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Ivan Stojmenovic Zixue Cheng Song Guo (Eds.) 131 Mobile and Ubiquitous Systems: Computing, Networking, and Services 10th International Conference, MOBIQUITOUS 2013 Tokyo, Japan, December 2–4, 2013 Revised Selected Papers 123 Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Editorial Board Ozgur Akan Middle East Technical University, Ankara, Turkey Paolo Bellavista University of Bologna, Bologna, Italy Jiannong Cao Hong Kong Polytechnic University, Hong Kong, Hong Kong Falko Dressler University of Erlangen, Erlangen, Germany Domenico Ferrari Università Cattolica Piacenza, Piacenza, Italy Mario Gerla UCLA, Los Angels, USA Hisashi Kobayashi Princeton University, Princeton, USA Sergio Palazzo University of Catania, Catania, Italy Sartaj Sahni University of Florida, Florida, USA Xuemin (Sherman) Shen University of Waterloo, Waterloo, Canada Mircea Stan University of Virginia, Charlottesville, USA Jia Xiaohua City University of Hong Kong, Kowloon, Hong Kong Albert Zomaya University of Sydney, Sydney, Australia Geoffrey Coulson Lancaster University, Lancaster, UK 131 More information about this series at http://www.springer.com/series/8197 Ivan Stojmenovic Zixue Cheng Song Guo (Eds.) • Mobile and Ubiquitous Systems: Computing, Networking, and Services 10th International Conference, MOBIQUITOUS 2013 Tokyo, Japan, December 2–4, 2013 Revised Selected Papers 123 Editors Ivan Stojmenovic University of Ottawa Ottawa, ON Canada ISSN 1867-8211 ISBN 978-3-319-11568-9 DOI 10.1007/978-3-319-11569-6 Zixue Cheng Song Guo School of Computer Science and Engineering The University of Aizu Tsuruga Fukushima Japan ISSN 1867-822X (electronic) ISBN 978-3-319-11569-6 (eBook) Library of Congress Control Number: 2014949557 Springer Cham Heidelberg New York Dordrecht London © Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2014 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer Permissions for use may be obtained through RightsLink at the Copyright Clearance Center Violations are liable to prosecution under the respective Copyright Law The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made The publisher makes no warranty, express or implied, with respect to the material contained herein Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Preface MobiQuitous 2013 has provided a successful forum for practitioners and researchers from diverse backgrounds to interact and exchange experiences about the design and implementation of mobile and ubiquitous systems We received 141 technical papers from all around the world All submissions received high-quality reviews from Technical Program Committee (TPC) members or selected external reviewers According to the review results, we have accepted 52 regular papers and 13 short papers for inclusion in the technical program of the main conference In the main technical program, we had two inspiring keynote speeches by Prof Xuemin (Sherman) Shen from University of Waterloo, Canada and Prof Nei Kato from Tohoku University, Japan, and 12 technical sessions, including 10 regular-paper sessions and two short-paper sessions Besides the main conference, we also had a joint International Workshop on Emerging Wireless Technologies for Future Mobile Networks (WEWFMN 2013) The conference successfully inspired many innovative directions in the fields of mobile applications, social networks, networking, and data management and services, all with a special focus on mobile and ubiquitous computing It is our distinct honor to present the best paper, Focus and Shoot: Efficient Identification over RFID Tags in the Specified Area, and the best-student paper, Protecting Movement Trajectories Through Fragmentation, for MobiQuitous 2013 The two papers were voted out based on the reviewers’ recommendations and on the papers’ significance, originality, and potential impact The technical program is the result of the hard work of many individuals We would like to thank all the authors for submitting their outstanding work to MobiQuitous 2013 We offer our sincere gratitude to the technical committee members and external reviewers, who worked hard to provide thorough, insightful, and constructive reviews in a timely manner We are grateful to the Steering Committee and Organizing Committee of MobiQuitous 2013, and especially to the TPC Chairs, Prof Guojun Wang from Central South University, China, Prof Kun Yang from University of Essex, UK, Prof Amiya Nayak from University of Ottawa, Canada, Prof Francesco De Pellegrini from Create-Net, Italy, and Prof Takahiro Hara from Osaka University, Japan for their invaluable support and insightful guidance Finally, we are grateful to all the participants in