Skip to main content
Log in

Network for hypersonic UCAV swarms

  • Review
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

Unmanned combat aerial vehicles (UCAVs) that swarm with both autonomous decision-making and cooperative attacking have been regarded as revolutionary elements of modern warfare. In such a swarm, inter-group connectivity must be ensured in a network to maintain a collective consensus. In recent years, academia and industry have made many efforts to achieve common tactical data link systems and commercial drone networks. However, the existing results have difficulty meeting the needs of cooperative autonomous UCAV swarms with both hypersonic mobility and time sensitivity in severe confrontation scenarios. In this article, we conduct an in-depth investigation of the network used for the hypersonic UCAV swarms, which can be considered as a special form of mobile wireless network. Furthermore, faced with specific functional demands, we summarize the main challenges of designing this dedicated network. In addition, a comprehensive survey of potential solutions for the network design is presented. Lastly, we discuss the possible capabilities of the network given the current forefront of technology, as well as remaining challenges and open issues.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Barry C L, Zimet E. UCAVs-technological, policy, and operational challenges. Defense Horizons, 2001. https://apps.dtic.mil/docs/citations/ADA421937

  2. Eshel T. US air force to experiment with counter-air defence cruise missiles. Defense Update, 2017. https://defense-update.com/20171221_gray-wolf.html

  3. Kopp C. Soviet/Russian cruise missiles. Air Power Australia, 2009. https://www.ausairpower.net/APA-Rus-Cruise-Missiles.html

  4. Lassman T. The tomahawk and U.S. cruise missile technology. Smithsonian National Air and Space Museum Story, 2017. https://airandspace.si.edu/stories/editorial/tomahawk-and-us-cruise-missile-technology

  5. Cummings M L, Guerlain S. The tactical tomahawk conundrum: designing decision support systems for revolutionary domains. In: Proceedings of 2003 IEEE International Conference on Systems, Man and Cybernetics, Washington, 2003. 2: 1583–1588

  6. Raytheon. Technical Manual Tomahawk Cruise Missile RGM/UGM-109 System Description. 2009

  7. American Security News Reports. Navy demonstrates new reconnaissance, redirection potential of Tomahawk missile. 2015. https://americansecuritynews.com/stories/510641816-navy-demonstrates-new-reconnaissance-redirection-potential-of-tomahawk-missile

  8. Taylor R. Net-enabled weapons. Intercom, 2004, 45: 11

    Google Scholar 

  9. Collins R. Rockwell Collins receives $18 million award for JSOW/Harpoon Net Enabled Weapon Data Link. 2007. http://www.defense-aerospace.com/articles-view/release/3/86215/rockwell-to-develop-data-link-for-jsowharpoon.html

  10. Southern Maryland News Net. World’s first net-enabled weapon completes developmental testing. 2012. https://smnewsnet.com/archives/8606/worlds-first-net-enabled-weapon-completes-developmental-testing/

  11. Rogoway T. The navy’s smart new stealth anti-ship missile can plan its own attack. Foxtrot Alpha, 2014. https://foxtrotalpha.jalopnik.com/the-navys-smart-new-stealth-anti-ship-missile-can-plan-1666079462

  12. Ilachinski A. AI, Robots, and Swarms: Issues, Questions, and Recommended Studies. CNA Analysis & Solutions, 2017. https://www.cna.org/CNA_files/PDF/DRM-2017-U-014796-Final.pdf

  13. Hudson S, Haataja S. Survive and project indirect fires. The U.S. Army Aviation and Missile Research, Development and Engineering Center (AMRDEC), 2018. https://www.army.mil/article/200241/survive_and_project_indirect_fires

  14. Jeon I S, Lee J I, Tahk M J. Homing guidance law for cooperative attack of multiple missiles. J Guidance Control Dyn, 2010, 33: 275–280

    Article  Google Scholar 

  15. Ren W, Beard R W, Mclain T W. Coordination variables and consensus building in multiple vehicle systems. In: Cooperative Control. Berlin: Springer, 2004. 309: 171–188

    Chapter  Google Scholar 

  16. Bekmezci I, Sahingoz O K, Temel S. Flying ad-hoc networks (FANETs): a survey. Ad Hoc Netw, 2013, 11: 1254–1270

    Article  Google Scholar 

  17. Saleem M, Di Caro G A, Farooq M. Swarm intelligence based routing protocol for wireless sensor networks: survey and future directions. Inf Sci, 2011, 181: 4597–4624

