6G deep learning
216056676
Redefining Wireless Communication for 6G: Signal Processing Meets Deep Learning
The year 2019 witnessed the rollout of 5G standard, which promises to offer significant data rate improvement over 4G. While 5G is still in its infancy, every there has been an increased shift in the research community for communication technologies beyond 5G. The recent emergence of machine learning (ML) approaches for enhancing wireless communications and empowering them with much desired intelligence holds immense potential for redefining wireless communication for 6G. In this article, we present the challenges associated with traditional ML and signal processing approaches, and how combining them towards a model driven approach can revolutionize the 6G physical layer. The goal of this article is to motivate hardware efficient model driven deep learning approaches to enable embedded edge learning capability for future communication networks.
ArXiv
Anu Jagannath Jithin Jagannath Tommaso Melodia
214624511
Deep Learning Based mmWave Beam Selection for 5G NR/6G With Sub 6 GHz Channel Information: Algorithms and Prototype Validation
In fifth generation (5G) communications, millimeter wave (mmWave) is one of the key technologies to increase the data rate. To overcome this technology's poor propagation characteristics, it is necessary to employ a number of antennas and form narrow beams. It becomes crucial then, especially for initial access, to attain fine beam alignment between a next generation NodeB (gNB) and a user equipment (UE) The current 5G New Radio (NR) standard, however, adopts an exhaustive search based beam sweeping, which causes time overhead of a half frame for initial beam establishment. In this paper, we propose a deep learning based beam selection, which is compatible with the 5G NR standard. To select a mmWave beam, we exploit sub 6 GHz channel information. We introduce a deep neural network (DNN) structure and explain how we estimate a power delay profile (PDP) of a sub 6 GHz channel, which is used as an input of the DNN. We then validate its performance with real environment based 3D ray tracing simulations and over the air experiments with a mmWave prototype. Evaluation results confirm that, with support from the sub 6 GHz connection, the proposed beam selection reduces the beam sweeping overhead by up to 79.3
IEEE Access
Min Soo Sim Yeon-Geun Lim Sang Hyun Park Linglong Dai Chan-Byoung Chae
211296496
Deep Learning for Ultra Reliable and Low Latency Communications in 6G Networks
In future 6th generation networks, URLLC will lay the foundation for emerging mission critical applications that have stringent requirements on end to end delay and reliability. Existing works on URLLC are mainly based on theoretical models and assumptions. The model based solutions provide useful insights, but cannot be directly implemented in practice. In this article, we first summarize how to apply data driven supervised deep learning and deep reinforcement learning in URLLC, and discuss some open problems of these methods. To address these open problems, we develop a multi level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC. The basic idea is to merge theoretical models and realworld data in analyzing the latency and reliability and training deep neural networks (DNNs) Deep transfer learning is adopted in the architecture to fine tune the pre trained DNNs in non stationary networks. Further considering that the computing capacity at each user and each mobile edge computing server is limited, federated learning is applied to improve the learning efficiency. Finally, we provide some experimental and simulation results and discuss some future directions.
IEEE Network
Changyang She Rui Dong Zhouyou Gu Zhanwei Hou Yonghui Li Wibowo Hardjawana Chenyang Yang Lingyang Song Branka Vucetic
221655719
A Tutorial of Ultra Reliable and Low Latency Communications in 6G: Integrating Theoretical Knowledge into Deep Learning
As one of the key communication scenarios in the 5th and also the 6th generation (6G) cellular networks, ultra reliable and low latency communications (URLLC) will be central for the development of various emerging mission critical applications. The state of the art mobile communication systems do not fulfill the end to end delay and overall reliability requirements of URLLC. A holistic framework that takes into account latency, reliability, availability, scalability, and decision making under uncertainty is lacking. Driven by recent breakthroughs in deep neural networks, deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLC in future 6G networks. This tutorial illustrates how to integrate theoretical knowledge (models, analysis tools, and optimization frameworks) of wireless communications into different kinds of deep learning algorithms for URLLC. We first introduce the background of URLLC and review promising network architectures and deep learning frameworks in 6G. To better illustrate how to improve learning algorithms with theoretical knowledge, we revisit model based analysis tools and cross layer optimization frameworks for URLLC. Following that, we examine the potential of applying supervised/unsupervised deep learning and deep reinforcement learning in URLLC and summarize related open problems. Finally, we provide simulation and experimental results to validate the effectiveness of different learning algorithms and discuss future directions.
ArXiv
Changyang She Chengjian Sun Zhouyou Gu Yonghui Li Chenyang Yang H Vincent Poor Branka Vucetic
221846596
Deep Learning based LAP Deployment and Aerial Infrastructure Sharing in 6G
Sixth Generation (6G) considers Low Altitude Platform (LAP) based aerial cells for improving coverage and capacity in cellular networks. The available literature on LAP based aerial cells has addressed the altitude and trajectory optimization for LAPs. However, aerial cell sharing amongst operators for capacity enhancement and resource utilization is not studied enough. To the best of our knowledge, for the first time, this paper proposes, (a) four architectures for aerial infrastructure sharing, (b) a novel element, i.e. Aerial Asset Manager (AAM) for deployment and sharing of LAPs by operators from one of the above four architectures. AAM utilizes a Long Short Term Memory (LSTM) based deep learning model for traffic forecasting. The forecast helps to plan the deployment and opportunistic sharing of LAPs amongst operators. The analytical model and simulations results reveal the forecasting accuracy of 96% for data traffic. AAM improves the capacity for operators, maximizes the resource utilization of the LAPs, i.e. the bandwidth utilization increases by 49% and minimizes the number of LAPs, i.e. the fleet size reduces by 45% in comparison to the available literatures.
