Ergodic Spectrum Management

Ergodic Spectrum Management (ESM)’s basic features are introduced as a cloud-based management of wireless connectivity that targets improvement of internet-user’s quality of experience. Ergodic Spectrum Management (or ESM) learns and exploits near-ergodicity, or time-consistency, to improve a communication-link connection’s stable and efficient use in time, space, and frequency; while using consumer quality of experience as the target metric. ESM methods can also improve existing radio resource management, particularly advancing unlicensed spectrum-use efficiency to levels at or exceeding those associated with licensed spectra, as shown herein. ESM’s use of learned probability distributions’ dimensional (time, space, and frequency) consistencies enables latency-insensitive remote-cloud-based resource management to be applied to wireless multi-user transmission. ESM methods are developed for 3 increasingly more effective stages that correspondingly increasingly rely on data collection and functional-profile (policy) guidance of physical-layer design choices. ESM application to either and both of existing and future unlicensed- and licensed-spectra networks is suggested as a means to improve overall wireless performance. Examples and field data are provided to show the potential of very large improvements in wireless system connectivity, throughput, and quality of experience.

[1]  Honglin Hu,et al.  Distributed Antenna Systems: Open Architecture for Future Wireless Communications , 2007 .

[2]  M. Goodarzi Dynamically Reconfigurable Optical-Wireless Back- haul/Fronthaul with Cognitive Control Plane for Small Cells and Cloud-RANs , 2015 .

[3]  Bo Rong,et al.  Scalable and Flexible Massive MIMO Precoding for 5G H-CRAN , 2017, IEEE Wireless Communications.

[4]  Anna Scaglione,et al.  Distributed Antenna Systems Open Architecture For Future Wireless Communications Wireless Networks And Le Communications , 2006 .

[5]  M. Shamim Kaiser Power Allocation for the Network Coded Cognitive Cooperative Network , 2011, 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing.

[6]  Erik G. Larsson,et al.  Fundamentals of massive MIMO , 2016, SPAWC.

[7]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[8]  C. Shannon,et al.  Communication In The Presence Of Noise , 1998, Proceedings of the IEEE.

[9]  Soumyajit Mandal,et al.  Wireless Communications and Applications Above 100 GHz: Opportunities and Challenges for 6G and Beyond , 2019, IEEE Access.

[10]  Bo Rong,et al.  SDN Controlled mmWave Massive MIMO Hybrid Precoding for 5G Heterogeneous Mobile Systems , 2016, Mob. Inf. Syst..

[11]  Xin Jin,et al.  K-Means Clustering , 2010, Encyclopedia of Machine Learning.

[12]  Jamie S. Evans,et al.  SCALE: A Low-Complexity Distributed Protocol for Spectrum Balancing in Multiuser DSL Networks , 2009, IEEE Transactions on Information Theory.

[13]  A. Leshem,et al.  Distributed coordination of spectrum and the prisoner~s dilemma , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[14]  Evgueni A. Haroutunian,et al.  Information Theory and Statistics , 2011, International Encyclopedia of Statistical Science.

[15]  C.E. Shannon,et al.  Communication in the Presence of Noise , 1949, Proceedings of the IRE.

[16]  Wei Yu,et al.  Iterative water-filling for Gaussian vector multiple-access channels , 2001, IEEE Transactions on Information Theory.

[17]  Wei Yu,et al.  Optimal multiuser spectrum balancing for digital subscriber lines , 2006, IEEE Transactions on Communications.

[18]  Heinrich Meyr,et al.  Synchronization in digital communications , 1990 .

[19]  Li Wang,et al.  Learning Radio Resource Management in RANs: Framework, Opportunities, and Challenges , 2018, IEEE Communications Magazine.

[20]  Marc Moonen,et al.  Iterative spectrum balancing for digital subscriber lines , 2005, IEEE International Conference on Communications, 2005. ICC 2005. 2005.

[21]  Hsiao-Hwa Chen,et al.  Radio Resource Management in Machine-to-Machine Communications—A Survey , 2018, IEEE Communications Surveys & Tutorials.

[22]  H. Vincent Poor,et al.  Cooperative Non-Orthogonal Multiple Access in 5G Systems , 2015, IEEE Communications Letters.

[23]  Günes Karabulut-Kurt,et al.  A Tutorial on Nonorthogonal Multiple Access for 5G and Beyond , 2018, Wirel. Commun. Mob. Comput..

[24]  Kristin L. Sainani,et al.  Logistic Regression , 2014, PM & R : the journal of injury, function, and rehabilitation.

[25]  Kazuki Maruta,et al.  Null-Space Expansion for Multiuser Massive MIMO Inter-User Interference Suppression in Time Varying Channels , 2017, IEICE Trans. Commun..

[26]  Peter Stone,et al.  Reinforcement learning , 2019, Scholarpedia.

[27]  Emil Björnson,et al.  Massive MIMO: ten myths and one critical question , 2015, IEEE Communications Magazine.

[28]  Victor C. M. Leung,et al.  Resource Allocation for Ultra-Dense Networks: A Survey, Some Research Issues and Challenges , 2019, IEEE Communications Surveys & Tutorials.

[29]  Klaus Moessner,et al.  A Survey of Radio Resource Management for Spectrum Aggregation in LTE-Advanced , 2014, IEEE Communications Surveys & Tutorials.

[30]  Athanasios V. Vasilakos,et al.  Full-Duplex Wireless Communications: Challenges, Solutions, and Future Research Directions , 2016, Proceedings of the IEEE.

[31]  Solomon Kullback,et al.  Information Theory and Statistics , 1960 .

[32]  D. Srinivasa Rao,et al.  QoS based Radio Resource Management Techniques for Next Generation MU-MIMO WLANs: A Survey , 2016 .

[33]  Marc Moonen,et al.  Autonomous Spectrum Balancing for Digital Subscriber Lines , 2007, IEEE Transactions on Signal Processing.

[34]  Nikos D. Sidiropoulos,et al.  Learning to optimize: Training deep neural networks for wireless resource management , 2017, 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[35]  Refik Caglar Kizilirmak,et al.  Non-Orthogonal Multiple Access (NOMA) for 5G Networks , 2016 .

[36]  Karim Djouani,et al.  A Survey of Resource Management Toward 5G Radio Access Networks , 2016, IEEE Communications Surveys & Tutorials.

[37]  Lena Schwartz Next Generation Wireless Lans 802 11n And 802 11ac , 2016 .

[38]  Aakanksha Chowdhery,et al.  A centralized multi-level water-filling algorithm for Dynamic Spectrum Management , 2009, 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers.

[39]  Abbas Jamalipour,et al.  Wireless communications , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[40]  Amir Leshem,et al.  Game Theoretic Dynamic Channel Allocation for Frequency-Selective Interference Channels , 2017, IEEE Transactions on Information Theory.

[41]  Rose Qingyang Hu,et al.  Spatial Domain Management and Massive MIMO Coordination in 5G SDN , 2015, IEEE Access.

[42]  Md. Shipon Ali,et al.  An Overview on Interference Management in 3GPP LTE-Advanced Heterogeneous Networks , 2015 .