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Deep Scanning—Beam Selection Based on Deep Reinforcement Learning in Massive MIMO Wireless Communication System
<span title="2020-11-04">2020</span>
<i title="MDPI AG">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ikdpfme5h5egvnwtvvtjrnntyy" style="color: black;">Electronics</a>
</i>
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In this paper, we investigate a deep learning based resource allocation scheme for massive multiple-input-multiple-output (MIMO) communication systems, where a base station (BS) with a large scale antenna ...
In particular, we propose Deep Scanning, in which a near-optimal beamforming vector can be found based on deep Q-learning. ...
In Reference [7] , which is improved from Reference [8] , two stage beamforming scheme is replaced with two separate neural network blocks, where each network works as antenna selection and RF chain ...
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/electronics9111844">doi:10.3390/electronics9111844</a>
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Neural Calibration for Scalable Beamforming in FDD Massive MIMO with Implicit Channel Estimation
[article]
<span title="2021-08-03">2021</span>
<i >
arXiv
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<span class="release-stage" >pre-print</span>
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In this paper, we propose a deep learning-based approach that directly optimizes the beamformers at the base station according to the received uplink pilots, thereby, bypassing the explicit channel estimation ...
Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. ...
CONCLUSIONS In this paper, we developed a neural calibration-based method for downlink beamforming in FDD massive MIMO systems with implicit channel estimation. ...
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Directional ASR: A New Paradigm for E2E Multi-Speaker Speech Recognition with Source Localization
[article]
<span title="2020-10-30">2020</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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All three functionalities of D-ASR: localization, separation, and recognition are connected as a single differentiable neural network and trained solely based on ASR error minimization objectives. ...
This paper proposes a new paradigm for handling far-field multi-speaker data in an end-to-end neural network manner, called directional automatic speech recognition (D-ASR), which explicitly models source ...
In MIMO-Speech as the spectral masks are estimated directly, it is hard for the network to learn this without reference signals. ...
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Learn to Communicate with Neural Calibration: Scalability and Generalization
[article]
<span title="2021-10-01">2021</span>
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arXiv
</i>
<span class="release-stage" >pre-print</span>
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In this paper, we propose a scalable and generalizable neural calibration framework for future wireless system design, where a neural network is adopted to calibrate the input of conventional model-based ...
The proposed neural calibration framework is applied to solve challenging resource management problems in massive multiple-input multiple-output (MIMO) systems. ...
In [14] , a deep unfolding neural network based on the structure of the WMMSE algorithm was developed for precoding in massive MIMO systems. ...
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Simultaneous Denoising and Dereverberation for Low-Latency Applications Using Frame-by-Frame Online Unified Convolutional Beamformer
<span title="2019-09-15">2019</span>
<i title="ISCA">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/trpytsxgozamtbp7emuvz2ypra" style="color: black;">Interspeech 2019</a>
</i>
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In addition, for both algorithms, the desired signal's relative transfer function (RTF) is estimated by online processing using a neural network based online mask estimation. ...
We derive two different online algorithms, one based on frame-by-frame recursive updating of the spatio-temporal covariance matrix of the captured signal, and the other on recursive least square estimation ...
In the experiments, we use a neural network for the online estimation of the masks. ...
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Deep Learning Framework for Hybrid Analog-Digital Signal Processing in mmWave Massive-MIMO Systems
[article]
<span title="2021-07-30">2021</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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Exploiting the fact that any complex matrix can be written as a scaled sum of two matrices with unit-modulus entries, a novel analog deep neural network (ADNN) structure is first developed which can be ...
In this paper, we present a general deep learning (DL) framework for efficient design and implementation of HSP-based massive-MIMO systems. ...
The network parameters are randomly initialized and subsequently adjusted by means of an iterative process in two-steps: 1) the output of the network is first calculated for each mini-batch (forward propagation ...
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Successive Interference Cancellation using LSTM in MIMO
<span title="2021-12-28">2021</span>
<i title="Smart Moves">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/f6stiz4i3fdudmhdc33nkhy2jy" style="color: black;">SMART MOVES JOURNAL IJOSCIENCE</a>
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And since the antenna is sending signals, evaluating the noise in a noisy channel is essential. ...
OFDM (orthogonal frequency division multiplexing) is a wireless network methodology that sends multiple data streams across a particular channel while effectiently handling inter-symbol interference and ...
INTRODUCTION Signal analysis refers to the process of condensing measured data in order to retrieve data about a distanced natural condition. ...
