Deep Learning for Frame Error Probability Prediction in BICM-OFDM Systems

In the context of wireless communications, we propose a deep learning approach to learn the mapping from the instantaneous state of a frequency selective fading channel to the corresponding frame error probability (FEP) for an arbitrary set of transmission parameters. We propose an abstract model of a bit interleaved coded modulation (BICM) orthogonal frequency division multiplexing (OFDM) link chain and show that the maximum likelihood (ML) estimator of the model parameters estimates the true FEP distribution. Further, we exploit deep neural networks as a general purpose tool to implement our model and propose a training scheme for which, even while training with the binary frame error events (i.e., ACKs / NACKs), the network outputs converge to the FEP conditioned on the input channel state. We provide simulation results that demonstrate gains in the FEP prediction accuracy with our approach as compared to the traditional effective exponential SIR metric (EESM) approach for a range of channel code rates, and show that these gains can be exploited to increase the link throughput.Comment: Submitted to 2018 IEEE International Conference on Acoustics, Speech and Signal Processin

The METIS 5G System Concept: Meeting the 5G Requirements

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] The development of every new generation of wireless communication systems starts with bold, high-level requirements and predictions of its capabilities. The 5G system will not only have to surpass previous generations with respect to rate and capacity, but also address new usage scenarios with very diverse requirements, including various kinds of machine-type communication. Following this, the METIS project has developed a 5G system concept consisting of three generic 5G services: extreme mobile broadband, massive machine-type communication, and ultra-reliable MTC, supported by four main enablers: a lean system control plane, a dynamic radio access network, localized contents and traffic flows, and a spectrum toolbox. This article describes the most important system-level 5G features, enabled by the concept, necessary to meet the very diverse 5G requirements. System-level evaluation results of the METIS 5G system concept are presented, and we conclude that the 5G requirements can be met with the proposed system concept.This work was supported in part by the European Commission under FP7, grant number ICT-317669 METIS.Tullberg, H.; Popovski, P.; Li, Z.; Uusitalo, MA.; Hoglund, A.; Bulakci, O.; Fallgren, M.... (2016). The METIS 5G System Concept: Meeting the 5G Requirements. IEEE Communications Magazine. 54(12):132-139. https://doi.org/10.1109/MCOM.2016.1500799CMS132139541

Architectures for Cognitive Radio Testbeds and Demonstrators – An Overview

Wireless communication standards are developed at an ever-increasing rate of pace, and significant amounts of effort is put into research for new communication methods and concepts. On the physical layer, such topics include MIMO, cooperative communication, and error control coding, whereas research on the medium access layer includes link control, network topology, and cognitive radio. At the same time, implementations are moving from traditional fixed hardware architectures towards software, allowing more efficient development. Today, field-programmable gate arrays (FPGAs) and regular desktop computers are fast enough to handle complete baseband processing chains, and there are several platforms, both open-source and commercial, providing such solutions. The aims of this paper is to give an overview of five of the available platforms and their characteristics, and compare the features and performance measures of the different systems

