多载波超密集网络的抗干扰分布式节能功率分配

何云; 申敏; 张梦

doi:10.11999/JEIT200388

多载波超密集网络的抗干扰分布式节能功率分配

doi: 10.11999/JEIT200388

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
重庆邮电大学通信核心芯片、协议及系统应用团队重庆 400065

基金项目: 重庆市教育委员会科学技术研究项目(KJQN201800618)

详细信息

作者简介:
何云：女，1979年生，博士生，研究方向为移动通信物理层算法、混合预编码

申敏：女，1963年生，教授，研究方向为通信核心芯片、协议与系统应用技术

张梦：女，1988年生，博士生，研究方向为移动通信物理层算法、网络安全

通讯作者:
何云　heyun@cqupt.edu.cn

中图分类号: TN929.5
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- 被引次数: 2
出版历程
- 收稿日期: 2020-05-15
- 修回日期: 2020-12-09
- 网络出版日期: 2020-12-21
- 刊出日期: 2021-07-10

Anti-Interference Distributed Energy-Efficient Power Allocation for Multi-Carrier Ultra-Dense Networks

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Innovation Team of Communication Core Chip, Protocols and System Application, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The Science and Technology Research Program of Chongqing Education Commission (KJQN201800618)

摘要

摘要: 该文研究多载波超密集网络(UDN)上行链路能效最优功率分配方案，基于非合作博弈论提出一种抗干扰分布式功率分配方案，使每个小区独立优化能效的同时抑制邻小区干扰。由于最大传输功率和QoS约束下的能效函数具有不易解决的非凸特性，且小小区间存在严重干扰。针对以上挑战，该文在最佳响应过程中设计了一种高精度低复杂度的阶梯注水算法，基于该算法利用干扰信道增益提出了一种多用户抗干扰功率分配算法。仿真结果和数值分析表明该算法运算复杂度低，且能在保证系统频谱效率的同时大幅度提升系统能效。
- 超密集网络 /
- 功率分配 /
- 抗干扰 /
- 阶梯注水算法
Abstract: The energy-efficient power allocation is studied in the uplink of the multi-carrier Ultra-Dense Networks (UDN). Based on the non-cooperative game theory, a distributed anti-interference power allocation scheme is proposed so that each cell can independently optimize energy efficiency while suppressing the inter-cell interference. Due to the fact that the energy efficiency problem under the constrains of the Quality of Service(QoS)and the maximum transmitter power is a challenging nonconvex problem and small cells suffer from the severe inter-cell interference, an accurate and low-complexity stair water-filling algorithm is proposed to solve the nonconvex problem in the best response process. Based on this algorithm, a multi-user anti-interference power allocation algorithm is proposed using interference channel gains. Simulation results and numerical analysis show that this algorithm can improve the system energy efficiency with no reduction in spectrum efficiency performance.
- Ultra-Dense Networks(UDN) /
- Power allocation /
- Anti-interference /
- Stair water-filling algorithm

HTML全文

图 1 超密集网络系统模型

下载: 全尺寸图片幻灯片

图 2 平均能效和平均频谱效率

下载: 全尺寸图片幻灯片

图 3 子载波功率

下载: 全尺寸图片幻灯片

图 4 迭代性能对比

下载: 全尺寸图片幻灯片

表 1 阶梯注水算法

(1) 通过遍历搜索获取注水水位和阶梯个数的上下界
(2) for $i = \underline L , ··· ,\bar L$
(3) 根据式(11)，求解阶梯区间 ${C_{k,i}}$ 内解集 ${{{T}}_{k,i}}$
(4) end
(5) 根据式(13)确定最优注水水位 $\mu _k^$ 和注水功率　　 ${\rm{WF}}({{{d}}_k}) = {\left[ {\mu _k^ - {{{d}}_k}} \right]^\dagger }$

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表 2 多用户抗干扰能效功率分配算法

(1) 选择初始点 ${{p}}(0) = {\rm{(}}{{{p}}_1}(0),{{{p}}_2}(0),···,{{{p}}_K}{\rm{(0)) = }}{\bf{0}}$ ，设置 $v{\rm{ = 0}}$
(2) while $\left\| {{{{p}}_k}(v + 1) - {{{p}}_k}(v)} \right\| > \varepsilon$
(3) for $k = 1,2,···,K$
(4) 测量 ${{\gamma }_k}(v + 1)$ 得到干扰 ${{{\overset{\frown} {{I}}} }_k}(v)$
(5) if ${{{\overset{\frown} {{I}}} }_k}(v{\rm{ + 1}}) > = {{{\overset{\frown} {{I}}} }_k}(v)$
(6) ${{p}}_k^* = {{{p}}_k}(v)$
(7) ${{{p}}_k}(v + 1) = \alpha \times {{p}}_k^* + (1 - \alpha ) \times {\rm{WF}}({{{p}}_{\backslash k}}(v{\rm{ + }}1))$
(8) else
(9) ${{p}}_{k}(v+1)=\beta \times {{p}}_{k}^{*}+(1-\beta )\times {{p}}_{k}(v)$
(10)end
(11) end
(12) end while

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表 3 仿真参数

变量	含义	取值
$N$	子载波数目	5
${N_0}$	噪声谱密度	$3.98 \times {10^{ - 19}}\;{\rm{ W/Hz}}$
$B$	总带宽	$1\;{\rm{ MHz}}$
${p_{\rm{c}}}$	电路消耗功率	$300\;{\rm{ mW}}$
$a$	传播指数	3.6
${\rm{cte}}$	传播常数	$2.57399 \times {10^{ - 2}}$
${d_{\min }}$	最小距离	$35\;{\rm{ m}}$
${d_{\max }}$	最大距离	$250\;{\rm{ m}}$
$K$	用户数	10
$M$	基站天线数	10

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