Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Wireless Personal Communications
Erschienen in: Wireless Personal Communications 3/2020

10.08.2020

Review on Positioning Technology of Wireless Sensor Networks

verfasst von: Mao Li, Feng Jiang, Cong Pei

Erschienen in: Wireless Personal Communications | Ausgabe 3/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

With the large-scale application of wireless sensor network, the position information of sensor nodes is more and more important. The position information of the unknown nodes are mainly depended on the beacon node the in wireless sensor network. First, the concept and characteristics of wireless sensor networks of the positioning technologies are briefly described. Then, the calculation methods of existing node positioning technologies are introduced. Next, the wireless sensors are described in detail from two aspects: range-based and range-free. Finally, summarizes the possible defects of positioning technology and looks forward to the future development of the node positioning technology.
Vorheriger Artikel Fast and More Scalable Positioning Method for Data Centers in LTE Networks
Nächster Artikel Dynamics of Epidemic Computer Virus Spreading Model with Delays

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Fei, Z., Li, B., Yang, S., et al. (2016). A survey of multi-objective optimization in wireless sensor networks: Metrics, algorithms and open problems. IEEE Communications Surveys and Tutorials, 99, 1–1.
2.
Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.
3.
Sohrabi, K., Gao, J., Ailawadhi, V., et al. (2000). Protocols for self-organization of a wireless sensor network. IEEE Personal Communications, 7(5), 16–27.
4.
Mao, G., Fidan, B., & Anderson, B. D. O. (2007). Wireless sensor network localization techniques. Computer Networks, 51(10), 2529–2553.MATH
5.
Yang, Z., & Liu, Y. (2010). Quality of trilateration: Confidence-based iterative localization. IEEE Transactions on Parallel and Distributed Systems, 21(5), 631–640.
6.
Keppel, E. (2010). Approximating complex surfaces by triangulation of contour lines. IBM Journal of Research and Development, 19(1), 2–11.MathSciNetMATH
7.
Bialer, O., Dan, R., & Weiss, A. J. (2013). Maximum-likelihood direct position estimation in dense multipath. IEEE Transactions on Vehicular Technology, 62(5), 2069–2079.
8.
Shah, S. F. A., Srirangarajan, S., & Tewfik, A. H. (2010). Implementation of a directional beacon-based position location algorithm in a signal processing framework. IEEE Transactions on Wireless Communications, 9(3), 1044–1053.
9.
Chan, F. K. W., & So, H. C. (2009). Accurate distributed range-based positioning algorithm for wireless sensor networks. IEEE Transactions on Signal Processing, 57(10), 4100–4105.MathSciNetMATH
10.
Alavi, B., & Pahlavan, K. (2006). Modeling of the TOA-based distance measurement error using UWB indoor radio measurements. IEEE Communications Letters, 10(4), 275–277.
11.
Cong, L., & Zhuang, W. (2001). Non-line-of-sight error mitigation in TDOA mobile location. Proceedings of IEEE Globecom Telecommunications, 1(2), 560–573.
12.
Niculescu, D., & Nath, B. (2003). Ad hoc positioning system (APS) using AOA. In Joint conference of the IEEE computer and communications. IEEE societies. IEEE (Vol. 3, pp. 1734–1743).
13.
Paul, A. S., & Wan, E. A. (2009). RSSI-based indoor localization and tracking using sigma-point Kalman Smoothers. IEEE Journal of Selected Topics in Signal Processing, 3(5), 860–873.
14.
Li, X., & Pahlavan, K. (2004). Super-resolution TOA estimation with diversity for indoor geolocation. IEEE Transactions on Wireless Communications, 3(1), 224–234.
15.
Tomic, S., & Beko, M. (2018). Exact Robust solution to TW-ToA-based target localization problem with clock imperfections. IEEE Signal Processing Letters, 25(4), 531–535.
16.
Wang, Y., Ma, S., & Chen, C. L. P. (2014). TOA-based passive localization in quasi-synchronous networks. IEEE Communications Letters, 18(4), 592–595.
17.
Tomic, S., Beko, M., Rui, D., et al. (2017). A robust bisection-based estimator for TOA-based target localization in NLOS environments. IEEE Communications Letters, 99, 1–1.
