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
Telecommunication Systems
Erschienen in: Telecommunication Systems 2/2016

01.10.2016

A review: detection techniques for LTE system

verfasst von: Mahmoud A. M. Albreem, Nur Afiqah Hani Binti Ismail

Erschienen in: Telecommunication Systems | Ausgabe 2/2016

Einloggen

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

search-config
loading …

Abstract

Long term evolution (LTE) has become the fastest developing mobile system technology and is considered as the fourth generation of wireless communication systems. Multiple-input multiple-output spatial multiplexing is a key technique employed in LTE system to boost the system capacity. With this gain comes the challenge of designing efficient detectors at the receiver side to decouple the transmitted data streams. The exhaustive search detector is optimum in terms of performance but it has a high computational load in terms of time and power. Owing to its high computational load, many detection techniques have been proposed in order to achieve an efficient detection performance with low complexity in terms of computational power. This paper reviews the detection techniques used in LTE including the maximum likelihood (ML) detection, zero forcing (ZF) detection, minimum mean square error (MMSE) detection, successive interference cancellation (SIC) detection, sphere decoding detection, lattice reduction and list decoding. It is shown that Maximum Likelihood detection has the best performance, but the complexity is too high. Other detectors like ZF or MMSE and nonlinear detectors employing SIC have much lower complexity, however, they do not provide detection performance close to that of the ML detection. The sphere detection algorithm has been shown to have low average computational complexity and achieves a quasi-ML performance which gives it an advantage over to other types of detection techniques.
Vorheriger Artikel Implementation of call admission control technique in HAP for enhanced QoS in wireless network deployment
Nächster Artikel Adoption of dynamic spectrum access technologies: a system dynamics approach

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

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 "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"

