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Deep Learning at the Edge for Channel Estimation in Beyond-5G Massive MIMO Mauro Belgiovine, Kunal Sankhe, Carlos Bocanegra, Debashri Roy, and Kaushik R

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Deep Learning at the Edge for Channel Estimation in Beyond-5G Massive MIMO Mauro Belgiovine, Kunal Sankhe, Carlos Bocanegra, Debashri Roy, and Kaushik R EDGE INTELLIGENCE FOR BEYOND 5G NETWORKS Deep Learning at the Edge for Channel Estimation in Beyond-5G Massive MIMO Mauro Belgiovine, Kunal Sankhe, Carlos Bocanegra, Debashri Roy, and Kaushik R. Chowdhury BSTRACT large antenna arrays. This article is motivated by A our desire to decouple the scale of deployment Massive multiple-input multiple-output with the limits of classical processing, especially as (mMIMO) is a critical component in upcoming 5G it pertains to the task of understanding the channel wireless deployment as an enabler for high data between a given antenna-receiver antenna-element rate communications. mMIMO is effective when pair for millimeter-wave (mmWave) communi- each corresponding antenna pair of the respec- cation. We accomplish this via training a deep tive transmitter-receiver arrays experiences an inde- learning (DL) architecture that offers the ability to pendent channel. While increasing the number of produce a robust and high fidelity channel matrix antenna elements increases the achievable data between the mobile user and the mMIMO BS in rate, at the same time computing the channel state a single forward pass. Since the overhead of the information (CSI) becomes prohibitively expen- DL-based channel estimation becomes irrespective sive. In this article, we propose to use deep learn- of the size of the antenna array, we believe this ing via a multi-layer perceptron architecture that approach will enable a fundamental leap toward exceeds the performance of traditional CSI pro- beyond 5G (B5G) standards where thousands of cessing methods like least square (LS) and linear coordinated antennas will become the new norm. minimum mean square error (LMMSE) estimation, Emerging B5G networks are envisioned to support thus leading to a beyond fifth generation (B5G) edge computing, which will enable rapid optimiza- networking paradigm wherein machine learning tion and reconfiguration of the network architec- fully drives networking optimization. By computing ture. This is a critical first step toward supporting the CSI of all pairwise channels simultaneously via requirements of emerging high-bandwidth and our deep learning approach, our method scales low-latency applications. Machine learning (ML) with large antenna arrays as opposed to tradi- and artificial intelligence (AI) algorithms running tional estimation methods. The key insight here is at the edge computing servers help to (i) scale to design the learning architecture such that it is the optimization problem without proportional implementable on massively parallel architectures, increase in complexity and (ii) enable fast response such as GPU or FPGA. We validate our approach close to the BS, thus meeting strict demands of a by simulating a 32-element array base station and time-varying wireless channel. We believe our use a user equipment with a 4-element array operating case of DL-enabled mmWave mMIMO demon- on millimeter-wave frequency band. Results reveal strates the need for tightly integrating AI into an improvement up to five and two orders of mag- emerging wireless standards, which remains a gap nitude in BER with respect to fastest LS estimation even in the ongoing 5G rollout today. and optimal LMMSE, respectively, substantially improving the end-to-end system performance and CHALLENGE IN CHANNEL ESTIMATION providing higher spatial diversity for lower SNR Channel estimation is the first step in the larger regions, achieving up to 4 dB gain in received processing chain associated with decoding the power signal compared to performance obtained data packet. Its objective is to identify the com- through LMMSE estimation. plex signal transformation imposed on the emit- ted wireless signal by the channel, and this is INTRODUCTION inferred via special information bits embedded in Large antenna arrays are revolutionizing wireless the packet preamble. For a spatially multiplexed communications and sensing, with manifestations system, this complex transformation is captured in programmable surfaces, gesture monitoring, via the so-called channel state information (CSI). and high rate data delivery through incorporation Knowing the CSI allows the transmitter to perform in the form of massive multiple-input multiple-out- additional precoding functions that maximize the put (mMIMO) systems. Already envisaged as a key signal energy in the direction of interest. Thus, component of 5G, mMIMO utilizes a number of delayed computation of CSI, or worse, an incor- antennas that can be one to two orders of magni- rect computation can quickly degrade the per- tude higher than the classical MIMO WiFi access formance in systems like mMIMO, where the CSI points and LTE base stations (BSs) available today. computation needs to be repeated several dozen However, despite the significant advances in edge times. computing capabilities, there are practical chal- In the context of the B5G use case we explore lenges in processing needs associated with such in this article, we consider time-division duplexing Digital Object Identifier: 10.1109/MWC.001.2000322 The authors are with Northeastern University, Boston. 