18.
Wang, W., Wang, G., Zhang, J., et al. (2017). Robust weighted least squares method for TOA-based localization under mixed LOS/NLOS conditions. IEEE Communications Letters, 21(10), 2226–2229.
19.
Pak, J. M., Ahn, C. K., Peng, S., et al. (2017). Distributed hybrid particle/FIR filtering for mitigating NLOS effects in TOA-based localization using wireless sensor networks. IEEE Transactions on Industrial Electronics, 64(6), 5182–5191.
20.
Gao, S., Zhang, F., & Wang, G. (2017). NLOS error mitigation for TOA-based source localization with unknown transmission time. IEEE Sensors Journal, 99, 1–1.
21.
Abu-Shaban, Z., Zhou, X., & Abhayapala, T. D. (2016). A novel TOA-based mobile localization technique under mixed LOS/NLOS conditions for cellular networks. IEEE Transactions on Vehicular Technology, 65(11), 8841–8853.
22.
.Ke M, Xu Y, Anpalagan A, et al. Distributed TOA-based Positioning in Wireless Sensor Networks: A Potential Game Approach[J]. IEEE Communications Letters, 2017, PP(99):1-1.
23.
.Li Y Y, Qi G Q, Sheng A D. Performance Metric on the Best Achievable Accuracy for Hybrid TOA/AOA Target Localization[J]. IEEE Communications Letters, 2018, PP(99):1-1.
24.
Chen, L., Thevenon, P., Seco-Granados, G., et al. (2016). Analysis on the TOA tracking with DVB-T signals for positioning. IEEE Transactions on Broadcasting, 62(4), 957–961.
25.
Nguyen, N. H., & Doğançay, K. (2016). Optimal geometry analysis for multistatic TOA localization. IEEE Transactions on Signal Processing, 64(16), 4180–4193.MathSciNetMATH
26.
Ho, K. C., & Chan, Y. T. (2002). Solution and performance analysis of geolocation by TDOA. IEEE Transactions on Aerospace and Electronic Systems, 29(4), 1311–1322.
27.
Li, X., Guo, F., Yang, L., et al. (2018). Improved solution for geolocating a known altitude source using TDOA and FDOA under random sensor location errors. Electronics Letters, 54(9), 597–599.
28.
Hmam, H. (2017). Optimal sensor velocity configuration for TDOA-FDOA geolocation. IEEE Transactions on Signal Processing, 99, 1–1.MathSciNetMATH
29.
Guo, F., Zhang, Z., & Yang, L. (2016). TDOA/FDOA estimation method based on dechirp. IET Signal Processing, 10(5), 486–492.
30.
Kim, D. G., Park, G. H., Park, J. O., et al. (2018). Computationally efficient TDOA/FDOA estimation for unknown communication signals in electronic warfare systems. IEEE Transactions on Aerospace and Electronic Systems, 99, 1–1.
31.
Wang, Y., & Wu, Y. (2017). An efficient semidefinite relaxation algorithm for moving source localization using TDOA and FDOA measurements. IEEE Communications Letters, 99, 1–1.
32.
Wang, G., Cai, S., Li, Y., et al. (2016). A bias-reduced nonlinear WLS method for TDOA/FDOA-based source localization. IEEE Transactions on Vehicular Technology, 65(10), 8603–8615.
33.
Noroozi, A., Oveis, A. H., Hosseini, M. R., et al. (2017). Improved algebraic solution for source localization from TDOA and FDOA measurements. IEEE Wireless Communications Letters, 99, 1–1.
34.
Qu, X., Xie, L., & Tan, W. (2017). Iterative constrained weighted least squares source localization using TDOA and FDOA measurements. IEEE Transactions on Signal Processing, 99, 1–1.MathSciNetMATH
35.
Cui, X., Yu, K., & Lu, S. (2018). Approximate closed-form TDOA-based estimator for acoustic direction finding via constrained optimization. IEEE Sensors Journal, 99, 1–1.
36.
Salari, S., Chan, F., Chan, Y. T., et al. (2018). TDOA estimation with compressive sensing measurements and hadamard matrix. IEEE Transactions on Aerospace and Electronic Systems, 99, 1–1.
37.
Zhu, Y., Deng, B., Jiang, A., et al. (2018). ADMM-based TDOA estimation. IEEE Communications Letters, 99, 1–1.
38.
Cao, H., Chan, Y. T., & So, H. C. (2017). Maximum likelihood TDOA estimation from compressed sensing samples without reconstruction. IEEE Signal Processing Letters, 24(5), 564–568.
39.
Zhang, Z., & Zhan, X. (2018). Statistical analysis of spoofing detection based on TDOA. IEEJ Transactions on Electrical and Electronic Engineering, 13(8), 190.
40.
Wang, Y., & Ho, K. C. (2018). Unified near-field and far-field localization for AOA and hybrid AOA-TDOA positionings. IEEE Transactions on Wireless Communications, 17(2), 1242–1254.
