Deep Learning for 5G Innovation Insights from Patents
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Patents are a good information resource for obtaining the state of the art of deep learning for 5G wireless telecommunications technology innovation insights. I.Deep Learning for 5G Technology Innovation Status Patents that specifically describe the major deep learning applications in 5G are a good indicator of the deep learning for 5G innovations in a specific innovation entity. To find the deep learning for 5G technology innovation status, patent applications in the USPTO as of June 5, 2020 that specifically describe the major deep learning applications in 5G are searched and reviewed. 24 published patent applications that are related to the key deep learning for 5G technology innovation are selected for detail analysis. II. Deep Learning for 5G Technology Innovation Details Patent information can provide many valuable insights that can be exploited for developing and implementing new technologies. Patents can also be exploited to identify new product/service development opportunities. MIMO Adaptive Antenna/Samsung Electronics MIMO Communication Channel/ Parallel Wireless Radio Access Network (RAN)/Beijing University of Posts and Telecommunications
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Deep Learning for 5G Innovation Insights from Patents
- 1. 1 ©2020 TechIPm, LLC All Rights Reserved http://www.techipm.com/ Deep Learning for 5G Innovation Insights from Patents Alex G. Lee1 Patents are a good information resource for obtaining the state of the art of deep learning for 5G wireless telecommunications technology innovation insights. I. Deep Learning for 5G Technology Innovation Status Patents that specifically describe the major deep learning applications in 5G are a good indicator of the deep learning for 5G innovations in a specific innovation entity. To find the deep learning for 5G technology innovation status, patent applications in the USPTO as of June 5, 2020 that specifically describe the major deep learning applications in 5G are searched and reviewed. 24 published patent applications that are related to the key deep learning for 5G technology innovation are selected for detail analysis. Following figure shows the deep learning for 5G patent application landscape with respect to the innovation entity. As shown in the figure, deep learning for 5G innovation entities are Samsung Electronics, Verizon. Virginia Tech, Ball Aerospace & Technologies, Beijing University of Posts and Telecommunications, Bluecom Systems and Consulting, CableLabs, DeepSig Inc., Futurewei Technologies, Google, Incelligent P.C., Korea Advanced Institute of Science and Technology, Micron Technology, Parallel Wireless, Inc., QoScience, Inc., Qualcomm, and Sony. 1 Alex G. Lee, Ph.D Esq., is a CTO and patent attorney at TechIPm, LLC.
- 3. 3 ©2020 TechIPm, LLC All Rights Reserved http://www.techipm.com/ Following figure shows the deep learning for 5G patent application landscape with respect to the key technology innovation field. As shown in the figure, MIMO is the most innovated deep learning for 5G technology followed by Cell Coverage Planning, Network Performance Optimization (QoS), Radio Resource Management, Radio Access Network (RAN), RF system, Adaptive Traffic Scheduler, Cognitive Radio (SDR), Communication Channel Modeling, Controlling Spectral Regrowth, Dynamic Software Defined Networking (DSDN), Network, Traffic Optimization, Predicting Received Signal Strength, Proactive Network Management, Transceiver Beam Management, and Wireless Scene Identification.
