Communication-Free Inter-Operator Interference Management in Shared Spectrum Small Cell Networks

Emergence of shared spectrum, such as the 3.5-GHz citizen broadband radio service (CBRS) band in the U.S., promises to broaden the mobile operator ecosystem and lead to proliferation of small cell deployments. We consider the inter-operator interference problem that arises when multiple small cell networks access the shared spectrum. Towards this end, we take a novel communication-free approach that seeks implicit coordination between operators without explicit communication. The key idea is for each operator to sense the spectrum through its mobiles to be able to model the channel vacancy distribution and extrapolate it for the next epoch. We use reproducing kernel Hilbert space kernel embedding of channel vacancy and predict it by vector-valued regression. This predicted value is then relied on by each operator to perform independent but optimal channel assignment to its base stations taking traffic load into account. Via numerical results, we show that our approach, aided by the above channel vacancy forecasting, adapts the spectrum allocation over time as per the traffic demands and more crucially, yields as good as or better performance than a coordination-based approach, even without accounting the overhead of the latter.

[1]  Mahesh K. Marina,et al.  GAVEL: strategy-proof ascending bid auction for dynamic licensed shared access , 2016, MobiHoc.

[2]  Carl P. Dettmann,et al.  Temporal Correlation of Interference and Outage in Mobile Networks over One-Dimensional Finite Regions , 2016, IEEE Transactions on Mobile Computing.

[3]  Mihaela van der Schaar,et al.  Cognitive MAC Protocols Using Memory for Distributed Spectrum Sharing Under Limited Spectrum Sensing , 2011, IEEE Transactions on Communications.

[4]  Edgar A. Valencia,et al.  Short-term time series prediction using Hilbert space embeddings of autoregressive processes , 2017, Neurocomputing.

[5]  Petri Mähönen,et al.  Riding the data tsunami in the cloud: myths and challenges in future wireless access , 2013, IEEE Communications Magazine.

[6]  Stavros Toumpis,et al.  Cooperative Relaying Under Spatially and Temporally Correlated Interference , 2013, IEEE Transactions on Vehicular Technology.

[7]  Michael L. Honig,et al.  Auction-Based Spectrum Sharing , 2006, Mob. Networks Appl..

[8]  Paul Honeine,et al.  Kernel autoregressive models using Yule-Walker equations , 2013, Signal Process..

[9]  Martin Haenggi Diversity Loss Due to Interference Correlation , 2012, IEEE Communications Letters.

[10]  Le Song,et al.  A Hilbert Space Embedding for Distributions , 2007, Discovery Science.

[11]  Fei Teng,et al.  Sharing of Unlicensed Spectrum by Strategic Operators , 2017, IEEE Journal on Selected Areas in Communications.

[12]  Thrasyvoulos Spyropoulos,et al.  Robust User Association for Ultra Dense Networks , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[13]  Fernando Paganini,et al.  Mechanism-based resource allocation for multimedia transmission over spectrum agile wireless networks , 2007, IEEE Journal on Selected Areas in Communications.

[14]  Mihaela van der Schaar,et al.  Learning to Compete for Resources in Wireless Stochastic Games , 2009, IEEE Transactions on Vehicular Technology.

[15]  Kin K. Leung Power control by interference prediction for broadband wireless packet networks , 2002, IEEE Trans. Wirel. Commun..

[16]  Christoph H. Lampert Predicting the future behavior of a time-varying probability distribution , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Martin Haenggi,et al.  Interference and Outage in Mobile Random Networks: Expectation, Distribution, and Correlation , 2014, IEEE Transactions on Mobile Computing.

[18]  Jeffrey G. Andrews,et al.  Characterizing Decentralized Wireless Networks with Temporal Correlation in the Low Outage Regime , 2012, IEEE Transactions on Wireless Communications.

[19]  C.-C. Jay Kuo,et al.  A Cognitive MAC Protocol Using Statistical Channel Allocation for Wireless Ad-Hoc Networks , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[20]  Klaus Moessner,et al.  Licensed Spectrum Sharing Schemes for Mobile Operators: A Survey and Outlook , 2016, IEEE Communications Surveys & Tutorials.

[21]  Chunming Qiao,et al.  A walk on the client side: Monitoring enterprise Wifi networks using smartphone channel scans , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[22]  Petri Ahokangas,et al.  Spectrum sharing using licensed shared access: the concept and its workflow for LTE-advanced networks , 2014, IEEE Wireless Communications.

