Joint Channel-Estimation/Decoding With Frequency-Selective Channels and Few-Bit ADCs

Sun, Peng; Wang, Zhongyong; Heath, Robert W.; Schniter, Philip

doi:10.1109/tsp.2018.2887189

Cited by 18 publications

(18 citation statements)

References 51 publications

(87 reference statements)

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“…The optimality of this method, however, is not guaranteed due to the use of OFDM signaling which is shown to be highly suboptimal when employing the few-bit ADCs [14]. A joint channel estimation-and-decoding technique that does not rely on the OFDM signaling was proposed in [29]. Unfortunately, this technique is still suboptimal in terms of the decoding performance, because it adopts the parametric BiGAMP algorithm based on Gaussian approximations.…”

Section: A Related Workmentioning

confidence: 99%

Robust Data Detection for MIMO Systems With One-Bit ADCs: A Reinforcement Learning Approach

Jeon

Lee

Poor

2020

IEEE Trans. Wireless Commun.

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The use of one-bit analog-to-digital converters (ADCs) at a receiver is a power-efficient solution for future wireless systems operating with a large signal bandwidth and/or a massive number of receive radio frequency chains. This solution, however, induces a high channel estimation error and therefore makes it difficult to perform the optimal data detection that requires perfect knowledge of likelihood functions at the receiver. In this paper, we propose a likelihood function learning method for multiple-input multipleoutput (MIMO) systems with one-bit ADCs using a reinforcement learning approach. The key idea is to exploit input-output samples obtained from data detection, to compensate the mismatch in the likelihood function. The underlying difficulty of this idea is a label uncertainty in the samples caused by a data detection error. To resolve this problem, we define a Markov decision process (MDP) to maximize the accuracy of the likelihood function learned from the samples. We then develop a reinforcement learning algorithm that efficiently finds the optimal policy by approximating the transition function and the optimal state of the MDP. Simulation results demonstrate that the proposed method provides significant performance gains for the optimal data detection methods that suffer from the mismatch in the likelihood function. Index TermsMultiple-input-multiple-output (MIMO), one-bit analog-to-digital converter (ADC), reinforcement learning, robust data detection, likelihood function learning.

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Section: A Related Workmentioning

confidence: 99%

Robust Data Detection for MIMO Systems With One-Bit ADCs: A Reinforcement Learning Approach

Jeon

Lee

Poor

2020

IEEE Trans. Wireless Commun.

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“…Very limited work has focused on the development of soft-output detection methods for MIMO systems with low-precision ADCs [31]- [36]. In our prior work [31], a weighted Hamming distance was used to compute the LLRs for the MIMO systems with one-bit ADCs; yet, this work does not take into account the ISI effect of mmWave channels.…”

Section: A Related Workmentioning

confidence: 99%

“…These frequency-domain techniques were shown to be fairly effective when the number of receive antennas is sufficiently larger than the number of simultaneously transmitted data streams at the transmitter. Recently, a joint soft-output detection and channel-decoding method has been developed in [36] on the basis of bilinear GAMP algorithm, but the algorithm is limited to the use for single-input single-output (SISO) systems. Moreover, none of the aforementioned methods in [32]- [36] guarantees the optimality in the soft-output detection performance, because all these methods compute the LLRs based on the approximate algorithms.…”

Section: A Related Workmentioning

confidence: 99%

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Soft-Output Detection Methods for Sparse Millimeter-Wave MIMO Systems With Low-Precision ADCs

Jeon

Hong

et al. 2019

IEEE Trans. Commun.

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The use of low-precision analog-to-digital converters (ADCs) is a low-cost and power-efficient solution for a millimeter wave (mmWave) multiple-input multiple-output (MIMO) system operating at sampling rates higher than a few Gsample/sec. This solution, however, can make significant frame-errorrates (FERs) degradation due to inter-subcarrier interference when applying conventional frequencydomain equalization techniques. In this paper, we propose computationally-efficient yet near-optimal soft-output detection methods for the coded mmWave MIMO systems with low-precision ADCs. The underlying idea of the proposed methods is to construct an extremely sparse inter-symbol-interference (ISI) channel model by jointly exploiting the delay-domain sparsity in mmWave channels and a high quantization noise caused by low-precision ADCs. Then we harness this sparse channel model to create a trellis diagram with a reduced number of states or a factor graph with very sparse edge connections.Using the reduced trellis diagram, we present a soft-output detection method that computes the loglikelihood ratios (LLRs) of coded bits by optimally combining the quantized received signals obtained from multiple receive antennas using a forward-and-backward algorithm. To reduce the computational complexity further, we also present a low-complexity detection method using the sparse factor graph to compute the LLRs in an iterative fashion based on a belief propagation algorithm. Simulations results demonstrate that the proposed soft-output detection methods provide significant FER gains compared to the existing frequency-domain equalization techniques in a coded mmWave MIMO system using oneor two-bit ADCs. Index TermsMillimeter wave communications, multiple-input-multiple-output (MIMO), low-precision analogto-digital converter (ADC), time-domain equalization, inter-symbol-interference (ISI) channel. Y.-S. Jeon, H. Do, and N. Lee are with the

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“…Various methods have been proposed. They include linear receiver [10]- [12], projected gradient method based algorithms [13], [14], fast adaptive shrinkage/thresholding algorithm (FASTA) based method [15], coding theory-based approach [16], supervised learning based method [17], and message-passing approaches, including generalized approximate message passing (GAMP) [18]- [21] and its extension bilinear GAMP (BiGAMP) [22] and parametric BiGAMP [23], vector AMP [24], and generalized Turbo (GTurbo) [25].…”

Section: Introductionmentioning

confidence: 99%

Reliable OFDM Receiver With Ultra-Low Resolution ADC

Wang

Shih

Wen

et al. 2019

IEEE Trans. Commun.

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The use of low-resolution analog-to-digital converters (ADCs) can significantly reduce power consumption and hardware cost. However, their resulting severe nonlinear distortion makes reliable data transmission challenging. For orthogonal frequency division multiplexing (OFDM) transmission, the orthogonality among subcarriers is destroyed. This invalidates conventional OFDM receivers relying heavily on this orthogonality. In this study, we move on to quantized OFDM (Q-OFDM) prototyping implementation based on our previous achievement in optimal Q-OFDM detection. First, we propose a novel Q-OFDM channel estimator by extending the generalized Turbo (GTurbo) framework formerly applied for optimal detection. Specifically, we integrate a type of robust linear OFDM channel estimator into the original GTurbo framework and derive its corresponding extrinsic information to guarantee its convergence. We also propose feasible schemes for automatic gain control, noise power estimation, and synchronization. Combined with the proposed inference algorithms, we develop an efficient Q-OFDM receiver architecture. Furthermore, we construct a proofof-concept prototyping system and conduct over-the-air (OTA) experiments to examine its feasibility and reliability. This is the first work that focuses on both algorithm design and system implementation in the field of low-resolution quantization communication. The results of the numerical simulation and OTA experiment demonstrate that reliable data transmission can be achieved.

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Joint Channel-Estimation/Decoding With Frequency-Selective Channels and Few-Bit ADCs

Cited by 18 publications

References 51 publications

Robust Data Detection for MIMO Systems With One-Bit ADCs: A Reinforcement Learning Approach

Robust Data Detection for MIMO Systems With One-Bit ADCs: A Reinforcement Learning Approach

Soft-Output Detection Methods for Sparse Millimeter-Wave MIMO Systems With Low-Precision ADCs

Reliable OFDM Receiver With Ultra-Low Resolution ADC

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