41.
Chen, C. Y., & Wu, W. R. (2018). Joint AoD, AoA, and channel estimation for MIMO-OFDM systems. IEEE Transactions on Vehicular Technology, 99, 1–1.
42.
Zhu, D., Choi, J., & Heath, R. W. (2017). Two-dimensional AoD and AoA acquisition for wideband mmWave systems with dual-polarized MIMO. IEEE Transactions on Wireless Communications, 99, 1–1.
43.
Zhu, D., Choi, J., & Heath, R. W. (2017). Auxiliary beam pair enabled AoD and AoA estimation in closed-loop large-scale millimeter-wave MIMO systems. IEEE Transactions on Wireless Communications, 16(7), 4770–4785.
44.
Amiri, R., Behnia, F., & Zamani, H. (2017). Efficient 3-D positioning using time-delay and AOA measurements in MIMO radar systems. IEEE Communications Letters, 99, 1–1.
45.
Noroozi, A., & Sebt, M. A. (2017). Algebraic solution for 3-D TDOA/AOA localization in MIMO passive radar. IET Radar Sonar Navigation, 12(1), 69.
46.
Tomic, S., Beko, M., & Rui, D. (2016). Distributed RSS-AoA based localization with unknown transmit powers. IEEE Wireless Communications Letters, 5(4), 392–395.
47.
Tomic, S., Beko, M., & Rui, D. (2017). 3-D target localization in wireless sensor network using RSS and AoA measurements. IEEE Transactions on Vehicular Technology, 99, 1–1.
48.
Tomic, S., Beko, M., Rui, D., et al. (2016). A closed-form solution for RSS/AoA target localization by spherical coordinates conversion. IEEE Wireless Communications Letters, 99, 1–1.
49.
Yin, J., Wan, Q., Yang, S., et al. (2015). A simple and accurate TDOA-AOA localization method using two stations. IEEE Signal Processing Letters, 23(1), 144–148.
50.
Chuang, S. F., Wu, W. R., & Liu, Y. T. (2015). High-resolution AoA estimation for hybrid antenna arrays. IEEE Transactions on Antennas and Propagation, 63(7), 2955–2968.MathSciNetMATH
51.
Shao, H. J., Zhang, X. P., & Wang, Z. (2014). Efficient closed-form algorithms for AOA based self-localization of sensor nodes using auxiliary variables. IEEE Transactions on Signal Processing, 62(10), 2580–2594.MathSciNetMATH
52.
Steendam, H. (2018). A 3-D positioning algorithm for AOA-based VLP with an aperture-based receiver. IEEE Journal on Selected Areas in Communications, 36(1), 23–33.
53.
Fascista, A., Ciccarese, G., Coluccia, A., et al. (2017). IEEE Signal Processing Letters, 24(99), 1–1.
54.
Tazawa, R., Honma, N., Miura, A., et al. (2018). RSSI-based localization using wireless beacon with three-element array. Ieice Transactions on Communications, 101(2), 67.
55.
Xue, W., Qiu, W., Hua, X., et al. (2017). Improved Wi-Fi RSSI measurement for indoor localization. IEEE Sensors Journal, 99, 1–1.
56.
Cho, S. Y. (2016). Measurement error observer-based IMM filtering for mobile node localization using WLAN RSSI measurement. IEEE Sensors Journal, 16(8), 2489–2499.
57.
Luo, Q., Peng, Y., Li, J., et al. (2016). RSSI-based localization through uncertain data mapping for wireless sensor networks. IEEE Sensors Journal, 16(9), 3155–3162.
58.
Pathak, P. H., Feng, X., Hu, P., et al. (2015). Visible light communication, networking, and sensing: A survey, potential and challenges. IEEE Communications Surveys & Tutorials, 17(4), 2047–2077.
59.
Bianchi, V., Ciampolini, P., & Munari, I. D. (2018). RSSI-based indoor localization and identification for ZigBee wireless sensor networks in smart homes. IEEE Transactions on Instrumentation and Measurement, 99, 1–10.
60.
Buffi, A., Michel, A., Nepa, P., et al. (2018). RSSI measurements for RFID tag classification in smart storage systems. IEEE Transactions on Instrumentation and Measurement, 99, 1–11.
61.
Booranawong, A., Jindapetch, N., & Saito, H. (2018). A system for detection and tracking of human movements using RSSI signals. IEEE Sensors Journal, 99, 1–1.
62.
Mahjoub, H. N., Tahmasbi-Sarvestani, A., Gani, S. M. O., et al. (2018). Composite α − μ based DSRC channel model using large data set of RSSI measurements. IEEE Transactions on Intelligent Transportation Systems, 99, 1–13.
63.
Xu, Z., Wang, R., Yue, X., et al. (2018). FaceME: Face-to-machine proximity estimation based on RSSI difference for mobile industrial human machine interaction. IEEE Transactions on Industrial Informatics, 99, 1–1.
64.
