Interference allocation for scheduler for green multimedia delivery

One of the key challenges in wireless networking is allocating the available radio resources to maximize key service delivery parameters such as the aggregate throughput and the multimedia quality of experience (QoE). We propose a novel and effective scheduling policy that allocates resource blocks, such that interference power is shifted toward capacity-saturated users, while improving the throughput of unsaturated users. The highlight of this paper is that the proposed scheme can dramatically improve the performance of cells that have a high discrepancy in its signal-to-noise ratio (SNR) distribution, which is typical in urban areas. The results show that a free-lunch (FL) solution is possible, whereby for negligible performance degradation in the saturated users, a large improvement in the nonsaturated users can be obtained. However, on average, the number of FL user pairings is low. By relaxing the degradation constraints, the non-FL (NFL) solution can yield a greater multiuser throughput gain. Motivated by a surge in mobile multimedia traffic, we further demonstrate that the proposed scheduling may have a profound impact on both energy efficiency and QoE of multimedia service delivery

Fair Coexistence of Scheduled and Random Access Wireless Networks: Unlicensed LTE/WiFi

We study the fair coexistence of scheduled and random access transmitters sharing the same frequency channel. Interest in coexistence is topical due to the need for emerging unlicensed LTE technologies to coexist fairly with WiFi. However, this interest is not confined to LTE/WiFi as coexistence is likely to become increasingly commonplace in IoT networks and beyond 5G. In this article we show that mixing scheduled and random access incurs and inherent throughput/delay cost, the cost of heterogeneity. We derive the joint proportional fair rate allocation, which casts useful light on current LTE/WiFi discussions. We present experimental results on inter-technology detection and consider the impact of imperfect carrier sensing.Comment: 14 pages, 8 figures, journa

Rate-Splitting Robustness in Multi-Pair Massive MIMO Relay Systems

Relay systems improve both coverage and system capacity. Toward this direction, a full-duplex (FD) technology, being able to boost the spectral efficiency by transmitting and receiving simultaneously on the same frequency and time resources, is envisaged to play a key role in future networks. However, its benefits come at the expense of self-interference (SI) from their own transmit signal. At the same time, massive multiple-input massive multiple-output systems, bringing unconventionally many antennas, emerge as a promising technology with huge degrees-of-freedom. To this end, this paper considers a multi-pair decode-and-forward FD relay channel, where the relay station is deployed with a large number of antennas. Moreover, the rate-splitting (RS) transmission has recently been shown to provide significant performance benefits in various multi-user scenarios with imperfect channel state information at the transmitter (CSIT). Engaging the RS approach, we employ the deterministic equivalent analysis to derive the corresponding sum-rates in the presence of interferences. Initially, numerical results demonstrate the robustness of RS in half-duplex (HD) systems, since the achievable sum-rate increases without bound, i.e., it does not saturate at high signal-to-noise ratio. Next, we tackle the detrimental effect of SI in FD. In particular, and most importantly, not only FD outperforms HD, but also RS enables increasing the range of SI over which FD outperforms HD. Furthermore, increasing the number of relay station antennas, RS appears to be more efficacious due to imperfect CSIT, since SI decreases. Interestingly, increasing the number of users, the efficiency of RS worsens and its implementation becomes less favorable under these conditions. Finally, we verify that the proposed DEs, being accurate for a large number of relay station antennas, are tight approximations even for realistic system dimensions.Peer reviewedFinal Accepted Versio

Accurate non-intrusive residual bandwidth estimation in WMNs