2 1536-1284/21/$25.00 © 2021 IEEE IEEE Wireless Communications • April 2021 (TDD) for mMIMO and assume that the channel mMIMO Trad. Channel Estimation varies slowly (coherence time of 10–100 ms [1]). Base Station In this regime of operation, two phases involving the BS and user equipment (UE) precede down- OFDM Channel 1 Rx Estimation link transmissions: Channel Sounding, in which 1 the UE performs CSI estimation for the complete OFDM Channel Rx Estimation MIMO channel and sends it back to the BS, and ⋮ Estimated CSI Data Transfer, in which the BS uses the received Channel CSI estimation to compute precoding weights for sounding directional beams. Thus, the CSI estimation must frame Deep Learning Channel Estimation be completed quickly in order to allow both the 1 Channel Sounding and Data Transfer phases to 1 be completed within the channel coherence time. Φ Such a hard threshold on timeliness ensures that ⋮ the BS can turn around its radio front-end and ⋮ leverage channel reciprocity for the downlink trans- ⋮ mission. Furthermore, by focusing on reducing the Φ Estimated CSI overhead associated with the CSI estimation step, : Number of subcarriers; : Number of transmit antenna; : Number of receive antennas; it may be possible to reduce the Channel Sound- ing phase. This in turn will allow more data to be : Orthogonal sequence of length for th transmit antenna : Number of time domain samples in the channel sounding frame transferred in the given channel coherence time, Φ − ultimately increasing the overall throughput of the FIGURE 1. Overview of deep-learning-based channel estimation for B5G mas- system. sive MIMO. SOLUTION OVERVIEW Our proposed approach of using DL aims to The model is trained in a regression fashion in address the above issues by constructing a chan- order to predict for each mMIMO sub-channel nel estimator that is able to obtain the complete the CSI in the frequency domain for the com- MIMO channel matrix by processing the incom- plete set of OFDM pilot and data sub-carriers. ing preambles in a single forward pass, irrespec- This allows learning directly a mapping from the tive of the number of antenna elements involved time-domain signal to its correspondent CSI in the in the system. For downlink, the BS sounds the frequency domain. The proposed DNN model channel by using a reference transmission, which architecture is presented later. allows the UE to estimate the channel using the By training the model on true CSI values proposed DL block. The UE transmits the channel obtained at high signal-to-noise ratio (SNR) level, estimation information back to the BS for calcula- we observe that the proposed method general- tion of the precoding needed for the subsequent izes well for low SNR scenarios and outperforms data transmission. We generate the dataset in the practical least square (LS) estimation in terms MATLAB, which we also release along with the of accuracy, while approaching or exceeding per- simulation code to accelerate further research on formance of linear minimum mean square error this topic. (LMMSE) and improving the end-to-end system performance in low SNR regimes, critical for fre- THE BENEFIT OF DEEP LEARNING quencies above 6 GHz band such as mmWave or Our goal is to leverage the massively parallel THz bands. nature of a type of DL called deep neural net- Moreover, to fully take advantage of this works (DNNs). Specifically, the key idea behind data-driven approach and increase robustness our proposed method is to estimate each of the of the DL pipeline, we add a denoising training sub-channels in the mMIMO channel matrix inde- step, in which we apply controlled additional white pendent from each other. We do so by exploit- Gaussian noise on the training samples. ing similarities in channel dynamics across spatial dimension and using an efficiently tuned DNN SUMMARY OF CONTRIBUTIONS model whose weights are trained in order to be • We propose a deep-learning-based CSI estima- shared across the entire antenna array. Thus, we tion method for mMIMO that incurs a fixed aim to retrieve the complete three-dimensional computational cost, irrespective of the number CSI matrix, where each dimension corresponds of antenna elements, by exploiting the inherent- to the number of receiver antennas, the number ly parallel nature of DNNs. of transmitter antennas, and the number of usable • We discuss the limitations of traditional esti- sub-carriers, by grouping all the received pream- mation techniques and compare the infer- bles in a single batch and processing it in a sin- ence time complexity of the state of the art gle forward step, as shown in Fig. 1. We design a in DL-based channel estimation with the pro- compact multi-layer perceptron (MLP) with only posed approach, demonstrating its suitability three hidden layers to jointly exploit the hierarchi- for edge applications. cal representational power of DNNs while keep- • We validate the performance of CSI estimation ing the execution time associated to its forward by simulating downlink transmissions between step low.
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