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.
Sawahashi, M., Abeta, S., Atarashi, H., Higuchi, K., Tanno M., & Ihara, T. (Jul, 2003). Broadband packet wireless access, NTT DoCoMo Technical Journal, 11(2), 844–848.
2.
ITU-R Recommendation (2003). Framework and overall objectives of the future development of IMT-2000 and systems beyond IMT-2000.
3.
Naik, G., et al. (2012). Challenges in the implementation of fourth generation wireless systems. IJERA, 2(2), 1353–1355.
4.
Montavont, N., & Noel, T. (2002). Handover management for mobile nodes in IPv6 networks. IEEE Communications Magazine, 40(8), 38–43.CrossRef
5.
Ekstrom, H., Furuskar, A., Karlsson, J., Meyer, M., Parkvall, S., Torsner, J., et al. (2006). Technical solutions for the 3G long-term evolution. Ericsson Res., Aachen, Germany, IEEE Communications Magazine, 44(3), 38–45.CrossRef
6.
Parkvall, S., Dahlman, E., Furuskar, A., et al. (Sept 2008). LTE-advanced-evolving LTE towards IMTAdvanced’. In Proceedings of 68th IEEE VTC, Calgary, Canada (pp. 1–5).
7.
sampath, H., et al. (2002). A fouth-generation MIMO-OFDM broadband wireless system: Design, performance and field trial resuIts”. IEEE Communications Magazine, 40, 143–149.CrossRef
8.
9.
Karakayali, M. K., Foschini, G. J., & Valenzuela, R. A. (2006). Network coordination for spectrally efficient communication in cellular systems. IEEE Wireless Communication, 13(4), 56–61.CrossRef
10.
Pereira, V., & Sousa, T. (2004). Evolution of mobile communications: From 1G to 4G. Coimbra: Department of Information Engineering of the University of Coimbra.
11.
Kumar, A., Sengupta, J., & Liu, Y.-F. (2012). 3GPP LTE: The future of mobile broadband. Wireless Personal Communications, 62(3), 671–686.CrossRef
12.
Astely, D., Dahlman, E., Furuskar, A., Jading, Y., Lindstrom, M., & Parkvall, S. (2009). LTE: The evolution of mobile broadband. IEEE Communication Magazine, 47(4), 44–51.CrossRef
13.
Gupta, M., Jha, S. C., Koc, A. T., & Vannithamby, R. (2013). Energy impact of emerging mobile internet applications on LTE networks: Issues and solutions. IEEE communications magazine, 51(2), 90–97.CrossRef
14.
Ghosh, A., Ratasuk, R., Mondal, B., Mangalvedhe, B. N., & Thomas, N. T. (2010). LTE-advanced: Next-generation wireless broadband technology. IEEE Wireless Communications, 17(3), 10–12.CrossRef
15.
Iwamura, M., Etemad, K., Fong, M., Nory, R., & Love, R. (2010). Carrier aggeration framework in 3GPP LTE-advanced. IEEE Comminications Magazine, 48(8), 60–67.CrossRef
16.
17.
Kanchi, S., Sandilya, S., Bhosale, D., Pitkar, A., & Gondhalekar, A. (2013). Overview of LTE-A Technology. Vidyalankar Institute of Technology.
18.
19.
20.
Boelcskei, H. (2006). MIMO-OFDM wireless systems: Basics, perspectives, and challenges. IEEE Transactions on Communications, 13(4), 31–37.
21.
Song, C.-K., & Kim, K.-S. (2007). Efficient signal feature detection method using spectral correlation function in the fading channel. International Journal of Contents, 3, 35–39.CrossRef
22.
de la Roche, G., Glazunov, A. A., & Allen, B. (2013). LTE advanced and next generation wireless networks channel modeling and propagation, ch 1, ch 2,ch 15. Chichester: Wiley.
23.
Huang, G., Nix, A., & Armour, S. (Sep. 2005). Decision feedback equalization in SC-FDMA. In Proceedings of IEEE Personal, Indoor and Mobile RAdion Communications (PIMRC O S), (pp. 1–5).
24.
Wong, K., Tsui, C., Cheng, R.K., Mow, W. (2002). A VLSI architecture of a K-best lattice decoding algorithm for MIMO channels. In Proceedinds of IEEE International Symposium Circuits Systems, vol 3 (pp. 273–276), Scottsdale, AZ, May 26–29.
25.
Kim, B., Choi, K. (2008). A very low complexity QRD-M algorithm based on limited tree search for MIMO systems. In Proceedings of IEEE VTC’08 Spring, (pp. 1246–1250).
26.
Ylioinas, J., & Juntti, M. (2009). Iterative joint detection, decoding, and channel estimation in turbo coded MIMO-OFDM. IEEE Transactions on Vehicular Technology, 58(4), 1784–1796.CrossRef
27.