MobiQuitous 2013 Zixue Cheng Ivan Stojmenovic Song Guo Organization Steering Committee Imrich Chlamtac Fausto Giunchiglia Tao Gu Tom La Porta Francesco De Pellegrini Chiara Petrioli Krishna Sivalingam Thanos Vasilakos Create-Net, Italy University of Trento, Italy University of Southern Denmark, Denmark Pennsylvania State University, USA Create-Net, Italy Universita di Roma “La Sapienza”, Italy University of Maryland at Baltimore, USA University of Western Macedonia, Greece Organizing Committee General Chairs Zixue Cheng Ivan Stojmenovic University of Aizu, Japan University of Ottawa, Canada General Co-chair Song Guo University of Aizu, Japan TPC Chairs Guojun Wang Kun Yang Amiya Nayak Francesco De Pellegrini Takahiro Hara Central South University, China University of Essex, UK University of Ottawa, Canada Create-Net, Italy Osaka University, Japan Local Chair Naohito Nakasato University of Aizu, Japan VIII Organization Workshop Chairs Chonggang Wang Baoliu Ye Shanzhi Chen InterDigital Communications, USA Nanjing University, China Datang Telecom Technology & Industry Group, China Publicity Chair Shui Yu Susumu Ishihara Hirozumi Yamaguchi Deakin University, Australia Shizuoka University, Japan Osaka University, Japan Publication Chair Lei Shu Guangdong University of Petrochemical Technology, China Web Chair Deze Zeng University of Aizu, Japan Conference Manager Ruzanna Najaryan EAI, Italy Technical Program Committee Jemal Abawajy Muhammad Bashir Abdullahi Christian Becker Roy Campbell Jiannong Cao Iacopo Carreras Liming Chen Marcus Handte Min Chen Franco Chiaraluce Michel Diaz Pasquale Donadio Wan Du Andrzej Duda Deakin University, Australia Federal University of Technology, Minna, Nigeria University of Mannheim, Germany University of Illinois at Urbana-Champaign, USA Hong Kong Polytechnic University, Hong Kong Create-Net, Italy University of Ulster, UK University of Duisburg-Essen, Germany Huazhong University of Science and Technology, China Polytechnical University of Marche, Italy LAAS-CNRS, France Alcatel-Lucent, Italy Nanyang Technological University, Singapore Grenoble Institute of Technology, France Organization Kary Framling Chris Gniady Teofilo Gonzalez Sergei Gorlatch Yu Gu Deke Guo Clemens Holzmann Henry Holtzman Susumu Ishihara Yoshiharu Ishikawa Xiaolong Jin Jussi Kangasharju Stephan Karpischek Fahim Kawsar Yutaka Kidawara Matthias Kranz Mo Li Xu Li Zhenjiang Li Xiaodong Lin Hai Liu Yunhuai Liu Tomas Sanchez Lopez Rongxing Lu Xiaofeng Lu Oscar Mayora Iqbal Mohomed Felix Musau Mirco Musolesi Sushmita Ruj Hedda R Schmidtke Joan Serrat Zhenning Shi Hiroshi Shigeno Stephan Sigg Philipp Sommer Danny Soroker Mineo Takai Ning Wang Song Wu Xiaofei Xing IX Aalto University, Finland University of Arizona, USA University of California at Santa Barbara, USA University of Münster, Germany Singapore University of Technology and Design, Singapore National University of Defense Technology, China University of Applied Sciences Upper Austria, Austria MIT Media Lab, USA Shizuoka University, Japan Nagoya University, Japan Institute of Computing Technology, Chinese Academy of Sciences, China University of Helsinki, Finland Swisscom (Switzerland) AG, Switzerland Bell Labs, USA NICT, Japan Universität Passau, Germany Nanyang Technological University, Singapore Huawei Technologies, Canada Nanyang Technological University, Singapore University of Ontario Institute of Technology, Canada HongKong Baptist University, Hong Kong TRIMPS, China EADS Innovation Works, UK University of Waterloo, Canada Xidian University, China Create-Net, Italy IBM T.J Watson Research Center, USA Kenyatta University, Kenya University of Birmingham, UK Indian Institute of Technology, India Carnegie Mellon University, USA Universitat Politècnica de Catalunya, Spain Orange Labs Beijing, China Keio University, Japan National Institute of Informatics, Japan CSIRO, Australia IBM T.J Watson Research Center, USA UCLA, USA and Osaka University, Japan University of Surrey, UK Huazhong University of Science and Technology, China Guangzhou University, China X Organization Ke Xu Hirozumi Yamaguchi Zhiwen Yu Haibo Zeng Jianming Zhang Yanmin Zhu Ali Ismail Tsinghua University, China Osaka University, Japan Northwestern Polytechnical University, China McGill University, Canada Changsha University of Science & Technology, China Shanghai Jiao Tong University, China Awad Al Azhar University, Egypt Small Cell Enhancement for LTE-Advanced Release 12 and Application of Higher Order Modulation Qin Mu(B) , Liu Liu, Huiling Jiang, and Hidetoshi Kayama DOCOMO Beijing Communications Laboratories Co., Ltd, Beijing, China {mu,liul,jiang,kayama}@docomolabs-beijing.com.cn Abstract The mobile data traffic is expected to