The multi-access scheme of 802.11 wireless networks imposes difficulties in achieving predictable service quality in multi-hop networks. In such networks, the residual capacity of wireless links should be estimated for resource allocation services such as flow admission control. In this paper, we propose an accurate and non-intrusive method to estimate the residual bandwidth of an 802.11 link. Inputs from neighboring network activity measurements and from a basic collision detection mechanism are fed to the analytical model so that the proposed algorithm calculates the maximum allowable traffic level for this link. We evaluate the efficiency of the method via OPNET simulations, and show that the percent estimation error is significantly lower than two other prominent estimation methods, bounded only between 2.5-7.5%. We also demonstrate that flow admission control is successfully achieved in a realistic WMN scenario. Flow control through our proposed algorithm keeps the unsatisfied traffic demand bounded and at a negligibly low level, which is less than an order of magnitude of the other two methods

A survey of IEEE 802.15.4 effective system parameters for wireless body sensor networks

This is the peer reviewed version of the following article: Moravejosharieh, Amirhossein, Lloret, Jaime. (2016). A survey of IEEE 802.15.4 effective system parameters for wireless body sensor networks.International Journal of Communication Systems, 29, 7, 1269-1292. DOI: 10.1002/dac.3098, which has been published in final form at http://doi.org/10.1002/dac.3098. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving[EN] Wireless body sensor networks are offered to meet the requirements of a diverse set of applications such as health-related and well-being applications. For instance, they are deployed to measure, fetch and collect human body vital signs. Such information could be further used for diagnosis and monitoring of medical conditions. IEEE 802.15.4 is arguably considered as a well-designed standard protocol to address the need for low-rate, low-power and low-cost wireless body sensor networks. Apart from the vast deployment of this technology, there are still some challenges and issues related to the performance of the medium access control (MAC) protocol of this standard that are required to be addressed. This paper comprises two main parts. In the first part, the survey has provided a thorough assessment of IEEE 802.15.4 MAC protocol performance where its functionality is evaluated considering a range of effective system parameters, that is, some of the MAC and application parameters and the impact of mutual interference. The second part of this paper is about conducting a simulation study to determine the influence of varying values of the system parameters on IEEE 802.15.4 performance gains. More specifically, we explore the dependability level of IEEE 802.5.4 performance gains on a candidate set of system parameters. Finally, this paper highlights the tangible needs to conduct more investigations on particular aspect(s) of IEEE 802.15.4 MAC protocol. Copyright (c) 2015 John Wiley & Sons, Ltd.Moravejosharieh, A.; Lloret, J. (2016). A survey of IEEE 802.15.4 effective system parameters for wireless body sensor networks. International Journal of Communication Systems. 29(7):1269-1292. https://doi.org/10.1002/dac.3098S12691292297Alrajeh, N. A., Lloret, J., & Canovas, A. (2014). A Framework for Obesity Control Using a Wireless Body Sensor Network. International Journal of Distributed Sensor Networks, 10(7), 534760. doi:10.1155/2014/534760Lopes I Silva B Rodrigues J Lloret J Proenca M A mobile health monitoring solution for weight control International Conference on Wireless Communications and Signal Processing (WCSP) Nanjing / China 2011 1 5Singh, N., Singh, A. K., & Singh, V. K. (2015). Design and performance of wearable ultrawide band textile antenna for medical applications. Microwave and Optical Technology Letters, 57(7), 1553-1557. doi:10.1002/mop.29131Lan, K., Chou, C.-M., Wang, T., & Li, M.-W. (2012). Using body sensor networks for motion detection: a cluster-based approach for green radio. Transactions on Emerging Telecommunications Technologies, 25(2), 199-216. doi:10.1002/ett.2559Lloret, J., Garcia, M., Catala, A., & Rodrigues, J. J. P. C. (2016). A group-based wireless body sensors network using energy harvesting for soccer team monitoring. International Journal of Sensor Networks, 21(4), 208. doi:10.1504/ijsnet.2016.079172Garcia M Catala A Lloret J Rodrigues J A wireless sensor network for soccer team monitoring International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS) Barcelona / Spain 2011 1 6Penders J Gyselinckx B Vullers R De Nil M Nimmala V van de Molengraft J Yazicioglu F Torfs T Leonov V Merken P Van Hoof C Human++: from technology to emerging health monitoring concepts 5th International Summer School and Symposium ISSS-MDBS on Medical Devices and Biosensors Hong Kong 2008 94 98Penders J Van de Molengraft J. Brown L Grundlehner B Gyselinckx B Van Hoof C Potential and challenges of body area networks for personal health Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC Minneapolis, U.S. 2009 6569 