Millimeter Wave MIMO Channel Estimation With One-Bit Receivers

Zhou, Runxin; Du, Huiqin; Zhang, Duoying

doi:10.1109/lcomm.2021.3123368

Cited by 6 publications

(6 citation statements)

References 21 publications

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“…Numerous studies have been devoted to study merging the low-resolution and 1-bit ADCs with the communication systems [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28]. The system performance analysis is considered in [7], [8], [9], and [10].…”

Section: Fully Digital Wide-bandwidth Massive Mimo Systemsmentioning

confidence: 99%

“…The seminal paper [19] estimates the single-input single-output (SISO) channel with low precision ADCs, whereas [20] addresses the channel estimation for the massive MIMO orthogonal frequency division multiplexing (OFDM). The mm-wave MIMO channel estimation is investigated in [21] and [22] for 1-bit and low-resolution ADCs, respectively, at the BS. For the data detection in systems with low-resolution ADCs, one side of the literature tackles the case of 1-bit ADCs [23], [24], [25], [26], and the other side has been developed for few-bit ADCs [9], [20], [27], [28].…”

Section: Fully Digital Wide-bandwidth Massive Mimo Systemsmentioning

confidence: 99%

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Enhanced Signal Detection and Constellation Design for Massive SIMO Communications With 1-Bit ADCs

et al. 2023

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In this paper, we investigate a transmitter and receiver design for a single-user massive SIMO (single-input multiple-output) system with 1-bit analog-to-digital converters (ADCs) at the base station (BS), where the user adopts higher-order modulation, e.g., 16-quadrature amplitude modulation (16-QAM), for the data transmission. For the channel estimation and the signal detection, linear least-squares (LS) estimation and maximum ratio combining (MRC) are respectively employed. In this context, we first introduce closedform formulas for the mean of the estimated symbols and for the correlation matrix between their real and imaginary parts considering the effect of 1-bit quantization. The study of the distribution of the estimated symbols indicates that, in presence of 1-bit ADCs, the conventional 16-QAM detector and the typical square 16-QAM modulation are not adequate. In light of this, we propose three novel symbol detectors and re-design the 16-QAM modulation in order to improve the symbol error rate (SER). Furthermore, the upper bound on the SER is analyzed based on the pair-wise error probability and the boundary equation between two regions is also studied. Through numerical results, the proposed framework, i.e., the symbol detector and the transmit constellation design, shows a significant enhancement in the SER performance against the conventional detector and the typical square 16-QAM modulation.INDEX TERMS Massive MIMO, 1-bit ADCs, data detection, upper bound on the SER, transmit constellation design.

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Section: Fully Digital Wide-bandwidth Massive Mimo Systemsmentioning

confidence: 99%

Section: Fully Digital Wide-bandwidth Massive Mimo Systemsmentioning

confidence: 99%

Enhanced Signal Detection and Constellation Design for Massive SIMO Communications With 1-Bit ADCs

et al. 2023

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“…Despite these attractive features of one-bit massive MIMO, the lack of signal level information also poses difficulty in channel estimation. There have been some endeavors trying to address this problem in the conventional MIMO communications without IRS [33]- [38]. In [33], a near maximum likelihood (nML) channel estimator and detector was proposed.…”

Section: Introductionmentioning

confidence: 99%

“…By taking into account the sparsity of the millimeter-wave MIMO channels, the estimation problem is formulated as a one-bit compressed sensing problem, and the expectation-maximization (EM) algorithm has been applied for maximum likelihood (ML) estimation and maximum a posteriori (MAP) estimation from one-bit measurements in [36] and [37], resp. The authors in [38] exploited the sparse and low-rank properties of the mmWave channel with one-bit ADCs to develop a mmWave channel estimation algorithm. Although various one-bit channel estimation algorithms have been proposed for the conventional MIMO communication systems, directly applying them to the IRS channels usually leads to performance degradation, because of the mismatch between the algorithms' prerequisites and the characteristics of the IRS channels; we will elaborate on this in the numerical results Sec.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the shared IRS-to-BS channel among the users makes IRS channels some distinct structure, which in general does not exhibit in the conventional MIMO channels. Therefore, by carefully exploiting the structure of the IRS channels, it is possible to custom-derive better estimation algorithms rather than just adapting the existing one-bit MIMO channel estimation algorithms [33]- [38] to the IRS channels. To this end, several estimation algorithms are proposed in this work with different levels of utilization of the IRS channel structures.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Channel Covariance Estimation and Two-Timescale Beamforming for IRS-Assisted Millimeter Wave Systems

Wang

Fang

Huang

et al. 2023

IEEE Trans. Wireless Commun.