    Article  Google Scholar 

  18. Wei M, Chen G, Cruz J B, et al. Multi-missile interception integrating new guidance law and game theoretic resource management. In: Proceedings of IEEE Aerospace Conference, Big Sky, 2008. 1–13

  19. Lee J I, Jeon I S, Tahk M J. Guidance law to control impact time and angle. IEEE Trans Aerosp Electron Syst, 2007, 43: 301–310

    Article  Google Scholar 

  20. Zhou J L, Yang J Y, Li Z K. Simultaneous attack of a stationary target using multiple missiles: a consensus-based approach. Sci China Inf Sci, 2017, 60: 070205

    Article  MathSciNet  Google Scholar 

  21. Zhao J B, Yang S X. Integrated cooperative guidance framework and cooperative guidance law for multi-missile. Chin J Aeronaut, 2018, 31: 546–555

    Article  Google Scholar 

  22. Betts R K. Cruise missiles: technology, strategy, politics. Washington Quart, 1981, 4: 66–80

    Article  Google Scholar 

  23. Kemburi K M. Diffusion of High-Speed Cruise Missiles in Asia: Strategic and Operational Implications. Policy Brief, Institute of Defence and Strategic Studies, Nanyang Technological University, 2014

  24. Zhao Q L, Dong X W, Chen J, et al. Coordinated guidance strategy for heterogeneous missiles intercepting hypersonic weapon. In: Proceedings of the 34th Chinese Control Conference (CCC), Hangzhou, 2015. 5170–5175

  25. Bouvry P, Chaumette S, Danoy G, et al. Using heterogeneous multilevel swarms of UAVs and high-level data fusion to support situation management in surveillance scenarios. In: Proceedings of 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Baden-Baden, 2016. 424–429

  26. Endsley M, Jones W M. Situation Awareness Information Dominance & Information Warfare. United States Air Force Armstrong Laboratory, 1997. https://www.researchgate.net/publication/235167825_Situation_Awareness_Information_Dominance_Information_Warfare

  27. Wang X K, Shen L C, Liu Z H, et al. Coordinated flight control of miniature fixed-wing UAV swarms: methods and experiments. Sci China Inf Sci, 2019, 62: 212204

    Article  MathSciNet  Google Scholar 

  28. Jeon I S, Lee J I, Tahk M J. Impact-time-control guidance law for anti-ship missiles. IEEE Trans Contr Syst Technol, 2006, 14: 260–266

    Article  Google Scholar 

  29. Harl N, Balakrishnan S N. Impact time and angle guidance with sliding mode control. IEEE Trans Contr Syst Technol, 2012, 20: 1436–1449

    Article  Google Scholar 

  30. Snyder M, Li C Y, Qu Z H. A new parameterized guidance law for cooperative air defense. In: Proceedings of the 50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, Nashville, 2012

  31. Zhao S Y, Zhou R. Cooperative guidance for multimissile salvo attack. Chin J Aeronaut, 2008, 21: 533–539

    Article  Google Scholar 

  32. Bai T T, Wang D B. Cooperative trajectory optimization for unmanned aerial vehicles in a combat environment. Sci China Inf Sci, 2019, 62: 010205

    Article  Google Scholar 

  33. Tsourdos A, White B, Shanmugavel M. Cooperative Path Planning of Unmanned Aerial Vehicles. Hoboken: John Wiley & Sons, 2010

    Book  Google Scholar 

  34. Wen G X, Chen C L P, Dou H, et al. Formation control with obstacle avoidance of second-order multi-agent systems under directed communication topology. Sci China Inf Sci, 2019, 62: 192205

    Article  MathSciNet  Google Scholar 

  35. Beard R W, McLain T W. Multiple UAV cooperative search under collision avoidance and limited range communication constraints. In: Proceedings of the 42nd IEEE Conference on Decision and Control, 2003

  36. Zhu B, Xie L H, Han D, et al. A survey on recent progress in control of swarm systems. Sci China Inf Sci, 2017, 60: 070201

    Article  MathSciNet  Google Scholar 

  37. Bourgault F, Furukawa T, Durrant-Whyte H F. Coordinated decentralized search for a lost target in a Bayesian world. In: Proceedings of 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), Las Vegas, 2003. 1: 48–53

