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SCIENCE CHINA Information Sciences, Volume 60, Issue 10: 100304(2017) https://doi.org/10.1007/s11432-017-9162-9

Group-based joint signaling and data resource allocation in MTC-underlaid cellular networks

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  • ReceivedApr 21, 2017
  • AcceptedJun 7, 2017
  • PublishedSep 5, 2017

Abstract

Machine-type communications (MTC) are gaining significant research attention as one of the most promising technologies for the fifth generation (5G) mobile networks. A critical issue handled by MTC is support for massive numbers of connections, which is a growing problem that will become increasingly challenging as MTC share spectrum resources with cellular communication. Here, not only the number of connections but also the data rate requirements of cellular users (CUEs) need to be considered. Given these issues, in this paper, we formulate a group-basedjoint signaling and data resource optimization model constrained by network resource and data rate requirements in order to maximize the number of connections. We also note that this problem is nonconvex and that obtaining an optimal solution is computationally complex for MTC with massive numbers of users (UEs). Therefore, we decompose the problem into group-based data aggregation and resource allocation subproblems.To solve these two subproblems, we develop an adaptive group head selection algorithm and a joint signaling and data resource allocation algorithm that satisfy both the data rate requirement and resource constraints, respectively. Our simulation results show that our proposed algorithms significantly improve the number of connections when compared with other classic methods. Furthermore, our results reveal that thelimiting factor on the number of connections changes with the ratio of the number of MTC UEs to that of CUEs and the ratio ofdata requirement of MTC UEs to that of CUEs. Finally, we note that our proposed group-based resource allocation algorithm can effectivelyimprove the number of connections, especially when more MTC UEs and a small amount of MTC data are present.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61461136002, 61631005) and Fundamental Research Funds for the Central Universities.


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  • Figure 1

    (Color online) System model.

  • Figure 2

    (Color online) Numbers of connections versus the ratio of data rate of MTC to that of cellular communications for our proposed algorithm (i.e., aggregation $K=10$) and other classic approaches.

  • Figure 3

    (Color online) Numbers of connections versus the number of CUEs to that of MTC UEs for our proposed algorithm (i.e., aggregation $K=10$) and other classic approaches.

  • Figure 4

    (Color online) Numbers of connections versus the number of preambles for our proposed algorithm (i.e., aggregation $K=10$) and other class approaches.

  • Figure 5

    (Color online) Numbers of connections versus the number of groups in our proposed algorithm.

  • Figure 6

    (Color online) Numbers of connections in terms of the number of iterations in our proposed algorithm.

  •   

    Algorithm 1 Group head selection

    Require:

    The number of group heads $K$, the maximum number of MTC UEs in a group $Z$ and the distance matrix ${\boldsymbol~E}$.

    Output:

    The set of group heads is selected by adopting the $K$-means method.

    if $\exists~\left|~{{{\cal~M}_j}}~\right|~>~Z,~\forall~j~\in~{\boldsymbol~K}$ then

    solve the transfer loss minimization problem and move MTC UEs into corresponding group heads using the Hungarian method.

    end if

  •   

    Algorithm 2 Power control and bandwidth allocation algorithm

    Require:

    Assume that all UEs are active, ${m_a}~\leftarrow~M$, ${n_a}~\leftarrow~K$, ${{\cal~U}_{TX}}~=~{{\cal~U}_C}~\cup~{{\cal~U}_M}$ and ${B_{\rm~d}}~=~B~-~{\rm~ceil}\left(~{\left(~{{m_a}~+~{n_a}}~\right)}\right)\Delta~B$.

    Output:

    1: Set ${\boldsymbol~F}~=~a{\boldsymbol~A}$ and calculate $\rho~\left(~{\boldsymbol~F}~\right)$. If $\rho~\left(~{\boldsymbol~F}~\right)~<~1$, i.e., the feasibility condition is satisfied, go to Step 4;otherwise, go to Step 2.

    2: If ${{\cal~U}_M}~\ne~\emptyset$, choose the column of ${{\cal~U}_M}$ such that $k~=~\mathop~{\arg~\max~}\nolimits_{k~\in~{{\cal~U}_M}}~{\left\|~{{{\boldsymbol~f}_k}}~\right\|_2}$; otherwise choose the column of ${{\cal~U}_C}$ such that $k~=~\mathop~{\arg~\max~}\nolimits_{k~\in~{{\cal~U}_c}}~{\left\|~{{{\boldsymbol~f}_k}}~\right\|_2}$.

    3: Delete the $k$-th row and the $k$-th column of ${\boldsymbol~F}$ and generate a new and reduced matrix ${\boldsymbol~F}$.

    if $k~\in~{{\cal~M}_l}$ then

    $\left|~{{{\cal~M}_l}}~\right|~\leftarrow~\left|~{{{\cal~M}_l}}~\right|~-~1$.

    if $\left|~{{{\cal~M}_l}}~\right|~=~0$ then

    Set ${n_a}~\leftarrow~{n_a}~-~1$.

    end if

    else

    ${m_a}~\leftarrow~{m_a}~-~1$.

    end if

    ${B_{\rm~d}}~=~B~-~{\rm~ceil}\left(~{\left(~{{m_a}~+~{n_a}}~\right)}\right)\Delta~B$. Return to Step 1.

    4: Solve the optimization problem and obtain the feasible solution $\left|~{{{\cal~K}_C}}~\right|$ and $\left|~{{{\cal~K}_M}}~\right|$.

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