Abouzar, P., Michelson, D. G., & Hamdi, M. (2016). RSSI-based distributed self-localization for wireless sensor networks used in precision agriculture. IEEE Transactions on Wireless Communications, 15(10), 6638–6650.
65.
Yiu, S., Dashti, M., Claussen, H., et al. (2017). Wireless RSSI fingerprinting localization. Signal Processing, 131, 235–244.
66.
Chen, W., Wang, W., Li, Q., et al. (2016). A crowd-sourcing indoor localization algorithm via optical camera on a smartphone assisted by Wi-Fi fingerprint RSSI. Sensors, 16(3), 410.
67.
Jiang, R., & Yang, Z. (2016). An improved centroid localization algorithm based on iterative computation for wireless sensor network. Acta Physica Sinica, 65(3), 100.
68.
Zhao, J., & Liu, Y. (2013). An improved Weighted Centroid Localization algorithm based on difference of estimated distances for Wireless Sensor Networks. Telecommunication Systems, 53(1), 25–31.MathSciNet
69.
Chaudhari, S., & Cabric, D. (2016). Cyclic weighted centroid algorithm for transmitter localization in the presence of interference. IEEE Transactions on Cognitive Communications & Networking, 2(2), 162–177.
70.
Linda, O., & Manic, M. (2012). Monotone centroid flow algorithm for type reduction of general type-2 fuzzy sets. IEEE Transactions on Fuzzy Systems, 20(5), 805–819.
71.
Zhai, D., & Mendel, J. M. (2012). Enhanced centroid-flow algorithm for computing the centroid of general type-2 fuzzy sets. IEEE Transactions on Fuzzy Systems, 20(5), 939–956.
72.
Yu, J., Liang, D., Gong, X., et al. (2019). Impact localization for composite plate based on Detrended Fluctuation Analysis and centroid localization algorithm using FBG sensors. Optik, 167, 50.
73.
Ngoc, M. T., & Park, D. C. (2018). Centroid neural network with pairwise constraints for semi-supervised learning. Neural Processing Letters, 10, 1–27.
74.
Tomic, S., & Mezei, I. (2016). Improvements of DV-Hop localization algorithm for wireless sensor networks. Telecommunication Systems, 61(1), 93–106.
75.
Mehrabi, M., Taheri, H., & Taghdiri, P. (2017). An improved DV-Hop localization algorithm based on evolutionary algorithms. Telecommunication Systems, 64(4), 1–9.
76.
Kaur, A., Kumar, P., & Gupta, G. P. (2018). Nature inspired algorithm-based improved variants of DV-Hop algorithm for randomly deployed 2D and 3D wireless sensor networks. Wireless Personal Communications, 1, 1–16.
77.
Zhou, C., Yang, Y., & Wang, Y. (2018). DV-Hop localization algorithm based on bacterial foraging optimization for wireless multimedia sensor networks. Multimedia Tools and Applications, 16, 1–11.
78.
Sharma, G., & Kumar, A. (2018). Improved DV-Hop localization algorithm using teaching learning based optimization for wireless sensor networks. Telecommunication Systems, 67(8), 1–16.
79.
Liu, Y., Chen, J., & Xu, Z. (2017). Improved DV-hop localization algorithm based on bat algorithm in wireless sensor networks. Ksii Transactions on Internet & Information Systems, 75, 11.
80.
Cheikhrouhou, O. M. G. B., & Alroobaea, R. (2018). A hybrid DV-hop algorithm using RSSI for localization in large-scale wireless sensor networks. Sensors, 18(5), 1469.
81.
Gui, L., Xiao, F. U., Zhang, Y., et al. (2018). DV-Hop localization with protocol sequence based access. IEEE Transactions on Vehicular Technology, 1(1), 20.
82.
Gui, L., Zhang, X., Ding, Q., et al. (2017). Reference anchor selection and global optimized solution for DV-hop localization in wireless sensor networks. Wireless Personal Communications, 96(4), 5995–6005.
83.
Zhao, W., Su, S., & Shao, F. (2017). Improved DV-hop algorithm using locally weighted linear regression in anisotropic wireless sensor networks. Wireless Personal Communications, 98(4), 1–19.
84.
Shahzad, F., Shaltami, T., & Shakshukhi, E. (2017). DV-maxHop: A fast and accurate range-free localization algorithm for anisotropic wireless networks. IEEE Transactions on Mobile Computing, 16(9), 2494–2505.
85.
Fan, L. H., Qiu, X. H., & Tang, Y. B. (2007). Discussion on self-location algorithm for wireless sensor networks. Telecommunications for Electric Power System, 10, 92.
86.
Years, I. R. (2015). Amorphous localization algorithm based on BP artificial neural network. International Journal of Distributed Sensor Networks, 2015, 6.