  38. Chen H, Wang X-M, Li Y. A survey of autonomous control for UAV. In: Proceedings of 2009 International Conference on Artificial Intelligence and Computational Intelligence, Shanghai, 2009. 267–271

  39. Clough B T. Metrics, schmetrics! How do you track a UAV’s autonomy. In: Proceedings of AIAA’s 1st Technical Conference and Workshop on Unmanned Aerospace Vehicles, Portsmouth, 2002

  40. Suresh M, Ghose D. Role of information and communication in redefining unmanned aerial vehicle autonomous control levels. Proc Instit Mech Eng Part G-J Aerosp Eng, 2010, 224: 171–197

    Article  Google Scholar 

  41. Perkins C E, Royer E M. Ad-hoc on-demand distance vector routing. In: Proceedings of the 2nd IEEE Workshop on Mobile Computing Systems and Applications, New Orleans, 1999. 90–100

  42. Jiang J F, Han G J. Routing protocols for unmanned aerial vehicles. IEEE Commun Mag, 2018, 56: 58–63

    Article  Google Scholar 

  43. Lee W I, Pyun J Y, Lee Y S, et al. Relative velocity based vehicle-to-vehicle routing protocol over ad-hoc networks. Int J Ad Hoc Ubiquitous Comput, 2013, 12: 14–22

    Article  Google Scholar 

  44. Zhang G Q, Zhang G Q. Communication network designing: transmission capacity, cost and scalability. Sci China Inf Sci, 2012, 55: 2454–2465

    Article  MathSciNet  MATH  Google Scholar 

  45. Yick J, Mukherjee B, Ghosal D. Wireless sensor network survey. Comput Netw, 2008, 52: 2292–2330

    Article  Google Scholar 

  46. Hall D L, Llinas J. An introduction to multisensor data fusion. Proc IEEE, 1997, 85: 6–23

    Article  Google Scholar 

  47. Merzoug M A, Boukerche A, Mostefaoui A. Efficient information gathering from large wireless sensor networks. Comput Commun, 2018, 132: 84–95

    Article  Google Scholar 

  48. Antonopoulos A, Verikoukis C, Skianis C, et al. Energy efficient network coding-based MAC for cooperative ARQ wireless networks. Ad Hoc Netw, 2013, 11: 190–200

    Article  Google Scholar 

  49. Popovski P, Nielsen J J, Stefanovic C, et al. Wireless access for ultra-reliable low-latency communication: principles and building blocks. IEEE Netw, 2018, 32: 16–23

    Article  Google Scholar 

  50. Sybis M, Wesolowski K, Jayasinghe K, et al. Channel coding for ultra-reliable low-latency communication in 5G systems. In: Proceedings of 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), Montreal, 2016. 1–5

  51. Pocovi G, Soret B, Pedersen K I, et al. MAC layer enhancements for ultra-reliable low-latency communications in cellular networks. In: Proceedings of 2017 IEEE International Conference on Communications Workshops (ICC Workshops), Paris, 2017. 1005–1010

  52. Popper C, Strasser M, Capkun S. Anti-jamming broadcast communication using uncoordinated spread spectrum techniques. IEEE J Sel Areas Commun, 2010, 28: 703–715

    Article  Google Scholar 

  53. Yue G. Antijamming coding techniques. IEEE Signal Process Mag, 2008, 25: 35–45

    Article  Google Scholar 

  54. Wang S, Wang J X, Zhang X D, et al. Performance of anti-jamming ad hoc networks using directional beams with group mobility. In: Proceedings of 2006 IFIP International Conference on Wireless and Optical Communications Networks, 2006

  55. Ram G, Mandal D, Kar R, et al. Craziness particle swarm optimization based hyper beamforming of linear antenna arrays. In: Proceedings of the 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC), Calcutta, 2014. 616–620

  56. Gong S Q, Xing C W, Chen S, et al. Polarization sensitive array based physical-layer security. IEEE Trans Veh Technol, 2018, 67: 3964–3981

    Article  Google Scholar 

  57. Lu X F, Wicker F D, Towsley D, et al. Detection probability estimation of directional antennas and omni-directional antennas. Wirel Pers Commun, 2010, 55: 51–63

