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Broadcasting And Multicasting Transmission In 5G New Radio

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ABSTRACT

Broadcast and multicast will be an important feature supported in 5G New Radio. In this paper a new transmission method is proposed to improve the receiver side User Equipment (UE) resource efficiency by using non-orthogonal multiplexing. The 5G-MBMS subsystem should provide a unified transmission platform for delivering mixed unicast, multicast, and broadcast services, with adaptive resource allocation and high spectrum efficiency. Up to the latest PTM system in the 3GPP Long Term Evolution (LTE), orthogonal multiplexing (OM) methods are used to combine the different types of services in one channel. This paper studies the capacity benefits of using power-based non-orthogonal multiplexing (PNOM) technology in 5G-MBMS. For LTE/5G systems delivering broadcast and multicast services, using P-NOM offers a solution to significantly increase the cell capacity for broadcast services, while providing broadcast services with required quality of service.

EXISTING METHOD

The FFT based MIMO-OFDM systems

DRAWBACKS

Low Data Transmission Due To High Bit Loss Interference

Hard decision decoding systems

PROPOSED MODIFICATION

DWT based MIMO OFDM with turbo decoding various modulation techniques BER analysis

4-QAM, 16QAM, 64 QAM

ADVANTAGES

  • Iterative soft decision based decoding methodology
  • Time Duration is less
  • Retransmission of data over the network decreased
  • Carrier recovery is high for other application is high

APPLICATION

  • Dual scenario rate capacity networks
  • City coverage area analysis on Improvement through WIFI connection.
  • Medical application through Emergency Analysis on mobile communication

SOFTWARE REQUIREMENT

  • MATLAB 2014a or above versions

REFERENCES

[1] M. Sandell, C. Luschi, P. Strauch, and R. Yan, .Iterative channel estimation using soft decision feedback,. in IEEE Global Telecommunications Conference, vol. 6, Sydney, NSW, 1998, pp. 3728.3733.

[2] M. Valenti, .Iterative channel estimation for turbo codes over fading channels,. in IEEE Wireless Communications and Networking Conference, vol. 3, 23-28 September 2000, pp. 1019.1024.

[3] B.-L. Yeap, C. Wong, and L. Hanzo, .Reduced complexity inphase/ quadrature-phase M-QAM turbo equalization using iterative channel estimation,. IEEE Transactions on Wireless Communications, vol. 2, no. 1, pp. 2.10, 2003.

[4] S. Song, A. Singer, and K.-M. Sung, .Turbo equalization with an unknown channel,. in Proceedings of IEEE International Conference? on Ascoustics, Speech and Signal Processing, vol. 3, 2002.

[5] .., .Soft input channel estimation for turbo equalization,. IEEE Transactions on Signal Processing, [see also IEEE Transactions on Acoustics, Speech and Signal Processing], vol. 52, pp. 2885.2894, 2004.

[6] R. Otnes and M. T?uchler, .Soft iterative channel estimation for turbo equalization: comparison of channel estimation algorithms,. in The 8th International Conference on Communication Systems, vol. 1, 2002, pp. 72.76.

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