grow beyond 1000 times by 2020 compared with it in 2010 In order to support 1000 times of capacity increase, improving spectrum efficiency is one of the important approaches Meanwhile, in Long Term Evolution (LTE)-Advanced, small cell and hotspot are important scenarios for future network deployment to increase the capacity from the network density domain Under such environment, the probability of high Signal to Interference plus Noise Ratio (SINR) region becomes larger which brings the possibility of introducing higher order modulation, i.e., 256 Quadrature Amplitude Modulation (QAM) to improve the spectrum efficiency In this paper, we will firstly introduce the ongoing small cell enhancement discussion in the 3rd Generation Partnership Project (3GPP) And then focus on the application of higher order modulation in small cell environment Important design issues and possible solutions will be analyzed particularly in the higher order modulation discussion Keywords: Higher order modulation release 12 · Small cell · LTE-advanced Introduction Long-term evolution (LTE) provides full IP packet-based radio access with low latency and adopts orthogonal frequency division multiple access (OFDMA) and single-carrier frequency division multiple access (SC-FDMA) in the downlink and uplink, respectively The 3rd generation partnership project (3GPP) finalized the radio interface specifications for the next generation mobile system as LTE release in 2008 [1,2] In Japan, the commercial service of LTE was launched in December, 2010 under the new service brand of “Xi” (crossy) [3] Meanwhile, in the 3GPP, there have been efforts targeting at establishing an enhanced LTE radio interface called LTE-Advanced (release 10 and beyond) [4,5] and the specification for LTE-Advanced release 11 is now freezed and the standardization for release 12 is started Mobile traffic data forecasts predict a tremendous growth in data traffic in the next several years and by some projections the traffic growth is expected to be c Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2014 I Stojmenovic et al (Eds.): MOBIQUITOUS 2013, LNICST 131, pp 794–805, 2014 DOI: 10.1007/978-3-319-11569-6 69 Small Cell Enhancement for LTE-Advanced Release 12 795 1000 times of the data from the previous decade [6] The primary drivers of this growth are the increased penetration of smartphone type handsets and various assorted mobile devices running a vast array of mobile applications, especially video applications Furthermore, end-user expectations on achievable data rates are also continuously raised Currently commercially available mobile networks typically offer end-user data rates up to in the order of a few Mbps In the future, there will be demands for typical end-user data rates of several tens of Mbps, with hundreds of Mbps or even several Gbps of data rates demanded in specific scenarios Explosive overall mobile data traffic and rising expectations on achievable data rates on end-user side challenge the mobile network operators To satisfy these future traffic-volume and end-user demands existing mobile networks need to evolve and be enhanced Since obtaining new spectrum is quite costprohibitive as well as difficult due to various government regulations in different parts of the world, other ways are being looked into to achieve this demand Most operators are increasingly drawn to the idea of network densification with small cells deployment to achieve the increasing traffic capacity [7] Currently, there is a study item (SI) called “Small Cell enhancements for E-UTRA and EUTRAN” in progress to explore the potential of small cells in the 3GPP standards body [8] The basic deployment of small cells that is being studied involves using an LTE base station that has much lower transmission power than a macro base station Several different types of deployments are being considered such as small cells using the same or different carrier frequency as the macro base station, small cells deployed indoors or outdoors, small cells deployed in areas with or without overlapping macro cell coverage and so on There are also some new techniques involved in the small cell enhancement discussion For example, efficient small cell discovery for power saving and traffic offloading, small cell power on/off for the interference coordination and higher order modulation in small cell for improved spectral efficiency etc In this paper, we focus on higher order modulation involved in the scenario where macro cell and small cell are deployed with separate carrier The rest of the paper are organized as follow Firstly, more detailed descriptions about small cell enhancement are provided in Sect Secondly, we elaborates the higher