    Article  Google Scholar 

  58. Zhang Q L, Gao F F, Sun Q, et al. Mainlobe jamming cancelation method for distributed monopulse arrays. Sci China Inf Sci, 2018, 61: 109301

    Article  MathSciNet  Google Scholar 

  59. Winters J H. Smart antennas for wireless systems. IEEE Pers Commun, 1998, 5: 23–27

    Article  Google Scholar 

  60. Bellofiore S, Balanis C A, Foutz J, et al. Smart-antenna systems for mobile communication networks. Part 1. Overview and antenna design. IEEE Antenna Propag Mag, 2002, 44: 145–154

    Article  Google Scholar 

  61. Sun C, Gao X Q, Jin S, et al. Beam division multiple access transmission for massive MIMO communications. IEEE Trans Commun, 2015, 63: 2170–2184

    Article  Google Scholar 

  62. Xiao Z Y, Xia P F, Xia X G. Enabling UAV cellular with millimeter-wave communication: potentials and approaches. IEEE Commun Mag, 2016, 54: 66–73

    Article  MathSciNet  Google Scholar 

  63. Tuncer T E, Yasar T K, Friedlander B. Narrowband and wideband DOA estimation for uniform and nonuniform linear arrays. In: Classical and Modern Direction-of-Arrival Estimation. Manhattan: Academic Press, 2009. 125–160

    Chapter  Google Scholar 

  64. Patwari N, Hero A O, Perkins M, et al. Relative location estimation in wireless sensor networks. IEEE Trans Signal Process, 2003, 51: 2137–2148

    Article  Google Scholar 

  65. Ranger J F O. Principles of JTIDS relative navigation. J Navi, 1996, 49: 22–35

    Article  Google Scholar 

  66. Cao N S, Zhao J, Ding Y Q. Realization and improvement of navigation function of Link16. Telecommun Engin, 2011, 51: 11–16

    Google Scholar 

  67. Godara L C. Application of antenna arrays to mobile communications. II. Beam-forming and direction-of-arrival considerations. Proc IEEE, 1997, 85: 1195–1245

    Article  Google Scholar 

  68. Qian J H, He Z S, Xie J L, et al. Null broadening adaptive beamforming based on covariance matrix reconstruction and similarity constraint. EURASIP J Adv Signal Process, 2017, 2017: 1

    Article  Google Scholar 

  69. Shen W Q, Bu X Y, Gao X Y, et al. Beamspace precoding and beam selection for wideband millimeter-wave MIMO relying on lens antenna arrays. IEEE Trans Signal Process, 2019, 67: 6301–6313

    Article  MathSciNet  MATH  Google Scholar 

  70. Niu Y, Li Y, Jin D P, et al. A survey of millimeter wave communications (mmWave) for 5G: opportunities and challenges. Wirel Netw, 2015, 21: 2657–2676

    Article  Google Scholar 

  71. Zhong W Z, Xu L, Zhu Q M, et al. MmWave beamforming for UAV communications with unstable beam pointing. China Commun, 2019, 16: 37–46

    Article  Google Scholar 

  72. Gutierrez F, Agarwal S, Parrish K, et al. On-chip integrated antenna structures in CMOS for 60 GHz WPAN systems. IEEE J Sel Areas Commun, 2009, 27: 1367–1378

    Article  Google Scholar 

  73. Li L M, Wang D M, Niu X K, et al. mmWave communications for 5G: implementation challenges and advances. Sci China Inf Sci, 2018, 61: 021301

    Article  Google Scholar 

  74. Sarwate D V, Pursley M B. Crosscorrelation properties of pseudorandom and related sequences. Proc IEEE, 1980, 68: 593–619

    Article  Google Scholar 

  75. Bingham B, Blair B, Mindell D. On the design of direct sequence spread-spectrum signaling for range estimation. In: Proceedings of OCEANS 2007, Vancouver, 2007

  76. Soobul Y, Chady K, Rughooputh H C S. Digital chaotic coding and modulation in CDMA. In: Proceedings of IEEE 6th Africon Conference in Africa (IEEE AFRICON), George, 2002

  77. Jiang H-Y, Fu C. A chaos-based high quality PN sequence generator. In: Proceedings of 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing, Dalian, 2008. 60–64

