Reliable Beamspace Channel Estimation for Millimeter-Wave Massive MIMO Systems with Lens Antenna Array
TLDR
In this article, a support detection (SD)-based channel estimation scheme was proposed to estimate the support of sparse beamspace channel with comparable or higher accuracy than conventional schemes, and the performance and complexity analyses were provided to prove that the proposed SD-based channel estimator can estimate the SBS with comparable performance and low pilot overhead.Abstract:
Millimeter-wave (mm-wave) massive MIMO with lens antenna array can considerably reduce the number of required radio-frequency (RF) chains by beam selection. However, beam selection requires the base station to acquire the accurate information of beamspace channel. This is a challenging task as the size of beamspace channel is large, while the number of RF chains is limited. In this paper, we investigate the beamspace channel estimation problem in mm-wave massive MIMO systems with lens antenna array. Specifically, we first design an adaptive selecting network for mm-wave massive MIMO systems with lens antenna array, and based on this network, we further formulate the beamspace channel estimation problem as a sparse signal recovery problem. Then, by fully utilizing the structural characteristics of the mm-wave beamspace channel, we propose a support detection (SD)-based channel estimation scheme with reliable performance and low pilot overhead. Finally, the performance and complexity analyses are provided to prove that the proposed SD-based channel estimation scheme can estimate the support of sparse beamspace channel with comparable or higher accuracy than conventional schemes. Simulation results verify that the proposed SD-based channel estimation scheme outperforms conventional schemes and enjoys satisfying accuracy even in the low SNR region as the structural characteristics of beamspace channel can be exploited.read more
Citations
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Millimeter Wave Communications for Future Mobile Networks
Ming Xiao,Shahid Mumtaz,Yongming Huang,Linglong Dai,Yonghui Li,Michail Matthaiou,George K. Karagiannidis,Emil Björnson,Kai Yang,Chih-Lin I,Amitabha Ghosh +10 more
TL;DR: A comprehensive survey of mmWave communications for future mobile networks (5G and beyond) is presented, including an overview of the solution for multiple access and backhauling, followed by the analysis of coverage and connectivity.
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Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems
TL;DR: The learned denoising-based approximate message passing (LDAMP) network is exploited and significantly outperforms state-of-the-art compressed sensing-based algorithms even when the receiver is equipped with a small number of RF chains.
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Channel Estimation for Orthogonal Time Frequency Space (OTFS) Massive MIMO
TL;DR: A 3D-structured orthogonal matching pursuit algorithm based channel estimation technique to solve the downlink channel estimation problem for OTFS massive MIMO.
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Prospective Multiple Antenna Technologies for Beyond 5G
TL;DR: Three new multiple antenna technologies that can play key roles in beyond 5G networks: cell-free massive MIMO, beamspace massive M IMO, and intelligent reflecting surfaces are surveyed.
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Multibeam 3-D-Printed Luneburg Lens Fed by Magnetoelectric Dipole Antennas for Millimeter-Wave MIMO Applications
TL;DR: In this article, a 3D-printed Luneburg lens with a simplified geometry is presented, where rod-type structures are employed as the unit cell of the gradient-index material to realize the required permittivity distribution in the lens.
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TL;DR: In this paper, the authors propose a multiuser communication architecture for point-to-point wireless networks with additive Gaussian noise detection and estimation in the context of MIMO networks.
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Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays
Fredrik Rusek,Daniel Persson,Buon Kiong Lau,Erik G. Larsson,Thomas L. Marzetta,Fredrik Tufvesson +5 more
TL;DR: The gains in multiuser systems are even more impressive, because such systems offer the possibility to transmit simultaneously to several users and the flexibility to select what users to schedule for reception at any given point in time.
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Scaling up MIMO: Opportunities and Challenges with Very Large Arrays
Fredrik Rusek,Daniel Persson,Buon Kiong Lau,Erik G. Larsson,Thomas L. Marzetta,Ove Edfors,Fredrik Tufvesson +6 more
TL;DR: Very large MIMO as mentioned in this paper is a new research field both in communication theory, propagation, and electronics and represents a paradigm shift in the way of thinking both with regards to theory, systems and implementation.