6572Ullah, S., Higgins, H., Braem, B., Latre, B., Blondia, C., Moerman, I., … Kwak, K. S. (2010). A Comprehensive Survey of Wireless Body Area Networks. Journal of Medical Systems, 36(3), 1065-1094. doi:10.1007/s10916-010-9571-3Cao, H., Leung, V., Chow, C., & Chan, H. (2009). Enabling technologies for wireless body area networks: A survey and outlook. IEEE Communications Magazine, 47(12), 84-93. doi:10.1109/mcom.2009.5350373Hall, P. S., Yang Hao, Nechayev, Y. I., Alomainy, A., Constantinou, C. C., Parini, C., … Bozzetti, M. (2007). Antennas and propagation for on-body communication systems. IEEE Antennas and Propagation Magazine, 49(3), 41-58. doi:10.1109/map.2007.4293935Mamaghanian, H., Khaled, N., Atienza, D., & Vandergheynst, P. (2011). Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes. IEEE Transactions on Biomedical Engineering, 58(9), 2456-2466. doi:10.1109/tbme.2011.2156795LAN-MAN Standards Committee the IEEE Computer Society IEEE standard for local and metropolitan area networks - part 15.4: low rate wireless personal area networks (LR-WPANs) 2011Petrova M Riihijarvi J Mahonen P Labella S Performance study of IEEE 802.15.4 using measurements and simulations IEEE Wireless Communications and Networking Conference (WCNC) Las Vegas, U.S. 2006 487 492Vaithiyanathan, J., Raju, R. K., & Sadayan, G. (2011). Performance Evaluation of IEEE 802.15.4 Using Association Process and Channel Measurement. Communications in Computer and Information Science, 409-417. doi:10.1007/978-3-642-22555-0_42Yazdi E Moravejosharieh A Willig A Pawlikowski K Coupling power and frequency adaptation for interference mitigation in IEEE 802.15.4-based mobile body sensor networks: part II 2014 Australasian Telecommunication Networks and Applications Conference (ATNAC) Melbourne, Australia 2014 105 110Pelegris P Banitsas K Investigating the efficiency of IEEE 802.15.4 for medical monitoring applications 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC Boston, U.S. 2011 8215 8218Ranjit, J. S., & Shin, S. (2013). A Modified IEEE 802.15.4 Superframe Structure for Guaranteed Emergency Handling in Wireless Body Area Network. Network Protocols and Algorithms, 5(2), 1. doi:10.5296/npa.v5i2.3375Jianliang Zheng, & Lee, M. J. (2004). Will IEEE 802.15.4 make ubiquitous networking a reality?: a discussion on a potential low power, low bit rate standard. IEEE Communications Magazine, 42(6), 140-146. doi:10.1109/mcom.2004.1304251Toscano E Lo Bello L Cross-channel interference in IEEE 802.15.4 networks IEEE International Workshop on Factory Communication Systems, 2008. WFCS 2008 Dresden, Germany 2008 139 148Bashir F Baek WS Sthapit P Pandey D young Pyun J Coordinator assisted passive discovery for mobile end devices in IEEE 802.15.4 2013 IEEE Consumer Communications and Networking Conference (CCNC) Las Vegas, U.S. 2013 601 604Tabatabaei Yazdi E Willig A Pawlikowski K Shortening orphan time in IEEE 802.15.4: what can be gained 2013 19th IEEE International Conference on Networks (ICON) Singapore 2013 1 6Park, T. R., Kim, T. H., Choi, J. Y., Choi, S., & Kwon, W. H. (2005). Throughput and energy consumption analysis of IEEE 802.15.4 slotted CSMA∕CA. Electronics Letters, 41(18), 1017. doi:10.1049/el:20051662Bianchi, G. (2000). Performance analysis of the IEEE 802.11 distributed coordination function. IEEE Journal on Selected Areas in Communications, 18(3), 535-547. doi:10.1109/49.840210IEEE Computer Society LAN MAN Standards Committee Wireless LAN medium access control (MAC) and physical layer (PHY) specifications 1997Pollin, S., Ergen, M., Ergen, S. C., Bougard, B., Der Perre, L. V., Moerman, I., … Catthoor, F. (2008). Performance Analysis of Slotted Carrier Sense IEEE 802.15.4 Medium Access Layer. IEEE Transactions on Wireless Communications, 7(9), 3359-3371. doi:10.1109/twc.2008.060057Xinhua Ling, Yu Cheng, Mark, J. W., & Xuemin Shen. (2008). A Renewal Theory Based Analytical Model for the Contention Access Period of IEEE 802.15.4 MAC. IEEE Transactions on Wireless Communications, 7(6), 2340-2349. doi:10.1109/twc.2008.070048Lee, C. Y., Cho, H. I., Hwang, G. U., Doh, Y., & Park, N. (2011). Performance modeling and analysis of IEEE 802.15.4 slotted CSMA/CA protocol with ACK mode. AEU - International Journal of Electronics and Communications, 65(2), 123-131. doi:10.1016/j.aeue.2010.02.007Wang, F., Zhao, Y., & Li, D. (2011). Analysis of CSMA/CA in IEEE 802.15.4. IET Communications, 5(15), 2187-2195. doi:10.1049/iet-com.2010.1007Zhu, J., Tao, Z., & Lv, C. (2011). Performance Evaluation of IEEE 802.15.4 CSMA/CA Scheme Adopting a Modified LIB Model. Wireless Personal Communications, 65(1), 25-51. doi:10.1007/s11277-011-0226-6Shu F Sakurai T Analysis of an energy conserving CSMA-CA GLOBECOM Washington DC, U.S. 2007 2536 2540Shu, F., & Sakurai, T. (2011). A new analytical model for the IEEE 802.15.4 CSMA-CA protocol. Computer Networks, 55(11), 2576-2591. doi:10.1016/j.comnet.2011.04.017Cano-Garcia, J. M., & Casilari, E. (2011). An empirical evaluation of the consumption of 802.15.4/ZigBee sensor motes in noisy environments. 