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SCIENCE CHINA Information Sciences, Volume 64 , Issue 4 : 140303(2021) https://doi.org/10.1007/s11432-020-2985-8

Energy-efficient design for mmWave-enabled NOMA-UAV networks

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  • ReceivedMar 16, 2020
  • AcceptedJul 17, 2020
  • PublishedMar 2, 2021

Abstract


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61871065, 61971194).


References

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  • Figure 1

    (Color online) Illustration of the mmWave-enabled NOMA-UAV network.

  • Table 1  

    Table 1Simulation parameters

    Parameter Value Parameter Value
    UAV altitude $H=120$ m Channel gain at reference distance $\beta_0=-30$ dB
    Noise power $\sigma^2=-100$ dBm The channel correlation threshold $\rho_0=0.05$
    Number of RF chains $N_{\rm~RF}=3$ Maximum transmit power of UAV $P_{\rm~max}~=~500$ mW
    Number of antennas at UAV $N_{\rm~u}=64$ Power consumed by one RF chain $P_{\rm~RF}=34.4$ mW
    Baseband power consumption$P_{\rm~BB}=200$ mW Realizable bits of quantized phase shifters $b=4$
  •   

    Algorithm 1 Correlation-based cluster head selection

    Require:$K$, $M$, and the channel vectors ${\boldsymbol~h}_{k},~k=1,2,\ldots,K$. The channel correlation threshold $\rho_0$.

    Output:The cluster head set $\mathcal{U}$.

    $\overline{\mathcal{H}}=[\left\|{\boldsymbol~h}_{1}\right\|_2,\left\|{\boldsymbol~h}_{2}\right\|_2,\ldots,\left\|{\boldsymbol~h}_{K}\right\|_2]$;$[\sim,\Theta]=\mbox{sort}(\overline{\mathcal{H}},~`{\rm~descend}$');

    $\mathcal{U}=\Theta~(1)$; $\mathcal{U}^c=\Theta/\mathcal{U}$;$\Lambda=\mathcal{U}$;

    $C_{\rm~idx}~=2$;

    while $C_{\rm~idx}~\leq~M$ do

    if $\Lambda=\emptyset$ then

    while $\Lambda=\emptyset$ do

    $\rho_0=\rho_0+\Delta\rho_0$;

    $\Lambda=\{j\in\mathcal{U}^c|\rho_{i,j}<\rho_0,~\forall~i\in\mathcal{U}\}$;

    end while

    end if

    $\Lambda=\{j\in\mathcal{U}^c|\rho_{i,j}<\rho_0,~\forall~i\in\mathcal{U}\}$;

    $\mathcal{U}=\mathcal{U}\cup\Lambda(1)$; $\mathcal{U}^c=\Theta/\mathcal{U}$;

    $C_{\rm~idx}~=C_{\rm~idx}+1$;

    end while

    return $\mathcal{U}$.

  •   

    Algorithm 2 Energy-efficient PA algorithm

    Require:Initialize feasible PA set $\{p_{m,k}^{(0)}\}$. Given the maximum tolerance $\epsilon^*$. Denote the index of iteration as $r=0$, $\lambda^{(0)}=0$, $\epsilon^{(0)}=10~\epsilon^*$.

    while $|\epsilon^r|>\epsilon^*$ do

    With given $\lambda^{(r)}$ and $\{p_{m,k}^{(r)}\}$, solve the problem (40) iteratively to converge to the new PA set denoted as $\{p_{m,k}^{(r+1)}\}$.

    Update $r~=~r+1$.

    Calculate $\lambda^{(r)}=\sum\nolimits_{m=1}^{M}\sum\nolimits_{k=1}^{K_m}{R}^{(r)}_{m,k}/(\sum\nolimits_{k=1}^{K_m}{p}^{(r)}_{m,k}+P_o)$,$\epsilon^{(r)}=\sum\nolimits_{m=1}^{M}\sum\nolimits_{k=1}^{K_m}{R}_{m,k}^{(r)}-\lambda^{(r-1)}(\sum\nolimits_{m=1}^{M}\sum\nolimits_{k=1}^{K_m}{p}_{m,k}^{r}+P_o)$;

    Denote the optimal solution as $~\{p_{m,k}^{*}~\}=~\{p_{m,k}^{(r)}~\}$;

    end while

    Output:The final solution of PA $~\{p_{m,k}^{*}~\}$.

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