- 4. © ©2020 TechIPm, Adaptive T Cognitive Radio (SDR) 4% Communication Channel Modeling 4% Controlling Spectral Regrowth 4% Dynamic Softwar Defined Networki (DSDN) 4% Network T Optimiza 4% Predicting Re Stre 4 LLC All Rights Re Traffic Scheduler 4% re ing Traffic tion eceived Signal ength 4% Proactive N Manage 4% De eserved http://ww Network ment Transceiv Manage 4% eep Learning f ww.techipm.com RF system 8% ver Beam ement % Wireless Scene 4% for 5G Key Te / Radio Acc Identification % echnology Lan MIMO 17% cess Network (RAN 8% ndscape USP Cell Coverag 8% Networ Optim ) TO 2020 2Q ge Planning % k Performance mization (QoS) 8% Radio Resource Management 8% 4
- 5. © A e t y l ©2020 TechIPm, A patent coun evolution of t technology in year) and the learning for 5 LLC All Rights Re nting for grow echnology inn nnovation grow publication d G technology 1 1 1 NumberofPatentApplications eserved http://ww wth in patentin novation. Foll wth trends. Si date by around y innovation a 0 2 4 6 8 10 12 14 2016 ww.techipm.com ng over a perio lowing patent ince there is u d two years, th activity is in ra 2017 Priority Yea / od of times ca t application a usually a time he patent appl apid growth s 2018 ar an be a good m activity chart lag between t lication activi stage. D A measuring too shows the dee the initial app ty chart indic Deep Learning for 5G Applications ol for monitor ep learning fo plication date ates that the d G Patent 5 ring the or 5G (priority deep
- 6. 6 ©2020 TechIPm, LLC All Rights Reserved http://www.techipm.com/ II. Deep Learning for 5G Technology Innovation Details Patent information can provide many valuable insights that can be exploited for developing and implementing new technologies. Patents can also be exploited to identify new product/service development opportunities. MIMO Adaptive Antenna/Samsung Electronics For next generation cellular systems such as 5G wireless communications system, efficient and unified radio resource management is desirable. To decrease propagation loss of the radio waves and increase the transmission coverage the 5G system implements the base station antenna that is the massive multiple-input multiple-output (MIMO) beam forming array antenna. US20190372644 illustrates a method for controlling and optimizing the broadcast beam for the base stations (jointly tune the antenna beam width and e-tilt angle). In a dynamic scenario where user equipments (UEs) are assumed to be moving according to some mobility pattern in a given cell of base station (BS) of 5G system, the antenna beam selection problem using deep reinforcement learning (Deep RL) includes two parts such as offline training and online deployment. The offline training part is to learn the UE distribution pattern from history data and to teach the neural network on the UE distribution pattern. After obtaining typical UE distribution patterns, these patterns together with ray-tracing data can be used to train the Deep RL network. After the neural network is trained, it can be deployed to provide beam guidance for the network online. In the Deep RL terminology, selecting the beam parameters (beam shape, tilt angles) can be regarded as the action. The measurements from the UEs in the network can be regarded as the observations. Based on the
- 7. 7 ©2020 TechIPm, LLC All Rights Reserved http://www.techipm.com/ observations, several reward values can be calculated. The reward can be the total number of connected UEs in the network based on the state and action taken. In DQN-based Deep RL, the Q-values are predicted using deep neural network. Input to the neural network is the UEs' state of the Deep RL environment, and output is the Q-values corresponding to the possible actions. Two identical neural networks are used in predicting the Q-values. One is used for computing the running Q-values--this neural network is called the evaluation network. The other neural network, called the target neural network is held fixed for some training duration, say for M episodes, and every M episode the weights of the evaluation neural network is transferred to the target neural network. Following table shows the detailed algorithm of the BS antenna beams are selected in dynamic scenarios for multiple sector case, where each sector can independently select own beam parameters to maximize the overall network coverage.
- 9. 9 ©2020 TechIPm, LLC All Rights Reserved http://www.techipm.com/ MIMO Communication Channel/ Parallel Wireless A large-scale MIMO system with high spectrum and energy efficiency is a very promising key technology for 5G wireless communications. For the large-scale MIMO system, accurate channel state information (CSI) acquisition is a challenging problem, especially when each user has to distinguish and estimate numerous channels coming from a large number of transmit antennas from BSs in the downlink as the overhead for channel estimation is linearly proportional to the number of antennas. US20190274108 illustrates a cost effective and reliable MIMO communication channel estimation method using deep learning technology. In communication channel estimation, superimposed pilot subcarriers are used due to the fact that there is no need to increase the number of subcarriers used even as the number of antennas used increases. Especially for FDD channel estimation, using superimposed pilot subcarriers, instead of traditional orthogonal subcarriers, can reduce the number of subcarriers required down from M*Np to Np; (M is the number of transmitter antennas; Np is the number of subcarriers required per Tx antenna). Exploiting deep learning to learn DL (downlink) channel estimation, a cost effective and reliable MIMO communication channel estimation can be possible: Offline training the first deep learning network to learn DL channel estimation is performed. The user equipment (UE) can use this process to obtain actual DL channel estimation during real-time operation (online) based on received inputs from the base-station side. Subsequently, via feeding these inputs to the offline trained deep-learning network, the UE can feed the learned DL channel estimation and obtain the compressed encoded feedback. Then, the compressed encoded feedback is sent back by the UE to the BS to minimize the UL feedback cost. The BS feeds the received compressed encoded feedback to the second half of trained deep learning network
- 10. 10 ©2020 TechIPm, LLC All Rights Reserved http://www.techipm.com/ to recover the DL channel estimation as obtained by the UE. Once the DL channel estimation is recovered, the BS can design the optimal precoding matrix accordingly. Via this procedure, both DL and UL transmission cost can be significantly reduced in the massive-MIMO context. Following figure shows an example deep learning network using the Convolutional Neural Network (CNN) model for DL (DL-CNN) channel estimation. The DL-CNN channel estimation is running per time slot per subcarrier. Assuming the training duration is T, and the number of training vectors (including input & channel vectors) is Y. The CNN will be trained offline with T*Y samples. The offline training process will be repeated to adapt to channel change. The interval is based on running performance. Channel multi-path parameters are acquired via channel sounding.