METIS research advances towards the 5G mobile and wireless system definition

[EN] The Mobile and wireless communications Enablers for the Twenty-twenty Information Society (METIS) project is laying the foundations of Fifth Generation (5G) mobile and wireless communication system putting together the point of view of vendors, operators, vertical players, and academia. METIS envisions a 5G system concept that efficiently integrates new applications developed in the METIS horizontal topics and evolved versions of existing services and systems. This article provides a first view on the METIS system concept, highlights the main features including architecture, and addresses the challenges while discussing perspectives for the further research work.Part of this work has been performed in the framework of the FP7 project ICT-317669 METIS, which is partly funded by the European Commission. The authors would like to acknowledge the contributions of their colleagues in METIS with special thanks to Petar Popovski, Peter Fertl, David Gozalvez-Serrano, Andreas Hoglund, Zexian Li, and Krystian Pawlak. Also thanks to Josef Eichinger and Malte Schellmann for the fruitful discussions during the revision of this article.Monserrat Del Río, JF.; Mange, G.; Braun, V.; Tullberg, H.; Zimmermann, G.; Bulakci, O. (2015). METIS research advances towards the 5G mobile and wireless system definition. EURASIP Journal on Wireless Communications and Networking. 2015(53):1-16. https://doi.org/10.1186/s13638-015-0302-9S116201553Cisco, in Global Mobile Data Traffic Forecast Update, 2014–2019 White Paper, February 2015. http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white_paper_c11-520862.pdfMETIS, in Mobile and wireless communications Enablers for the Twenty-twenty Information Society, EU 7th Framework Programme project, http://www.metis2020.com .ICT-317669 METIS project, in Scenarios, requirements and KPIs for 5G mobile and wireless system, Deliverable D1.1, May 2013, https://www.metis2020.com/documents/deliverables/B Ahlgren, C Dannewitz, C Imbrenda, D Kutscher, B Ohlman, A survey of information-centric networking. IEEE Commun Mag 50(7), 26–36 (2012)A Osseiran, F Boccardi, V Braun, K Kusume, P Marsch, M Maternia, O Queseth, M Schellmann, H Schotten, H Taoka, H Tullberg, MA Uusitalo, B Timus, M Fallgren, Scenarios for the 5G mobile and wireless communications: the vision of the METIS project. IEEE Commun Mag 52(5), 26–35 (2014)D Gomez-Barquero, D Calabuig, JF Monserrat, N Garcia and J Perez-Romero, Hopfield neural network - based approach for joint dynamic resource allocation in heterogeneous wireless networks, in Proceedings 64th IEEE Vehicular Technology Conference (VTC), Montreal. 2006JF Monserrat, P Sroka, G Auer, J Cabrejas, D Martin-Sacristan, A Mihovska, R Rossi, A. Saul, R. Schoenen, Advanced Radio Resource Management for IMT-Advanced in WINNER+ (II), in Proc. Future Network and Mobile Summit, pp.1-9, June 2010.F Boccardi, RW Heath, A Lozano, TL Marzetta, P Popovski, Five disruptive technology directions for 5G. IEEE Commun Mag 52(2), 74–80 (2014)JG Andrews, S Buzzi, C Wan, SV Hanly, A Lozano, ACK Soong, JC Zhang, What will 5G be? IEEE J Sel Area Comm 32(6), 1065–1082 (2014)MN Tehrani, M Uysal, H Yanikomeroglu, Device-to-device communication in 5G cellular networks: challenges, solutions, and future directions. IEEE Commun Mag 52(5), 86–92 (2014)N Bhushan, L Junyi, D Malladi, R Gilmore, D Brenner, A Damnjanovic, R Sukhavasi, C Patel, S Geirhofer, Network densification: the dominant theme for wireless evolution into 5G. IEEE Commun Mag 52(2), 82–89 (2014)K Okino, T Nakayama, C Yamazaki, H Sato, Y Kusano, Pico Cell Range Expansion with Interference Mitigation toward LTE-Advanced Heterogeneous Networks, in Proc. of IEEE International Conference on Communications (ICC), 2011.P Mugen, L Dong, W Yao, L Jian Li, C Hsiao-Hwa, Self-configuration and self-optimization in LTE-advanced heterogeneous networks. IEEE Commun Mag 51(5), 36–45 (2013)I Siomina, D Yuan, Load Balancing in Heterogeneous LTE: Range Optimization via Offset and Load-coupling Characterization, in Proc. of IEEE Int. Conference on Communications (ICC). June 2012.KI Pedersen, Y Wang, B Soret, F Frederiksen, eICIC Functionality and Performance for LTE HetNet Co-Channel Deployments, in Proc. of IEEE Vehicular Technology Conf, Sep 2012X Gu, X Deng, Q Li, L Zhang, W Li, Capacity Analysis and Optimization in Heterogeneous Network with Adaptive Cell Range Control, Int. J. Antennas. Propag. 