一種人與人和物到物業(yè)務(wù)共存下的異構(gòu)蜂窩網(wǎng)絡(luò)柔性接入策略
doi: 10.11999/JEIT190676 cstr: 32379.14.JEIT190676
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陸軍工程大學(xué)野戰(zhàn)工程學(xué)院 南京 210007
A Flexible Network Access Scheme in Heterogeneous Cell Networks with H2H and M2M Coexistence
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College of Field Engineering, Army Engineering University, Nanjing 210007, China
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摘要:
針對(duì)人與人(H2H)和物到物(M2M)業(yè)務(wù)共存的異構(gòu)無(wú)線網(wǎng)絡(luò)，該文設(shè)計(jì)了一種根據(jù)業(yè)務(wù)特性的代理節(jié)點(diǎn)的網(wǎng)絡(luò)選擇策略，用博弈論對(duì)以保障兩類業(yè)務(wù)服務(wù)質(zhì)量(QoS)需求和網(wǎng)絡(luò)負(fù)載均衡為目標(biāo)的代理節(jié)點(diǎn)網(wǎng)絡(luò)選擇問(wèn)題進(jìn)行建模，并分析了該博弈模型納什均衡(NE)的存在性和可行性；同時(shí)，提出了基于學(xué)習(xí)自動(dòng)機(jī)的分布式網(wǎng)絡(luò)-信道選擇算法(DNCSALA)，求得該博弈的納什均衡。仿真結(jié)果表明，所提算法能夠獲得與窮舉搜索算法相近的性能，可滿足共存場(chǎng)景中不同類型業(yè)務(wù)的QoS需求并提高網(wǎng)絡(luò)資源利用率。
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關(guān)鍵詞:
- 異構(gòu)蜂窩網(wǎng) /
- H2H和M2M共存 /
- 網(wǎng)絡(luò)選擇 /
- 博弈論
Abstract:Considering the problem of agents’ network selection for Human-to-Human(H2H) and Machine-to-Machine (M2M) traffic in heterogeneous wireless networks, an agents’ network selection scheme based on the characteristic of traffic is designed. Game theory is adopted to solve the problem of network selection to satisfy difference in traffic’s Quality of Service (QoS) requirements. The existence and feasibility of the Nash Equilibrium (NE) of the proposed game are also analyzed. Then, a Distributed Network-Channe Selection Algorithm based on Learning Automata (DNCSALA) is presented to obtain the NE of the proposed game. In simulations, the proposed algorithm can achieve a near optimal performance compared to the exhaustive search, satisfy the QoS requirements of different types of traffic, and improves the efficiency of network resources.
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圖 2 DNCSALA中剛性代理節(jié)點(diǎn)4的行動(dòng)概率進(jìn)化曲線圖，(
${\gamma ^{\rm ave}} = 5$ dB,${M_f} = 4$ ,${M_r} = 4$ ,${{}_{\rm req}} = \{ 0.5,0.6,0.7,0.7\} $ )表 1 基于學(xué)習(xí)自動(dòng)機(jī)的分布式網(wǎng)絡(luò)選擇算法(DNCSALA)
(1) 首先，初始化每個(gè)代理節(jié)點(diǎn)第0時(shí)刻的行為概率分布${ {{p} }_i}(0)$為$p_{ik}^j(0) = {1 / {\left(1 + \displaystyle\sum\nolimits_{j \in {\cal{N}}} {{K_j}} \right)}}$, $\forall i \in {\cal M},j \in {\cal N}$。每一個(gè)代理節(jié)點(diǎn)根據(jù)自 己的行為概率分布${ {{p} }_i}(0)$選擇一個(gè)行為②； (2) 在每一個(gè)時(shí)刻$t > 0$，每一個(gè)代理節(jié)點(diǎn)都根據(jù)當(dāng)前時(shí)刻的概率分布${ {{p} }_i}(0)$選擇一個(gè)行為(${s_i}(t)$)； (3) 基站根據(jù)所有代理節(jié)點(diǎn)的行為，計(jì)算出收益，并將其廣播給所有代理節(jié)點(diǎn)； (4) 在獲得反應(yīng)函數(shù)之后，每一個(gè)代理節(jié)點(diǎn)根據(jù)式(16)，更新自己的行為概率分布, 其中$0 < {\zeta ^s} < 1$表示步長(zhǎng)參數(shù)；
$\left. \begin{aligned} & {p_{ik}^j(t + 1) = p_{ik}^j(t) - {\zeta ^s}{\gamma _i}(t)p_{ik}^j(t),\quad \quad \quad\quad {s_i}(t) \ne {\rm{CH} }_{ik}^j} \\ & {p_{ik}^j(t + 1) = p_{ik}^j(t) + {\zeta ^s}{\gamma _i}(t)(1 - p_{ik}^j(t)),\quad \;\,{s_i}(t) = {\rm{CH} }_{ik}^j} \end{aligned} \right\}\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\qquad\qquad (16)$(5) 如果對(duì)于任意$i \in {\cal M}$, 其行為概率分布存在一個(gè)元素接近1，確切地說(shuō)等于0.99，那么算法停止。否則，跳轉(zhuǎn)到步驟(2)。 
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