Low-Complexity Zero-Forcing Precoding for XL-MIMO Transmissions

Ribeiro, Lucas N.; Schwarz, Štefan; Haardt, Martin

doi:10.23919/eusipco54536.2021.9616090

Cited by 13 publications

(4 citation statements)

References 8 publications

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“…Also, mmWave communications did not consider in [23] and [25]. In [28], authors proposed two low complexity ZF approximation schemes for precoding and user grouping. Those schemes are based on dividing the antennas at BS into sub-arrays and grouping users according to their elevation angles.…”

Section: Related Workmentioning

confidence: 99%

Performance of Large Intelligent Surfaces in Multiuser Millimeter Wave MIMO-NOMA Systems

Arafat,

Maher,

El-Mahdy

et al. 2023

IEEE Access

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Large Intelligent Surfaces (LIS) are considered a candidate technology for 6G networks due to its simple deployment and relatively low cost. Recently, Millimeter Wave (mmWave) communication has attracted much attention due to its huge spectrum resources (30-300 GHz), which supports gigabits per second data rates. However, it suffers from severe path loss compared to path attenuation at lowfrequency bands. Massive MIMO compensates this path loss by adding more antennas in a small area. Nonorthogonal multiple access (NOMA) is one of the multiple access techniques that increases the data rate of the system. In this paper, we integrate LIS with multi-user MIMO-NOMA in the mmWave band to enhance the overall system performance. Hybrid precoding is performed by having a zero-forcing digital precoder at the base station and an analog precoding at the LIS. Users are grouped into clusters to minimize multi-user interference. Joint optimization of power allocation of NOMA users, LIS phases, and gains is proposed to maximize the energy efficiency of the system. Closed form expressions for the optimum phases and gains 1 This article has been accepted for publication in IEEE Access.

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Section: Related Workmentioning

confidence: 99%

Performance of Large Intelligent Surfaces in Multiuser Millimeter Wave MIMO-NOMA Systems

Arafat,

Maher,

El-Mahdy

et al. 2023

IEEE Access

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“…Besides, they often neglected the spatial non-stationary property [19]. Moreover, to reduce the complexity of the signal processing, some new algorithms, e.g., variational message passing (VMP) [17], [20], mean-angle-based zero-forcing (MZF), and tensor ZF (TZF) [21] have been proposed. To the best knowledge of the authors, even though the above algorithms based on the ZF have low signal processing complexity, the SE performance is still unsatisfactory.…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Precoding for Extremely Large-Scale MIMO Over Non-Stationary Channels

Xu¹,

Wang²,

Xiao³

et al. 2023

Preprint

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Extremely large-scale multiple-input-multiple-output (XL-MIMO) is a promising technology for the future sixthgeneration (6G) networks to achieve higher performance. In practice, various linear precoding schemes, such as zero-forcing (ZF) and regularized zero-forcing (RZF) precoding, are capable of achieving both large spectral efficiency (SE) and low bit error rate (BER) in traditional massive MIMO (mMIMO) systems. However, these methods are not efficient in extremely large-scale regimes due to the inherent spatial non-stationarity and high computational complexity. To address this problem, we investigate a low-complexity precoding algorithm, e.g., randomized Kaczmarz (rKA), taking into account the spatial non-stationary properties in XL-MIMO systems. Furthermore, we propose a novel mode of randomization, i.e., sampling without replacement rKA (SwoR-rKA), which enjoys a faster convergence speed than the rKA algorithm. Besides, the closed-form expression of SE considering the interference between subarrays in downlink XL-MIMO systems is derived. Numerical results show that the complexity given by both rKA and SwoR-rKA algorithms has 51.3% reduction than the traditional RZF algorithm with similar SE performance. More importantly, our algorithms can effectively reduce the BER when the transmitter has imperfect channel estimation.

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“…As a result, a user may merely see only partial antennas instead of the entire array, and these antennas that can be observed by the user are referred to its visibility region (VR) [9]. The existence of VR may reduce the array gain, on the other hand, it can also help reduce the complexity of precoding and user scheduling [7,[10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, the performances of MRC and linear minimum mean squared error (LMMSE) receiver in the presence of VR were analyzed in [12]. Considering a non-stationary propagation channel, the authors leveraged user grouping and plane-wave approximation to reduce the complexity of precoding in XL-MIMO, and developed two novel ZF precoders in [10]. Besides, precoding design and several user scheduling schemes were also elaborated by utilizing the independence of different users' VR in [7,11,13].…”

Section: Introductionmentioning

confidence: 99%

Average Spectral Efficiency for TDD-based non-Stationary XL-MIMO with VR Estimation

Zhang

Han

et al. 2022

2022 14th International Conference on Wireless Communications and Signal Processing (WCSP)

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Extra large-scale multiple-input multiple-output (XL-MIMO) is a key technology for future wireless communication systems. This paper considers the effects of visibility region (VR) at the base station (BS) in a non-stationary multiuser XL-MIMO scenario, where only partial antennas can receive users' signal. In time division duplexing (TDD) mode, we first estimate the VR at the BS by detecting the energy of the received signal during uplink training phase. The probabilities of two detection errors are derived and the uplink channel on the detected VR is estimated. In downlink data transmission, to avoid cumbersome Monte-Carlo trials, we derive a deterministic approximate expression for ergodic average energy efficiency (EE) with the regularized zero-forcing (RZF) precoding. In frequency division duplexing (FDD) mode, the VR is estimated in uplink training and then the channel information of detected VR is acquired from the feedback channel. In downlink data transmission, the approximation of ergodic average EE is also derived with the RZF precoding. Invoking approximate results, we propose an alternate optimization algorithm to design the detection threshold and the pilot length in both TDD and FDD modes. The numerical results reveal the impacts of VR estimation error on ergodic average EE and demonstrate the effectiveness of our proposed algorithm.

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Low-Complexity Zero-Forcing Precoding for XL-MIMO Transmissions

Cited by 13 publications

References 8 publications

Performance of Large Intelligent Surfaces in Multiuser Millimeter Wave MIMO-NOMA Systems

Performance of Large Intelligent Surfaces in Multiuser Millimeter Wave MIMO-NOMA Systems

Low-Complexity Precoding for Extremely Large-Scale MIMO Over Non-Stationary Channels

Average Spectral Efficiency for TDD-based non-Stationary XL-MIMO with VR Estimation

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