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Recently, intelligent reflecting surface (IRS)-assisted communication has gained considerable attention due to its advantage in extending the coverage and compensating the path loss with low-cost passive metasurface. This paper considers the uplink channel estimation for IRS-aided multiuser massive multiinput single-output (MISO) communications with one-bit ADCs at the base station (BS). The use of one-bit ADC is impelled by the low-cost and power efficient implementation of massive antennas techniques. However, the passiveness of IRS and the lack of signal level information after one-bit quantization make the IRS channel estimation challenging. To tackle this problem, we exploit the structured sparsity of the user-IRS-BS cascaded channels and develop three channel estimators, each of which utilizes the structured sparsity at different levels. Specifically, the first estimator exploits the elementwise sparsity of the cascaded channel and employs the sparse Bayesian learning (SBL) to infer the channel responses via the type-II maximum likelihood (ML) estimation. However, due to the one-bit quantization, the type-II ML in general is intractable. As such, a variational expectation-maximization (EM) algorithm is custom-derived to iteratively compute an ML solution. The second estimator utilizes the common row-structured sparsity induced by the IRS-to-BS channel shared among the users, and develops another type-II ML solution via the block SBL (BSBL) and the variational EM. To further improve the performance of BSBL, a third two-stage estimator is proposed, which can utilize both the common rowstructured sparsity and the column-structured sparsity arising from the limited scattering around the users. Simulation results show that the more diverse structured sparsity is exploited, the better estimation performance is achieved, and that the proposed estimators are superior to state-of-the-art one-bit estimators.

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Performance of Quantized Massive MIMO With Fronthaul Rate Constraint Over Quasi-Static Channels

et al. 2023

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We provide a rigorous framework for characterizing and numerically evaluating the error probability achievable in the uplink and downlink of a fully digital quantized multiuser multiple-input multiple-output (MIMO) system. We assume that the system operates over a quasi-static channel that does not change across the finite-length transmitted codewords, and only imperfect channel state information (CSI) is available at the base station (BS) and at the user equipments. The need for the novel framework developed in this paper stems from the fact that, for the quasi-static scenario, commonly used signal-to-interferenceand-distortion-ratio expressions that depend on the variance of the channel estimation error are not relatable to any rigorous information-theoretic achievable-rate bound. We use our framework to investigate how the performance of a fully digital massive MIMO system subject to a fronthaul rate constraint, which imposes a limit on the number of samples per second produced by the analog-to-digital and digital-to-analog converters (ADCs and DACs), depends on the number of BS antennas and on the precision of the ADCs and DACs. In particular, we characterize, for a given fronthaul constraint, the trade-off between the number of antennas and the resolution of the data converters, and discuss how this trade-off is influenced by the accuracy of the available CSI. Our framework captures explicitly the cost, in terms of spectral efficiency, of pilot transmission-an overhead that the outage capacity, the classic asymptotic metric used in this scenario, cannot capture. We present extensive numerical results that validate the accuracy of the proposed framework and allow us to characterize, for a given fronthaul constraint, the optimal number of antennas and the optimal resolution of the converters as a function of the transmitted power and of the available CSI.INDEX TERMS Finite blocklength, fronthaul rate constraint, low-precision converters, multi-user massive multiple-input multiple-output (MIMO), quasi-static scenario.

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Millimeter Wave MIMO Channel Estimation With One-Bit Receivers

Cited by 6 publications

References 21 publications

Enhanced Signal Detection and Constellation Design for Massive SIMO Communications With 1-Bit ADCs

Enhanced Signal Detection and Constellation Design for Massive SIMO Communications With 1-Bit ADCs

Spatial Channel Covariance Estimation and Two-Timescale Beamforming for IRS-Assisted Millimeter Wave Systems

Performance of Quantized Massive MIMO With Fronthaul Rate Constraint Over Quasi-Static Channels

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