2011 International Conference on Networking, Sensing and Control. doi:10.1109/icnsc.2011.5874886Baz, M., Mitchell, P. D., & Pearce, D. A. J. (2013). Versatile Analytical Model for Delay and Energy Evaluation in WPANs: A Case Study for IEEE 802.15.4 CSMA-CA. Wireless Personal Communications, 75(1), 415-445. doi:10.1007/s11277-013-1370-yLiu Q Czylwik A A priority-based adaptive service differentiation scheme for IEEE 802.15.4 sensor networks Proceedings of European Wireless 2014; 20th European Wireless Conference Barcelona, Spain 2014 1 6Golmie, N., Cypher, D., & Rebala, O. (s. f.). Performance evaluation of low rate WPANs for medical applications. IEEE MILCOM 2004. Military Communications Conference, 2004. doi:10.1109/milcom.2004.1494952Misic, J., Misic, V. B., & Shafi, S. (s. f.). Performance of IEEE 802.15.4 beacon enabled PAN with uplink transmissions in non-saturation mode - access delay for finite buffers. First International Conference on Broadband Networks. doi:10.1109/broadnets.2004.61Mišić, J., Shafi, S., & Mišić, V. B. (2005). The impact of MAC parameters on the performance of 802.15.4 PAN. Ad Hoc Networks, 3(5), 509-528. doi:10.1016/j.adhoc.2004.08.002Anastasi, G., Conti, M., & Di Francesco, M. (2011). A Comprehensive Analysis of the MAC Unreliability Problem in IEEE 802.15.4 Wireless Sensor Networks. IEEE Transactions on Industrial Informatics, 7(1), 52-65. doi:10.1109/tii.2010.2085440Lee, B.-H., Al Rasyid, M. U. H., & Wu, H.-K. (2012). Analysis of superframe adjustment and beacon transmission for IEEE 802.15.4 cluster tree networks. EURASIP Journal on Wireless Communications and Networking, 2012(1). doi:10.1186/1687-1499-2012-219Zimmerling, M., Ferrari, F., Mottola, L., Voigt, T., & Thiele, L. (2012). pTunes. Proceedings of the 11th international conference on Information Processing in Sensor Networks - IPSN ’12. doi:10.1145/2185677.2185730Rohm, D., Goyal, M., Hosseini, H., Divjak, A., & Bashir, Y. (2009). Configuring Beaconless IEEE 802.15.4 Networks Under Different Traffic Loads. 2009 International Conference on Advanced Information Networking and Applications. doi:10.1109/aina.2009.84Jin-Shyan Lee. (2006). Performance evaluation of IEEE 802.15.4 for low-rate wireless personal area networks. IEEE Transactions on Consumer Electronics, 52(3), 742-749. doi:10.1109/tce.2006.1706465De Paz Alberola, R., & Pesch, D. (2012). Duty cycle learning algorithm (DCLA) for IEEE 802.15.4 beacon-enabled wireless sensor networks. Ad Hoc Networks, 10(4), 664-679. doi:10.1016/j.adhoc.2011.06.006Barbieri, A., Chiti, F., & Fantacci, R. (2006). WSN17-2: Proposal of an Adaptive MAC Protocol for Efficient IEEE 802.15.4 Low Power Communications. IEEE Globecom 2006. doi:10.1109/glocom.2006.989Jeon, J., Lee, J. W., Ha, J. Y., & Kwon, W. H. (2007). DCA: Duty-Cycle Adaptation Algorithm for IEEE 802.15.4 Beacon-Enabled Networks. 2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring. doi:10.1109/vetecs.2007.35Kang, M., Chong, J., Hyun, H., Kim, S., Jung, B., & Sung, D. (2007). Adaptive Interference-Aware Multi-Channel Clustering Algorithm in a ZigBee Network in the Presence of WLAN Interference. 2007 2nd International Symposium on Wireless Pervasive Computing. doi:10.1109/iswpc.2007.342601Yi, P., Iwayemi, A., & Zhou, C. (2011). Developing ZigBee Deployment Guideline Under WiFi Interference for Smart Grid Applications. IEEE Transactions on Smart Grid, 2(1), 110-120. doi:10.1109/tsg.2010.2091655Tang, L., Wang, K.-C., Huang, Y., & Gu, F. (2007). Channel Characterization and Link Quality Assessment of IEEE 802.15.4-Compliant Radio for Factory Environments. IEEE Transactions on Industrial Informatics, 3(2), 99-110. doi:10.1109/tii.2007.898414Sha M Xing G Zhou G Liu S Wang X C-MAC: model-driven concurrent medium access control for wireless sensor networks IEEE INFOCOM 2009 Rio de Janeiro, Brazil 2009 1845 1853 10.1109/INFCOM.2009.5062105Peizhong Yi, Iwayemi, A., & Chi Zhou. (2010). Frequency agility in a ZigBee network for smart grid application. 2010 Innovative Smart Grid Technologies (ISGT). doi:10.1109/isgt.2010.5434747Torabi N Wong W Leung VCM A robust coexistence scheme for IEEE 802.15.4 wireless personal area networks IEEE Consumer Communications and Networking Conference (CCNC) Las Vegas, U.S. 2011 1031 1035 10.1109/CCNC.2011.5766322IEEE standard for local and metropolitan area networks - part 15.6: wireless body area networks IEEE Std 802.15.6-2012 2012 1 271 10.1109/IEEESTD.2012.6161600Kim, S., Kim, S., Kim, J.