268 research outputs found
DOA Estimation for Hybrid Massive MIMO Systems using Mixed-ADCs: Performance Loss and Energy Efficiency
Due to the power consumption and high circuit cost in antenna arrays, the
practical application of massive multipleinput multiple-output (MIMO) in the
sixth generation (6G) and future wireless networks is still challenging.
Employing lowresolution analog-to-digital converters (ADCs) and hybrid analog
and digital (HAD) structure is two low-cost choice with acceptable performance
loss. In this paper, the combination of the mixedADC architecture and HAD
structure employed at receiver is proposed for direction of arrival (DOA)
estimation, which will be applied to the beamforming tracking and alignment in
6G. By adopting the additive quantization noise model, the exact closedform
expression of the Cramer-Rao lower bound (CRLB) for the HAD architecture with
mixed-ADCs is derived. Moreover, the closed-form expression of the performance
loss factor is derived as a benchmark. In addition, to take power consumption
into account, energy efficiency is also investigated in our paper. The
numerical results reveal that the HAD structure with mixedADCs can
significantly reduce the power consumption and hardware cost. Furthermore, that
architecture is able to achieve a better trade-off between the performance loss
and the power consumption. Finally, adopting 2-4 bits of resolution may be a
good choice in practical massive MIMO systems.Comment: 11 pages, 7 figure
Hybrid Precoding for Multiuser Millimeter Wave Massive MIMO Systems : A Deep Learning Approach
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In multi-user millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems, hybrid precoding is a crucial task to lower the complexity and cost while achieving a sufficient sum-rate. Previous works on hybrid precoding were usually based on optimization or greedy approaches. These methods either provide higher complexity or have sub-optimum performance. Moreover, the performance of these methods mostly relies on the quality of the channel data. In this work, we propose a deep learning (DL) framework to improve the performance and provide less computation time as compared to conventional techniques. In fact, we design a convolutional neural network for MIMO (CNN-MIMO) that accepts as input an imperfect channel matrix and gives the analog precoder and combiners at the output. The procedure includes two main stages. First, we develop an exhaustive search algorithm to select the analog precoder and combiners from a predefined codebook maximizing the achievable sum-rate. Then, the selected precoder and combiners are used as output labels in the training stage of CNN-MIMO where the input-output pairs are obtained. We evaluate the performance of the proposed method through numerous and extensive simulations and show that the proposed DL framework outperforms conventional techniques. Overall, CNN-MIMO provides a robust hybrid precoding scheme in the presence of imperfections regarding the channel matrix. On top of this, the proposed approach exhibits less computation time with comparison to the optimization and codebook based approaches.Peer reviewe
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