order modulation in small cell including the motivation, design issues and comparison of possible solutions in Sect Finally, we conclude this paper in Sect 2.1 Small Cell Enhancement Deployment Scenario Identified in 3GPP 3GPP decided in September 2012 to start a study on the scenarios and requirements of small cell enhancements This study was completed successfully in December 2012 and the agreed deployment scenarios and relevant technical requirements are captured in a technical report [9]; these are briefly introduced hereinafter 796 Q Mu et al Fig Small cell deployment Enhanced small cells can be deployed both with macro coverage and stand alone, both indoor and outdoor, and support both ideal and non-ideal back hauls Enhanced small cells can also be deployed sparsely or densely An illustration of possible deployment scenarios is shown in Fig [9] 2.1.1 With and Without Macro Coverage Small cell enhancement should target the deployment scenario in which small cell nodes are deployed under the coverage of one or more than one overlaid macro-cell layer(s) in order to boost the capacity of already deployed cellular network And it should also work without macro coverage, for example, in deep indoor situations 2.1.2 Outdoor and Indoor Small cell enhancement should target both outdoor and indoor small cell deployments A key differentiator between indoor and outdoor scenarios is mobility support In indoor scenarios, users normally stay stationary or move at very low speeds In outdoor scenarios, operators may deploy small cell nodes to cover certain busy streets where relatively higher terminal speeds can be expected 3GPP has decided to focus on low terminal speeds (up to km/h) for indoor and medium terminal speeds (up to 30 km/h and potentially higher) for outdoor scenarios 2.1.3 Backhaul The backhaul, which generally means the link connecting the radio access network and core network, is another important aspect for small cell enhancement, especially when considering the potentially large number of small cell nodes to be deployed 3GPP has decided both the ideal backhaul (i.e., very high throughput and very low latency backhaul such as dedicated point-to-point connection using optical fiber) and non-ideal backhaul (i.e., typical backhaul widely used in the market such as xDSL) should be studied Small Cell Enhancement for LTE-Advanced Release 12 797 2.1.4 Sparse and Dense Small cell enhancement should consider sparse and dense small cell deployments In some scenarios (e.g., hotspot indoor/outdoor places, etc.), single or a few small cell node(s) are sparsely deployed, e.g to cover the hotspot(s) Meanwhile, in some scenarios (e.g., dense urban, large shopping mall, etc.), a lot of small cell nodes are densely deployed to support huge traffic over a relatively wide area covered by the small cell nodes The coverage of the small cell layer is generally discontinuous between different hotspot areas Each hotspot area can be covered by a group of small cells, i.e a small cell cluster For mobility/connectivity performance, both sparse and dense deployments should be considered with equal priority 2.1.5 Synchronization Both synchronized and un-synchronized scenarios should be considered between small cells as well as between small cells and macro cell(s) For specific operations e.g interference coordination, carrier aggregation and inter-eNodeB coordinated multiple point transmission, small cell enhancement can benefit from synchronized deployments with respect to small cell search/measurements and interference/resource management Therefore time synchronized deployments of small cell clusters are prioritized in the study and new means to achieve such synchronization shall be considered 2.1.6 Spectrum Small cell enhancement should address the deployment scenario in which different frequency bands are separately assigned to macro layer and small cell layer, respectively, where F1 and F2 in Fig correspond to different carriers in different frequency bands Small cell enhancement should be applicable to all existing and as well as future cellular bands, with special focus on higher frequency bands, e.g., the 3.5 GHz band, to enjoy the more available spectrum and wider bandwidth Co-channel deployment scenarios between macro layer and small cell layer should be considered as well 2.1.7 Traffic In a small cell deployment, it is likely that the traffic will fluctuate greatly since the number of users per small cell node is typically not large due to the small coverage area It is also likely that the user distribution is very nonuniform and fluctuates between the small cell nodes It is also expected that the traffic could be highly asymmetrical, either downlink- or