Hochwald, B., & ten Brink, S. (2003). Achieving near-capacity on a Multiple Antenna Channel. IEEE Trans. Commun., 51(3), 389–399.CrossRef
28.
Lovasz, L. (1986). An algoriihmic theory of nwnbrrs, graph, and cormdry. Philadelphia, PA: Society for Industrial and Applied Mathematics.
29.
Kusume, K., Dietl, G., Abe, T., Taoka, H. & Nagata, S., (2010) System level performance of downlink MU-MIMO transmission for 3GPP LTEAdvanced. In Proceedings of VTC 2010-Spring, (pp. 1–5).
30.
Choi, W., Negi, R. & Cioffi, J.M. (June 2000). Combined ML and DFE decoding for the V-BLAST system. In Proceedings of IEEE ICC, (pp. 1243–1248).
31.
Im, T. H. (2007). A low complexity QRM-MLD for MIMO systems. In IEEE Vehicular Technology Conference, VTC2007-Spring, (pp. 2243–2247).
32.
Foschini, G. J., & Gans, M. J. (1998). On limits of wireless communications in a fading environment when using multiple antennas. Wireless Personal Communications, 6(3), 311–335.CrossRef
33.
Cho, K., & Yoon, D. (2002). On the general BER expression of one- and twodemensional amplitude modulations. IEEE Transactions on Communication, 50(7), 1074–1080.CrossRef
34.
Wang, C. (2007). On the performance of the MIMO zero-forcing receiver in the presence of channel estimation error. IEEE Transactions on Wireless Communications, 6(3), 805–810.CrossRef
35.
Fan, H.-Y. (2002). MIMO detection schemes for wireless communications. MPhil. Thesis, The Hong Kong University of Science and Technology.
36.
Chiani, M., Dardari, D., & Simon, M. K. (2003). New exponential bounds and approximations for the computation of error probability in fading channels. IEEE Transaction on Wireless Communication, 2, 840–845.CrossRef
37.
Bolliger, L., Loeliger, H. A., & Vogel, C. (2010) Simulation, MMSE estimation, and interpolation of sampled contiuous-time signals using fatcor graphs. Information theory & Application workshop (ITA), UCSD, La Jalla.
38.
Edfors, O., Sandell, M. & Beek, J., et al. (1996). OFDM channel estimation by singular value decomposition. IEEE VTC’96, (pp. 923–927).
39.
Manfred, Z., & Klaus, D. A. (2002). Multipath model for the powerline channel. IEEE Transactions on Communications [C], 54(4), 553–559.
40.
Ketonen, J., Juntti, M., Ylioinas, J. & Cavallaro, J. R. (2012). Implementation of LS, MMSE and SAGE channel estimators for mobile MIMO-OFDM. In Proceedings of Annual Asilomar Conference on Signals, Systems and Computers Pacific Grove, USA, (pp. 1092–1096).
41.
Simko, M., Wu, D., Mehlfuehrer, C., Eilert, J., & Liu, D. (2011). Implementation aspects of channel estimation for 3GPP LTE terminals. In Proceedings of European Wireless Conference, Vienna, Austria, (pp. 27–29).
42.
Myung, H. G., Lim, J., & Goodman, D. J. (2006). Single carrier FDMA for uplink wireless transmission. IEEE Vehicular Technology Magazine, 1, 30–38.CrossRef
43.
Gollakota, S., Perli, S. D., & Katabi, D. (2009). Interference alignment and cancellation. In Proceedings of the ACM SIGCOMM 2009 conference on Data communication. ACM, (pp. 159–170).
44.
An, J., Xu, B., Zhou, R., & Wang, A. (2008). Successive interference cancellation detection for multicode STBC-MIMO systems. In IEEE 4th International Conference on Wireless Communication, Networking and Mobile Computing (WiCOM ’08) (pp. 1–4), Dalian, China.
45.
Qin, C., Santhapuri, N., Sen, S. & Nelakuditi, S. (2010). Known interference cancellation: Resolving collisions due to repeated transmissions. In IEEE WiMesh.
46.
Wubben, A. & Kammeyer, K. -D. (2006). Low complexity successive interference cancellation for per-antenna-coded MIMO-OFDM schemes by applying parallel-SQRD. In IEEE 63rd Vehicular Technology Conference (VTC 06), (pp. 2183–2187), Melbourne, Australia.
47.
Thomas, T. A., Baum K. L. & Vook, F. W. (2003). Modulation and coding rate selection to improve successive cancellation reception in OFDM and spread OFDM MIMO systems. In IEEE International Conference on Communication (ICC 03), (pp. 2842–2846), Alaska, USA.
48.
