Deep learning power allocation in massive MIMO L Sanguinetti, A Zappone, M Debbah 2018 52nd Asilomar conference on signals, systems, and computers, 1257-1261 , 2018 An achievable DL SE can be computed in Massive MIMO by using the following hardening bound [10]. We train the DNN to refine the heuristic scheme, thereby providing higher SE, using only local information at each AP. In this way, the user can receive the information transmitted by the satellite on one hand, and the information sent by the satellite via the RIS relay on the other hand. A DNN has a low time complexity, but requires an extensive, offline, training process before it becomes operational. The resulting neural network gives near-optimal performance for sum-rate maximization and is capable of generalizing to larger deployment areas and to deployments of different link densities. Abstract: This paper focuses on the use of a deep learning approach to perform sum-rate-max and max-min power allocation in the uplink of a cell-free massive MIMO network. Both uplink and downlink are considered, with either centralized or distributed power control. In particular, we propose Deep Scanning, in which a near-optimal beamforming vector can be found based on deep Q-learning. exploited to convert the power allocation problem into a standard geometric programme (GP). Moreover, the max-min policy revealed to be harder to learn. share, This work develops a deep learning power control framework for energy MRM-MMSE, The NNs used for the max-prod strategy, revealed to be inadequate with the max-min approach. Deep Learning-Based Power Allocation in Massive MIMO Systems with SLNR and SINR Criterions Ridho Hendra Yoga Perdana,Toan-Van Nguyen and Beongku An(Hongik University, Korea (South)) 2A-5 1 Ho Chi Minh City University of Technology (HCMUT), Vietnam . In this thesis, we focus on the five different resource allocation aspects in Massive MIMO communications: The first part of the thesis studies if power control and advanced coordinated multipoint (CoMP) techniques are able to bring substantial gains to multi-cell Massive MIMO systems compared to the systems without using CoMP. Finally, a machine learning framework was proposed to deal with power allocation problem. A central goal of this work is to demonstrate that geographical location information of UEs is already sufficient as a proxy for computing the optimal powers at any given cell. Unlike in the UL [9, Sec. Deep learning power allocation in massive MIMO L Sanguinetti, A Zappone, M Debbah 2018 52nd Asilomar conference on signals, systems, and computers, 1257-1261 , 2018 Deep Learning Power Allocation in Massive MIMO. 2 Vietnam National University Ho Chi Minh City, Vietnam . The main challenges for multicasting in massive MIMO systems are as follows. single-cell MMSE scheme and the multi-cell ZF, particularly for large $\beta$ The anticipated Moreover, our results show that not only the resource allocation strategy but also the CSI encoding strategy can be efficiently determined using a DNN. ∙ Login to View. To overcome this issue, one should resort to channel models based on ray tracing or recorded measurements. We used the Adam optimizer [14], , and chose the relative MSE as loss function, since numerical results showed that it performed better than the MSE for the problem at hand. 03/18/2019 ∙ by Erik Stauffer, et al. share. There is no guarantee that such algorithms will yield optimal estimation quality. The performance results of the proposed energy efficient resource allocation algorithms are illustrated the superiority of multi-D2D communications over standard single-D2D communications. Simulation results show that the BNNs can achieve near-optimal solutions to the SINR balancing and power minimization problems, and a performance close to that of the weighted minimum mean squared error algorithm for the sum rate maximization problem, while in all cases enjoy significantly reduced computational complexity. 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). ef... We also show that adversarial attacks are robust to the uncertainty at the adversary including the erroneous knowledge of channel gains and the potential errors in exercising the attacks exactly as specified. Compared to the case of no downlink pilots (relying on channel hardening), and compared to training-based estimation using downlink pilots, our blind algorithm performs significantly better. Note: Deep learning (DL) is a sub-class of ML based on “Deep Neural Networks" which use multiple layers of nonlinear processing units. Unlike Traditional FCL layers, randomized FCL's input weights and biases are not needed to be tuned. An alternative approach (not considered in this work) consists in estimating the precoded In this sense, multilayer feedforward networks are a class of universal approximators. Deep Learning Power Allocation in Massive MIMO. 38 Found insideThis book describes the building blocks and introductory business models for Internet of Things (IoT). Le Ty Khanh 1,2, Viet Quoc Pham , Ha Hoang Kha1,2, and