uplink-centric Traffic load distribution in the time domain and spatial domain could be uniform or non-uniform 798 Q Mu et al 2.1.8 Backward Compatibility Backward compatibility, that is, the possibility for legacy user equipment (UE) to access a small-cell node/carrier, shall be guaranteed and the ability for legacy UE to benefit from small-cell enhancements can be considered 2.2 New Techniques to Improve the Spectral Efficiency and Capacity in Small Cell 2.2.1 Small Cell Discovery Different from in the homogeneous deployment, the UEs in small cell should always perform inter-frequency measurement in order to detect the surrounding small cells timely and make maximum use of them Thus a well designed discovery signal and discovery mechanism will not only improve the power efficiency but also facilitate neighboring cell identification and synchronization 2.2.2 Small Cell On/Off The traffic in small cell fluctuates greatly in time domain and spatial domain When there is no traffic, small cell should go “off” state quickly for more power saving On the other hand, small cell should be activated timely when traffic arrives In addition, when one cell experiences severe interference from neighboring cells, turning off some neighboring cells could mitigate interference and improve the overall performance Hence manners to support prompt and flexible switch between small cell “on” state and small cell “off” state is quite beneficial for both power saving and interference reduction 2.2.3 Higher Order Modulation Sperate frequency for macro cell and small cell is the prioritized scenario Due to the absence of strong interference from Macro cell high SINR performance is easily achieved which provides another chance to solve the explosive data problem from spectral efficiency dimension by introducing higher order modulation schemes In 3GPP meeting discussion, many operators and vendors show their great interests [10–12] In the following section, we will introduce this technique in details 3.1 Higher Order Modulation in Small Cell Motivation The basic premise of 256 QAM introduction is that there should be some scenarios satisfy the usage SINR requirement Figure shows the spectral efficiency performance for various modulation schemes The spectral efficiency is defined as (1−block error rate )×modulation order ×coding rate The results are obtained by using piratical channel coding (Turbo coding) in AWGN scenario From Fig 2, it is observed that the switch point between 256 QAM and 64 QAM is around Small Cell Enhancement for LTE-Advanced Release 12 799 Fig Spectral efficiency Fig SINR distribution 20 dB Therefore, the precondition for 256 QAM introduction is there should be some small cell scenarios where some UEs could experience SINR larger than 20 dB In small cell deployment, the prioritized scenario is separate frequency for macro cell and small cell As mentioned above, good SINR performance could be achieved due to the absence of strong interference from macro Figure shows the SINR geometry for different small cell density In the figure, N denotes the 800 Q Mu et al Table CQI table in LTE Rel8-Rel11 CQI index Modulation Code rateX1024 Efficiency Out of range QPSK 78 0.1523 QPSK 120 0.2344 QPSK 193 0.3770 QPSK 308 0.6016 QPSK 449 0.8770 QPSK 602 1.1758 16 QAM 378 1.4766 16 QAM 490 1.9141 16 QAM 616 2.4063 10 64 QAM 466 2.7305 11 64 QAM 567 3.3223 12 64 QAM 666 3.9023 13 64 QAM 772 4.5234 14 64 QAM 873 5.1152 15 64 QAM 948 5.5547 number of small cells per macro sector It is observed that more than 20 % UEs could reach the SINR larger than 20 dB in sparse small cell deployment Thus more than 20 % UEs could benefit from 256 QAM in the sparse small cell deployment High cell throughput gain is expected from introducing 256 QAM in small cell 3.2 Design Issues in Implement 256 QAM In LTE release 8, quadrature phase shift keying (QPSK), 16 QAM and 64 QAM have been specified for data transmission These modulation schemes are implemented in the channel quality indicator (CQI) table and modulation and coding scheme (MCS) table [13] The CQI table is mainly used to assist the channel quality feedback from UEs to eNodeBs The original CQI table includes 16 levels of different modulation and coding schemes, as shown in Table UEs feed back the effective SINR by conveying the CQI index with bits in uplink control indicator(UCI) The mapping from effective SINR to a corresponding CQI value is carried out such that a BLER lower than 0.1 is achieved by using corresponding modulation and coding rate The MCS table is mainly used to inform the detailed modulation and coding schemes for physical downlink shared channel (PDSCH) or physical uplink shared channel (PUSCH) transmission The original