  78. Leon D, Balkir S, Hoffman M W, et al. Pseudo-chaotic PN-sequence generator circuits for spread spectrum communications. IEE Proc Circ Dev Syst, 2004, 151: 543–550

    Article  Google Scholar 

  79. Nawkhare R, Tripathi A, Pokle P. DS-SS communication system using pseudo chaotic sequences generator. In: Proceedings of 2013 International Conference on Communication Systems and Network Technologies, Gwalior, 2013

  80. Rohde G K, Nichols J M, Bucholtz F. Chaotic signal detection and estimation based on attractor sets: applications to secure communications. Chaos, 2008, 18: 013114

    Article  Google Scholar 

  81. Quyen N X, Duong T Q, Vo N S, et al. Chaotic direct-sequence spread-spectrum with variable symbol period: a technique for enhancing physical layer security. Comput Netw, 2016, 109: 4–12

    Article  Google Scholar 

  82. Hu H P, Liu L F, Ding N D. Pseudorandom sequence generator based on the Chen chaotic system. Comput Phys Commun, 2013, 184: 765–768

    Article  MathSciNet  Google Scholar 

  83. Tayebi A, Berber S, Swain A. Security enhancement of fix chaotic-DSSS in WSNs. IEEE Commun Lett, 2018, 22: 816–819

    Article  Google Scholar 

  84. Kajiwara A, Nakagawa M. A new PLL frequency synthesizer with high switching speed. IEEE Trans Veh Technol, 1992, 41: 407–413

    Article  Google Scholar 

  85. Jiang T, Tang Z X, Zhang B. Design of frequency synthesizer based on DDS+PLL. J Electron Measurement Instrument, 2009, 2009: 91–95

    Article  Google Scholar 

  86. Kang J J, Teh K C. Performance of coherent fast frequency-hopped spread-spectrum receivers with partial-band noise jamming and AWGN. IEE Proc Commun, 2005, 152: 679–685

    Article  Google Scholar 

  87. Perez S, Alonso J B, Travieso C M, et al. Design of a synchronous FFHSS modulator on a FPGA with system generator. Wseas Trans Circ Syst, 2009, 8: 641–650

    Google Scholar 

  88. Lee J H, Yu B S, Lee S. Probability of error for a hybrid DS/SFH spread-spectrum system under tone jamming. In: Proceedings of IEEE Conference on Military Communications, Monterey, 1990

  89. Geraniotis E A. Noncoherent hybrid DS-SFH spread-spectrum multiple-access communications. IEEE Trans Commun, 1986, 34: 862–872

    Article  Google Scholar 

  90. Lou D, Li Z-Q, Li F-L. The range and velocity measurement performance of DS/FH hybrid spread spectrum signal with the interference. In: Proceedings of the 2nd International Conference on Computer Science and Network Technology, Changchun, 2012. 1405–1408

  91. Lekkakos D, Kragh F, Robertson C. Performance analysis of a Link-16 compatible waveform using errors-and-erasures decoding when corrupted by pulse-noise interference. In: Proceedings of IEEE Military Communications Conference, Boston, 2009

  92. Lekkakos D, Robertson R C. Performance analysis of a LINK-16/JTIDS compatible waveform transmitted over a channel with pulse-noise interference. In: Proceedings of 2009 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, Victoria, 2009

  93. Olama M M, Ma X, Kuruganti T P, et al. Hybrid DS/FFH spread-spectrum: a robust, secure transmission technique for communication in harsh environments. In: Proceedings of Military Communications Conference, Baltimore, 2011

  94. Olama M M, Killough S M, Kuruganti T, et al. Design, implementation, and evaluation of a hybrid DS/FFH spread-spectrum radio transceiver. In: Proceedings of 2014 IEEE Military Communications Conference, Baltimore, 2014

  95. Killough S M, Olama M M, Kuruganti T, et al. FPGA-based implementation of a hybrid DS/FFH spread-spectrum transceiver. In: Proceedings of 2013 World Congress in Computer Science, Computer Engineering, and Applied Computing (WORLDCOMP’13), 2013

  96. Shirvanimoghaddam M, Mohammadi M S, Abbas R, et al. Short block-length codes for ultra-reliable low latency communications. IEEE Commun Mag, 2019, 57: 130–137

