Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction

Koda, Yusuke; Park, Jihong; Bennis, Mehdi; Yamamoto, Koji; Nishio, Takayuki; Morikura, Masahiro; Nakashima, Kouichi

doi:10.1109/lcomm.2020.2978824

Cited by 48 publications

(28 citation statements)

References 31 publications

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“…Under the assumption that the channel gains and AoDs are obtained from the preceding channel estimation process, while also considering that some of those paths might be under random blockage during the actual data transmission, one may notice that possible combinations of blockage patterns due to ω k b,u can be randomly generated and utilized as a training dataset for problem (11). This is based on the established fact that the path blockage probabilities of mmWave channels can be estimated, as demonstrated in [22]- [24].…”

Section: Proposed Methods Part 1: Update Rules a Reformulation As Trained Empirical Risk Minimization Problemmentioning

confidence: 99%

“…It has also been demonstrated that the blockage probability of each channel path component can be estimated and predicted [22]- [24], and thus utilized in the design of robust beamformers with the objective of mitigating performance losses that would result from the aforementioned channel uncertainties [26], [28], [29].…”

Section: Channel and System Model A Motivation: Path Blockage In Mmwavementioning

confidence: 99%

“…The ever-growing demands for data-rate and massive wireless connectivity have driven the fifth generation (5G) standard to incorporate technologies that exploit the millimeter wave (mmWave) spectrum available in the [24][25][26][27][28][29][30] bands [1]- [3]. This trend is expected to continue, with 5G New Radio (NR) aiming to support spectrum bands up to 71 [GHz] in Release 17.…”

Section: Introductionmentioning

confidence: 99%

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A Stochastic Gradient Descent Approach for Hybrid mmWave Beamforming With Blockage and CSI-Error Robustness

et al. 2021

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In this study, we consider the downlink beamforming problem in millimeter wave (mmWave) systems subjected to both path blockages and imperfect channel state information (CSI), and propose a new robust hybrid sum-outage minimizing design as a solution. We first formulate the problem as an empirical risk minimization (ERM) stochastic learning problem, whose solution can be obtained by the alternate iteration of a baseband digital and a radio frequency (RF) analog Riemann manifold-constrained beamforming updates through a mini-batch stochastic gradient descent (MSGD) approach, with gradient minimizing update rules given in closed-form, and learning rates optimized based on the lower-bounds of the corresponding Lipschitz constants. Unlike existing solutions to the path blockage-robust mmWave beamforming problem, wherein out-of-band side information is required or perfect CSI is assumed, our method relies only on the estimates and statistical knowledge of the channel's angles of departure (AoD) and complex gains, which are simultaneously captured in a Bernoulli-Gaussian model and used to generate the training data for the MSGD-based optimizer. Further, unlike preceding fully-digital or fullyconnected hybrid contributions, the proposed scheme assumes a virtually-configured partially-connected setup; therefore, it is compatible with coordinated multipoint (CoMP) architectures, which are known to be crucial in terms of exploiting the full potential of mmWave systems. Simulation results confirm the effectiveness of our MSGD-based robust hybrid CoMP mmWave beamformer in mitigating the effects of path blockage and CSI error, demonstrating its superiority to state-of-the-art (SotA) alternatives.INDEX TERMS Millimeter wave systems, distributed hybrid beamforming, stochastic gradient descent, cooperative multi-point downlink beamforming.

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Section: Proposed Methods Part 1: Update Rules a Reformulation As Trained Empirical Risk Minimization Problemmentioning

confidence: 99%

Section: Channel and System Model A Motivation: Path Blockage In Mmwavementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Stochastic Gradient Descent Approach for Hybrid mmWave Beamforming With Blockage and CSI-Error Robustness

et al. 2021

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“…With these benefits, SL has recently been adopted in medical applications wherein dispersed private health records should be exploited without sharing raw data [21], [75] [79]. SL has also been known for its robustness against non-IID data distributions, and applied for fusing heterogeneous vision and radio-frequency (RF) modalities to predict millimeter-wave channels [80]- [82].…”

