云無線接入網(wǎng)絡(luò)高能效功率分配和波束成形聯(lián)合優(yōu)化算法
doi: 10.11999/JEIT180218 cstr: 32379.14.JEIT180218
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1.
南京郵電大學(xué)物聯(lián)網(wǎng)學(xué)院 ??南京 ??210023
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2.
南京郵電大學(xué)通信與信息工程學(xué)院 ??南京 ??210023
基金項(xiàng)目: 江蘇省博士后基金(SBH17024)，江蘇省高校自然科學(xué)基金(15KJB510026)，江蘇省自然科學(xué)基金(BK20150866)，南京郵電大學(xué)引進(jìn)人才基金(NY215046, NY217056)，國家自然科學(xué)基金(61801237, 61701255)
Energy Efficient Joint Power Allocation and Beamforming for Cloud Radio Access Network
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1.
College of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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2.
College of Telecommunications & Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Funds: The Postdoctoral Fund of Jiangsu Province (SBH17024), The Jiangsu University of Natural Science Foundation (15KJB510026), The Natural Science Foundation of Jiangsu Province (BK20150866), The Introduction of Talent Fund of Nanjing University of Posts and Telecommunications (NY215046, NY217056), The National Natural Science Fundation of China (61801237, 61701255)
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摘要: 針對云無線接入網(wǎng)絡(luò)(C-RAN)的資源分配問題，該文采用max-min公平準(zhǔn)則作為優(yōu)化準(zhǔn)則，以C-RAN用戶的能量效率作為優(yōu)化目標(biāo)函數(shù)，在滿足最大發(fā)射功率和最小傳輸速率約束條件下，通過最大化最差鏈路的能量效率來實(shí)現(xiàn)用戶發(fā)射功率和無線遠(yuǎn)端射頻單元(RRHs)波束成形向量的聯(lián)合優(yōu)化。上述優(yōu)化問題屬于非線性、分式規(guī)劃問題，為了方便求解，首先將原優(yōu)化問題轉(zhuǎn)化為差分形式的優(yōu)化問題，然后通過引入變量將差分形式的、非平滑優(yōu)化問題轉(zhuǎn)化為平滑優(yōu)化問題。最終，提出一種雙層迭代功率分配和波束成形算法。在仿真實(shí)驗(yàn)中，將該文算法與傳統(tǒng)的非能效資源分配算法和能量效率最大化算法進(jìn)行了比較，實(shí)驗(yàn)結(jié)果證明該文算法在改進(jìn)C-RAN能量效率和提高資源分配公平性方面的有效性。
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關(guān)鍵詞:
- 云無線接入網(wǎng)絡(luò) /
- 能量效率 /
- 功率分配 /
- 波束成形
Abstract: The resource allocation for Cloud Radio Access Network (C-RAN) is investigated. The max-min fairness criterion is used as the optimization criterion and the Energy Efficiency (EE) of C-RAN users is taken as the optimization objective function, by maximizing the EE of the worst link under the constraints of maximum transmit power and minimum transmit rate, the user transmit power and Remote Radio Heads (RRHs) beamforming vectors are jointly optimized. The above optimization problem belongs to the nonlinear and fractional programming problem. First, the original nonconvex optimization problem is transformed into an equivalent optimization problem in subtractive form. Then, by introducing a new variable, non-smooth equivalent optimization problem is transformed into a smooth optimization problem. Finally, a two-layer iterative power allocation and beamforming algorithm is proposed. The proposed algorithm is compared with traditional non-EE resource allocation algorithm and EE maximization algorithm. The experimental results show that the proposed algorithm is effective in improving the EE and the fairness of resource allocation.-
Key words:
- Cloud Radio Access Network (C-RAN) /
- Energy efficiency /
- Power allocation /
- Beamforming
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表 1 Dinkelbach算法求解優(yōu)化問題式(6)
外循環(huán)： (1)根據(jù)式(9)和式(10)計(jì)算 $P_{\rm lb}^{\rm{total}}$and $P_{\rm ub}^{\rm{total}}$，令 $t = 1$, ${\mathbb{I}^1} =( - \infty, $
$+\infty)$，選取 ${\lambda ^1} \in {\mathbb{I}^1}$，并設(shè)置迭代終止精度閾值 $\varepsilon > 0$(2)對于 ${\lambda ^t}$，通過求解優(yōu)化問題式(6)得到 ${p_{n,t}}$和 ${{w}_{n,t}}$,n=1,2,···,N (3)計(jì)算 $F\left( {{\lambda ^t}} \right) = \mathop {\min }\limits_n \left\{ {{R_n}\left( {{p_{n,t}},{{w}_{n,t}}} \right) - \lambda P_n^{\rm{total}}\left( {{p_{n,t}}} \right)} \right\}$ (4)根據(jù)式 (11) 和式 (12)更新區(qū)間 $\left[ {\alpha _{\min }^t,\alpha _{\max }^t} \right]$ (5)更新 ${\mathbb{I}^{t + 1}} = {\mathbb{I}^t} \cap \left[ {\alpha _{\min }^t,\alpha _{\max }^t} \right]$，并選取 ${\lambda ^t} \in {\mathbb{I}^{t + 1}}$ (6)如果 $\left| {F\left( {{\lambda ^t}} \right)} \right| \ge \varepsilon $，令 $t = t + 1$ (7)重復(fù)步驟2～步驟6，直到 $\left| {F\left( {{\lambda ^t}} \right)} \right| < \varepsilon $ 
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表 2 求解優(yōu)化問題式(13)的步驟
內(nèi)循環(huán)： (1)初始化 ${\alpha _n},{\beta _n},{\chi _n}$, ${{w}_{n}}$ (2)repeat (3)根據(jù)式(19)更新 ${p_n}$ 由式(21)、式(23)計(jì)算 ${\varGamma _n}$和 ${\phi _n}$，根據(jù)式(22)計(jì)算 ${{w}_{n}}$ (4) $\tau = \tau + 1$, 根據(jù)公式(26)，式(27)，式(28)更新 ${\alpha _n}\left( \tau \right)$, ${\beta _n}\left( \tau \right)$,
${\chi _n}\left( \tau \right)$(5)until ${\alpha _n}\left( {\tau + 1} \right)$, ${\beta _n}\left( {\tau + 1} \right)$, ${\chi _n}\left( {\tau + 1} \right)$收斂
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