- 11. 11 ©2020 TechIPm, LLC All Rights Reserved http://www.techipm.com/ Radio Access Network (RAN)/Beijing University of Posts and Telecommunications A flexible and dynamic RAN can perform the end-to-end self-configuration of the RAN, sense the quality of the user's experiences and network performance in real time, and perform predictive analysis and intelligent optimization. According to application scenarios supported 5G wireless communication system, the RAN slices can be classified into four typical types, including a wide-area seamless coverage slice, a hot-spot high- capacity slice, a low-power large-connection slice and a low-delay high-reliability slice. In practical network deployment, a new slice type can be added according to the network requirements. US20200178093 illustrates the RAN performance evaluation of the existing slices2 using deep learning technology. The RAN performance evaluation is performed for the existing slices in the network to reduce the complexity of network operations and the instability of network performance that are caused by the frequent change of the global network configuration. The evaluation module evaluates the network in the current network according to historical wireless transmission data of the network and the terminal measurement report that are collected by the data collection processor, performs a reserving, adding or deleting operation for the existing slices in the network, so as to determine the global slice setting. The evaluation process includes: according to real-time wireless transmission data of the network and the terminal measurement data that are collected by the data collection processor from the RAN, and by using a Convolutional Neural Network (CNN) and a Recurrent Neural 2 The network performance includes a set of network spectrum efficiency, network transmission delay, network connection number, network power consumption efficiency, network delay jitter, and extreme transmission speed.
- 12. 12 ©2020 TechIPm, LLC All Rights Reserved http://www.techipm.com/ Network (RNN), extracting a spatial feature and temporal feature of the data respectively as show in the following figure, obtaining a network performance status level corresponding to the current slice, and reporting it to the centralized evaluation module. For different types of slices, the weight values of different network performance status levels in the set are different. The CNN and the RNN can provide a mapping relationship between wireless transmission data, terminal measurement data and network performance statuses through a training process of historical measurement data of the network. According to the real-time wireless transmission data and terminal measurement data, a network performance status level can be obtained: According to the historical wireless transmission data and the terminal measurement data obtained in the RAN and by using the CNN and the RNN, the spatial feature and temporal
- 13. 13 ©2020 TechIPm, LLC All Rights Reserved http://www.techipm.com/ feature of the data are extracted respectively. Then, the network performance status level corresponding to the historical wireless transmission data and the terminal measurement data is determined. The determined network performance status is compared with the actual network performance status, and the values of the weight parameters of the CNN and the RNN are adjusted according to a comparison result. Based on the RAN performance evaluation, RAN self-optimization within the slice can be done through Deep Reinforcement Learning (DRL). The current networking mode and resource allocation of the slice are used as status variables of the DRL and are input to a node supporting the training of the deep learning model. By using the service level and resources utilization efficiency obtained based on the historical resource configuration scheme in the data collection processor, an intra-slice resource allocation adjustment strategy is output to realize the highest network resource utilization efficiency and the best service performance level. The to-be-adjusted resource allocation ratio of each slice is used as an action variable of the DRL. After the adjustment of the resource allocation, the network resource utilization efficiency collected by the data collection processor and the performance indicators of each slice are used as DRL reward variables. Then, the online learning is performed through a Deep Q Network (DQN). Following figure shows a flowchart illustrating a process of deriving a resource adjustment strategy within a slice by using a DRL training model.