Interference Efficiency-based Base Station Selection and Power Allocation Algorithm for Multi-cell Heterogeneous Wireless Networks
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摘要:
针对多蜂窝多用户异构无线网络干扰管理和效率提升问题，该文研究了基于干扰效率最大的下行链路基站(BS)-用户匹配和功率分配问题。首先，考虑宏用户和微蜂窝用户的服务质量，将问题建模为多变量混合整数非线性规划问题。其次将原问题分解为基站选择和功率分配两个子问题。针对基站选择问题，利用凸优化问题获得最优基站选择策略；针对功率分配问题，利用二次变换法和Dinkelbach辅助变量法，将功率分配问题转换为凸优化问题求解。仿真结果表明，与现有算法对比，该算法具有较好的干扰效率和干扰控制性能。
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-用户匹配选择算法
初始化微蜂窝网络能服务的最大用户数${K^n}$，最小用户速率需求门限$R_{n,k}^{\min }$和发射功率${p_n}(t) = {p_0}$； 初始化拉格朗日乘子${\beta _k}(0) = {\beta _{k,0}}$, ${\chi _n}(0) = {\chi _{n,0}}$和${\lambda _{n,k}}(0) = {\lambda _{n,k,0}}$；初始化网络用户数量和基站用户数$M,N$和$K$；初始化
步长${s_1}(t),{s_2}(t)$和${s_3}(t)$。初始化第$n$个微蜂窝所接入用户数量集合为${U_n} = \varnothing $, $\left| {{U_n}} \right|$为集合中有多少个元素。While $t \le {T^{\max } }$或者${\left\| { {\varphi }(t + 1) - {\varphi }(t)} \right\|_2} \le \varepsilon $；其中${T^{\max }}$为最大迭代次数；$\varepsilon $为拉格朗日乘子收敛精度；${\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 $\left| { {U_n} } \right| \le {K^n}$ 根据式(9)计算${n^*}$，从而根据式(8)计算${\alpha _{n,k}}$；根据式(10)—式(12)更新拉格朗日乘子。 Else Break; End if End For 将用户编号$k$存储在${U_n}$中。 End For End while 
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表 2 最优功率分配算法
初始化微蜂窝网络能服务的最大用户数${K^n}$，最小用户速率需求门限$R_{n,k}^{\min }$和发射功率${p_n}(t) = {p_0}$； 初始化拉格朗日乘子，网络用户数量和基站用户数，初始化步长和干扰效率。
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 }}$为最大迭代次数；$\varepsilon $为收敛精度；For m=1:1:M For k=1:1:K For n=1:1:N 根据式(21)、式(22)计算变量${x_{n,k}}$和最优功率${p_n}$； 根据式(23)—式(25)更新拉格朗日乘子${\theta _n},{\mu _m},\lambda _{n,m}^{{p} }$。 End For End For End For Until $t = {T_{\max }}$或收敛。
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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