基于干擾效率多蜂窩異構(gòu)無線網(wǎng)絡(luò)最優(yōu)基站選擇及功率分配算法
doi: 10.11999/JEIT190419
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1.
重慶郵電大學(xué)通信與信息工程學(xué)院 重慶 400065
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2.
山東大學(xué)山東省無線通信技術(shù)重點(diǎn)實(shí)驗(yàn)室 濟(jì)南 250100
Interference Efficiency-based Base Station Selection and Power Allocation Algorithm for Multi-cell Heterogeneous Wireless Networks
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School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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2.
Shandong Provincial Key Laboratory of Wireless Communication Technologies, Shandong University, Jinan 250100, China
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摘要:
針對(duì)多蜂窩多用戶異構(gòu)無線網(wǎng)絡(luò)干擾管理和效率提升問題，該文研究了基于干擾效率最大的下行鏈路基站(BS)-用戶匹配和功率分配問題。首先，考慮宏用戶和微蜂窩用戶的服務(wù)質(zhì)量，將問題建模為多變量混合整數(shù)非線性規(guī)劃問題。其次將原問題分解為基站選擇和功率分配兩個(gè)子問題。針對(duì)基站選擇問題，利用凸優(yōu)化問題獲得最優(yōu)基站選擇策略；針對(duì)功率分配問題，利用二次變換法和Dinkelbach輔助變量法，將功率分配問題轉(zhuǎn)換為凸優(yōu)化問題求解。仿真結(jié)果表明，與現(xiàn)有算法對(duì)比，該算法具有較好的干擾效率和干擾控制性能。
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關(guān)鍵詞:
- 異構(gòu)無線網(wǎng)絡(luò) /
- 干擾效率 /
- 資源分配 /
- 基站選擇
Abstract:To solve interference management and efficiency improvement of multi-cell multi-user heterogeneous wireless networks, the downlink Base Station (BS)-user matching and power allocation problem are studied to maximize the interference efficiency of femtocells. Firstly, consideration of quality of service of macro cell users and femtocell users, the problem is formulated as a multivariate mixed integer nonlinear programming problem. Secondly, the problem is decomposed into two subproblems. The BS selection problem is solved by convex optimization technique. The power allocation problem is firstly converted into a convex one by using quadratic transformation method and Dinkelbach approach, then the problem is resolved by using Lagrange dual methods and subgradient methods. Simulations results show the effectiveness of the proposed algorithm by comparing with the existing algorithms in terms of interference efficiency and interference management.
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表 1 基站-用戶匹配選擇算法
初始化微蜂窩網(wǎng)絡(luò)能服務(wù)的最大用戶數(shù)${K^n}$，最小用戶速率需求門限$R_{n,k}^{\min }$和發(fā)射功率${p_n}(t) = {p_0}$； 初始化拉格朗日乘子${\beta _k}(0) = {\beta _{k,0}}$, ${\chi _n}(0) = {\chi _{n,0}}$和${\lambda _{n,k}}(0) = {\lambda _{n,k,0}}$；初始化網(wǎng)絡(luò)用戶數(shù)量和基站用戶數(shù)$M,N$和$K$；初始化
步長${s_1}(t),{s_2}(t)$和${s_3}(t)$。初始化第$n$個(gè)微蜂窩所接入用戶數(shù)量集合為${U_n} = \varnothing $, $\left| {{U_n}} \right|$為集合中有多少個(gè)元素。While $t \le {T^{\max } }$或者${\left\| { {\varphi }(t + 1) - {\varphi }(t)} \right\|_2} \le \varepsilon $；其中${T^{\max }}$為最大迭代次數(shù)；$\varepsilon $為拉格朗日乘子收斂精度；${\varphi }(t) = {[{\beta _k}(t),{\chi _n}(t),{\lambda _{n,k} }(t)]^{\rm{T} } }$。 For k=1:1:K For n=1:1:N if $\left| { {U_n} } \right| \le {K^n}$ 根據(jù)式(9)計(jì)算${n^*}$，從而根據(jù)式(8)計(jì)算${\alpha _{n,k}}$；根據(jù)式(10)—式(12)更新拉格朗日乘子。 Else Break; End if End For 將用戶編號(hào)$k$存儲(chǔ)在${U_n}$中。 End For End while 
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表 2 最優(yōu)功率分配算法
初始化微蜂窩網(wǎng)絡(luò)能服務(wù)的最大用戶數(shù)${K^n}$，最小用戶速率需求門限$R_{n,k}^{\min }$和發(fā)射功率${p_n}(t) = {p_0}$； 初始化拉格朗日乘子，網(wǎng)絡(luò)用戶數(shù)量和基站用戶數(shù)，初始化步長和干擾效率。
While $j \le J$ 或者$\left| {\dfrac{ {\displaystyle\sum\nolimits_{n = 1}^N {\displaystyle\sum\nolimits_{k = 1}^K { {\alpha _{n,k} }{R_{n,k} }(j)} } } }{ {\displaystyle\sum\nolimits_{m = 1}^M {\displaystyle\sum\nolimits_{n = 1}^N { {p_n}(j){h_{n,m} } } } } } - \eta (j - 1)} \right| > \varepsilon $；其中${T^{\max }}$為最大迭代次數(shù)；$\varepsilon $為收斂精度；For m=1:1:M For k=1:1:K For n=1:1:N 根據(jù)式(21)、式(22)計(jì)算變量${x_{n,k}}$和最優(yōu)功率${p_n}$； 根據(jù)式(23)—式(25)更新拉格朗日乘子${\theta _n},{\mu _m},\lambda _{n,m}^{{p} }$。 End For End For End For Until $t = {T_{\max }}$或收斂。