El-Hajjar, M., & Hanzo, L. (2010). Multifunctional MIMO systems: A combined diversity and multiplexing design perspective. IEEE Wireless Communication, 17(2), 73–79.CrossRef
49.
Katti, S. Shyamnath G., & Dina K. (2007). Embracing wireless interference: Analog network coding. In SIG Communication.
50.
Albreem, M. A. M. & Salleh, M. F. M. (2012). Lattice sphere decoding technique assisted optimum detection for block data transmission systems. In 2nd IEEE International Conference on Cyber-Enabled Distributed Computing and knowledge Discovery, (CyberC2012), Sanya, China.
51.
Mansour, M. M., Alex, S. P., & Jalloul, L. M. A. (2014). Reduced complexity soft-output MIMO sphere detectors-part I: Algorithmic optimizations. IEEE Transactions onSignal Processing, 62, 5505–5520.CrossRef
52.
Mostafa, E.-K., & Haris, V. (2009). Performance of sphere decoding of block codes. IEEE Transactions on Communication, 57(10), 2940–2950.CrossRef
53.
Albreem, M. A. M. (2015). An efficient lattice sphere decoding technique for multi-carrier systems. Wireless Personal Communications, 52(3), 1825–1831.
54.
Debbah, M., Loubaton P. & de Courville M. (2003). The spectral efficiency of linear precoders. IEEE Inf. Theory Workshop, (pp. 90–93), Paris, France.
55.
Qiao, S. (2008). A deterministic method for choosing search radii in sphere decoding. Hamilton, Ontario, Canada.
56.
Mennega, B., & Fettweis, G. (2009). Search sequence determination for tree search baseddetection algorithms. In IEEE SARNOFF09 Symposium, pp. 1–6.
57.
Zhou, G., Xu, W., & Bauch, G. (2013). An ordering scheme exploiting modulation information for tree search based detection in interferencelimited MIMO systems. In IEEE on Wireless Communication Letters.
58.
Jafarkhani, H. (2001). A quasi-orthogonal space-time block code. IEEE Transactions on Commununication, 49(1), 1–4.CrossRef
59.
Albreem, M. A. M., & Salleh, M. F. M. (2015). Regularized lattice sphere decoding for block data transmission systems. Wireless Personal Communications, 82(3), 1833–1850.
60.
Zhao, F., Qiao, S. (2006). Radius selection algoritms for sphere decoding, Canada.
61.
Fu, W. (2011). Improved sphere decoding algorithm in TD-LTE system, 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN), (pp. 514–517).
62.
Guo, Z., & Nilsson, P. (2006). Algorithm and implementation of the K-best sphere decoding for MIMO detection. IEEE Journal on Selected Areas in Communications, 24(3), 491–503.CrossRef
63.
Jald’en, J., Ottersten, B. (2005). Parallel Implementation of a Soft Output Sphere Decoder. In Proceedings of Asilomar Conference on Signals, Systems, and Computers.
64.
Albreem Mahmoud, A. M., Salleh, M. F. M., & Babu, S. (2011). Reduced complexity optimum detector for block data transmission systems using lattice sphere decoding technique. IEICE Electronics Express (ELEX), 8, 644–649.CrossRef
65.
Studer, C., Burg, A., & Bolcskei, H. (2008). Soft-output sphere decoding: Algorithms and VLSI implementation. IEEE Journal on Selected Areas in Communications, 26(2), 290–300.CrossRef
66.
Wang, R., & Giannakis, G. (2004). Approaching near-capacity with reduced-complexity soft sphere decoding. Proceedings of IEEE Wireless Communications and Newtorking Conference (WCNC), 3, 1620–1625.
67.
Albreem, Mahmoud A. M., & Salleh, M. F. M. (2014). Lattice sphere decoding technique for block data transmission systems with special channel matrices. Wireless Personal Communications, 79(1), 265–277.CrossRef
68.
E.-U. Technical Specification Group RAN. (2007). 36.211 Physical Channels and Modulation, v8.0.0. Rep: Tech.
69.
Verd’u, S. (1998). Multiuser detection. Cambridge: Cambridge University Press.
70.
Agrell, E., Eriksson, T., Vardy, A., & Zeger, K. (2002). Closest point search in lattices. IEEE Transactions on Information Theory, 48(8), 2201–2214.CrossRef
71.
Mow, W. (1994). Maximum likelihood sequence estimation from the lattice viewpoint. IEEE Transactions on Information Theory, 40(5), 1591–1600.CrossRef
72.
Aubert, S., Nouvel, F. & Nafkha, A. (2009). Complexity gain of QR decomposition based sphere decoder in LTE receivers. 70th IEEE on Vehicular Technology Conference Fall (VTC 2009-Fall), (pp 1–5).