87.
Shen, S., Yang, B., Qian, K., et al. (2016). An improved amorphous localization algorithm for wireless sensor networks. In International conference on NETWORKING and network applications (pp. 69–72). IEEE.
88.
Shen, S., Qian, K., Yang, B., et al. (2018). An improved amorphous algorithm in wireless sensor network based on approximate equilateral triangle beacon selection. In International conference on NETWORKING and network applications (pp. 54–60). IEEE.
89.
Zhang, R., Claussen, H., Haas, H., et al. (2016). Energy efficient visible light communications relying on amorphous cells. IEEE Journal on Selected Areas in Communications, 34(4), 894–906.
90.
Feng, S., Li, X., Zhang, R., et al. (2016). Hybrid positioning for the amorphous-cell assisted user-centric visible light downlink. IEEE Photonics Journal, 4(3), 100–108.
91.
Li, X., Chen, L., Wang, J., et al. (2015). Fuzzy system and Improved APIT (FIAPIT) combined range-free localization method for WSN. Ksii Transactions on Internet and Information Systems, 9(7), 2414–2434.
92.
Yuan, Y., Huo, L., Wang, Z., et al. (2018). Secure APIT localization scheme against sybil attacks in distributed wireless sensor networks. IEEE Access, 6, 27629–27636.
93.
Liu, Z., Feng, X., Zhang, J., et al. (2016). An improved GPSR algorithm based on energy gradient and APIT grid. Journal of Sensors, 2016(2), 4027–4032.
94.
Li, P., \& Zhang, W. (2010). Based on the cyclic refinement APIT localization algorithm for wireless sensor networks. In Control conference (pp. 4753–4756). IEEE.
95.
Chen, B., Sun, J., Xu, W. B., et al. (2012). A mixed localization algorithm based on RSSI and APIT with fitness analysis and optimization. In International symposium on distributed computing and applications to business, engineering and science (pp. 164–168). IEEE.
96.
Jain, S., Singh, A., Kaur, A., et al. (2017). Improved APIT localization algorithm in wireless sensor networks. In International conference on signal processing, computing and control (pp. 77–81).
97.
Xiong, X., & Yan, C. (2014). Three-dimensional localization algorithm of APIT based on fermat-point divided for wireless sensor networks. In Seventh international symposium on computational intelligence and design (pp. 521–524). IEEE Computer Society.
98.
Wen, W., Dong, Z., Chen, G., et al. (2017). Energy efficient data collection scheme in mobile wireless sensor networks. In International conference on advanced information NETWORKING and applications workshops (pp. 226–230).
99.
Liang, J., Shao, J., Xu, Y., et al. Sensor network localization in constrained 3-D spaces. In IEEE international conference on mechatronics and automation (pp. 49–54). IEEE.
100.
Zhang, P., Lu, J., & Wang, Q. (2016). Performance bounds for relative configuration and global transformation in cooperative localization. Ict Express, 2(1), 14–18.
101.
Tang, T., Liu, H., Song, H., et al. (2016). Support vector machine based range-free localization algorithm in wireless sensor network. In International conference on machine learning and intelligent communications (pp. 150–158). Springer International Publishing.
Metadaten
Titel
Review on Positioning Technology of Wireless Sensor Networks
verfasst von
Mao Li
Feng Jiang
Cong Pei
Publikationsdatum
10.08.2020
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 3/2020
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-020-07667-7

Weitere Artikel der Ausgabe 3/2020

Wireless Personal Communications 3/2020 Zur Ausgabe

ReviewPaper

Performances of OSIC Detector of an UpLink OFDM Massive-MIMO System in Rayleigh and Ricain Fading Channels

OriginalPaper

DFT-Spread OTFS Communication System with the Reductions of PAPR and Nonlinear Degradation

OriginalPaper

Performance Analysis Over Unified Generalized Composite Fading Model

OriginalPaper

A HGGSA Approach to Beam Forming for DOA Estimation in Smart Antenna Arrays

OriginalPaper

Assessment of Denim and Photo Paper Substrate-Based Microstrip Antennas for Wearable Biomedical Sensing

OriginalPaper

Low Complexity High-Performance Precoding Algorithms for mm-Wave MU-MIMO Communication System

Neuer Inhalt

    Bildnachweise