    Article  Google Scholar 

  97. van Wonterghem J, Alloum A, Boutros J J, et al. On short-length error-correcting codes for 5G-NR. Ad Hoc Netw, 2018, 79: 53–62

    Article  Google Scholar 

  98. Gaudio L, Ninacs T, Jerkovits T, et al. On the performance of short tail-biting convolutional codes for ultra-reliable communications. In: Proceedings of the 11th International ITG Conference on Systems, Communications and Coding (SCC 2017), Hamburg, 2017

  99. Han Y S, Wu T Y, Chen P N, et al. A low-complexity maximum-likelihood decoder for tail-biting convolutional codes. IEEE Trans Commun, 2018, 66: 1859–1870

    Article  Google Scholar 

  100. Dolinar S, Divsalar D, Pollara F. Code Performance as a Function of Block Size. TMO Progress Report 42–133, 1998

  101. Jerkovits T, Matuz B. Turbo code design for short blocks. In: Proceedings of the 8th Advanced Satellite Multimedia Systems Conference and the 14th Signal Processing for Space Communications Workshop (ASMS/SPSC), Palma de Mallorca, 2016

  102. Liva G, Paolini E, Matuz B, et al. Short turbo codes over high order fields. IEEE Trans Commun, 2013, 61: 2201–2211

    Article  Google Scholar 

  103. Tonnellier T, Leroux C, Le Gal B, et al. Lowering the error floor of turbo codes with CRC verification. IEEE Wirel Commun Lett, 2016, 5: 404–407

    Article  Google Scholar 

  104. Hassan A, Dessouky M I, Abouelazm A E, et al. Evaluation of complexity versus performance for turbo code and LDPC under different code rates. In: Proceedings of the 4th International Conference on Advances in Satellite and Space Communications (SPACOMM 2012), Chamonix, 2012

  105. Xu H Z, Chen C, Zhu M, et al. Nonbinary LDPC cycle codes: efficient search, design, and code optimization. Sci China Inf Sci, 2018, 61: 089303

    Article  MathSciNet  Google Scholar 

  106. Chen K, Niu K, Lin J R. Improved successive cancellation decoding of polar codes. IEEE Trans Commun, 2013, 61: 3100–3107

    Article  Google Scholar 

  107. Jiang M, Li Z Y, Yang X, et al. Partial CRC-aided decoding of 5G-NR short codes using reliability information. Sci China Inf Sci, 2019, 62: 080303

    Article  MathSciNet  Google Scholar 

  108. R1-1611081-Final Report of 3GPP TSG RAN WG1 #86bis v1.0.0. Final Minutes Report, 2016. https://www.3gpp.org/ftp/tsg_ran/WG1_RL1/TSGR1_86b/Report/

  109. Amat A G I, Liva G. Finite-length analysis of irregular repetition slotted ALOHA in the waterfall region. IEEE Commun Lett, 2018, 22: 886–889

    Article  Google Scholar 

  110. Namboodiri V, DeSilva M, Deegala K, et al. An extensive study of slotted Aloha-based RFID anti-collision protocols. Comput Commun, 2012, 35: 1955–1966

    Article  Google Scholar 

  111. Jayasuriya A, Perreau S, Dadej A, et al. Hidden vs. exposed terminal problem in ad hoc networks. Dissertation for Ph.D. Degree. Mawson Lakes: Institute for Telecommunications Research University of South Australia, 2004

    Google Scholar 

  112. Wattanamongkhol N. Performance comparison of collision resolution algorithms with amount of feedback information. In: Proceedings of the 8th International Symposium on Wireless and Pervasive Computing (ISWPC), Taipei, 2013

  113. Laya A, Kalalas C, Vazquez-Gallego F, et al. Goodbye, ALOHA! IEEE Access, 2016, 4: 2029–2044

    Article  Google Scholar 

  114. Clark S M, Hoback K A, Zogg S J F. Statistical priority-based multiple access system and method. Patent US7680077, 2002

  115. Wang L, Li H, Liu Z F. Research and pragmatic-improvement of statistical priority-based multiple access protocol. In: Proceedings of the 2nd IEEE International Conference on Computer and Communications (ICCC), Chengdu, 2016

  116. Rong B N, Zhang Z S, Zhao X, et al. Robust superimposed training designs for MIMO relaying systems under general power constraints. IEEE Access, 2019, 7: 80404–80420