Section: Split Learning (Sl)mentioning

confidence: 99%

Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications

et al. 2021

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Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond. By imbuing intelligence into the network edge, edge nodes can proactively carry out decision-making, and thereby react to local environmental changes and disturbances while experiencing zero communication latency. To achieve this goal, it is essential to cater for high ML inference accuracy at scale under time-varying channel and network dynamics, by continuously exchanging fresh data and ML model updates in a distributed way. Taming this new kind of data traffic boils down to improving the communication efficiency of distributed learning by optimizing communication payload types, transmission techniques, and scheduling, as well as ML architectures, algorithms, and data processing methods. To this end, this article aims to provide a holistic overview of relevant communication and ML principles, and thereby present communication-efficient and distributed learning frameworks with selected use cases. SIGNIFICANCE AND MOTIVATIONThe pursuit of extremely stringent latency and reliability guarantees is essential in the fifth generation (5G) communication system and beyond [1], [2]. In a wirelessly automated factory, the remote control of assembly robots should provision the same level of target latency and reliability offered by existing wired factory systems. To this end, for instance, control packets should be delivered within 1 ms with 99.99999% reliability [3]- [5]. Things are becoming even more challenging in the emerging mission-critical applications beyond 5G. A prime example is the forthcoming nonterrestrial networks consisting of a massive constellation of low-altitude earth orbit (LEO) satellites [6]- [11]. Given such

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“…In [16], a proactive handover management framework was proposed to make handover decisions by using camera images and DRL. In [17], a multimodal split learning method based on convLSTM networks was presented to predict mmWave received power through camera images and radio frequency signals while considering communication efficiency and privacy protection. All aforementioned papers are summarized in Table I for comparison purposes.…”

Section: An Overview Of Related Workmentioning

confidence: 99%

Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies, Opportunities, and Challenges

Tian

Pan

Alouini

2021

IEEE Open J. Commun. Soc.

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Deep learning (DL) has seen great success in the computer vision (CV) field, and related techniques have been used in security, healthcare, remote sensing, and many other areas. As a parallel development, visual data has become universal in daily life, easily generated by ubiquitous low-cost cameras. Therefore, exploring DL-based CV may yield useful information about objects, such as their number, locations, distribution, motion, etc. Intuitively, DL-based CV can also facilitate and improve the designs of wireless communications, especially in dynamic network scenarios. However, so far, such work is rare in the literature. The primary purpose of this article, then, is to introduce ideas about applying DL-based CV in wireless communications to bring some novel degrees of freedom to both theoretical research and engineering applications. To illustrate how DL-based CV can be applied in wireless communications, an example of using a DL-based CV with a millimeter-wave (mmWave) system is given to realize optimal mmWave multipleinput and multiple-output (MIMO) beamforming in mobile scenarios. In this example, we propose a framework to predict future beam indices from previously observed beam indices and images of street views using ResNet, 3-dimensional ResNext, and a long short-term memory network. The experimental results show that our frameworks achieve much higher accuracy than the baseline method, and that visual data can significantly improve the performance of the MIMO beamforming system. Finally, we discuss the opportunities and challenges of applying DL-based CV in wireless communications. Index Terms-Computer vision, deep learning, multiple-input and multiple-output, beamforming, beam tracking, long shortterm memory, wireless communications I. INTRODUCTION Recently, deep learning (DL) has seen great success in the computer vision (CV) field. DL networks comprise networks such as deep neural networks, deep belief networks, recurrent neural networks (RNNs), and convolutional neural networks (CNNs). Many DL networks with various structures have emerged with the availability of large image and video datasets

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Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction

Cited by 48 publications

References 31 publications

A Stochastic Gradient Descent Approach for Hybrid mmWave Beamforming With Blockage and CSI-Error Robustness

A Stochastic Gradient Descent Approach for Hybrid mmWave Beamforming With Blockage and CSI-Error Robustness

Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications

Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies, Opportunities, and Challenges

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