One-Bit Sphere Decoding for Uplink Massive MIMO Systems With One-Bit ADCs

Jeon, Yo-Seb; Lee, Namyoon; Hong, Song‐Nam; Heath, Robert W.

doi:10.1109/twc.2018.2827028

Cited by 114 publications

(101 citation statements)

References 32 publications

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“…While the potential of binary sampling has been extensively studied, e.g., regarding wireless communication capabilities [3]- [10], signal reconstruction error [11]- [13], estimation sensitivity [14]- [19], and detection reliability [20]- [22], here we focus on the analysis of the sensing latency [23].…”

Section: A Motivationmentioning

confidence: 99%

Latency Analysis for Sequential Detection in Low-Complexity Binary Radio Systems

Stein

Faub

2019

IEEE Trans. Commun.

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We consider the problem of making a quick decision in favor of one of two possible physical signal models while the numerical measurements are acquired by sensing devices featuring minimal digitization complexity. Therefore, the digital data streams available for statistical processing are binary and exhibit temporal and spatial dependencies. To handle the intractable multivariate binary data model, we first consider sequential tests for exponential family distributions. Within this generic probabilistic framework, we identify adaptive approximations for the log-likelihood ratio and the Kullback-Leibler divergence. The results allow designing sequential detectors for binary radio systems and analyzing their average run-time along classical arguments of Wald. In particular, the derived tests exploit the spatio-temporal correlation structure of the analog sensor signals engraved into the binary measurements. As an application, we consider the specification of binary sensing architectures for cognitive radio and GNSS spectrum monitoring where our results characterize the sequential detection latency as a function of the temporal oversampling and the number of antennas. Finally, we evaluate the efficiency of the proposed algorithms and illustrate the accuracy of our analysis via Monte-Carlo simulations.

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Section: A Motivationmentioning

confidence: 99%

Latency Analysis for Sequential Detection in Low-Complexity Binary Radio Systems

Stein

Faub

2019

IEEE Trans. Commun.

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“…Data detection methods for MIMO systems with one-bit ADCs have been intensively studied in the literature [13]- [21]. For frequency flat channels, the optimal maximum-likelihood (ML) detection method and its low-complexity variations were developed in [15]- [18]. Particularly, in [17], [18], it was proven that the optimal ML detection is equivalent to the minimum weighted Hamming distance decoding in which the weights are determined by the likelihood functions.…”

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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“…, T t do {Training for parameter learning} 2: Therefore, for the implementation, the number of co-scheduled uplink users should be chosen to meet the constraint of pilot overheads or a semi-supervised-learning and reinforcement-learning methods can be used as in [29] and [30]. In addition, the complexity of the A-ML detector can be further reduced using one-bit sphere decoding in [15].…”

Section: Algorithmmentioning

confidence: 99%

Supervised-Learning for Multi-Hop MU-MIMO Communications With One-Bit Transceivers

Kim

Hong

Lee

2019

IEEE J. Select. Areas Commun.

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This paper considers a nonlinear multi-hop multi-user multiple-input multiple-output (MU-MIMO) relay channel, in which multiple users send information symbols to a multi-antenna base station (BS) with one-bit analog-to-digital converters via intermediate relays, each with one-bit transceiver. To understand the fundamental limit of the detection performance, the optimal maximum-likelihood (ML) detector is proposed with the assumption of perfect and global channel state information (CSI) at the BS. This multi-user detector, however, is not practical due to the unrealistic CSI assumption and the overwhelming detection complexity. These limitations are addressed by presenting a novel detection framework inspired by supervised-learning. The key idea is to model the complicated multihop MU-MIMO channel as a simplified channel with much fewer and learnable parameters. One major finding is that, even using the simplified channel model, a near ML detection performance is achievable with a reasonable amount of pilot overheads in a certain condition. In addition, an online supervised-learning detector is proposed, which adaptively tracks channel variations. The idea is to update the model parameters with a reliably detected data symbol by treating it as a new training (labelled) data. Lastly, a multi-user detector using a deep neural network is proposed. Unlike the modelbased approaches, this model-free approach enables to remove the errors in the simplified channel model, while increasing the computational complexity for parameter learning. Via simulations, the detection performances of classical, model-based, and model-free detectors are thoroughly compared to demonstrate the effectiveness of the supervised-learning approaches in this channel.

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One-Bit Sphere Decoding for Uplink Massive MIMO Systems With One-Bit ADCs

Cited by 114 publications

References 32 publications

Latency Analysis for Sequential Detection in Low-Complexity Binary Radio Systems

Latency Analysis for Sequential Detection in Low-Complexity Binary Radio Systems

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

Supervised-Learning for Multi-Hop MU-MIMO Communications With One-Bit Transceivers

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