73.
Barbero, L., & Thompson, J. (2006) Performance analysis of a fixed complexity sphere decoder in high-dimensional mimo systems, 4.
74.
Schnorr, C. P., & Euchner, M. (1994). Lattice basis reduction: Improved practical algorithms and solving subset sum problems. Mathematical Programming, 66(2), 181–191.CrossRef
75.
Viterbo, E., & Boutros, J. (1999). A universal lattice code decoder for fading channels. IEEE Transactions on Information Theory, 45, 1639–1642.CrossRef
76.
Ren, X., & Wang, H. (2009). Research of sphere decoding detection algorithm in LTE-A system. In 2012 8th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), (pp. 1–4).
77.
Xia, Xiaomei, Yang, Honglin (2008). Method of minimizing the initial radius of sphere decoding, China, 200610116780.1[P].
78.
Wubben, D., Bohnke, R., Khun, V., & Kammeyer, K.-D. (2001). Efficient algorithm for decoding layered space-time codes. Electronics Letters, 37(22), 1348–1350.CrossRef
79.
Liu, Q., Yang L. (2004). A novel method for initial radius selection of sphere decoding. In IEEE Semiannual Vehicular Technology Conference VTC-Fall, 2004 (p. 128), Los Angeles, USA.
80.
Kim, K. J., Yue, J., Iltis, R. A., & Gibson, J. D. (2005). A QRD-M/Kalman filter based detection and channel estimation algorithm for MIMO-OFDM systems. IEEE Transactions on Wireless Communications, 4(2), 710–721.CrossRef
81.
Lee, K. F., & Williams, D. B. (2000). A space-frequency transmitter diversity technique for OFDM systems. Global Telecommunications Conference, 3, 1473–1477.CrossRef
82.
Ying, Li, Xin, W. A. N. G., & Ji-bo, W. E. I. (2006). Simulation of a complex sphere decoding algorithm. Journal of System Simulation, 18(10), 2980–2983.
83.
Zamir, R., Shamai, S., & Erez, U. (2002). Nested linear/lattice codes for structured multiterminal binning. IEEE Transactions on Information Theory, 48(6), 1250–1276.CrossRef
84.
Sey sen, M. (1993). Simultaneous reduction of a lattice basis and its reciprocal basis. Combinatorica, 13(3), 363–376.CrossRef
85.
Qi, X., & Holt, K. (2007). A lattice-reduction-aided soft demapper for high-rate coded mimo-ofdm systems. IEEE Signal Processing Letters, 14(5), 305–308.CrossRef
86.
Rekaya, G., Belfiore, J.-C. & Viterbo, E. (Oct. 2004). A very efficient lattice reduction tool on fast fading channels. In Proceedings of IEEE International Symposium on Information Theory and Its Applications (ISITA) (pp. 714–717), Parma, Italy.
87.
Windpassinger, C. & Fischer, R.F.H. (Mar 2003). Low-complexity near-maximumlikelihood detection and precoding for MIMO systems using lattice reduction. Proceedings of IEEE Information Theory Workshop (ITW) (pp. 345–348), Paris, France.
88.
Sandell, M., Lillie, A., McNamara, D., Ponnampalam, V. & Milford, D. (May 2007) Complexity study of lattice reduction forMIMO detection. In IEEE Wireless Communications and Networking Conference, (pp. 1421–1424).
89.
Wang, J., & Daneshrad, B. (March 2005). A comparative study of mimo detection algorithms for wideband spatial multiplexing systems. In 2005 IEEE Wireless Communications and Networking Conference, vol. 1, (pp. 408–413).
90.
Zhou, G.T., Viberg, M. & Mckelvey, T. (2001). Superimposed periodic pilots for blind channel estimation. In IEEE Thirty-Fifth Asilomar Conf. on Signals Systems and Computers, (pp. 653- 657).
91.
Lenstra, A. K., Lenstra, J. H. W., & Lovasz, L. (1982). Factorizing polynomials with rational coefficients. Mathematische Annalen, 261, 515–534.CrossRef
92.
Gan, Y. H., Ling, C., & Mow, W. H. (2009). Complex lattice reduction algorithm for low-complexity full-diversity MIMO detection. IEEE Transactions on Signal Processing, 57(7), 2701–2710.CrossRef
93.
Yao H., Wornell, G.W. (Nov. 2002). Lattice-reduction-aided detectors for MIMO communication systems. In Proceedings of IEEE Global Communications Conference (GLOBECOM), Taipei, Taiwan.
94.
Datta, P. & Kaushal, S. (2014). Exploration and comparison of different 4G technologies implementations: A survey. In Engineering and Computational Sciences (RAECS), (pp. 1–6).
