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Georgios B Giannakis - One of the best experts on this subject based on the ideXlab platform.

  • capacity scaling of wireless networks with Complex Field network coding
    International Conference on Acoustics Speech and Signal Processing, 2009
    Co-Authors: Tairan Wang, Georgios B Giannakis
    Abstract:

    Network coding in wired networks has been shown to achieve considerable throughput gains relative to traditional routing networks. While the ergodic capacity of wireless multihop networks is unknown, the scaling of capacity with the number of nodes (n) has recently received increasing attention. While existing works mainly focus on networks with n source-destination pairs, this paper deals with capacity scaling in any-to-any wireless links, where each node communicates with all other nodes. Complex Field network coding (CFNC) is adopted at the physical layer to allow n nodes exchanging information with simultaneous transmissions from multiple sources. A hierarchical CFNC-based scheme is developed and shown to achieve asymptotically (as n → ∞) optimal quadratic capacity scaling in a dense network, where the area is fixed and the density of nodes increases. This is possible by dividing the network into many clusters, with each cluster sub-divided into many sub-clusters, hierarchically.

  • capacity scaling of wireless networks with Complex Field network coding
    Journal of Communications, 2009
    Co-Authors: Tairan Wang, Georgios B Giannakis
    Abstract:

    Network coding in wired networks has been shown to achieve considerable throughput gains relative to traditional routing networks. For wireless multihop networks however, the ergodic capacity is unknown. In this context, the scaling of capacity with the number of nodes ( n ) has recently received increasing attention. While existing works mainly focus on networks with n source-destination pairs, this paper deals with capacity scaling in any-toany wireless links, where each node communicates with all other nodes. Complex Field network coding (CFNC) is adopted at the physical layer to allow n nodes exchanging information with simultaneous transmissions from multiple sources. As n increases, a hierarchical CFNC-based scheme is developed and shown to achieve asymptotically optimal quadratic capacity scaling in a dense network, where the area is fixed and the density of nodes increases. This is possible by dividing the network into many clusters, with each cluster sub-divided into many sub-clusters, hierarchically. As a result, information is transmitted on multi-input multi-output based multiple access channels and broadcast channels. When generalized to extended networks, where the density of nodes is fixed and the area increases linearly with n , the hierarchical CFNC scheme is shown to scale as n 3 −α/ 2 for a path loss exponent α ≥ 2, which is asymptotically optimal when α 3.

  • Complex Field network coding for multiuser cooperative communications
    IEEE Journal on Selected Areas in Communications, 2008
    Co-Authors: Tairan Wang, Georgios B Giannakis
    Abstract:

    Multi-source relay-based cooperative communications can achieve spatial diversity gains, enhance coverage and potentially increase capacity when multiuser detection is used to effect maximum likelihood demodulation. If considered for large networks, traditional relaying entails loss in spectral efficiency that can be mitigated through network coding at the physical layer. These considerations motivate the Complex Field network coding (CFNC) approach introduced in this paper. Different from network coding over the Galois Field, where wireless throughput is limited as the number of sources increases, CFNC always achieves throughput as high as 1/2 symbol per source per channel use. In addition to improved throughput, CFNC- based relaying achieves full diversity gain regardless of the underlying signal-to-noise-ratio (SNR) and the constellation used. Furthermore, the CFNC approach is general enough to allow for transmissions from sources to a common destination as well as simultaneous information exchanges among sources.

  • combining galois with Complex Field coding for space time communications
    European Transactions on Telecommunications, 2003
    Co-Authors: Renqiu Wang, Zhengdao Wang, Georgios B Giannakis
    Abstract:

    A joint scheme combining error-control coding (ECC) with Complex-Field coding (CFC) is proposed for space–time communications through flat or frequency-selective fading multiple-input multiple-output (MIMO) channels. It is shown that the diversity gain of this joint coding scheme is the product of the free distance of the ECC, the Complex-Field block encoder size, and the number of receiving antennas, which is computationally prohibitive to achieve using ECC alone. The decoding Complexity with the proposed iterative decoder is comparable to a system without CFC. Numerical simulations for perfectly interleaved and HIPERLAN channels with both single and multiple antennas show great potential of the proposed joint coding scheme. Copyright © 2003 AEI

