Dergiler / Turkish Journal of Electrical Engineering and Computer Science / 2015 / Cilt: 23 - Sayı: 5

OFDMA-based multicast with multiple base stations

Sayfa
1372–1383
Yayın Tarihi
2015-08-13
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

We also propose a greedy subchannel and bit allocation algorithm that is close to optimal.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:We consider an orthogonal frequency division multiple access (OFDMA)-based multicast system where multiple base stations transmit a multicast session to a multicast group.
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Temel Bulgular & Veri:The goal is to maximize the multicast rate (i.e. the minimum achievable user rate) subject to a total power constraint.
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Sonuç & Katkı:We also propose a greedy subchannel and bit allocation algorithm that is close to optimal.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Multicast · orthogonal frequency division multiplexing · erasure codes

Özet

We consider an orthogonal frequency division multiple access (OFDMA)-based multicast system where multiple base stations transmit a multicast session to a multicast group. The goal is to maximize the multicast rate (i.e. the minimum achievable user rate) subject to a total power constraint. We assume the use of an erasure code (e.g., a Reed--Solomon code) or rateless code (e.g., Luby transform code). This facilitates each user to accumulate rates from their best subchannels, so that the achievable multicast rate is not limited to the worst user. The resource allocation problem involves determining the transmitting base station at each OFDMA subchannel, the number of bits to be transmitted at each subchannel, and the set of nodes to decode the bits at each subchannel. We formulate the problem as a mixed binary integer linear programming and find the optimal solution as a benchmark. We also propose a greedy subchannel and bit allocation algorithm that is close to optimal.

Abstract

We consider an orthogonal frequency division multiple access (OFDMA)-based multicast system where multiple base stations transmit a multicast session to a multicast group. The goal is to maximize the multicast rate (i.e. the minimum achievable user rate) subject to a total power constraint. We assume the use of an erasure code (e.g., a Reed--Solomon code) or rateless code (e.g., Luby transform code). This facilitates each user to accumulate rates from their best subchannels, so that the achievable multicast rate is not limited to the worst user. The resource allocation problem involves determining the transmitting base station at each OFDMA subchannel, the number of bits to be transmitted at each subchannel, and the set of nodes to decode the bits at each subchannel. We formulate the problem as a mixed binary integer linear programming and find the optimal solution as a benchmark. We also propose a greedy subchannel and bit allocation algorithm that is close to optimal.

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Bu Makaleye Atıf Yap

KAZEZ, A. C., GİRİCİ, T. (2015). OFDMA-based multicast with multiple base stations. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 23 - Sayı: 5, 1372–1383. https://ajindex.com/articles/187514

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Kaynakça (18)

  1. Jiang T, Xiang W, Chen H-H, Ni Q. Multicast broadcast services support in OFDMA-based WiMAX systems [advances in mobile multimedia]. IEEE Commun Mag 2007; 45: 78–86.
  2. Liu J, Chen W, Cao Z, Letaief KB. Dynamic power and sub-carrier allocation for OFDMA-based wireless multicast systems. In: 2008 IEEE International Conference on Communications (ICC ’08); 19–23 May 2008;
  3. Beijing, China. New York, NY, USA: IEEE. pp. 2607–2611.
  4. Ma Y, Letaief KB, Wang Z, Murch R, Wu Z. Multiple description coding-based optimal resource allocation for OFDMA multicast service. In: 2010 IEEE Global Telecommunications Conference (Globecom ‘10); 6–10 December 2010;
  5. Miami, FL, USA. New York, NY USA: IEEE. pp. 1–5.
  6. Suh C, Mo J. Resource allocation for multicast services in multicarrier wireless communications. IEEE T Wirel Commun 2008; 7: 27–31.
  7. Wolf J, Wyner A, Ziv J. Source coding for multiple descriptions. Bell Syst Tech J 1980; 59: 1417–1426.
  8. Bakanoglu K, Minquan M, Hang L, Saurabh M. Adaptive resource allocation in multicast OFDMA systems. In: 2010 IEEE International Conference on Wireless Communications and Networking (WCNC ‘10); 18–21 April 2010;
  9. Sydney, Australia. New York, NY, USA: IEEE. pp. 1–6.
  10. Girici T, Sokmen FI. OFDM based multicast using erasure codes. In: IEEE 2012 Signal Processing and Commu- nications Applications Conference (SIU ‘12); 18–20 April 2012;
  11. Antalya, Turkey. New York, NY, USA: IEEE. pp. 1–4.
  12. Jin J, Li B. Cooperative multicast scheduling with random network coding in WiMAX. In: 2009 International Workshop on Quality of Service (IWQoS ‘09); 1–9 July 2009;
  13. Charleston SC, USA. Washington, DC, USA: ACM. pp. 1–9. Luby M. LT codes. In: 2002 Annual IEEE Symposium on Foundations of Computer Science; 16–19 November 2002;
  14. Vancouver, Canada. New York, NY, USA: IEEE. pp. 271–282. Shokrollahi A. Raptor codes. In: 2004 IEEE International Symposium on Information Theory; 27 June–2 July 2004;
  15. Chicago IL, USA. New York, NY, USA: IEEE. p. 36. Molisch AF, Mehta NB, Yedidia J, Zhang J. Performance of fountain codes in collaborative relay networks. IEEE T Wirel Commun 2007; 6: 4108–4119.
  16. Draper SC, Liu L, Molisch AF, Yedidia JS. Cooperative transmission for wireless networks using mutual-information accumulation. IEEE T Inform Theory 2011; 57: 5151–5162.
  17. Urgaonkar R, Neely MJ. Optimal routing with mutual information accumulation in wireless networks. IEEE J Sel Areas Commun 2012; 30: 1730–1737.
  18. Liu Y, Yang J, Draper SC. Exploiting route diversity in multi-packet transmission usin