95.
Burg, A., Felber, N., & Fichtner, W. (2003). A 50 mbps 4 \(\times \) 4 maximum likelihood decoder for multiple-input multiple-output systems with qpsk modulation. In IEEE International Conference on Circuits and Systems (pp. 322-325).
96.
Wolniansky, P.W., Foschini, G.J., Golden, G.D. & Valenzuela, R.A. (1998). V-BLAST: An architecture for realizing very high data rates over the rich-scattering wireless channel. In Proceedings of International Symposium Signals, Systems, Electronics (ISSSE) (pp. 295–300), Pisa, Italy, Sept 29–Oct 2.
97.
Tah erzadeh, M., Mobasher, A., & Khandani, A. K. (2007). Communication over MIMO broadcast channels using lattice-basis reduction. IEEE Transactions on Information Theory, 53(12), 4567–4582.CrossRef
98.
Jal dén, J., Elia, P. (2009). LR-aided MMSE lattice decoding is DMT optimal for all approximately universal codes. In Proceedings of IEEE International Symposium on Information Theory (ISIT) (pp. 1263–1267), Seoul, Korea.
99.
Liao, C.-F., Lan, F.-C., & Huang, Y.-H. (May 2011). Latency-constrained low-complexity lattice reduction for MMMO-OFDM systems. In IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 1677–1680).
100.
Foschini, G. J. (1996). Layered space-time architecture for wireless communications in a fading environment when using multiple-element antenna. Bell Labs Technical Journal, 1, 41–59.CrossRef
101.
Wubben, D., Bohnke, R., Kuhn, V., Kammeyer K.-D. (May 2004) MMSE-based lattice-reduction for near-ML detection of MIMO systems. In ITG Workshop on Smart Antennas, (pp. 106–113).
102.
Bäro, S., Hagenauer, J. & Witzke, M. (2003). Iterative detection of MIMO transmission using a list-sequential (LISS) detector. In Proceedings of the IEEE International Conference on Communications, 2003. ICC’03, vol. 4.
103.
Zhang, W. & Ma, X. (Mar. 2007). Approaching optimal performance by lattice-reduction aided soft detectors. In Proceedings of Conference on Information Sciences and Systems (CISS) (pp. 818–822), Baltimore, MD.
104.
Jaldén, J., & Elia, P. (2010). DMT optimality of LR-aided linear decoders for a general class of channels, lattice designs, and system models. IEEE Transactions on Infomation Theory, 56(10), 4765–4780.CrossRef
105.
Eckert, S. (2006). Investigations on different approaches for equalization in mimo ofdm systems in terms of performance and complexity.
106.
Fincke, U., & Pohst, M. (2000). Improved methods for calculating vectors of short length in a lattice, including a complexity analysis. Mathematics of Computation, 44, 463–471.CrossRef
107.
Albreem, Mahmoud A. M., & Salleh, M. F. M. (2013). Near-an-lattice sphere decoding technique assisted optimum detection for block data transmissions systems. IEICE Transactions on Communications, E96–B(01), 356–359.CrossRef
Metadaten
Titel
A review: detection techniques for LTE system
verfasst von
Mahmoud A. M. Albreem
Nur Afiqah Hani Binti Ismail
Publikationsdatum
01.10.2016
Verlag
Springer US
Erschienen in
Telecommunication Systems / Ausgabe 2/2016
Print ISSN: 1018-4864
Elektronische ISSN: 1572-9451
DOI
https://doi.org/10.1007/s11235-015-0112-8

Weitere Artikel der Ausgabe 2/2016

Telecommunication Systems 2/2016 Zur Ausgabe

OriginalPaper

A streaming flow-based technique for traffic classification applied to 12 + 1 years of Internet traffic

OriginalPaper

A compatibility strategy for enabling secure and efficient ITS communications in today’s Internet

OriginalPaper

Optimal pilot design for OFDM systems with non-contiguous subcarriers based on semi-definite programming

OriginalPaper

Localization error modeling of hybrid fingerprint-based techniques for indoor ultra-wideband systems

OriginalPaper

Adoption of dynamic spectrum access technologies: a system dynamics approach

OriginalPaper

Stochastic geometry analysis of downlink energy efficiency for a relay deployment scheme in relay-assisted cellular networks

Neuer Inhalt

    Bildnachweise