[23]  Martin Haenggi,et al.  Managing Interference Correlation Through Random Medium Access , 2013, IEEE Transactions on Wireless Communications.

[24]  H. Lian Nonlinear functional models for functional responses in reproducing kernel hilbert spaces , 2007, math/0702120.

[25]  Kaushik R. Chowdhury,et al.  A survey on MAC protocols for cognitive radio networks , 2009, Ad Hoc Networks.

[26]  Injong Rhee,et al.  QuickSense: Fast and energy-efficient channel sensing for dynamic spectrum access networks , 2013, 2013 Proceedings IEEE INFOCOM.

[27]  Martin Haenggi,et al.  The Local Delay in Mobile Poisson Networks , 2013, IEEE Transactions on Wireless Communications.

[28]  Bikramjit Singh,et al.  Coordination protocol for inter-operator spectrum sharing in co-primary 5G small cell networks , 2015, IEEE Communications Magazine.

[29]  Le Song,et al.  A unified kernel framework for nonparametric inference in graphical models ] Kernel Embeddings of Conditional Distributions , 2013 .

[30]  Carlo Fischione,et al.  Spectrum Pooling in MmWave Networks: Opportunities, Challenges, and Enablers , 2016, IEEE Communications Magazine.

[31]  Kentaro Ishizu,et al.  Resource allocation in shared spectrum access communications for operators with diverse service requirements , 2016, EURASIP J. Adv. Signal Process..

[32]  Marco Gramaglia,et al.  Mobile traffic forecasting for maximizing 5G network slicing resource utilization , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[33]  Christian Bettstetter,et al.  Temporal Correlation of Interference in Wireless Networks with Rayleigh Block Fading , 2012, IEEE Transactions on Mobile Computing.

[34]  Xuemin Shen,et al.  HC-MAC: A Hardware-Constrained Cognitive MAC for Efficient Spectrum Management , 2008, IEEE Journal on Selected Areas in Communications.

[35]  Chang Liu,et al.  Investing in shared spectrum , 2017, 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).

[36]  Christian Bettstetter,et al.  Semi-Blind Interference Prediction in Wireless Networks , 2017, MSWiM.

[37]  Martin Haenggi,et al.  Temporal Correlation of the Interference in Mobile Random Networks , 2011, 2011 IEEE International Conference on Communications (ICC).

[38]  Ananthram Swami,et al.  Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework , 2007, IEEE Journal on Selected Areas in Communications.

[39]  Amitabha Das,et al.  A survey on MAC protocols in OSA networks , 2009, Comput. Networks.

[40]  Fabrice Valois,et al.  Learning Driven Mobility Control of Airborne Base Stations in Emergency Networks , 2019, PERV.

[41]  Constantinos B. Papadias,et al.  Opportunistic beamforming for secondary users in licensed shared access networks , 2014, 2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP).

[42]  Jihoon Ryoo,et al.  Design and implementation of an end-to-end architecture for 3.5 GHz shared spectrum , 2015, 2015 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).

[43]  Zhi Ding,et al.  Opportunistic spectrum access in cognitive radio networks , 2008, IJCNN.

[44]  Stavros Toumpis,et al.  How does interference dynamics influence packet delivery in cooperative relaying? , 2013, MSWiM.

[45]  Xu Chen,et al.  Database-Assisted Distributed Spectrum Sharing , 2013, IEEE Journal on Selected Areas in Communications.

[46]  Kentaro Ishizu,et al.  Heterogeneous Networks in Shared Spectrum Access Communications , 2017, IEEE Journal on Selected Areas in Communications.

[47]  Holger Claussen,et al.  On the Fundamental Characteristics of Ultra-Dense Small Cell Networks , 2017, IEEE Network.

[48]  Hui Wang,et al.  A Survey on MAC Protocols for Opportunistic Spectrum Access in Cognitive Radio Networks , 2008, 2008 International Conference on Computer Science and Software Engineering.

[49]  Rouzbeh Razavi,et al.  Small cell networks : deployment, management, and optimization , 2018 .

[50]  Mérouane Debbah,et al.  Simultaneous Spectrum Sensing and Data Reception for Cognitive Spatial Multiplexing Distributed Systems , 2017, IEEE Transactions on Wireless Communications.

[51]  Martin Haenggi,et al.  Spatial and temporal correlation of the interference in ALOHA ad hoc networks , 2009, IEEE Communications Letters.