  • combining galois with Complex Field coding for space time communications
    European Wireless Conference, 2003
    Co-Authors: Renqiu Wang, Zhengdao Wang, Georgios B Giannakis
    Abstract:

    A joint scheme combining error-control coding (ECC) with Complex-Field coding (CFC) is proposed for space-time communications through flat or frequency-selective fading multiple-input multiple-output (MIMO) channels. It is shown that the diversity gain of this joint coding scheme is the product of the free distance of the ECC, the Complex-Field block encoder size, and the number of receiving antennas, which is computationally prohibitive to achieve using ECC alone. The decoding Complexity with the proposed iterative decoder is comparable to a system without CFC. Numerical simulations for perfectly interleaved and HIPERLAN channels with both single and multiple antennas show great potential of the proposed joint coding scheme.

Tairan Wang - One of the best experts on this subject based on the ideXlab platform.

  • capacity scaling of wireless networks with Complex Field network coding
    International Conference on Acoustics Speech and Signal Processing, 2009
    Co-Authors: Tairan Wang, Georgios B Giannakis
    Abstract:

    Network coding in wired networks has been shown to achieve considerable throughput gains relative to traditional routing networks. While the ergodic capacity of wireless multihop networks is unknown, the scaling of capacity with the number of nodes (n) has recently received increasing attention. While existing works mainly focus on networks with n source-destination pairs, this paper deals with capacity scaling in any-to-any wireless links, where each node communicates with all other nodes. Complex Field network coding (CFNC) is adopted at the physical layer to allow n nodes exchanging information with simultaneous transmissions from multiple sources. A hierarchical CFNC-based scheme is developed and shown to achieve asymptotically (as n → ∞) optimal quadratic capacity scaling in a dense network, where the area is fixed and the density of nodes increases. This is possible by dividing the network into many clusters, with each cluster sub-divided into many sub-clusters, hierarchically.

  • capacity scaling of wireless networks with Complex Field network coding
    Journal of Communications, 2009
    Co-Authors: Tairan Wang, Georgios B Giannakis
    Abstract:

    Network coding in wired networks has been shown to achieve considerable throughput gains relative to traditional routing networks. For wireless multihop networks however, the ergodic capacity is unknown. In this context, the scaling of capacity with the number of nodes ( n ) has recently received increasing attention. While existing works mainly focus on networks with n source-destination pairs, this paper deals with capacity scaling in any-toany wireless links, where each node communicates with all other nodes. Complex Field network coding (CFNC) is adopted at the physical layer to allow n nodes exchanging information with simultaneous transmissions from multiple sources. As n increases, a hierarchical CFNC-based scheme is developed and shown to achieve asymptotically optimal quadratic capacity scaling in a dense network, where the area is fixed and the density of nodes increases. This is possible by dividing the network into many clusters, with each cluster sub-divided into many sub-clusters, hierarchically. As a result, information is transmitted on multi-input multi-output based multiple access channels and broadcast channels. When generalized to extended networks, where the density of nodes is fixed and the area increases linearly with n , the hierarchical CFNC scheme is shown to scale as n 3 −α/ 2 for a path loss exponent α ≥ 2, which is asymptotically optimal when α 3.

  • Complex Field network coding for multiuser cooperative communications
    IEEE Journal on Selected Areas in Communications, 2008
    Co-Authors: Tairan Wang, Georgios B Giannakis
    Abstract:

    Multi-source relay-based cooperative communications can achieve spatial diversity gains, enhance coverage and potentially increase capacity when multiuser detection is used to effect maximum likelihood demodulation. If considered for large networks, traditional relaying entails loss in spectral efficiency that can be mitigated through network coding at the physical layer. These considerations motivate the Complex Field network coding (CFNC) approach introduced in this paper. Different from network coding over the Galois Field, where wireless throughput is limited as the number of sources increases, CFNC always achieves throughput as high as 1/2 symbol per source per channel use. In addition to improved throughput, CFNC- based relaying achieves full diversity gain regardless of the underlying signal-to-noise-ratio (SNR) and the constellation used. Furthermore, the CFNC approach is general enough to allow for transmissions from sources to a common destination as well as simultaneous information exchanges among sources.

Naoki Urakawa - One of the best experts on this subject based on the ideXlab platform.

Koji Igarashi - One of the best experts on this subject based on the ideXlab platform.

Yasuhiro Kawabata - One of the best experts on this subject based on the ideXlab platform.