2014(215803), 10 (2014)K Smiljkovikj, P Popovski, L Gavrilovska, Analysis of the Decoupled Access for DL and UL in Wireless Heterogeneous Networks, in IEEE Wireless Communications Letters, in press, doi:10.1109/LWC.2015.2388676.P Agyapong, M. Iwamura, D. Staehle, W. Kiess, A. Benjebbour, Design considerations for a 5G network architecture. IEEE Commun Mag 52(11), 65–75 (2014)L Yan, X Fang, Reliability Evaluation of 5G C/U-plane Decoupled Architecture for High-speed Railway. EURASIP J Wirel Commun Netw 2014, 127 (2014)B Zafar, S Gherekhloo, M Haardt, Analysis of multihop relaying networks: communication between range-limited and cooperative nodes. IEEE Veh Technol Mag 7(3), 40–47 (2012)Study on Mobile Relay for Evolved Universal Terrestrial Radio Access (E-UTRA), 3GPP TR 36.836, V2.0.2, July 2013.A Krendzel, LTE-A Mobile Relay Handling: Architecture Aspects, in Proc. of the 19th European Wireless Conference (EW), Guildford, UK, pp. 1–6, 2013.M Khanfouci, Y Sui, A Papadogiannis, and M Färber, Moving Relays and Mobility aspects, ARTIST4G project deliverable D3.5c-v2.0, 2012.F Haider, M Dianati, and R Tafazolli, A Simulation Based Study of Mobile Femtocell Assisted LTE Networks, in Proc. Of the 7th International Wireless Communications and Mobile Computing Conference (IWCMC), Istanbul, Turkey, pp. 2198–2203, 2011F Haider, W Haiming, H Haas, Y Dongfeng, W Haiming, G Xiqi, Y Xiao-Hu, E Hepsaydir, Spectral efficiency enalysis of mobile Femtocell based cellular systems, in Proc. of the 13th International Conference on Communication Technology (ICCT), Jinan, pp. 347–351, September 2011.ICT-317669 METIS project, Initial report on horizontal topics, first results and 5G system concept, Deliverable D6.2, April 2014, https://www.metis2020.com/documents/deliverables/Study on LTE Device to Device Proximity Services, 3GPP TR 36.843, 2014.V Yazıcı, UC Kozat, M Oguz, Sunay, A new control plane for 5G network architecture with a case study on unified handoff, mobility, and routing management. IEEE Commun Mag 52(11), 76–85 (2014)F Malandrino, C Casetti, C-F Chiasserini, Toward D2D-enhanced heterogeneous networks. IEEE Commun Mag 52(11), 94–100 (2014)A Asadi, Q Wang, V Mancuso, A survey on device-to-Device communication in cellular networks. IEEE Commun Surv Tutor 16(4), 1801–1819 (2014)D Feng, L Lu, YY Wu, GY Li, G Feng, S Li, Device-to-device communications underlaying cellular networks. IEEE Trans Commun 61(8), 3541–3551 (2013)C Xu, L Song, Z Han, Q Zhao, X Wang, X Cheng, B Jiao, Efficiency resource allocation for device-to-device underlay communication systems: a reverse iterative combinatorial auction based approach. IEEE J Sel Area Comm 31(9), 348–358 (2013)S Lingyang, D Niyato, H Zhu, E Hossain, Game-theoretic resource allocation methods for device-to-device communication. IEEE Wireless Commun 21(3), 136–144 (2014)G Aloi, M Di Felice, V Loscrì, P Pace, G Ruggeri, Spontaneous smartphone networks as a user-centric solution for the future internet. IEEE Commun Mag 52(12), 26–33 (2014)PA Frangoudis, GC Polyzos, Security and performance challenges for user-centric wireless networking. IEEE Commun Mag 52(12), 48–55 (2014)ITU-R M.2079, in Technical and operational information for identifying Spectrum for the terrestrial component of future development of IMT-2000 and IMT-Advanced, 2006AB MacKenzie, LA DaSilva, Application of signal processing to addressing wireless data demand [in the spotlight]. IEEE Signal Process Mag 29(6), 168–166 (2012)X Cheng, Y Koucheryavy, Y Li, F Zhao, T Znati (ed.), Dynamic Spectrum Access for Throughput, Delay, and Fairness Enhancement