Divide and Conquer: One-bit MIMO-OFDM Detection by Inexact Expectation Maximization

Shao, Mingjie; Ma, Wing-Kin

doi:10.1109/icassp39728.2021.9414559

Cited by 5 publications

(6 citation statements)

References 21 publications

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“…Many PG convergence proofs make strong use of the identity (45). As an important discovery, we found that the EM surrogate function g in (36) possesses the following structure…”

Section: Insight Behind the Convergence Results And Prior Workmentioning

confidence: 99%

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Accelerated and Deep Expectation Maximization for One-Bit MIMO-OFDM Detection

Shao,

Ma,

Liu

et al. 2024

IEEE Trans. Signal Process.

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In this paper we study the expectation maximization (EM) technique for one-bit MIMO-OFDM detection (OMOD). Arising from the recent interest in massive MIMO with one-bit analog-to-digital converters, OMOD is a massive-scale problem. EM is an iterative method that can exploit the OFDM structure to process the problem in a per-iteration efficient fashion. In this study we analyze the convergence rate of EM for a class of approximate maximum-likelihood OMOD formulations, or, in a broader sense, a class of problems involving regression from quantized data. We show how the SNR and channel conditions can have an impact on the convergence rate. We do so by making a connection between the EM and the proximal gradient methods in the context of OMOD. This connection also gives us insight to build new accelerated and/or inexact EM schemes. The accelerated scheme has faster convergence in theory, and the inexact scheme provides us with the flexibility to implement EM more efficiently, with convergence guarantee. Furthermore we develop a deep EM algorithm, wherein we take the structure of our inexact EM algorithm and apply deep unfolding to train an efficient structured deep net. Simulation results show that our accelerated exact/inexact EM algorithms run much faster than their standard EM counterparts, and that the deep EM algorithm gives promising detection and runtime performances.

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“…Many PG convergence proofs make strong use of the identity (45). As an important discovery, we found that the EM surrogate function g in (36) possesses the following structure…”

Section: Insight Behind the Convergence Results And Prior Workmentioning

confidence: 99%

“…Applying the above two equations to (36), and using g(θ |θ ) = f (θ ), we obtain (46). The proof is complete.…”

Section: Insight Behind the Convergence Results And Prior Workmentioning

confidence: 99%

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Accelerated and Deep Expectation Maximization for One-Bit MIMO-OFDM Detection

Shao,

Ma,

Liu

et al. 2024

IEEE Trans. Signal Process.

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“…We start with the SQNR. From the received signal model (10), we see that the signal part √ ρh T i xt scales with √ ρ|α i |A. Here, it is important to note that A describes the maximum input signal amplitude, i.e., ∥ xt ∥ IQ−∞ ≤ A for all t. We define the SQNR of user i as the ratio of the square of the received signal scale factor √ ρ|α i |A to the quantization and noise power, which can be shown to be…”

Section: Sqnr Maximization In a User Targeted Fashionmentioning

confidence: 99%

“…In this paper we are interested in the context of multiuser massive MIMO downlink with lowresolution DACs at the transmitter. It is important to mention that massive MIMO uplink with low-resolution ADCs at the receiver is another key topic; the reader is referred to the literature, such as [2]- [5], [9], [10] and the references therein, for details. In coarsely quantized MIMO downlink precoding, the existing studies can be taxonomized into two types, namely, the precode-then-quantize type and the direct signal design type.…”

Section: Introductionmentioning

confidence: 99%

Spatial Sigma-Delta Modulation for Coarsely Quantized Massive MIMO Downlink: Flexible Designs by Convex Optimization

Keung,

2024

IEEE Open J. Signal Process.

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This paper considers the context of multiuser massive MIMO downlink precoding with low-resolution digital-to-analog converters (DACs) at the transmitter. This subject is motivated by the consideration that it is expensive to employ high-resolution DACs for practical massive MIMO implementations. The challenge with using low-resolution DACs is to overcome the detrimental quantization error effects. Recently, spatial Sigma-Delta (Σ∆) modulation has arisen as a viable way to put quantization errors under control. This approach takes insight from temporal Σ∆ modulation in classical DAC studies. Assuming a 1D uniform linear transmit antenna array, the principle is to shape the quantization errors in space such that the shaped quantization errors are pushed away from the user-serving angle sector. In the previous studies, spatial Σ∆ modulation was performed by direct application of the basic firstand second-order modulators from the Σ∆ literature. In this paper, we develop a general Σ∆ modulator design framework for any given order, for any given number of quantization levels, and for any given angle sector. We formulate our design as a problem of maximizing the signal-to-quantization-and-noise ratios (SQNRs) experienced by the users. The formulated problem is convex and can be efficiently solved by available solvers. Our proposed framework offers the alternative option of focused quantization error suppression in accordance with channel state information. Our framework can also be extended to 2D planar transmit antenna arrays. We perform numerical study under different operating conditions, and the numerical results suggest that, given a moderate number of quantization levels, say, 5 to 7 levels, our optimization-based Σ∆ modulation schemes can lead to bit error rate performance close to that of the unquantized counterpart.

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Binary MIMO Detection via Extreme Learning Machines in 5G Network and Beyond

Keung,

Qureshi

2023

2023 IEEE Globecom Workshops (GC Wkshps)

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Divide and Conquer: One-bit MIMO-OFDM Detection by Inexact Expectation Maximization

Cited by 5 publications

References 21 publications

Accelerated and Deep Expectation Maximization for One-Bit MIMO-OFDM Detection

Accelerated and Deep Expectation Maximization for One-Bit MIMO-OFDM Detection

Spatial Sigma-Delta Modulation for Coarsely Quantized Massive MIMO Downlink: Flexible Designs by Convex Optimization

Binary MIMO Detection via Extreme Learning Machines in 5G Network and Beyond

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