Hybrid Precoding With Partially Connected Structure for Millimeter Wave Massive MIMO OFDM: A Parallel Framework and Feasibility Analysis

Dsouza, Kevin Bradley; Prasad, K. N. R. Surya Vara; Bhargava, V.K.

doi:10.1109/twc.2018.2874227

Cited by 29 publications

(22 citation statements)

References 42 publications

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“…where p k,i is the allocated power for the k th user at subcarrier i. After applying the inverse fast Fourier transforms (IFFT) and adding the cyclic prefixes (CP), the analog RF beamformer F ∈ C M ×N RF developed via low-cost phase-shifters is employed, which is identical for all subcarriers 3 [10]- [12]. Then, the transmitted data signal at subcarrier i is written as:…”

Section: System Modelmentioning

confidence: 99%

“…We here propose a new genetic algorithm based resource allocation (GA-RA) technique for MU-mMIMO-OFDM systems. As the RA optimization problem defined in (12) aims to find the optimal allocated powers for K users at subcarrier i, it lies on K-dimensional search space. Thus, each chromosome contains K genes.…”

Section: Genetic Algorithm Based Resource Allocationmentioning

confidence: 99%

“…To address the frequency selectivity issue, the orthogonal frequency division multiplexing (OFDM) technique is widely considered for the wideband signal transmission [10]. In [10]- [12], OFDMbased HP scheme is investigated for mMIMO systems, where one RF beamformer is designed for all subcarriers, while the number of BB precoders should be the same as the number of subcarriers.…”

Section: Introductionmentioning

confidence: 99%

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Energy-Efficient Throughput Maximization in mmWave MU-Massive-MIMO-OFDM: Genetic Algorithm based Resource Allocation

Koç¹,

Bishe²,

Le‐Ngoc³

2022

Preprint

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This paper develops a new genetic algorithm based resource allocation (GA-RA) technique for energy-efficient throughout maximization in multi-user massive multiple-input multiple-output (MU-mMIMO) systems using orthogonal frequency division multiplexing (OFDM) based transmission. We employ a hybrid precoding (HP) architecture with three stages: (i) radio frequency (RF) beamformer, (ii) baseband (BB) precoder, (iii) resource allocation (RA) block. First, a single RF beamformer block is built for all subcarriers via the slow time-varying angle-of-departure (AoD) information. For enhancing the energy efficiency, the RF beamformer aims to reduce the hardware cost/complexity and total power consumption via a low number of RF chains. Afterwards, the reduced-size effective channel state information (CSI) is utilized in the design of a distinct BB precoder and RA block for each subcarrier. The BB precoder is developed via regularized zero-forcing technique. Finally, the RA block is built via the proposed GA-RA technique for throughput maximization by allocating the power and subcarrier resources. The illustrative results show that the throughput performance in the MU-mMIMO-OFDM systems is greatly enhanced via the proposed GA-RA technique compared to both equal RA (EQ-RA) and particle swarm optimization based RA (PSO-RA). Moreover, the performance gain ratio increases with the increasing number of subcarriers, particularly for low transmission powers.

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Section: System Modelmentioning

confidence: 99%

Section: Genetic Algorithm Based Resource Allocationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy-Efficient Throughput Maximization in mmWave MU-Massive-MIMO-OFDM: Genetic Algorithm based Resource Allocation

Koç¹,

Bishe²,

Le‐Ngoc³

2022

Preprint

View full text Add to dashboard Cite

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“…The quadratic sub‐problem

()(, {scriptQ}_{2}^{()(, n)})

has Q real variables. The Upper limit of the number of iterations is

O ()(, Q^{3.5} log ()(, 1 / ε))

[30, 31], where

ε

is the accurate parameter [32]. The complexity of sub‐problem

()(, {scriptQ}_{1}^{()(, n)})

O ()(, N_{normalr} N_{normalt}^{})

, due to matrix multiplications and conjugate transpose.…”

Section: Mmse‐based Hybrid Precoding Designmentioning

confidence: 99%

Near‐optimal hybrid precoding for millimeter wave massive MIMO systems via cost‐efficient Sub‐connected structure

et al. 2020

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“…To address these issues, the hybrid beamforming structure has emerged as a promising solution, which allows the AP to employ a smaller number of RF chains than that of antenna elements [23]- [33]. Considering the tradeoff between performance versus complexity, different types of hybrid beamforming (e.g., fully-connected [24], [25], [27]- [29], [31], [32] and partially-connected [23], [26], [30], [33]) with different kinds of RF electronic modules (e.g., analog switches [23], [26] and analog phase shifters [24], [25], [27]- [33]) have been investigated. In particular, the fully-connected hybrid beamforming is appealing as it can achieve better performance with each RF chain connected to all antennas, while the partially-connected hybrid beamforming shows a lower complexity but compromised performance, since it allows every RF chain to connect to only part of antennas.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Beamforming for Massive MIMO Over-the-Air Computation

Zhai¹,

Chen²,

Xu³

et al. 2020

Preprint

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Over-the-air computation (AirComp) has been recognized as a promising technique in Internet-of-Things (IoT) networks for fast data aggregation from a large number of wireless devices. However, as the number of devices becomes large, the computational accuracy of AirComp would seriously degrade due to the vanishing signalto-noise ratio (SNR). To address this issue, we exploit the massive multiple-input multiple-output (MIMO) with hybrid beamforming, in order to enhance the computational accuracy of AirComp in a cost-effective manner. In particular, we consider the scenario with a large number of multi-antenna devices simultaneously sending data to an access point (AP) equipped with massive antennas for functional computation over the air. Under this setup, we jointly optimize the transmit digital beamforming at the wireless devices and the receive hybrid beamforming at the AP, with the objective of minimizing the computational mean-squared error (MSE) subject to the individual transmit power constraints at the wireless devices. To solve the non-convex hybrid beamforming design optimization problem, we propose an alternating-optimization-based approach, in which the transmit digital beamforming and the receive analog and digital beamforming are optimized in an alternating manner. In particular, we propose two computationally efficient algorithms to handle the challenging receive analog beamforming problem, by exploiting the techniques of successive convex approximation (SCA) and block coordinate descent (BCD), respectively. It is shown that for the special case with a fully-digital receiver at the AP, the achieved MSE of the massive MIMO AirComp system is inversely proportional to the number of receive antennas. Furthermore, numerical results

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Hybrid Precoding With Partially Connected Structure for Millimeter Wave Massive MIMO OFDM: A Parallel Framework and Feasibility Analysis

Cited by 29 publications

References 42 publications

Energy-Efficient Throughput Maximization in mmWave MU-Massive-MIMO-OFDM: Genetic Algorithm based Resource Allocation

Energy-Efficient Throughput Maximization in mmWave MU-Massive-MIMO-OFDM: Genetic Algorithm based Resource Allocation

Near‐optimal hybrid precoding for millimeter wave massive MIMO systems via cost‐efficient Sub‐connected structure

Hybrid Beamforming for Massive MIMO Over-the-Air Computation

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