FDD Massive MIMO Uplink and Downlink Channel Reciprocity Properties: Full or Partial Reciprocity?

Zhong, Zhimeng; Li, Fan; Ge, Shibin

doi:10.1109/globecom42002.2020.9322570

Cited by 41 publications

(18 citation statements)

References 13 publications

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“…Motivated by the partial reciprocity of uplink and downlink channels [31], [32], [48], we use the following steps to estimate the downlink channel: I) we estimate the frequencyindependent underlying channel parameters, namely, the magnitudes of path gains, delays, AoAs and AoDs during the uplink training; and II) the frequency-specific underlying channel parameters, i.e., the phase of each propagation path, are estimated via downlink training. Using this strategy, we shift the burden in FDD downlink channel estimation to the BS, which is, anyway, responsible for uplink channel estimation.…”

Section: Contribution and Methodologymentioning

confidence: 99%

“…Indeed, it is shown via measurements and theoretical investigations that not all channel parameters are reciprocal in uplink and downlink. In particular, there is no reciprocity between the phases of different multi-path components for FDD uplink and downlink channels [31]. Intuitively, this is expected given the sensitivity of the phase to operating frequency.…”

Section: B Related Workmentioning

confidence: 99%

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Deep Generative Models for Downlink Channel Estimation in FDD Massive MIMO Systems

Mirzaei,

ShahbazPanahi,

Adve

et al. 2022

Preprint

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It is well accepted that acquiring downlink channel state information in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems is challenging because of the large overhead in training and feedback. In this paper, we propose a deep generative model (DGM)-based technique to address this challenge. Exploiting the partial reciprocity of uplink and downlink channels, we first estimate the frequencyindependent underlying channel parameters, i.e., the magnitudes of path gains, delays, angles-of-arrivals (AoAs) and angles-ofdepartures (AoDs), via uplink training, since these parameters are common in both uplink and downlink. Then, the frequencyspecific underlying channel parameters, specifically, the phase of each propagation path, are estimated via downlink training using a very short training signal. In the first step, we incorporate the underlying distribution of the channel parameters as a prior into our channel estimation algorithm. We use DGMs to learn this distribution. Simulation results indicate that our proposed DGM-based channel estimation technique outperforms, by a large gap, the conventional channel estimation techniques in practical ranges of signal-to-noise ratio (SNR). In addition, a near-optimal performance is achieved using only few downlink pilot measurements.

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Section: Contribution and Methodologymentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

Deep Generative Models for Downlink Channel Estimation in FDD Massive MIMO Systems

Mirzaei,

ShahbazPanahi,

Adve

et al. 2022

Preprint

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“…In this section, we show the details of our proposed partial channel reciprocity-based codebook. Since the multipath angle-delay distribution of the UL and DL are reciprocal [19] [18], there exists a partial reciprocity in terms of the signal subspace for UL and DL wideband channel covariance matrices. Our method makes use of such a partial reciprocity and the low-dimensional structure of the statistical channel information in both spatial and frequency domains.…”

Section: A Partial Channel Reciprocity-based Codebookmentioning

confidence: 99%

“…In this paper, we propose a novel CSI feedback framework based on an idea of joint spatial-frequency domain precoded reference signal. This framework is enabled by the partial UL/DL reciprocity of angle-delay distributions, which was recently validated by measurements [18]. In the traditional methods, the precoding is always performed in spatial domain.…”

Section: Introductionmentioning

confidence: 99%

A Partial Channel Reciprocity-based Codebook for Wideband FDD Massive MIMO

Yin¹,

Gesbert²

2022

Preprint

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The acquisition of channel state information (CSI) in Frequency Division Duplex (FDD) massive MIMO has been a formidable challenge. In this paper, we address this problem with a novel CSI feedback framework enabled by the partial reciprocity of uplink and downlink channels in the wideband regime. We first derive the closed-form expression of the rank of the wideband massive MIMO channel covariance matrix for a given angle-delay distribution. A low-rankness property is identified, which generalizes the well-known result of the narrow-band uniform linear array setting. Then we propose a partial channel reciprocity (PCR) codebook, inspired by the lowrankness behavior and the fact that the uplink and downlink channels have similar angle-delay distributions. Compared to the latest codebook in 5G, the proposed PCR codebook scheme achieves higher performance, lower complexity at the user side, and requires a smaller amount of feedback. We derive the feedback overhead necessary to achieve asymptotically errorfree CSI feedback. Two low-complexity alternatives are also proposed to further reduce the complexity at the base station side. Simulations with the practical 3GPP channel model show the significant gains over the latest 5G codebook, which prove that our proposed methods are practical solutions for 5G and beyond.

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“…The selected channels represent propagation environments typical for indoor transmission (Indoor A and B) and outdoor-to-indoor transmission when the base station is located outdoors. They cover cases of the most frequently occurring propagation conditions including also indoor-to-outdoor transmission scenarios on the basis of channel reciprocity property [24]. Every channel model is characterized by a tapped-delay line model in the time domain.…”

Section: Carriermentioning

confidence: 99%

Estimation of Compressible Channel Impulse Response for OFDM Modulated Transmissions

Dziwoki

Kucharczyk

2021

Electronics

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Channel estimation scheme for OFDM modulated transmissions usually combines an initial block-pilot-assisted stage with a tracking one based on comb or scattered pilots distributed among user data in the signal frame. The channel reconstruction accuracy in the former stage has a significant impact on tracking efficiency of the channel variations and the overall transmission quality. The paper presents a new block-pilot-assisted channel reconstruction procedure based on the DFT-based approach and the Least Square impulse response estimation. The proposed method takes into account a compressibility feature of the channel impulse response and restores its coefficients in groups of automatically controlled size. The proposition is analytically explained and tested in a OFDM simulation environment. The popular DFT-based methods including compressed sensing oriented one were used as references for comparison purposes. The obtained results show a quality improvement in terms of Bit Error Rate and Mean Square Error measures in low and mid ranges of signal-to-noise ratio without significant computational complexity growth in comparison to the classical DFT-based solutions. Moreover, additional multiplication operations can be eliminated, compared to the competitive, in terms of estimation quality, compressed sensing reconstruction method based on greedy approach.

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FDD Massive MIMO Uplink and Downlink Channel Reciprocity Properties: Full or Partial Reciprocity?

Cited by 41 publications

References 13 publications

Deep Generative Models for Downlink Channel Estimation in FDD Massive MIMO Systems

Deep Generative Models for Downlink Channel Estimation in FDD Massive MIMO Systems

A Partial Channel Reciprocity-based Codebook for Wideband FDD Massive MIMO

Estimation of Compressible Channel Impulse Response for OFDM Modulated Transmissions

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