更新 $j = j + 1$和$\eta (j) = \frac{ {\displaystyle\sum\nolimits_{n = 1}^N {\displaystyle\sum\nolimits_{k = 1}^K { {\alpha _{n,k} }{R_{n,k} }(j - 1)} } } }{ {\displaystyle\sum\nolimits_{m = 1}^M {\sum\nolimits_{n = 1}^N { {p_n}(j - 1){h_{n,m} } } } } }$。End while
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DAMNJANOVIC A, MONTOJO J, WEI Yongbin, et al. A survey on 3GPP heterogeneous networks[J]. IEEE Wireless Communications, 2011, 18(3): 10–21. doi: 10.1109/MWC.2011.5876496 徐勇軍, 李國權(quán), 徐鵬, 等. 異構(gòu)無線網(wǎng)絡(luò)資源分配算法研究綜述[J]. 重慶郵電大學(xué)學(xué)報(bào): 自然科學(xué)版, 2018, 30(3): 289–299. doi: 10.3979/j.issn.1673-825X.2018.03.001XU Yongjun, LI Guoquan, XU Peng, et al. Survey on resource allocation in heterogeneous wireless network[J]. Journal of Chongqing University of Posts and Telecommunications:Natural Science Edition, 2018, 30(3): 289–299. doi: 10.3979/j.issn.1673-825X.2018.03.001 ZHANG Haijun, LIU Hao, CHENG Julian, et al. Downlink energy efficiency of power allocation and wireless backhaul bandwidth allocation in heterogeneous small cell networks[J]. IEEE Transactions on Communications, 2018, 66(4): 1705–1716. doi: 10.1109/TCOMM.2017.2763623 XIE Renchao, YU F R, JI Hong, et al. Energy-efficient resource allocation for heterogeneous cognitive radio networks with femtocells[J]. IEEE Transactions on Wireless Communications, 2012, 11(11): 3910–3920. doi: 10.1109/TWC.2012.092112.111510 BACCI G, BELMEGA E V, MERTIKOPOULOS P, et al. Energy-aware competitive power allocation for heterogeneous networks under QoS constraints[J]. IEEE Transactions on Wireless Communications, 2015, 14(9): 4728–4742. doi: 10.1109/TWC.2015.2425397 LI Rui, CAO Ning, MAO Minghe, et al. Load-aware energy efficiency with unequal user priority in downlink heterogeneous network system[J]. IEEE Access, 2019, 7: 106275–106283. doi: 10.1109/ACCESS.2019.2920149 MIAO Jie, HU Zheng, YANG Kun, et al. Joint power and bandwidth allocation algorithm with QoS support in heterogeneous wireless networks[J]. IEEE Communications Letters, 2012, 16(4): 479–481. doi: 10.1109/LCOMM.2012.030512.112304 CHOI Y, KIM H, HAN S W, et al. Joint resource allocation for parallel multi-radio access in heterogeneous wireless networks[J]. IEEE Transactions on Wireless Communications, 2010, 9(11): 3324–3329. doi: 10.1109/TWC.2010.11.100045 FOOLADIVANDA D and ROSENBERG C. Joint resource allocation and user association for heterogeneous wireless cellular networks[J]. IEEE Transactions on Wireless Communications, 2013, 12(1): 248–257. doi: 10.1109/TWC.2012.121112.120018 NGO D T, KHAKUREL S, and LE-NGOC T. Joint subchannel assignment and power allocation for OFDMA femtocell networks[J]. IEEE Transactions on Wireless Communications, 2014, 13(1): 342–355. doi: 10.1109/TWC.2013.111313.130645 BOYD S and VANDENBERGHE L. Convex Optimization[M]. Cambridge, UK: Cambridge University Press, 2004. SHEN Kaiming and YU Wei. Fractional programming for communication systems-Part I: Power control and beamforming[J]. IEEE Transactions on Signal Processing, 2018, 66(10): 2616–2630. doi: 10.1109/TSP.2018.2812733 FANG Fang, DING Zhiguo, LIANG Wei, et al. Optimal energy efficient power allocation with user fairness for uplink MC-NOMA systems[J]. IEEE Wireless Communications Letters, 2019, 8(4): 1133–1136. doi: 10.1109/LWC.2019.2908912 XU Yongjun, LI Guoquan, YANG Yang, et al. Robust resource allocation and power splitting in SWIPT enabled heterogeneous networks: A robust minimax approach[J]. IEEE Internet of Things Journal, 2019, 6(6): 10799–10811. doi: 10.1109/JIOT.2019.2941897 XU Quansheng, LI Xi, JI Hong, et al. Energy-efficient resource allocation for heterogeneous services in OFDMA downlink networks: Systematic perspective[J]. IEEE Transactions on Vehicular Technology, 2014, 63(5): 2071–2082. doi: 10.1109/TVT.2014.2312288 WANG Haining, WANG Jiaheng, and DING Zhi. Distributed power control in a two-tier heterogeneous network[J]. IEEE Transactions on Wireless Communications, 2015, 14(12): 6509–6523. doi: 10.1109/TWC.2015.2456055 -
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