2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)
Tushar Vrind Sudharshan Rao Lalit Pathak Debabrata Das
220265459
Deep Learning Based Load Balancing for improved QoS towards 6G
Deep learning has made great strides lately with the availability of powerful computing machines and the advent of user friendly programming environments. It is anticipated that the deep learning algorithms will entirely provision the majority of operations in 6G. One such environment where deep learning can be the right solution is load balancing in future 6G intelligent wireless networks. Load balancing presents an efficient, cost effective method to improve the data process capability, throughput, and expand the bandwidth, thus enhancing the adaptability and availability of networks. Hence a load balancing algorithm based on Long Short Term Memory(LSTM) deep neural network is proposed through which the coverage area of base station changes according to geographic traffic distribution, catering the requirement for future generation 6G heterogeneous network. The LSTM model performance is evaluated by considering three different scenarios, and the results were presented. Load variance coefficient(LVC) and load factor(LF) are introduced and validated over two wireless network layouts(WNL) to study the Quality of Service(QoS) and load distribution. The proposed method shows a decrease of LVC by 98.311% and 99.21% for WNL1, WNL2 respectively.
Vishnu Vardhan Nimmalapudi Ajith Kumar Mengani Roopa Vuppula Rahul Jashvantbhai Pandya
225118950
Trustworthy Deep Learning in 6G Enabled Mass Autonomy: From Concept to Quality of Trust Key Performance Indicators
Mass autonomy promises to revolutionize a wide range of engineering, service, and mobility industries. Coordinating complex communication among hyperdense autonomous agents requires new artificial intelligence (AI) enabled orchestration of wireless communication services beyond 5G and 6G mobile networks. In particular, safety and mission critical tasks will legally require both transparent AI decision processes and quantifiable quality of trust (QoT) metrics for a range of human end users (consumer, engineer, and legal) We outline the concept of trustworthy autonomy for 6G, including essential elements such as how explainable AI (XAI) can generate the qualitative and quantitative modalities of trust. We also provide XAI test protocols for integration with radio resource management and associated key performance indicators (KPIs) for trust. The research directions proposed will enable researchers to start testing existing AI optimization algorithms and develop new ones with the view that trust and transparency should be built in from the design through the testing phase.
IEEE Vehicular Technology Magazine
Chen Li Weisi Guo Schyler C Sun Saba Al-Rubaye Antonios Tsourdos
222097768
Channel Estimation for Intelligent Reflecting Surface Enabled Terahertz MIMO Systems: A Deep Learning Perspective
Terahertz (THz) communications have been recognized as a promising technology to provide sufficient spectrum resources and ultra high data rate for sixth generation (6G) wireless communication networks. To compensate the coverage hole caused by the propagation features of THz waves, an intelligent reflecting surface (IRS) is proposed to create the controllable propagation environment. However, the channel acquisition is particularly complicated for the IRS enabled THz multiple input multiple output (MIMO) system, since the IRS is lack of the signal processing capability. To this end, we firstly convert the channel estimation problem into a spare recovery problem by utilizing the sparsity nature of the THz channel. Then a deep learning based channel estimation (DL CE) scheme is developed to solve the sparse recovery problem by revealing the relationship between the received signals and path gains. Simulation results demonstrate that, in contrast with classic compressed sensing based methods, our proposed DL CE method achieves a better recovery performance and greatly decreases the computational complexity.
2020 IEEE/CIC International Conference on Communications in China (ICCC Workshops)
Zhuoxun Li Zhi Chen Xinying Ma Wenjie Chen
214761397
Deep learning based likelihood ascent search detection algorithm for uplink multiuser Massive MIMO system
The research on low complexity detection algorithm for Massive multiple input and multiple output (MIMO) system is one of the hot spots in 5G and 6G communications. At the leading edge of the artificial intelligent field, the deep learning technology has been employed and won many achievements in wireless communications. The deep learning technology has considerable performance for dealing with the big data issue. Actually, The inherent problem of the low complexity detection in Massive MIMO system is similar to that of processing the big data with high dimension. In this paper, a deep learning based likelihood ascent search (DPLAS) detection algorithm is proposed on the basis of the graphical detection model for uplink multiuser Massive MIMO system. Compared to the counterpart algorithms, the proposed DPLAS detection algorithm obtains a better BER performance of $10^ 6} at the average received signal to noise ratio (SNR) of 10 dB and requires a lower average received SNR of 8 dB to achieve the theoretical spectral efficiency along with the polynomial computational complexity of \mathrm{O}(N_{T}{2} per symbol for graphical 4 quadrature amplitude modulation signal when $N_{T}=N_{R}=256$ where NT and NR denote the number of transmitting and receiving antennas, respectively.
2020 International Conference on Computing, Networking and Communications (ICNC)
Lin Li Weixiao Meng
208088173
A Survey of Intelligent Reflecting Surfaces (IRSs) Towards 6G Wireless Communication Networks with Massive MIMO 2.0
Intelligent reflecting surfaces (IRSs) tune wireless environments to increase spectrum and energy efficiencies. In view of much recent attention to the IRS concept as a promising technology for 6G wireless communications, we present a survey of IRSs in this paper. Specifically, we categorize recent research studies of IRSs as follows. For IRS aided communications, the summary includes capacity/data rate analyses, power/spectral optimizations, channel estimation, deep learning based design, and reliability analysis. Then we review IRSs implementations as well as the use of IRSs in secure communications, terminal positioning, and other novel applications. We further identify future research directions for IRSs, with an envision of the IRS technology playing a critical role in 6G communication networks similar to that of massive MIMO in 5G networks. As a timely summary of IRSs, our work will be of interest to both researchers and practitioners working on IRSs for 6G networks.
Jun Zhao