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SCIENTIA SINICA Informationis, Volume 47, Issue 10: 1411-1434(2017) https://doi.org/10.1360/N112017-00036

Synergetic communication-and-computation optimization in software-defined hyper-cellular networks

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  • ReceivedFeb 15, 2017
  • AcceptedApr 1, 2017
  • PublishedAug 23, 2017

Abstract

Recently, the cloud radio access network (C-RAN) architecture has been proposed to enhance the cost effectiveness and flexibility of traditional cell-centric radio access networks. However, the massive fronthaul bandwidth required to centralize baseband computations in C-RAN results in extremely high costs. This paper summarizes our previous efforts toward solving this problem. We proposed the software-defined hyper-cellular network (SDHCN) based on the control/data separation principle. Under the proposed SDHCN framework, we studied two mechanisms that can greatly reduce fronthaul costs through the joint deployment of communicational and computational resources. First, we quantitatively characterized the relationship between the size of virtual base station (VBS) pools and the gains from computational statistical multiplexing by using queueing theory. We then showed that the marginal gain diminishes quickly with a growing pool size. Therefore, it is most economical to deploy mid-sized VBS pools. Finally, we proposed a genetic algorithm for baseband function splitting within a graph-clustering framework. This algorithm provides splitting schemes that can flexibly achieve different tradeoffs between fronthaul and computational costs based on different design preferences.


Funded by

国家重点基础研究发展计划(973)(2012CB316000)

国家自然科学基金(61461136004)

国家自然科学基金(61571265)

国家自然科学基金(91638204)


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  • Figure 1

    (Color online) Illustration of the software-defined hyper cellular architecture (SDHCN). VM: virtual machine; RIE: radio interface equipment

  • Figure 2

    (Color online) Illustration of a heterogeneous virtual base station (VBS) pool. VBSs are classified into $V$ groups. VBSs in the same group have the same amount of radio servers, while VBSs from all groups share $N$ computational servers in the data center

  • Figure 3

    (Color online) State transition illustration for a virtual base station (VBS) pool with two VBSs. Axis $k_1$ and axis $k_2$ denote the number of active sessions in the two VBSs, respectively. $K=3$, $N=4$, $f_1(n)~=~n\mu_0$

  • 图 4

    (网络版彩图)实时业务会话阻塞率相对于归一化计算服务器预留量的变化曲线. $M_1=40$, $a_1=20$, $P_1^{\mathrm{bth}}=1\mathrm{E}{-2}$, $K_1=30$

  • 图 5

    (网络版彩图)延时容忍业务会话阻塞率相对于归一化计算服务器预留量的变化曲线. $M_1=100$, $a_1=0.5$, $P_1^{\mathrm{bth}}=5\mathrm{E}{-4}$, $K_1=10$

  • 图 6

    (网络版彩图)多种业务量和服务质量需求下实时业务会话阻塞率相对于归一化计算服务器预留量的变化曲线. 池规模: $M=40$

  • Figure 7

    (Color online) Blocking probability vs. normalized amount of c-server reservation with different pool size. Vertical solid lines mark out the critical tradeoff points. Vertical dashed lines mark out the large pool limit. (a) $a$=20, $K$=28, $P^{\rm~bth}$=0.02; (b) $a$=20, $K$=30, $P^{\rm~bth}$=0.01; (c) $a$=32, $K$=44, $P^{\rm~bth}$=0.01

  • Figure 8

    (Color online) Critical tradeoff point vs. pool size. The solid lines mark the exact value while the dotted lines mark out the approximation. The dashed lines mark out the large pool limit. The ratio between the multiplexing gain with a pool size of $50$ and the large pool limit is also shown. (a) $a$=20, $K$=28, $P^{\rm~bth}$=0.02; (b) $a$=20, $K$=30, $P^{\rm~bth}$=0.01; protectłinebreak (c) $a$=32, $K$=44, $P^{\rm~bth}$=0.01

  • Figure 9

    (Color online) Illustration for the flexible splitting of baseband functions

  • Figure 10

    (Color online) The simplified baseband processing structure used in simulation

  • 图 11

    (网络版彩图)前传与计算代价的折衷关系. $D(p)=30$

  • 图 12

    (网络版彩图)协作多点传输(CoMP)对聚类结果的影响. $\alpha=0.05$, $D(p)=30$

  • Table 1   Node weights
    Index Node Weight Index Node Weight
    1 radioTX 0 7 mod 0.1
    2 radioRX 0 8 demod 0.1
    3 fft 1 9 code 0.1
    4 ifft 1 10 decode 2
    5 MIMOtx 0.5 11 sourceDL 0
    6 MIMOrx 0.5 12 sinkUL 0
  • Table 2   Computational cost function $c_{\rm~c}(i,\xi)$
    Cell site Data center
    $2^{\sum_{\xi(v)=i}{\gamma(i)}}$ $0$
  • Table 3   Fronthaul cost function $c_{\rm~f}(i,j,\xi)$
    Cluster Cost
    Inside cell sites 0
    Inside data center 0
    Between cell sites $4^{\sum_{\xi(e)=(i,j)}{\omega(e)}}$
    Between cell sites and data center $2^{\sum_{\xi(e)=(i,j)}{\omega(e)}}$
  • Table 4   Delay function $d(p;\xi)$
    Cell site Data center
    $\sum_{v~\in~p}{(\gamma(v)\sum_{\xi(w)=\xi(v)}\gamma(v))}$ $0$
  • Table 5   Other algorithm parameters
    Parameter Value
    Population $20$
    Init. function Graph-based init.
    Seed nodes RadioTx, radioRx, sourceDL, sinkUL
    Selection function Roulette
    Crossover function Dispersive
    Mutation function Graph-based (Probability $0.4$)
    Delay penalty $10$

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