-W., & Eom, D.-S. (2012). Flexible beacon scheduling scheme for interference mitigation in body sensor networks. 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON). doi:10.1109/secon.2012.6275772Bradai N Fourati LC Kamoun L Performance analysis of medium access control protocol for wireless body area networks 27th International Conference on Advanced Information Networking and Applications Workshops (WAINA) Barcelona, Spain 2013 916 921Moravejosharieh A Yazdi ET Study of resource utilization in IEEE 802.15.4 wireless body sensor network, part I: the need for enhancement IEEE 16th International Conference on Computational Science and Engineering (CSE) Sydney, Australia 2013 1226 1231Moravejosharieh A Yazdi ET Willig A Study of resource utilization in IEEE 802.15.4 wireless body sensor network, part II: greedy channel utilization 19th IEEE International Conference on Networks (ICON) Singapore 2013 1 6Moravejosharieh A Yazdi E Willig A Pawlikowski K Adaptive channel utilisation in IEEE 802.15.4 wireless body sensor networks: continuous hopping approach Australasian Telecommunication Networks and Applications Conference (ATNAC) Melbourne, Australia 2014 93 98 10.1109/ATNAC.2014.7020880Moravejosharieh, A. H. (2015). Frequency-Adaptive Approach In IEEE 802.15.4 Wireless Body Sensor Networks: Continuous-Assessment or Periodic-Assessment? International Journal of Information, Communication Technology and Applications, 1(1), 19. doi:10.17972/ajicta2015113Moravejosharieh A Yazdi E Pawlikowski K Sirisena H Adaptive channel utilisation in IEEE 802.15.4 wireless body sensor networks: adaptive phase-shifting approach International Telecommunication Networks and Applications Conference (ITNAC) Sydney, Australia 2015 93 98Bian, K., Park, J.-M., & Gao, B. (2014). Channel Assignment for Multi-hop Cognitive Radio Networks. Cognitive Radio Networks, 101-116. doi:10.1007/978-3-319-07329-3_6Bian, K., Park, J.-M., & Gao, B. (2014). Coexistence-Aware Spectrum Sharing for Homogeneous Cognitive Radio Networks. Cognitive Radio Networks, 61-75. doi:10.1007/978-3-319-07329-3_4Wu C Yan H Huo H A multi-channel MAC protocol design based on IEEE 802.15.4 standard in industry 2012 10th IEEE International Conference on Industrial Informatics (INDIN) Beijing, China 2012 1206 1211 10.1109/INDIN.2012.6300916Incel, O. D. (2011). A survey on multi-channel communication in wireless sensor networks. Computer Networks, 55(13), 3081-3099. doi:10.1016/j.comnet.2011.05.020Kim Y Shin H Cha H Y-MAC: an energy-efficient multi-channel MAC protocol for dense wireless sensor networks Proceedings of the 7th International Conference on Information Processing in Sensor Networks IPSN '08 St. Louis MO, U.S. 2008 53 63Demirkol, I., Ersoy, C., & Alagoz, F. (2006). MAC protocols for wireless sensor networks: a survey. IEEE Communications Magazine, 44(4), 115-121. doi:10.1109/mcom.2006.1632658Wykret T Correia L Macedo D Giacomin J Andrade L Evaluation and avoidance of interference in WSN: a multi-radio node prototype using dynamic spectrum allocation IFIP Wireless Days (WD) Valencia, Spain 2013 1 3 10.1109/WD.2013.6686533Doyle L Sutton P Nolan K Lotze J Ozgul B Rondeau T Fahmy S Lahlou H DaSilva L Experiences from the IRIS testbed in dynamic spectrum access and cognitive radio experimentation IEEE Symposium on New Frontiers in Dynamic Spectrum Singapore 2010 1 8 10.1109/DYSPAN.2010.5457835Ansari, J., Zhang, X., & Mahonen, P. (2010). Multi-radio medium access control protocol for wireless sensor networks. International Journal of Sensor Networks, 8(1), 47. doi:10.1504/ijsnet.2010.034066Liu Z Wu W A dynamic multi-radio multi-channel MAC protocol for wireless sensor networks 2nd International Conference on Communication Software and Networks (ICCSN) Singapore 2010 105 109Xu, W., Trappe, W., & Zhang, Y. (2008). Defending wireless sensor networks from radio interference through channel adaptation. ACM Transactions on Sensor Networks, 4(4), 1-34. doi:10.1145/1387663.1387664Kim Y Shin H Cha H Y-MAC: an energy-efficient multi-channel MAC protocol for dense wireless sensor networks Proceedings of the 7th IEEE Computer Society International Conference on Information Processing in Sensor Networks IPSN '08 Washington, DC, USA 2008 53 63Tae Hyun Kim, Jae Yeol Ha, & Sunghyun Choi. (2009). Improving Spectral and Temporal Efficiency of Collocated IEEE 802.15.4 LR-WPANs. IEEE Transactions on Mobile Computing, 8(12), 1596-1609. doi:10.1109/tmc.2009.85Chowdhury, K. R., Nandiraju, N., Chanda, P., Agrawal, D. P., & Zeng, Q.