Blind channel estimation for massive MIMO

Peken, Ture; Vanhoy, Garrett; Bose, T.

doi:10.1007/s10470-017-0943-1

Cited by 39 publications

(16 citation statements)

References 17 publications

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“…STBCs make it possible to apply blind estimation of the channel by observing only the received signals [24], [25], [26], and leveraging this MIMO's blind estimation capability with STBC is what we propose in this paper for improving the resiliency of deep learning based RF fingerprinting to channel condition variations.…”

Section: ) Transmit Diversity and Space-time Block Codingmentioning

confidence: 99%

Leveraging Multiple Transmissions and Receptions for Channel-Agnostic Deep Learning-Based Network Device Classification

Basha,

Hamdaoui

2021

Preprint

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The accurate identification of wireless devices is critical for enabling automated network access monitoring and authenticated data communication in large-scale networks; e.g., IoT. RF fingerprinting has emerged as a solution for device identification by leveraging the transmitter unique manufacturing impairments. Although deep learning is proven efficient in classifying devices based on the hardware impairments fingerprints, DL models perform poorly due to channel variations. That is, although training and testing neural networks using data generated during the same period achieve reliable classification, testing them on data generated at different times degrades the accuracy substantially, an already well recognized problem within the community. To the best of our knowledge, we are the first to propose to leverage MIMO capabilities to mitigate the channel effect and provide a channel-resilient device classification. We show that for AWGN channels, combining multiple received signals improves the testing accuracy by up to 30%. We also show that for Rayleigh channels, blind channel estimation enabled by MIMO increases the testing accuracy by up to 40% when the models are trained and tested over the same channel, and by up to 60% when the models are tested on a channel that is different from that used for training.

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Section: ) Transmit Diversity and Space-time Block Codingmentioning

confidence: 99%

Leveraging Multiple Transmissions and Receptions for Channel-Agnostic Deep Learning-Based Network Device Classification

Basha,

Hamdaoui

2021

Preprint

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“…A straightforward (and common) way to address the scaling ambiguity is through the transmission of a short training preamble. 11 The above updates can be equivalently written (cf. Appendix C) as…”

Section: Tensor-based Formulationmentioning

confidence: 99%

“…10 Also referred to as componentwise optimization [78]. 11 Alternative ways include appropriate normalization of one of the factors (e.g., [45]) or the transmission of a pilot sequence at one of the subcarriers (e.g., [23], [79]).…”

Section: Tensor-based Formulationmentioning

confidence: 99%

A Tensor-based Approach to Joint Channel Estimation / Data Detection in Flexible Multicarrier MIMO Systems

Kofidis

2019

Preprint

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Filter bank-based multicarrier (FBMC) systems have attracted increasing attention recently in view of their many advantages over the classical cyclic prefix (CP)-based orthogonal frequency division multiplexing (CP-OFDM) modulation. However, their more advanced structure (resulting in, for example, self interference) complicates signal processing tasks at the receiver, including synchronization, channel estimation and equalization. In a multiple-input multiple-output (MIMO) configuration, the multi-antenna interference has also to be taken into account. (Semi-) blind receivers, of increasing interest in (massive) MIMO systems, have been little studied so far for FBMC and mainly for the single-antenna case only. The design of such receivers for flexible MIMO FBMC systems, unifying a number of existing FBMC schemes, is considered in this paper through a tensorbased approach, which is shown to encompass existing joint channel estimation and data detection approaches as special cases, adding to their understanding and paving the way to further developments. Simulation-based results are included, for realistic transmission models, demonstrating the estimation and detection performance gains from the adoption of these receivers over their training only-based counterparts. I. INTRODUCTIONF ILTER bank-based multicarrier (FBMC) systems [1] have recently attracted increasing attention as a competent alternative to the classical cyclic prefix (CP)-based orthogonal frequency division multiplexing (CP-OFDM) modulation, in a wide range of applications, including both wireless (e.g., ad-hoc public safety) and wired (e.g., fiber optics) communications [2]. The potential of FBMC transmission stems from its increased ability to carrying a flexible spectrum shaping, along with a major increase in spectral efficiency and robustness to synchronization requirements, features of fundamental importance in modern and future networks. However, its more advanced structure (resulting in, for example, self interference) complicates signal processing tasks at the receiver, including synchronization, channel estimation and equalization [2]. In a multiple-input multiple-output (MIMO) configuration, the multi-antenna interference has also to be taken into account, which can be a challenge in, for example, offset quadrature amplitude modulation (OQAM)-based FBMC [3]. Nevertheless FBMC(/OQAM) is being also considered for massive MIMO systems [4], [5] and has also shown its potential in the envisaged millimeter wave (mmWave) communications [6].

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“…As there is a large number of antennas used by massive MIMO, therefore the channels estimations associated with such antennas will also be large which is a tedious task because of the enormous increasing dimensions of the channel matrix. Many channel estimation techniques for CIR are proposed in the related research literature which can be classified into superimposed [ 11 ], training [ 12 ], pilot [ 13 ], blind [ 14 ], and semi-blind [ 15 ] techniques, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

Spectral and Energy Efficient Low-Overhead Uplink and Downlink Channel Estimation for 5G Massive MIMO Systems

Khan

Zafar

Jan

et al. 2018

Entropy

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Uplink and Downlink channel estimation in massive Multiple Input Multiple Output (MIMO) systems is an intricate issue because of the increasing channel matrix dimensions. The channel feedback overhead using traditional codebook schemes is very large, which consumes more bandwidth and decreases the overall system efficiency. The purpose of this paper is to decrease the channel estimation overhead by taking the advantage of sparse attributes and also to optimize the Energy Efficiency (EE) of the system. To cope with this issue, we propose a novel approach by using Compressed-Sensing (CS), Block Iterative-Support-Detection (Block-ISD), Angle-of-Departure (AoD) and Structured Compressive Sampling Matching Pursuit (S-CoSaMP) algorithms to reduce the channel estimation overhead and compare them with the traditional algorithms. The CS uses temporal-correlation of time-varying channels to produce Differential-Channel Impulse Response (DCIR) among two CIRs that are adjacent in time-slots. DCIR has greater sparsity than the conventional CIRs as it can be easily compressed. The Block-ISD uses spatial-correlation of the channels to obtain the block-sparsity which results in lower pilot-overhead. AoD quantizes the channels whose path-AoDs variation is slower than path-gains and such information is utilized for reducing the overhead. S-CoSaMP deploys structured-sparsity to obtain reliable Channel-State-Information (CSI). MATLAB simulation results show that the proposed CS based algorithms reduce the feedback and pilot-overhead by a significant percentage and also improve the system capacity as compared with the traditional algorithms. Moreover, the EE level increases with increasing Base Station (BS) density, UE density and lowering hardware impairments level.

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Blind channel estimation for massive MIMO

Cited by 39 publications

References 17 publications

Leveraging Multiple Transmissions and Receptions for Channel-Agnostic Deep Learning-Based Network Device Classification

Leveraging Multiple Transmissions and Receptions for Channel-Agnostic Deep Learning-Based Network Device Classification

A Tensor-based Approach to Joint Channel Estimation / Data Detection in Flexible Multicarrier MIMO Systems

Spectral and Energy Efficient Low-Overhead Uplink and Downlink Channel Estimation for 5G Massive MIMO Systems

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