledge [17]. However, training such a black-box-based network requires a lot of 1 A matrix A = UΣV is unitarily-invariant if U, Σ and V are mutually independent, and U, V are Haar-distributed. The independent and identically distributed (i.i.d.) Gaussian matrix is a typical unitarily-invariant matrix.

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Section: B Oamp-net2 Detectormentioning

confidence: 99%

Section: ) Learnable Variablesmentioning

confidence: 99%

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Model-Driven Deep Learning for MIMO Detection

Wen

Jin

et al. 2020

IEEE Trans. Signal Process.

334

164

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“…Joint signal detection and channel decoding (JDD) iteration is referred to as "turbo receiver" in this paper. The unfolded MIMO turbo receiver is based on the idea of unfolding the traditional turbo receiver [12], [13] using a deep NN (DNN) [20]. As shown in Fig.…”

Section: B Principles Of Unfolded Mimo Turbo Receivermentioning

confidence: 99%

Meta Learning-Based MIMO Detectors: Design, Simulation, and Experimental Test

Zhang

et al. 2021

IEEE Trans. Wireless Commun.

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Deep neural networks (NNs) have exhibited considerable potential for efficiently balancing the performance and complexity of multiple-input and multiple-output (MIMO) detectors. However, existing NN-based MIMO detectors are difficult to be deployed in practical systems because of their slow convergence speed and low robustness in new environments. To address these issues systematically, we propose a receiver framework that enables efficient online training by leveraging the following simple observation: although NN parameters should adapt to channels, not all of them are channel-sensitive. In particular, we use a deep unfolded NN structure that represents iterative algorithms in signal detection and channel decoding modules as multi layer deep feed forward networks. An expectation propagation (EP) module, called EPNet, is established for signal detection by unfolding the EP algorithm and rendering the damping factors trainable. An unfolded turbo decoding module, called TurboNet, is used for channel decoding. This component decodes the turbo code, where trainable NN units are integrated into the traditional max-log-maximum a posteriori decoding procedure. We demonstrate that TurboNet is robust for channels and requires only one off-line training. Therefore, only a few damping factors in EPNet must be re-optimized online. An online training mechanism based on meta learning is then developed. Here, the optimizer, which is implemented by long short-term memory NNs, is trained to update damping factors efficiently by using a small training set such that they can quickly adapt to new environments. Simulation results indicate that the proposed receiver significantly outperforms traditional receivers and that the online learning mechanism can quickly adapt to new environments. Furthermore, an over-the-air platform is presented to demonstrate the significant robustness of the proposed receiver in practical deployment.

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“…We first review the parameter updating methods used in the related approaches [13], [34]- [37], and then explain the ones used in Algorithm 1. Following [36], some parameters must be tuned, including the minimum allowed variance, ǫ, the damping procedure, β, and the number of iterations, L. The first parameter guarantees non-negativity and the second one determines the stability and the speed of convergence of the algorithm.…”

Section: B Oamp-netmentioning

confidence: 99%

Artificial Intelligence-Aided Receiver for a CP-Free OFDM System: Design, Simulation, and Experimental Test

et al. 2019

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Orthogonal frequency division multiplexing (OFDM), usually with sufficient cyclic prefix (CP), has been widely applied in various communication systems. The CP in OFDM consumes additional resource and reduces spectrum and energy efficiency. However, channel estimation and signal detection are very challenging for CP-free OFDM systems. In this paper, we propose a novel artificial intelligence (AI)aided receiver (AI receiver) for a CP-free OFDM system. The AI receiver includes a channel estimation neural network (CE-NET) and a signal detection neural network based on orthogonal approximate message passing (OAMP), called OAMP-NET. The CE-NET is initialized by the least-square channel estimation algorithm and refined by a linear minimum mean-squared error neural network. The OAMP-NET is established by unfolding the iterative OAMP algorithm and adding several trainable parameters to improve the detection performance. We first investigate their performance under different channel models through extensive simulation and then establish a real transmission system using a 5G rapid prototyping system for an over-the-air (OTA) test. Based on our study, the AI receiver can estimate

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Self and Turbo Iterations for MIMO Receivers and Large-Scale Systems

Cited by 27 publications

References 16 publications

Model-Driven Deep Learning for MIMO Detection

Model-Driven Deep Learning for MIMO Detection

Meta Learning-Based MIMO Detectors: Design, Simulation, and Experimental Test

Artificial Intelligence-Aided Receiver for a CP-Free OFDM System: Design, Simulation, and Experimental Test

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