-A. (2009). Channel allocation and medium access control for wireless sensor networks. Ad Hoc Networks, 7(2), 307-321. doi:10.1016/j.adhoc.2008.03.004Deylami, M., & Jovanov, E. (2012). A distributed and collaborative scheme for mitigating coexistence in IEEE 802.15.4 based WBANs. Proceedings of the 50th Annual Southeast Regional Conference on - ACM-SE ’12. doi:10.1145/2184512.2184514Deylami, M. N., & Jovanov, E. (2014). A Distributed Scheme to Manage The Dynamic Coexistence of IEEE 802.15.4-Based Health-Monitoring WBANs. IEEE Journal of Biomedical and Health Informatics, 18(1), 327-334. doi:10.1109/jbhi.2013.2278217Deylami M Jovanov E An implementation of a distributed scheme for managing the dynamic coexistence of wireless body area networks Southeastcon, 2013 Proceedings of IEEE Jacksonville, U.S. 2013 1 6 10.1109/SECON.2013.6567446Cavallari, R., Martelli, F., Rosini, R., Buratti, C., & Verdone, R. (2014). A Survey on Wireless Body Area Networks: Technologies and Design Challenges. IEEE Communications Surveys & Tutorials, 16(3), 1635-1657. doi:10.1109/surv.2014.012214.00007Chen, M., Gonzalez, S., Vasilakos, A., Cao, H., & Leung, V. C. M. (2010). Body Area Networks: A Survey. Mobile Networks and Applications, 16(2), 171-193. doi:10.1007/s11036-010-0260-8Movassaghi, S., Abolhasan, M., Lipman, J., Smith, D., & Jamalipour, A. (2014). Wireless Body Area Networks: A Survey. IEEE Communications Surveys & Tutorials, 16(3), 1658-1686. doi:10.1109/surv.2013.121313.00064Patel, M., & Wang, J. (2010). Applications, challenges, and prospective in emerging body area networking technologies. IEEE Wireless Communications, 17(1), 80-88. doi:10.1109/mwc.2010.5416354ULLAH, S., KHAN, P., ULLAH, N., SALEEM, S., HIGGINS, H., & Sup KWAK, K. (2009). A Review of Wireless Body Area Networks for Medical Applications. International Journal of Communications, Network and System Sciences, 02(08), 797-803. doi:10.4236/ijcns.2009.28093Boulis, A., Smith, D., Miniutti, D., Libman, L., & Tselishchev, Y. (2012). Challenges in body area networks for healthcare: the MAC. IEEE Communications Magazine, 50(5), 100-106. doi:10.1109/mcom.2012.6194389Pantelopoulos A Bourbakis N A survey on wearable biosensor systems for health monitoring 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vancouver, Canada 2008 4887 4890 10.1109/IEMBS.2008.4650309Takei, K., Honda, W., Harada, S., Arie, T., & Akita, S. (2014). Toward Flexible and Wearable Human-Interactive Health-Monitoring Devices. Advanced Healthcare Materials, 4(4), 487-500. doi:10.1002/adhm.201400546Caldeira, J. M. L. P., Rodrigues, J. J. P. C., & Lorenz, P. (2013). Intra-Mobility Support Solutions for Healthcare Wireless Sensor Networks–Handover Issues. IEEE Sensors Journal, 13(11), 4339-4348. doi:10.1109/jsen.2013.2267729Carrano, R. C., Passos, D., Magalhaes, L. C. S., & Albuquerque, C. V. N. (2014). Survey and Taxonomy of Duty Cycling Mechanisms in Wireless Sensor Networks. IEEE Communications Surveys & Tutorials, 16(1), 181-194. doi:10.1109/surv.2013.052213.00116Sudevalayam, S., & Kulkarni, P. (2011). Energy Harvesting Sensor Nodes: Survey and Implications. IEEE Communications Surveys & Tutorials, 13(3), 443-461. doi:10.1109/surv.2011.060710.00094Khanafer, M., Guennoun, M., & Mouftah, H. T. (2014). A Survey of Beacon-Enabled IEEE 802.15.4 MAC Protocols in Wireless Sensor Networks. IEEE Communications Surveys & Tutorials, 16(2), 856-876. doi:10.1

LTE-LAA 성능 향상을 위한 MAC 계층 기법

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2019. 2. 최성현.3GPP long term evolution (LTE) operation in unlicensed spectrum is emerging as a promising technology in achieving higher data rate with LTE since ultra-wide unlicensed spectrum, e.g., about 500 MHz at 5–6 GHz range, is available in most countries. Recently, 3GPP has finalized standardization of licensed-assisted access (LAA) for LTE operation in 5 GHz unlicensed spectrum, which has been a playground only for Wi-Fi. In this dissertation, we propose the following three strategies to enhance the performance of LAA: (1) Receiver-aware COT adaptation, (2) Collision-aware link adaptation, and (3) Power and energy detection threshold adaptation. First, LAA has a fixed maximum channel occupancy time (MCOT), which is the maximum continuous transmission time after channel sensing, while Wi-Fi may transmit for much shorter time duration. As a result, when Wi-Fi coexists with LAA, Wi-Fi airtime and throughput can be much less than those achieved when Wi-Fi coexists with another Wi-Fi. To guarantee fair airtime and improve throughput of Wi-Fi, we propose a receiver-aware channel occupancy time (COT) adaptation ( RACOTA ) algorithm, which observes Wi-Fi aggregate MAC protocol data unit (A-MPDU) frames and matches LAAs COT to the duration of A-MPDU frames when any Wi-Fi receiver has more data to