MCS table which is an extension of CQI table includes 32 levels of different modulation and coding schemes and of them are reserved for future use eNodeBs Small Cell Enhancement for LTE-Advanced Release 12 801 inform UEs of the detailed modulation and coding scheme in the table by conveying the corresponding MCS index with bits in downlink control indicator (DCI) In order to support 256 QAM in current LTE-Advanced system, the new entries of 256 QAM should be merged in the CQI table and MCS table However, how to implement the new entries in these tables needs careful investigation by taking the performance gain, impact on the control signaling, specification effort etc into consideration 3.3 Possible Solutions Based on whether to change the sizes of the tables and corresponding indicators, there are potential alternatives to implement the 256 QAM in current network 3.3.1 Alt.1 Extend the Tables and Corresponding Indicators This alternative is a simple extension of the existing MCS/CQI tables Contents based on 256 QAM can be attached at the end of the tables This method guarantees the performance of UEs supporting 256 QAM since all the existing modulation levels can be reused However, the changed table size will lead to the changed payload size of relevant indicator, e.g., DCI formats with larger MCS indication field and channel state reporting with larger CQI feedback bits should be defined The increased payload sizes in general result in reduced robustness of the DCI and UCI From the standardization aspect, additional specification effort is needed to specify the new DCI format and channel status reporting 3.3.2 Alt.2 Keep the Same Table Size and Refine the Content Keeping the same sizes of the tables has the advantage that the sizes of the relevant indicators are not affected Hence, less specification effort is required due to no need to define new DCI format and channel status reporting To keep less change on the interpretation of the new content in CQI/MCS table, a possible method is to remove some entries from the extended table in Alt.1 (extended table is formed by attaching new entries at the end of the existing tables) The entries contribute less cell throughput improvement will be removed The removed entries could be the original content or the newly added content It is noted that some MCS used in severe channel condition should be kept to maintain the basic service requirement The detailed criteria for the remove is related to the spectral efficiency improvement brought by one entry and the possibility of the entry to be used Both factors contribute to the cell throughput For one entry other than entries used in severe channel condition, if it brings little improvement in the spectral efficiency or has little possibility to be used, it could be removed because of less improvement on the cell throughput By this way, the cell throughput performance could be guaranteed to the largest extent 802 Q Mu et al Table Simulation parameters Deployment scenarios Heterogeneous network with small cells within one macro cell sector Carrier configuration Macro@ GHz Small cell @3.5 GHz System bandwidth 10 MHz Channel model ITU-UMa for Macro ITU-UMi for small cell Number of UEs 10,20,30 UE (per macro sector) DL transmission scheme SU-MIMO with rank adaptation UE speed km/h Tx power (Ptotal) Macro:46 dBm Small cell: 30 dBm Traffic model Full buffer Number of TX and RX antennas For macro: × For small cell: × Antenna configuration CPA UE receiver MMSE Feedback scheme Rel-8 RI/CQI/PMI based on Rel-8 2Tx codebook EVM 28 dB (4 %) Relative gain of Alt.1 Relative gain of Alt.2 95% user throughput of baseline 95% user throughput of Alt.1 95% user throughput of Alt.2 45 95% user throughput(Mbps) 40 35 30 20 30 25 20 10 15 Relative gain(%) 50 10 0 10 20 Number of UEs per sector 30 40 Fig 95 % user throughput performance 3.4 Performance Evaluation and Discussion To compare the performance of these alternatives System level simulation is performed The baseline for comparison is current LTE-Advanced performance Small Cell Enhancement for LTE-Advanced Release 12 Relative gain of Alt.1 Relative gain of Alt.2 Average user throughput of baseline Average user throughput of Alt.1 Average user throughput of Alt.2 Average user throughput(Mbps) 18 16 14 30 20 12 10 10 Relative gain(%) 20 803 0 10 20 Number of UEs per sector 30 40 Fig Average user throughput performance Relative gain of Alt.1 Relative gain of Alt.2 Edge user throughput of baseline Edge user throughput of Alt.1 Edge user throughput of Alt.2 4.5 Edge user throughput(Mbps) 3.5 30 20 2.5 10 1.5 Relative gain(%) 0.5 0 10 20 Number of UEs per sector 30 40 Fig Cell edge user throughput performance with rank adaption 