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Massive MIMO Beam Management in Sub-6 GHz 5G NR
[article]
<span title="2022-04-12">2022</span>
<i >
arXiv
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<span class="release-stage" >pre-print</span>
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We design a neural network based on an autoencoder architecture that uses a beamspace observation in combination with RF environment characteristics to improve the synchronization signal (SS) burst codebook ...
Beam codebooks are a new feature of massive multiple-input multiple-output (M-MIMO) in 5G new radio (NR). ...
In [7] , the concept was extended by replacing the key network parameters with a neural network structure inspired by autoencoders. ...
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Implicit Filter-and-sum Network for Multi-channel Speech Separation
[article]
<span title="2020-11-17">2020</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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Various neural network architectures have been proposed in recent years for the task of multi-channel speech separation. ...
Among them, the filter-and-sum network (FaSNet) performs end-to-end time-domain filter-and-sum beamforming and has shown effective in both ad-hoc and fixed microphone array geometries. ...
Despite the advances in time-frequency domain neural beamformers where a neural network is used to assist the conventional beamformers for better robustness and performance [1] [2] [3] [4] [5] [6] [7] ...
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Intelligent Radio Signal Processing: A Survey
[article]
<span title="2021-06-04">2021</span>
<i >
arXiv
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<span class="release-stage" >pre-print</span>
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Intelligent signal processing for wireless communications is a vital task in modern wireless systems, but it faces new challenges because of network heterogeneity, diverse service requirements, a massive ...
This survey covers four intelligent signal processing topics for the wireless physical layer, including modulation classification, signal detection, beamforming, and channel estimation. ...
The calculations in these two steps can be expressed as follows: E − step : J θ|θ (t) c =E z|x,θ (t) c [ln p (z|θ)] , (4) M − step : θ (t+1) c = arg max θ J θ|θ (t) c , (5) where J θ|θ (t) c refers to ...
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MIMO-SPEECH: End-to-End Multi-Channel Multi-Speaker Speech Recognition
[article]
<span title="2019-10-16">2019</span>
<i >
arXiv
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<span title="2019-10-15">2019</span>
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It is comprised of: 1) a monaural masking network, 2) a multi-source neural beamformer, and 3) a multi-output speech recognition model. ...
MIMO-Speech is a fully neural end-to-end framework, which is optimized only via an ASR criterion. ...
[27] further shows that the neural beamforming function in a multi-channel end-to-end system can enhance the signals. ...
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End-to-End Multi-speaker ASR with Independent Vector Analysis
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<span title="2022-04-01">2022</span>
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We propose a frontend for joint source separation and dereverberation based on the independent vector analysis (IVA) paradigm. ...
It uses the fast and stable iterative source steering algorithm together with a neural source model. ...
Unlike in neural beamforming, the network models a single source. ...
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Intelligent Radio Signal Processing: A Survey
<span title="">2021</span>
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The calculations in these two steps can be expressed as follows: E − step : J θ|θ (t) c =E z|x,θ (t) c [ln p (z|θ)] , (4) M − step : θ (t+1) c = arg max θ J θ|θ (t) c , (5) where J θ|θ (t) c refers to ...
raw data by using a neural network [40] . ...
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Contextual Bandit Learning for Machine Type Communications in the Null Space of Multi-Antenna Systems
<span title="">2019</span>
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Two function approximation methods, a) linear full posterior sampling, and, b) neural networks are proposed for optimal selection of MTD for transmission for the given beamformer. ...
In particular, a cellular system is considered with a multi-antenna BS in which a receive beamformer is designed to maximize the rate of a cellular user, and, a machine type aggregator (MTA) that receives ...
Two function approximation methods, a) linear full posterior sampling, and, b) neural networks are proposed for optimal selection of MTD for transmission for the given beamformer. ...
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Beam Management in Millimeter-wave Communications for 5G and Beyond
<span title="">2020</span>
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Massive MIMO is one of the promising techniques to improve spectral efficiency and network performance for reaching its targeted multi-gigabit throughput in 5G systems. ...
INDEX TERMS 5G NR systems, beam management, physical layer, beyond 5G, hybrid beamforming, AI based beam management. 13282 This work is licensed under a Creative Commons Attribution 4.0 License. ...
The measurement can be based on downlink CSI-RS transmitted by a base station in Case 1 or based on uplink Sounding Reference Signal (SRS) transmitted by a UE in Case 2. ...
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