receive. Moreover, RACOTA detects saturation of Wi-Fi traffic and adjusts COT only if Wi-Fi traffic is saturated. We prototype saturation detection algorithm of RACOTA with commercial off-the-shelf Wi-Fi device and show that RACOTA detects saturation of Wi-Fi networks accurately. Through ns-3 simulations, we demonstrate that RACOTA provides airtime fairness between LAA and Wi-Fi while achieves up to 334% Wi-Fi throughput gain. Second, the link adaptation scheme of the conventional LTE, adaptive modulation and coding (AMC), cannot operate well in the unlicensed band due to intermittent collisions. Intermittent collisions make LAA eNB lower modulation and coding scheme (MCS) for the subsequent transmission and such unnecessarily lowered MCS significantly degrades spectral efficiency. To address this problem, we propose a collision-aware link adaptation algorithm ( COALA ). COALA exploits k-means unsupervised clustering algorithm to discriminate channel quality indicator (CQI) reports which are measured with collision interference and selects the most suitable MCS for the next transmission. By prototype-based experiments, we demonstrate that COALA detects collisions accurately, and by conducting ns-3 simulations in various scenarios, we also show that COALA achieves up to 74.9% higher user perceived throughput than AMC. Finally, we propose PETAL to mitigate the negative impact of spatial reuse (SR) operation. We first design the baseline algorithm, which operates SR aggressively, and show that the baseline algorithm degrades the throughput performance severely when the UE is close to an interferer. Our proposed algorithm PETAL estimates and compares the spectral efficiency for the SR operation and non-SR operation. Then, PETAL operates SR only if the spectral efficiency of SR operation is expected to be higher than the case of non-SR operation. Our simulation verifies the performance of PETAL in various scenarios. When two pair of an eNB and a UE coexists, PETAL improves the throughput by up to 329% over the baseline algorithm. In summary, we identify interesting problems that appeared with LAA and shows the impact of the problems through the extensive simulations and propose compelling algorithms to solve the problems. The airtime fairness between Wi-Fi and LAA is improved with COT adaptation. Furthermore, link adaptation accuracy and SR operation are improved by exploiting CQI reports history. The performance of the proposed schemes is verified by system level simulation.비면허 대역에서의 LTE 동작은 더 높은 데이터 전송률을 달성하는 유망한 기술로 부각되고 있다. 최근 3GPP는 기존 Wi-Fi 기술이 사용하던 5 GHz 비면허 대역에서 LTE를 사용하는 licensed-assisted access (LAA) 기술의 표준화를 완료하였다. 본 논문에서 우리는 LAA의 성능을 향상시키기 위해 다음과 같은 세 가지 전략을 제안한다: (1) 수신기 인식 채널 점유 시간 적응, (2) 충돌 인식 링크 적응, (3) 전력 및 에너지 검출 역치 적응. 첫째, LAA는 고정된 최대 채널 점유 시간을 가지고 있고 그 시간 만큼 연속적으로 전송할 수 있는 반면, Wi-Fi는 비교적 짧은 시간 동안만 연속적으로 전송할 수 있다. 그 결과 Wi-Fi가 LAA와 공존할 때 Wi-Fi의 airtime과 수율 성능은 Wi-Fi가 또 다른 Wi-Fi와 공존할 때에 비하여 저하되게된다. 따라서 우리는 Wi-Fi의 airtime과 수율 성능을 향상시키기 위하여 Wi-Fi의 A-MPDU 프레임 전송 시간에 맞추어 LAA의 채널 점유 시간을 조절하는 수신기 인식 채널 점유 시간 적응 기법인 RACOTA를 제안한다. RACOTA 는 포화 감지 결과 Wi-Fi 수신기가 더 받을 데이터가 있다고 판단될 때에만 채널 점유 시간을 조절한다. 우리는 RACOTA 의 포화 감지 알고리즘을 상용 Wi-Fi 장비에 구현하여 이를 바탕으로 실측을 통해 RACOTA 가 공존하는 Wi-Fi의 포화 여부를 정확하게 감지해냄을 보인다. 또한 우리는 ns-3 시뮬레이션을 통하여 RACOTA 를 사용하는 LAA가 공존하는 Wi-Fi에게 공정한 airtime을 제공하고 기존 LAA와 공존하는 Wi-Fi 대비 최대 334%의 Wi-Fi 수율 성능 향상을 가져옴을 보인다. 둘째, 간헐적인 충돌이 발생할 수 있는 비면허 대역에서는 기존 LTE의 링크 적응 기법인 adaptive modulation and coding (AMC)이 잘 동작하지 않을 수 있다. 간헐적인 충돌은 LAA 기지국으로 하여금 modulation and coding scheme (MCS)을 낮추어서 다음 전송을 하도록 하는데 다음 전송 시에 충돌이 발생하지 않는다면 불필요하게 낮춘 MCS로 인해 주파수 효율이 크게 저하된다. 이러한 문제를 해결하기위해 우리는 충돌 인식 링크 적응 기법인 COALA 를 제안한다. COALA 는 k-means 무감독 클러스터링 알고리즘을 사용하여 channel quality indicator (CQI) 리포트 중 충돌 간섭에 영향을 받은 CQI 리포트들을 구별해내고 이를 통해 다음 전송을 위한 최적의 MCS를 선택한다. 우리는 실측을 통하여 COALA 가 정확하게 충돌을 감지해냄을 보인다. 또한 우리는 다양한 환경에서의 ns-3 시뮬레이션을 통하여 COALA 가 AMC 대비 최대 74.9%의 사용자 인식 수율 성능 향상을 가져옴을 보인다. 셋째, 우리는 공간 재사용 동작의 부작용을 최소화하기 위하여 수신 단말을 고려하여 전송 파워 및 에너지 검출 역치를 적응적으로 조절하는 PETAL 알고리즘을 제안한다. 우리는 먼저 수신 단말을 고려하지 않고 공격적으로 공간 재사용 동작을 하는 baseline 알고리즘을 설계하고 다양한 환경에서의 시뮬레이션을 통하여 수신 단말이 간섭원에 가까운 경우 baseline 알고리즘의 성능이 심각하게 열화됨을 보인다. 제안하는 알고리즘인 PETAL 은 수신 단말로부터 받은 CQI 리포트 정보와 채널 점유 시점까지의 평균 대기 시간을 이용하여 공간 재사용 동작을 할 때 예상되는 주파수 효율과 공간 재사용 동작을 하지 않을 때 예상되는 주파수 효율을 비교하여 공간 재사용 동작을 할 때 예상되는 주파수 효율이 더 클 때에만 공간 재사용 동작을 한다. 우리는 다양한 환경에서의 ns-3 시뮬레이션을 통하여 PETAL 이 baseline 알고리즘 대비 최대 329%의 수율 성능 향상을 가져옴을 보인다. 요약하자면, 우리는 LAA의 등장과 함께 새롭게 대두되는 흥미로운 문제들을 확인하고 문제들의 심각성을 다양한 환경에서의 시뮬레이션을 통하여 살펴보고 이 러한 문제들을 해결할 수 있는 알고리즘들을 제안한다. Wi-Fi와 LAA 사이의 airtime 공정성은 LAA의 연속 전송 시간을 적응적으로 조절하여 개선할 수 있다. 또한 링크 적응 정확도와 공간 재사용 동작의 효율성은 CQI 리포트들의 분포를 이용하여 개선할 수 있다. 