    Article  Google Scholar 

  117. Khatiwada B, Moh S. A novel multi-channel MAC protocol for directional antennas in ad hoc networks. Wirel Pers Commun, 2015, 80: 1095–1112

    Article  Google Scholar 

  118. Feng J, Ren P Y, Yan S C. A deafness free MAC protocol for ad hoc networks using directional antennas. In: Proceedings of the 4th IEEE Conference on Industrial Electronics and Applications (ICIEA 2009), Xi’an, 2009

  119. Abdullah A A, Cai L, Gebali F. DSDMAC: dual sensing directional MAC protocol for ad hoc networks with directional antennas. IEEE Trans Veh Technol, 2012, 61: 1266–1275

    Article  Google Scholar 

  120. Na W, Park L, Cho S. Deafness-aware MAC protocol for directional antennas in wireless ad hoc networks. Ad Hoc Netw, 2015, 24: 121–134

    Article  Google Scholar 

  121. Inzillo V, de Rango F, Quintana A A, et al. An adaptive beamforming time with round-robin MAC algorithm for reducing energy consumption in MANET. J Sensor Actuator Netw, 2018, 7: 50

    Article  Google Scholar 

  122. Nasrallah Y Y, Al-Anbagi I, Mouftah H T. Mobility impact on the performance of electric vehicle-to-grid communications in smart grid environment. In: Proceedings of 2015 IEEE Symposium on Computers and Communication (ISCC), Larnaca, 2015

  123. Takata M, Bandai M, Watanabe T. An extended directional MAC for location information staleness in ad hoc networks. In: Proceedings of the 25th IEEE International Conference on Distributed Computing Systems Workshops, Columbus, 2005

  124. Arafat M Y, Moh S. A survey on cluster-based routing protocols for unmanned aerial vehicle networks. IEEE Access, 2019, 7: 498–516

    Article  Google Scholar 

  125. Li Y, Shirani R, St-Hilaire M, et al. Improving routing in networks of unmanned aerial vehicles: reactive-greedy-reactive. Wirel Commun Mob Comput, 2012, 12: 1608–1619

    Article  Google Scholar 

  126. Rosati S, Kruzelecki K, Heitz G, et al. Dynamic routing for flying ad hoc networks. IEEE Trans Veh Technol, 2016, 65: 1690–1700

    Article  Google Scholar 

  127. Liu X X. A survey on clustering routing protocols in wireless sensor networks. Sensors, 2012, 12: 11113–11153

    Article  Google Scholar 

  128. Zang C H, Zang S H. Mobility prediction clustering algorithm for UAV networking. In: Proceedings of 2011 IEEE Global Communications Conference (GLOBECOM), Houston, 2011

  129. Shu J, Ge Y F, Liu L L, et al. Mobility prediciton clustering routing in UAVs. In: Proceedings of 2011 International Conference on Computer Science and Network Technology, Harbin, 2011

  130. Danoy G, Brust M R, Bouvry P. Connectivity stability in autonomous multi-level UAV swarms for wide area monitoring. In: Proceedings of the 5th ACM International Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications, 2015

  131. Dai H-N, Ng K-W, Wong R C-W, et al. On the capacity ofmulti-channel wireless networks using directional antennas. In: Proceedings of the 27th Conference on Computer Communications (IEEE INFOCOM 2008), Phoenix, 2008

  132. Gankhuyag G, Shrestha A P, Yoo S J. Robust and reliable predictive routing strategy for flying ad-hoc networks. IEEE Access, 2017, 5: 643–654

    Article  Google Scholar 

  133. Li H P, Xu Z. Routing protocol in VANETs equipped with directional antennas: topology-based neighbor discovery and routing analysis. Wirel Commun Mobile Comput, 2018. doi: https://doi.org/10.1155/2018/7635143

  134. An S N, He Z S, Li J G, et al. Micrometer accuracy phase modulated radar for distance measurement and monitoring. IEEE Sens J, 2020, 20: 2919–2927

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. U1636125, 6180011907, U1836201).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhongshan Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Luo, S., Zhang, Z., Wang, S. et al. Network for hypersonic UCAV swarms. Sci. China Inf. Sci. 63, 140311 (2020). https://doi.org/10.1007/s11432-019-2765-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-019-2765-7

Keywords

Navigation