95 % user throughput, average user throughput and % user throughput will be evaluated Furthermore, to model the RF impairment in realistic network, % error vector magnitude (EVM) effect are assumed on the eNodeB side Other detailed parameters are listed in Table According to the simulation results in Figs 4, and 6, we observe that both alternatives could achieve significant gain in 95 % UE throughput and average UE throughput Small gain is obtained in % user throughput % user throughput represents the cell edge user performance For cell edge users, they have less chance of reaching high SINR to support 256 QAM This is why the small gain is caused Due to less entries supported in tables of Alt.2, it provides relative coarse channel condition information compared with Alt.1 as the same range of SINR 804 Q Mu et al is covered by less amount of indicators Thus Alt.2 suffers slightly performance loss compared with Alt.1 More supported indicators in Alt.1 yields slightly throughput gain But on the other hand, it also pays more signaling overhead and specification effort for this little gain Considering the tradeoff between the throughput gain and signaling overhead, specification effort pain It is better to use Alt.2 to implement 256 QAM in small cell Conclusion Small cell deployments are key tools to satisfy future traffic-volume and enduser service-level demands Higher order modulation scheme based on small cell deployment further improves the ability In this article, we first introduce the typical small cell deployment and the related features Then focus on higher order modulation which is one of important technique for small cell enhancement In the discussion, we elaborate the motivation and analyze the main design issues for 3GPP Based on the analysis, we show our consideration on the possible solutions Performance evaluation and further discussion are performed to analyze the merits and demerits of the solutions Based on the analysis, we observe that Alt.2 achieves similar performance gain to Alt.1 with less overhead and 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Xiaodong, S.: Trends in small cell enhancements in LTE advanced IEEE Commun Mag 512, 98–105 (2013) 3GPP, RP-122033: New Study Item Description: Small Cell enhancements for EUTRA and E-UTRAN CHigher layer aspects, TSG-RAN, 58 meeting, June 2012 3GPP, TR36.932 (V12.0.0), Scenarios and Requirements for Small Cell Enhancements for E-URTA and E-UTRAN, December 2012 10 3GPP, R1–130136, ZTE, Consideration on high order modulation for small cell, RAN1 72 meeting, February 2013 Small Cell Enhancement for LTE-Advanced Release 12 805 11 3GPP, R1–130341, Hitachi Ltd, Views on 256 QAM for small cell enhancement emph RAN1 72 meeting, February 2013 12 3GPP, R1–130404, NTT DOCOMO, Enhanced Transmission Scheme for Small Cell Enhancement, emph RAN1 72 meeting, Febrary 2013 13 3GPP, Evolved Universal Terrestrial Radio Access (E-UTRA); LTE physical layer; General description, ser.TS, December 2010, no TS36.213, Rel-10 V9.2.0, June 2010 Author Index Abawajy, Jemal H 726 Alanezi, Khaled 640 Amintoosi, Haleh 262 Anagnostopoulou, Maria 699 Arcega, Lorena 737 Arnrich, Bert 181 Bae, Jun-Young 500 Bagrodia, Rajive 716 Bandyopadhyay, Soma 710 Banerjee, Ansuman 615 Banerjee, Dipyaman 653 Banerjee, Nilanjan 116, 615, 653 Banhazi, Thomas M 30 Baratchi, Mitra 102 Barnard, Kobus 396 Basirat, Amir Hussein 66 Beigl, Michael 435 Benzina, Amal 422 Beresford, Alastair R 195 Bergsträßer, Sonja 155 Bexheti, Agon 181 Bhattacharya, Sourav 409 Bhattacharyya, Abhijan 710 Bhaumik, Chirabrata 447 Boreli, Roksana 487 Bruns, Christoph 422 Buda, Andrea 743 Canovas, Oscar 460 Carlson, Darren 694 Castelluccia, Claude 500 Cetina, Carlos 737 Chakraborty, Dipanjan 116, 653 Chandel, Vivek 447 Chattopadhyay, Soumi 615 Chaudhary, Yetesh 409 Chen, Guanling 688, 721 Chen, Guihai 732 Chen, Yinjie 512 Chen, Yuanfang 142 Choi, Jong-Seok 549 Choudhury, Anirban Dutta 447 Collins, Justin 716 Coskun, Tayfur 422 Crespi, Noel 142 D’Angelo, Lorenzo T 576 Dai, Haipeng 732 Daniels, Wilfried 524 del Cid Garcia, Pedro Javier 524 Devlic, Alisa 209 Dey, Prasenjit 628 Dhurandher, Sanjay K 757 Dimopoulos, Dimitris 699 Ding, Xiang 688 Dorfmeister, Florian 276 Drury, Jill 688 Dürr, Frank 17, 303 Efrat, Alon 396 Ekambaram, Vijay 474 Elmongui, Hicham G 78 Font, Jaime 737 Främling, Kary 743 Friginal, Jesύs 748 Fu, Xinwen 512 Furqan, Fatima 768 Gao, Bo 89 Ghose, Avik 447 Gniady, Chris 396 Goel, Utkarsh 224 Goldfeder, Steven 564 Goto, Keisuke 52 Gottron, Christian 155 Gravenhorst, Franz 181 Gu, Tao 370 Guiochet, Jérémie 748 Guo, Yike 37 Han, Dongsoo 129 Hara, Takahiro 52 Havinga, Paul J.M 102 He, Ligang 89 He, Yuanduo 808 Author Index Hoang, Doan B 768 Hock, Mario 