제안하는 알고리즘들의 성능은 시스템 레벨 시뮬레이션을 통하여 검증되었다.1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Overview of Existing Approaches . . . . . . . . . . . . . . . . . . . 2 1.3 Main Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3.1 RACOTA: Receiver-Aware Channel Occupancy Time Adaptation for LTE-LAA . . . . . . . 2 1.3.2 COALA: Collision-Aware Link Adaptation for LTE-LAA . . 3 1.3.3 PETAL: Power and Energy Detection Threshold Adaptation for LAA . . . . . . . . . . . . . . 4 1.4 Organization of the Dissertation . . . . . . . . . . . . . . . . . . . . 5 2 RACOTA:Receiver-AwareChannelOccupancyTimeAdaptationforLTE- LAA 6 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.3 MAC Mechanisms of Wi-Fi and LAA . . . . . . . . . . . . . . . . . 10 2.3.1 Wi-Fi MAC Operation . . . . . . . . . . . . . . . . . . . . . 10 2.3.2 LAA Listen-Before-Talk (LBT) Mechanism . . . . . . . . . . 11 2.3.3 Wide Bandwidth Operation . . . . . . . . . . . . . . . . . . 13 2.4 Coexistence performance of LAA and Wi-Fi . . . . . . . . . . . . . . 14 2.4.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 14 2.4.2 Unfairness between LAA and Wi-Fi . . . . . . . . . . . . . . 15 2.5 Receiver-Aware COT Adaptation Algorithm . . . . . . . . . . . . . . 17 2.5.1 Saturation Detection (SD) . . . . . . . . . . . . . . . . . . . 20 2.5.2 Receiver-Aware COT Decision . . . . . . . . . . . . . . . . . 22 2.6 Performance Evaluation of SD Algorithm . . . . . . . . . . . . . . . 22 2.6.1 Measurement Setup . . . . . . . . . . . . . . . . . . . . . . . 22 2.6.2 PPDUMaxTime Detection . . . . . . . . . . . . . . . . . . . 24 2.6.3 Saturation Detection Performance . . . . . . . . . . . . . . . 26 2.7 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.7.1 Saturated Traffic Scenario . . . . . . . . . . . . . . . . . . . 28 2.7.2 Unsaturated Traffic Scenario . . . . . . . . . . . . . . . . . . 30 2.7.3 Bursty Traffic Scenario . . . . . . . . . . . . . . . . . . . . . 30 2.7.4 Heterogeneous Wi-Fi Traffic Generation Scenario . . . . . . 31 2.7.5 Multiple Node Scenario . . . . . . . . . . . . . . . . . . . . 34 2.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3 COALA: Collision-Aware Link Adaptation for LTE-LAA 36 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.2 Backgound and Related Work . . . . . . . . . . . . . . . . . . . . . 38 3.2.1 LAA and LBT . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2.2 AMC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.2.3 Inter-Cell Interference Cancellation . . . . . . . . . . . . . . 39 3.2.4 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.3 Impact of Collision to Link Adaptation . . . . . . . . . . . . . . . . . 41 3.4 COALA: Collision-aware Link Adaptation . . . . . . . . . . . . . . . 47 3.4.1 CQI Clustering Algorithm . . . . . . . . . . . . . . . . . . . 48 3.4.2 Collision Detection and Collision Probability Estimation . . . 48 3.4.3 Suitable MCS Selection . . . . . . . . . . . . . . . . . . . . 49 3.5 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.5.1 Prototype-based Feasibility Study . . . . . . . . . . . . . . . 51 3.5.2 Contention Collision with LAA eNBs . . . . . . . . . . . . . 53 3.5.3 Hidden Collision . . . . . . . . . . . . . . . . . . . . . . . . 57 3.5.4 Bursty Hidden Collision . . . . . . . . . . . . . . . . . . . . 58 3.5.5 Contention Collision with Wi-Fi Transmitters . . . . . . . . . 58 3.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4 PETAL: Power and Energy Detection Threshold Adaptation for LAA 62 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.2 Backgound and Related Work . . . . . . . . . . . . . . . . . . . . . 64 4.2.1 Energy Detection Threshold . . . . . . . . . . . . . . . . . . 64 4.2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.3 Baseline Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.3.1 Design of the Baseline Algorithm . . . . . . . . . . . . . . . 65 4.3.2 Performance of the Baseline Algorithm . . . . . . . . . . . . 66 4.4 PETAL: Power and Energy Detection Threshold Adaptation . . . . . 68 4.4.1 CQI Management . . . . . . . . . . . . . . . . . . . . . . . . 69 4.4.2 Success Probability and Airtime Ratio Estimation . . . . . . . 69 4.4.3 CQI Clustering Algorithm . . . . . . . . . . . . . . . . . . . 71 4.4.4 SR Decision . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.5 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.5.1 Two Cell Scenario . . . . . . . . . . . . . . . . . . . . . . . 73 4.5.2 Coexistence with Standard LAA . . . . . . . . . . . . . . . . 75 4.5.3 Four Cell Scenario . . . . . . . . . . . . . . . . . . . . . . . 76 4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5 Concluding Remarks 79 5.1 Research Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 79 Abstract (In Korean) 88 감사의 글 92Docto