Beamspace MIMO transceivers for low-complexity and near-optimal communication at mm-wave frequencies

Song, Gi Hong; Brady, John; Sayeed, A.M.

doi:10.1109/icassp.2013.6638490

Cited by 42 publications

(36 citation statements)

References 18 publications

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“…Furthermore, the channel model described in (13) is a stochastic multipath channel [35]. Many discussions about the channel have been raised over the last years.…”

Section: Matrix Completion Problemmentioning

confidence: 99%

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A Framework to Channel Feedback and Reconstruction using Matrix Completion in Massive MIMO Systems

Valduga

Almeida²,

Silva³

et al. 2018

JCIS

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Abstract-In this paper, we are interested in the problem of channel feedback and reconstruction in frequency division duplexing (FDD)-based massive multiple-input-multiple-output (MIMO) systems. We propose a general framework that allows reducing the overhead significantly in the uplink feedback control channel when assuming massive antenna arrays at both ends of the wireless link. The fundamental idea of the proposed method is to explore the low-rank structure of the channel for its accurate reconstruction at the transmitter side (Tx) with very few uplink feedback information, using matrix completion techniques. The proposed framework consists of two stages. First, upon reception of downlink pilots, the receiver side (Rx) undersamples either the received pilot data matrix or the estimated channel matrix and feeds back only a fraction of their entries to the Tx, throwing away the remaining ones. Then, under the assumption of reduced scattering propagation, the Tx capitalizes on matrix completion to recover either the downlink pilots or to directly reconstruct the downlink channel. We consider two application examples: i) backhauling communication and ii) a multi-user scenario with perfect and imperfect instantaneous channel knowledge. Due to data/channel undersampling, energy consumption at a receiver can be reduced during the uplink feedback transmission. Simulation results show that, compared to the conventional full-feedback approach, which requires feedback of the entire data/channel matrix, the proposed solution can decrease the feedback load in more than 90% for low-rank channels, while providing good channel estimation accuracy, bit error rate (BER) and goodput assuming maximum ratio transmission (MRT) precoding.

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“…Furthermore, the channel model described in (13) is a stochastic multipath channel [35]. Many discussions about the channel have been raised over the last years.…”

Section: Matrix Completion Problemmentioning

confidence: 99%

“…Hence, the angular spread seen by the BS is small, and the number of incoming signal paths is limited. Also, several related works assume similar insights [7], [35], [41]- [47]. Thereby, for the considered scenarios in this work, the channel matrix is assumed to have low-rank.…”

Section: Matrix Completion Problemmentioning

confidence: 99%

A Framework to Channel Feedback and Reconstruction using Matrix Completion in Massive MIMO Systems

Valduga

Almeida²,

Silva³

et al. 2018

JCIS

View full text Add to dashboard Cite

show abstract

“…Similar to (20), each variable can be optimized based on the Lagrangian multiplier method. Therefore, the first-order optimality condition of the Lagrangian function of (27) with respect to f i yields:…”

Section: B the Proposed Pdd Based Algorithmmentioning

confidence: 99%

“…Furthermore, the phase shifters needed in the hybrid structure are replaced by a switching network, which decreases the cost and complexity of the RF hardware as well as improving the power budget. In [20], beamspace MIMO is studied under multi-path propagation conditions, while its extensions to multiuser scenarios are considered in [21], [22]. Within this context, a critical problem of mmWave lens array systems is the design of effective beam selection and digital precoding schemes.…”

Section: Introductionmentioning

confidence: 99%

Joint Design of Beam Selection and Precoding Matrices for mmWave MU-MIMO Systems Relying on Lens Antenna Arrays

Guo

Zhao

Shi

et al. 2018

IEEE J. Sel. Top. Signal Process.

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Abstract-Wireless transmission relying on lens antenna arrays is becoming more and more attractive for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems using a limited number of radio frequency (RF) chains due to the lens' energy-focusing capability. In this paper, we consider the joint design of the beam selection and precoding matrices in order to maximize the sum-rate of a downlink single-sided lens MU-MIMO mmWave system under transmit power constraints. We first formulate the optimization problem into a tractable form using the popular weighted minimum mean squared error (WMMSE) approach. To solve this problem, we then propose an efficient joint beam selection and precoding design algorithm based on the innovative penalty dual decomposition (PDD) method. To reduce the design complexity, we also propose a simplified algorithm by combining the interference-aware beam selection (IA-BS) scheme with the WMMSE approach. Simulation results demonstrate that our proposed algorithms can converge in a few iterations and achieve near-optimal performance when compared to the fully digital precoding scheme, thus enabling them to outperform the competing methods.

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“…Some research to demonstrate the practicability of mm-wave beamformer systems has been done in [5], [6] and particularly the mitigation of interference cancellation using beamspace multiple-input multiple-output (MIMO) has been discussed in [7]. Outdoor propagation measurements carried out in the 28 GHz band illustrate that simple beam combining in itself can improve gain, counteract pathloss and shadow fading [8].…”

mentioning

confidence: 99%

Spatiotemporal-MIMO Channel Estimator and Beamformer for 5G

Sridhar

Gabillard

Manikas

2016

IEEE Trans. Wireless Commun.

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Abstract-With requirements of spiralling data rates and limited spectrum availability, there is an increased interest in mmwave beamformer-based communications for 5G. For upcoming cellular networks, the critical point is to exploit the increased number of employable antennas at both Tx and Rx to (i) combat increased path loss, (ii) tackle higher interference due to higher user density and (iii) handle multipath effects in frequency selective channels. Towards this, a multi-beam spatiotemporal superresolution beamforming framework is proposed in this paper as a promising candidate to design beampatterns that mitigate/suppress co-channel interference and deliver massive gain in the desired directions. Initially, channel and signal models suitable for the mm-wave MIMO system are presented using the manifold vectors of both Tx and Rx antenna arrays. Based on these models, a novel subspace-based channel estimator is employed which estimates delays, directions, velocities and fading coefficients of the desired signal paths. This information is then exploited by the proposed spatiotemporal beamformer to provide massive array gain that combats path loss without increasing the number of antenna array elements and to be tolerant to the nearfar problem in a high interference environment. The performance of the proposed channel estimator and beamformer is examined using computer simulation studies.Index Terms-MIMO, frequency selective channel, mm-wave, spatiotemporal beamformer, channel estimation.

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Beamspace MIMO transceivers for low-complexity and near-optimal communication at mm-wave frequencies

Cited by 42 publications

References 18 publications

A Framework to Channel Feedback and Reconstruction using Matrix Completion in Massive MIMO Systems

A Framework to Channel Feedback and Reconstruction using Matrix Completion in Massive MIMO Systems

Joint Design of Beam Selection and Precoding Matrices for mmWave MU-MIMO Systems Relying on Lens Antenna Arrays

Spatiotemporal-MIMO Channel Estimator and Beamformer for 5G

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