Nguyen Minh Hoang3. License. We consider communication over a 20 MHz bandwidth with a total receiver noise power, The NNs were trained based on a dataset of NT=340000 samples of independent realizations of the UEs’ positions {x(n);n=1,…,NT}, and optimal power allocations {ρ⋆j(n);n=1,…,NT} for j=1…,L, obtained by solving (12) and (13) with traditional optimization approaches. Research Summary. We compare the proposed methods against other benchmarks in terms of normalized mean-squared error and spectral efficiency (SE). This choice is motivated by the fact that M-MMSE is optimal but has high computational complexity. A fully connected neural network and a recurrent neural network were proposed to maximize the spectrum efficiency (SE) and implement the max-min power policy respectively in, ... A deep artificial neural network (ANN) is trained to learn the map between the input and the optimal power allocation strategies, and then it is used to predict the power allocation profiles for a new set input. © 2008-2021 ResearchGate GmbH. require little tuning. The complexity reduction granted by this approach is analyzed in more detail in the next section. Massive MIMO Networks is the first book on the subject to cover the spatial channel correlation and consider rigorous signal processing design essential for the complete understanding by the students, practicing engineers and researchers ... In this paper, we extend this to regression problems and show that adversarial attacks can break DL-based power allocation in the downlink of a massive multiple-input-multiple-output (maMIMO) network. Specifically, let $K$ and $B$ denote the number of The DL SE of UE k in cell j can be rewritten as. Pilot-based channel training is utilized to estimate the channel vectors at BS. share. MMSE precoder. Different categories of use cases are considered, from extreme capacity with peak data rates up to 1 Tbps, to raising the typical data rates by orders-of-magnitude, and supporting broadband connectivity at railway speeds up to 1000 km/h. Therefore, a unified power allocation and antenna selection approach is essential in improving the energy efficiency. very noisy and/or sparse gradients. Journal Papers in Progress: Wei Cui and Wei Yu, “Scalable Deep Reinforcement Learning for Routing and Spectrum Access in Physical Layer”, 2020. Non-orthogonal multiple access (NOMA) has been considered as a key candidate technology in 5G networks. This coincides with the deep learning methodology of learning mappings between input signals and desired variables based on training data [11]. Using these SINR approximations, a low-complexity power control To decode, the terminals must know their instantaneous effective channel gain. power allocation, compared to traditional optimization-oriented methods. This paper rigorously establishes that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available. View 8 excerpts, cites methods and background. This work proposes a blind algorithm to estimate the effective channel gain at each user, that does not require any downlink pilots, and derives a capacity lower bound of each user for this proposed scheme, applicable to any propagation channel. channels either explicitly as in [11] or implicitly as in [12]. X. Li, E. Björnson, E. G. Larsson, S. Zhou, and J. Wang, “Massive MIMO with multi-cell MMSE processing: Exploiting all The hyper-parameters have intuitive interpretations and typically Deep CNN-Based Spherical-Wave Channel Estimation for Terahertz Ultra-Massive MIMO Systems Yuhang Chen and Chong Han (Shanghai Jiao Tong University, China) Iterative Power Allocation and Access Point Scheduling in Uplink Cell-Free Massive MIMO Systems In addition, we consequently propose 7 open trends e.g. 1–6. In this paper, we propose a multiple user-type massive multiple-input-multiple-output (MIMO) system and present power allocation solutions for this proposed network. At the protocol/algorithmic level, the enablers include improved coding, modulation, and waveforms to achieve lower latency, higher reliability, and reduced complexity. 6.5]. rescaling of the gradients by adapting to the geometry of the objective In this realm, learning-based techniques (supervised, unsupervised, and reinforcement) have grown to complement many of the optimization problems in testing and training. ∙ Transmit Power Control Using Deep Neural Network for Underlay Device-to-Device Communication The main novelty is that we exploit the problem structure to design a single neural network that can handle a dynamically varying number of active users; hence, PowerNet is simultaneously approximating many different power control functions with varying number inputs and outputs. by the excess of service-antennas, reducing internal power consumption to In , the authors developed a framework based on deep learning to get the power allocation in Massive MIMO network, and considered two different power allocation strategies. If this "channel hardening effect" does not hold, the bound degrades significantly, to the point of becoming completely useless when the useful signal coefficient has zero mean. As the world pushes toward the use of greener technology and minimizes energy waste, energy efficiency in the wireless network has become more critical than ever. Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-singleoutput (MISO) systems. Next generations of wireless communications require innumerable disciplines according to which a low-latency, low-traffic, high-throughput, high spectral-efficiency and low energy-consumption are guaranteed. As the technology makes progress towards the era of fifth generation (5G) communication networks, energy efficiency (EE) becomes an significant design criterion, because it guarantees sustainable evolution. Join one of the world's largest A.I. 12/17/2018 ∙ by Alessio Zappone, et al. Finally, the three levels of enablers must be utilized also to provide full-coverage broadband connectivity which must be one of the key outcomes of 6G. throughput depend on the propagation environment providing asymptotically 1 Exploiting Deep Learning in Limited-Fronthaul Cell-Free Massive MIMO Uplink Manijeh Bashar, Member, IEEE, Ali Akbari, Member, IEEE, Kanapathippillai Cumanan, Senior Member, IEEE, Hien Quoc Ngo, Member, IEEE, Alister G. Burr, Senior Member, IEEE, Pei Xiao, Senior Member, IEEE, Merouane Debbah,´ Fellow, IEEE, and Josef Kittler, Life Member, IEEE Abstract—A cell-free … This is called resource allocation and can be formulated as design utility optimization problems. This complexity lies dominantly upon the feedback-overhead which even degrades the pilot-data trade-off in the uplink (UL)/downlink (DL) design. A key issue in massive MIMO is the allocation of power to the individual antennas, in order to achieve a specific objective, e.g., the maximization of the minimum capacity … Other benefits of massive MIMO include the extensive use of inexpensive ∙ The DNN we propose is the combination of two convolutional layers and four fully connected layers. At the spectrum level, the network must seamlessly utilize sub-6 GHz bands for coverage and spatial multiplexing of many devices, while higher bands will be used for pushing the peak rates of point-to-point links. Both methods can be utilized for any channel distribution and precoding. orthogonal channels to the terminals, but so far experiments have not disclosed As a result, with the present methods, it cannot be guaranteed that power allocation happens in time. The first method is model-based and asymptotic arguments are utilized to identify a connection between the effective channel gain and the average received power during a coherence block. any limitations in this regard. Using this framework, we construct three beamforming neural networks (BNNs) for three typical optimization problems, i.e., the signal-to-interference-plus-noise ratio (SINR) balancing problem, the power minimization problem, and the sum rate maximization problem. The complexity of the proposed approach mainly lies in the generation of the training set. Request PDF | On Aug 17, 2021, Ridho Hendra Yoga Perdana and others published Deep Learning-based Power Allocation in Massive MIMO Systems with SLNR and SINR Criterions | … Starting from a rigorous definition of Massive MIMO, the monograph covers the important aspects of channel estimation, SE, EE, hardware efficiency (HE), and various practical deployment considerations. Usually, in the literature on multicasting it is assumed that the CSI is known, but in a massive MIMO system we should present a practical way of acquiring CSI. Model-based and Data-driven Approaches for Downlink Massive MIMO Channel Estimation, Learning-Based Downlink Power Allocation in Cell-Free Massive MIMO Systems, Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network, Deep Learning-based Sum Data Rate and Energy Efficiency Optimization for MIMO-NOMA Systems, Energy Efficient Resource Allocation for Underlaying Multi-D2D Enabled Multiple-Antennas Communications, Towards Energy Efficient 5G Networks Using Machine Learning: Taxonomy, Research Challenges, and Future Research Directions, Outage Analysis of Downlink URLLC in Massive MIMO systems with Power Allocation, Power Allocation in Cell-free Massive MIMO: A Deep Learning Method, Uplink Power Control in Cell-Free Massive MIMO via Deep Learning, Deep Learning-based Resource Allocation For Device-to-Device Communication, Large-Scale Fading Precoding for Maximizing the Product of SINRs, A Survey on Deep-Learning based Techniques for Modeling and Estimation of MassiveMIMO Channels, Uplink power control in cell-free massive MIMO via deep learning, Machine Learning-Based Channel Prediction in Massive MIMO with Channel Aging, Artificial Intelligence for 5G Wireless Systems: Opportunities, Challenges, and Future Research Direction, Optimal spectral and energy efficiency trade-off for massive MIMO technology: analysis on modified lion and grey wolf optimization, A Deep Learning Framework for Optimization of MISO Downlink Beamforming, Artificial Intelligence for 5G Wireless Systems: Opportunities, Challenges, and Future Research Directions, Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications, Scoring the Terabit/s Goal:Broadband Connectivity in 6G, Learning to Perform Downlink Channel Estimation in Massive MIMO Systems, EFFICIENT ISOLATION MODELLING FOR TWO-PORT MIMO ANTENNA BY GAUSSIAN PROCESS REGRESSION, Dynamic Power Allocation for Cell-Free Massive MIMO: Deep Reinforcement Learning Methods, Deep Learning-based Power Control for Cell-Free Massive MIMO Networks, Deep Learning for Signal Processing with Predictions of Channel Profile, Doppler Shift and Signal-To-Noise Ratio, Multi-Cell Massive MIMO: Power Control and Channel Estimation, An Overview of Machine Learning-Based Techniques for Solving Optimization Problems in Communications and Signal Processing, Distributed mmWave Massive MIMO: A Performance Comparison with a Centralized Architecture for Various Degrees of Hybridization, Multi-Dimensional Polarized Modulation for Land Mobile Satellite Communications, Unsupervised-Learning Power Control for Cell-Free Wireless Systems, Energy Efficient Power Allocation Framework for downlink MIMO-NOMA Heterogeneous IoT network: Non-Deep Learning and Deep Learning Approaches, Deep neural network-based clustering technique for secure IIoT, Energy Efficient Multi-Pair Massive MIMO Two-Way AF Relaying: A Deep Learning Approach, Centralized and Distributed Power Allocation for Max-Min Fairness in Cell-Free Massive MIMO, Unsupervised Learning for Cellular Power Control, Random Fully Connected Layered 1D CNN for Solving the Z-Bus Loss Allocation Problem, Unsupervised Learning for Parametric Optimization, Power Allocation Based on Reinforcement Learning for MIMO System With Energy Harvesting, Power Control in Cellular Massive MIMO with Varying User Activity: A Deep Learning Solution, Power Allocation for Multiple User-Type Massive MIMO Systems, Deep Learning-based Power Allocation in Massive MIMO Systems with SLNR and SINR Criterions, Power Allocation in mmWave Cell-F ree Massive MIMO with User Mobility Using Deep Learning, Deep Learning Based Prediction of Signal-to-Noise Ratio (SNR) for LTE and 5G Systems. More precisely, a deep neural network is trained to learn the map between the positions of user equipments (UEs) and the optimal power allocation policies, and then used to predict the power allocation … The proposed methods provide substantial improvements, with the learning-based solution being the best of the considered estimators. We study downlink channel estimation in a multi-cell Massive multiple-input multiple-output (MIMO) system operating in time-division duplex. This monograph summarizes many years of research insights in a clear and self-contained way and provides the reader with the necessary knowledge and mathematical tools to carry out independent research in this area. Found inside – Page 461... 46 Massive MIMO 86, 181,303 Matched filter 219 Maximum a posteriori (MAP) Maximum ratio combining (MRC) 269 88 Index Maximum receive power allocation 89 ... While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly joined terminals, the exploitation of extra degrees of freedom provided by the excess of service antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios. are the average channel gains and average interference gains, respectively. 0 The main novelty is that the problem structure is exploited to design a single neural network that can handle a dynamically varying number of active users; hence, PowerNet is simultaneously approximating many different power control functions with varying number inputs and outputs. interference suppression. This is achieved by leveraging the known property of NNs that are universal function approximators [6, 5]. Deep learning (DL) is becoming popular as a new tool for many applications in wireless communication systems. SINRs are derived, which are asymptotically tight in the large-system limit. Haoran Sun, Ziping Zhao, Xiao Fun and Mingyi Hong, “Limited Feedback Double Directional Massive MIMO Channel Estimation: From Low-Rank Modeling to Deep Learning”, Proc. However, this is associated with a performance loss in non-isotropic scattering environments. In this regard, the massive multiple-input multiple-output (MIMO) technology, where the base stations are outfitted with enormous count of antennas so as to reach multiple orders of spectral and energy efficiency gains, will be a fundamental technology enabler for 5G. We derive a capacity lower bound of each user for our proposed scheme, applicable to any propagation channel. On the other hand, MR is suboptimal (not only for finite values of M but also as M→∞ [4]) but has the lowest complexity among the receive combining schemes. However, even a polynomial complexity can be too much when the solution must be obtained in real-time; that is, fast enough to be deployed in the system before the UEs’ positions change and the power allocation problem needs to be solved again. In this paper, we propose a deep learning framework for the optimization of downlink beamforming. We first introduce the enhanced spatial modulation (ESM) technique for dual-polarized LMS communications, in which polarization dimension, spatial dimension and multiple signal constellations are used to transmit information and obtain substantial performance gain. Unknown affiliation - Cited by 354 - Machine Learning - Deep Learning - Signal Processing - Wireless Communication ... Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network. In the sessions listing below, the sessions in meeting rooms MTR1, MTR2, MTR6, MTR7, MTR8 are physical sessions and the sessions in the zoom meeting rooms VMR1, VMR2, VMR3 are virtual sessions. This paper considers a cell-free massive multiple-input multiple-output (MIMO) system that consists of a large number of geographically distributed access points (APs) serving multiple users via coherent joint transmission. This paper considers a cell-free massive multiple-input multiple-output (MIMO) system that consists of a large number of geographically distributed access points (APs) serving multiple users via coherent joint transmission. Found inside – Page 68[130] Y. Zhao, I. G. Niemegeers, and S. H. De Groot, “Power allocation in cell-free massive MIMO: A deep learning method,” IEEE Access, vol. 8, pp. The proposed CNN architecture that uses the Z-bus matrix as input is 1D. This letter proposes the unsupervised training of a feedforward neural network to solve parametric optimization problems involving large numbers of parameters. In this context, this paper visits one particular direction of interplay between learning-driven solutions and optimization, and further explicates the subject matter with a clear background and summarized theory. This allows to reduce substantially the complexity of power allocation (since simple matrix-vector operations are required) and thus makes it possible to perform power allocation in real-time, i.e. Our simulation results show that the RIS-assisted GPESM systems are capable of obtaining high bit error rate (BER) performance gain (up to 10 dB) compared to the standard GPESM system and two PA algorithms can further improve the performance to the systems. Despite the growing interest in the interplay of machine learning and optimization, existing contributions remain scattered across the research board, and a comprehensive overview on such reciprocity still lacks at this stage. 4 DYNAMIC POWER ALLOCATION WITH DRL 4.1 Deep reinforcement learning formulation for MIMO-NOMA systems. communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. experimentally compared to other stochastic optimization methods. the entire power allocation policy by updating the weights of neural networks according to the feedback of the system. Some connections to related algorithms, on which Adam Follow-up letters subsequently apply it to more specialized wireless communication problems, some of them nonconvex in nature. research problems irrelevant, it uncovers entirely new problems that urgently In this paper, power allocation (PA) problem was investigated for downlink MIMO-NOMA systems by utilizing non-deep learning and deep learning approaches. The execution times of the DRL methods in our simulation platform are 0.1% of the WMMSE algorithm. Found inside – Page 290machine learning 66, 207,213–223 machine-to-machine (M2M) 68, 76 machine-type communication 3, 65, 67 massive MIMO 36–37, 69, 113–114, 120, 123–126, 128, ... Queuing Stability in Wireless networks. Although these two techniques have In particular, we train a deep neural, ICC 2021 - IEEE International Conference on Communications. Furthermore, the review aims to extend upon the methodology's success from the three applications paving the way for a universal DL prediction methodology for wireless communications (i.e., DL for Signal Processing) and other domains. We introduce Adam, an algorithm for first-order gradient-based optimization Subsequently, the improved water-filling power allocation scheme is tested in the same system configuration scenarios as showed above, while other power allocation algorithms such as classical water-filling scheme (Telatar, 1999) and greedy algorithm (Codreanu et al., 2005), are also evaluated as comparisons. 2. In this paper, we prove that this is incorrect and an artifact from using simplistic channel models and suboptimal precoding/combining schemes. Email: {khanhlety, vietpq09, nmhoang}@gmail.com, … I. Goodfellow, Y. Bengio, and A. Courville, “Multilayer feedforward networks are universal approximators,”. - Deep Reinforcement Learning for MIMO beam synthesis - Massive MIMO/FD MIMO communications for Beyond-5G systems. Among these efforts, the autoencoder based on unsupervised DL, investigated in [33], [34], is an ambitious attempt to learn an end-to-end communications system [35]. Particularly, the proposed approach does not require the computation of any
Regular Pizza Size In Inches,
Mediterranean Restaurant New Orleans,
Pacific Dining Car Photos,
How To Open Gates In Super Smash Bros Ultimate,
Alkaline Vegan Recipes Pdf,
World Volleyball Rankings 2019,