In Cognitive Radio Networks, EURASIP J Wirel Commun Netw, November 2014MR Akdeniz, Y Liu, MK Samimi, S Sun, S Rangan, TS Rappaport, E Erkip, Millimeter wave channel modeling and cellular capacity evaluation. IEEE J Sel Area Comm 32(6), 1164–1179 (2014)A Adhikary, E Al Safadi, M Samimi, R Wang, G Caire, TS Rappaport, AF Molisch, Joint spatial division and multiplexing for mm-wave channels. IEEE J Sel Area Comm 32(6), 1239–1255 (2014)K Pentikousis, Y Wang, W Hu, Mobileflow: toward software-defined mobile networks. IEEE Commun Mag 51(7), 44–53 (2013)E3 D2.4. Cognitive Function mapping to Networks Architectures, Standard Engineering and Software Technologies for Cognitive Radios, E3 Project Deliverable 2.4, December 2009.R Wang, H Hu, X Yang, Potentials and challenges of C-RAN supporting Multi-RATs toward 5G mobile networks. IEEE. Access. 2(1187), 1195 (2014)V Jungnickel, K Manolakis, W Zirwas, B Panzner, V Braun, M Lossow, M Sternad, R Apelfrojd, T Svensson, The role of small cells, coordinated multipoint, and massive MIMO in 5G. IEEE Commun Mag 52(5), 44–51 (2014)E Larsson, O Edfors, F Tufvesson, T Marzetta, Massive MIMO for next generation wireless systems. IEEE Commun Mag 52(2), 186–195 (2014)W Roh, S Ji-Yun, P Jeongho, L Byunghwan, L Jaekon, K Yungsoo, C Jaeweon, C Kyungwhoon, F Aryanfar, Millimeter-wave Beamforming as an Enabling Technology for 5G Cellular Communications: Theoretical Feasibility and Prototype Results. IEEE Commun Mag 2(2), 106–113 (2014)AL Swindlehust, E Ayanoglu, P Heydari, F Capolino, Millimeter-wave massive MIMO: the next wireless revolution? IEEE Commun Mag 52(9), 56–62 (2014)L Lu, GY Li, AL Swindlehurst, A Ashikhmin, Z Rui, An overview of massive MIMO: benefits and challenges. IEEE J Sel Top Signal Process 8(5), 742–758 (2014)S Roger, D Calabuig, J Cabrejas, JF Monserrat, Multi-user non-coherent detection for downlink MIMO communication. IEEE Signal Process Lett 21(10), 1225–1229 (2014)X Wang, M Chen, T Taleb, A Ksentini, V Leung, Cache in the air: exploiting content caching and delivery techniques for 5G systems. IEEE Commun Mag 52(2), 131–139 (2014)ETSI ISG NFV (Operator Group), Network Functions Virtualisation – Network Operator Perspectives on Industry Progress, Updated White Paper, October 2013NGMN Alliance, in Suggestions on potential solutions for C-RAN, White Paper, January 2013ETSI ISG NFV, Network Functions Virtualisation (NFV); Virtual Network Functions Architecture, v1.1.1, Dec 2014.A Tzanakaki, MP Anastasopoulos, GS Zervas, BR Rofoee, R Nejabati, D Simeonidou, Virtualization of heterogeneous wireless-optical network and IT infrastructures in support of cloud and mobile cloud services. IEEE Commun Mag 51(8), 155–161 (2013)A Manzalini, R Saracco, C Buyukkoc, P Chemuouil, S Kukliński, A Gladisch, M Fukui, W Shen, M Fujiwara, K Shimano, E Dekel, D Soldani, M Ulema, W Cerroni, F Callegati, G Schembra, V Riccobene, C Mas Machuca, A Galis, J Mueller, Software-Defined Networks for Future Networks and Services: Main Technical Challenges and Business Implications, IEEE Workshop SDN4FNS, 1–16, 2013CEPT ECC, in Licensed Shared Access (LSA), ECC Report 205, February 2014IEEE 802.11, in IEEE 802.11-2012 Part11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, IEEE Standard, March 2012D Martín-Sacristán, JF Monserrat, J Cabrejas-Peñuelas, D Calabuig, S Garrigas, N Cardona, On the way towards fourth-generation mobile: 3GPP LTE and LTE-Advanced. EURASIP J Wirel Commun Netw 2009, 10 (2009)ICT-317669 METIS, Final report on architecture, Deliverable D6.4, January 2015, https://www.metis2020.com/documents/deliverables/ICT-317669 METIS, Report on simulation results and evaluations, Deliverable D6.5, February 2015, https://www.metis2020.com/documents/deliverables/Ö Bulakci, Z Ren, C Zhou, J Eichinger, P Fertl, S Stanczak, Dynamic Nomadic Node Selection for Performance Enhancement in Composite Fading/Shadowing Environments, (IEEE VTC 2014-Spring, Seoul, South Korea)ICT-317669 METIS, Final report on network-level solutions, Deliverable D4.3 Version 1, February 201