435 Huang, Ke 721 Huang, Yu 370 Huber, Manuel 422 Hughes, Danny 250, 524 Iyer, Aakash 653 Jeon, Seokseong 129 Ji, Yusheng 435 Jiang, Huiling 794 Jiang, Lintong 732 Jin, Haozhun 168 Joosen, Wouter 250, 524 Joshi, Anupam 116 Julien, Christine 316 Kaafar, Mohamed Ali 487 Kanhere, Salil S 262 Karim, Benzidane 537 Kayama, Hidetoshi 794 Khan, Asad I 66 Khoudali, Saad 537 Killijian, Marc-Olivier 748 Kim, Minyoung 549 Klinker, Gudrun 422 Kobayashi, Vladimer 705 Kolarovszki, Peter 358 Korp, Maximiliano 396 Kraunelis, Joshua 512 Kronbauer, Artur H 667 Kubler, Sylvain 743 Lauradoux, Cédric 500 Lhérisson, Pierre-René 705 Li, Xuandong 688 Liang, Chieh-Jan Mike 168 Ling, Zhen 512 Linnhoff-Popien, Claudia 276 Liotou, Eirini 588 Liu, Jia 602 Liu, Liu 794 Loke, Seng W 237 Lopez-de-Teruel, Pedro E 460 Low, Tobias 30 Lu, Jian 370 Lu, Sanglu 344 Lueth, Tim C 576 Lungaro, Pietro 209 Lv, Lin 142 Madhikermi, Manik 743 Maier, Patrick 422 Maret, Pierre 705 McCarthy, Tim 549 Merakos, Lazaros 588 Meratnia, Nirvana 102 Michiels, Sam 250 Mishra, Shivakant 640 Mittal, Sumit 116, 653 Miyyapuram, Ajay 224 Mohan, Amit M 628 Mu, Qin 794 Muaremi, Amir 181 Muhlenbach, Fabrice 705 Nagi, Magdy H 78 Nandakumar, Vikrant 474 Neuhaeuser, Jakob 576 Ninggal, Mohd Izuan Hafez Nishio, Shojiro 52 Ortiz, Antonio M 726 142 Pal, Arpan 710 Passas, Nikos 588 Philipp, Damian 17 Phuttharak, Jurairat 237 Preuveneers, Davy 250 Qi, Haxia 30 Rai, Angshu 116 Rajput, Nitendra 409, 628 Ramakrishna, Venkatraman 409 Ranasinghe, Damith C 330, 384, 683 Ravindran, B 116 Rice, Andrew 195 Rothermel, Kurt 17, 303 Rousseau, Franck 500 Ruge, Lukas 694 Ruiz-Ruiz, Antonio J 460 Sankarkumar, Rengamathi 683 Santos, Celso A S 667 Sarne, David 564 Sasaki, Yuya 52 Sathyan, Thuraiappah 683 Scholz, Markus 435 Segall, Zary 209 Seiter, Julia 181 Sekkaki, Abderrahim 537 Shamoun, Simon 564 Author Index Sharma, Deepak Kumar 757 Sharma, Vivek 474 Shi, Qinfeng 384 Shrivastava, Kundan 409 Shu, Lei 142 Sigg, Stephan 435 Siris, Vasilios A 699 Smith, Guillaume 487 Srinivasan, Balasubramaniam 66 Srivastava, Saurabh 409 Stachowiak, Jarosław 17 Stehr, Mark-Oliver 549 Steinmetz, Ralf 155 Suh, Young-Joo 129 Tao, Xianping 370 Thoelen, Klaas 250 Tollmar, Konrad 209 Tönnis, Marcus 422 Torres, Roberto Luis Shinmoto Tröster, Gerhard 181, 435 Trujillo-Rasua, Rolando 289 Tsolkas, Dimitris 588 Tung, Qiyam 396 Vaculík, Juraj 358 Vasilakos, Athanasios V 344 Wagner, Daniel T 195 Wang, Jiangtao Wang, Liang 370 Wang, Linzhang 688, 721 Wang, Yasha 384 Wernke, Marius 303 White, Jerome 409 Wiesner, Kevin 276 Wittie, Mike P 224 Wolf, Lars 435 Woungang, Isaac 757 Wu, Chao 37 Wu, Di 37 Wu, Jie 344 Wu, Xiaobing 732 Xie, Hongwei 370 Xie, Lei 344 Xing, Michael 316 Xu, Jing 688 Yan, Shulin Yang, Kun Yang, Qing Yang, Yang Yin, Yafeng Yu, Chansu 37 602 224 168 344 129 Zakhary, Victor 78 Zhang, Chunhui 721 Zhang, Li 168 Zhang, Lianming 602 Zhanikeev, Marat 782 Zhao, Wei 512 Zhaoy, Feng 168 Zhou, Hong 30 Zhou, Mingue 330 809 ... http://www.springer.com/series/8197 Ivan Stojmenovic Zixue Cheng Song Guo (Eds.) • Mobile and Ubiquitous Systems: Computing, Networking, and Services 10th International Conference, MOBIQUITOUS 2013 Tokyo, Japan,... successful forum for practitioners and researchers from diverse backgrounds to interact and exchange experiences about the design and implementation of mobile and ubiquitous systems We received 141... Future Mobile Networks (WEWFMN 2013) The conference successfully inspired many innovative directions in the fields of mobile applications, social networks, networking, and data management and services,

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Mục lục

  • Preface

  • Organization

  • Contents

  • Main Conference Session

  • OPSitu: A Semantic-Web Based Situation Inference Tool Under Opportunistic Sensing Paradigm

    • 1 Introdution

    • 2 Running Example

    • 3 System Overview

      • 3.1 Key Concepts

      • 3.2 Architecture

      • 4 Situation Inference Engine Implementation

        • 4.1 Semi-automatic SIR Decomposition

        • 4.2 Topological-Ordering Based CA Reasoning

        • 4.3 Similarity-Based Merging and Decision

        • 5 Experimental Evaluation

          • 5.1 Experimental Methodology

          • 5.2 Experimental Result

          • 6 Related Work

          • 7 Conclusion

          • References

          • Model-Driven Public Sensing in Sparse Networks

            • 1 Introduction

            • 2 System Model and Goals

              • 2.1 System Model and Architecture

              • 2.2 Problem Statement

              • 3 Optimized Query Execution in Dense Systems

                • 3.1 Multivariate Gaussian Distribution

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