Värdering vid köp av onoterade företag i dörrbranschen

Syftet med denna uppsats är att genomföra tre företagsvärderingar av onoterade företag i dörrbranschen: ett historiskt och två framåtblickande och undersöka vilka problem som uppstår i värderingsprocessen för en uppköpare inom dörrbranschen

Reinforcement Learning for Efficient and Tuning-Free Link Adaptation

Wireless links adapt the data transmission parameters to the dynamic channel state -- this is called link adaptation. Classical link adaptation relies on tuning parameters that are challenging to configure for optimal link performance. Recently, reinforcement learning has been proposed to automate link adaptation, where the transmission parameters are modeled as discrete arms of a multi-armed bandit. In this context, we propose a latent learning model for link adaptation that exploits the correlation between data transmission parameters. Further, motivated by the recent success of Thompson sampling for multi-armed bandit problems, we propose a latent Thompson sampling (LTS) algorithm that quickly learns the optimal parameters for a given channel state. We extend LTS to fading wireless channels through a tuning-free mechanism that automatically tracks the channel dynamics. In numerical evaluations with fading wireless channels, LTS improves the link throughout by up to 100% compared to the state-of-the-art link adaptation algorithms.QC 20210503</p

Reinforcement Learning for Efficient and Tuning-Free Link Adaptation

Wireless links adapt the data transmission parameters to the dynamic channel state -- this is called link adaptation. Classical link adaptation relies on tuning parameters that are challenging to configure for optimal link performance. Recently, reinforcement learning has been proposed to automate link adaptation, where the transmission parameters are modeled as discrete arms of a multi-armed bandit. In this context, we propose a latent learning model for link adaptation that exploits the correlation between data transmission parameters. Further, motivated by the recent success of Thompson sampling for multi-armed bandit problems, we propose a latent Thompson sampling (LTS) algorithm that quickly learns the optimal parameters for a given channel state. We extend LTS to fading wireless channels through a tuning-free mechanism that automatically tracks the channel dynamics. In numerical evaluations with fading wireless channels, LTS improves the link throughout by up to 100% compared to the state-of-the-art link adaptation algorithms.QC 20210503</p