Robust Energy-efficient Resource Allocation Algorithm in D2D Communication Networks with Imperfect CSI
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摘要: 针对设备到设备(D2D)直连通信网络传统最优资源分配算法在随机信道时延、信道估计误差影响下鲁棒性弱的问题，该文在考虑参数不确定性影响的条件下，提出D2D用户总能效最大的鲁棒资源分配算法。考虑干扰功率门限、用户最小速率需求、最大传输功率和子信道分配约束，建立了下垫式频谱共享模式下多用户D2D网络资源分配模型。基于有界信道不确定性模型，利用最坏准则方法将原非凸鲁棒资源分配问题转换为确定性的凸优化问题。然后利用拉格朗日对偶理论求得资源分配的解析解。仿真结果表明所提出的算法具有很好的鲁棒性。Abstract: Due to the impact of random channel delays and channel estimation errors, traditional optimal resource allocation algorithms in Device-to-Device (D2D) communication networks have weak robustness. In this paper, a robust resource allocation algorithm for the energy-efficient maximization of D2D users is proposed under parametric uncertainties. Specifically, a multi-user resource allocation model in the D2D network with an underlay spectrum sharing mode is established under the constraints of the interference power threshold, the minimum rate requirement, the maximum transmit power, and the sub-channel allocation. Based on the bounded channel uncertainty models, the original non-convex robust resource allocation problem is converted into a deterministic and convex one by using the worst-case approach. Accordingly, the analytical solution of the robust resource allocation problem is obtained by using Lagrange dual theory. Simulation results demonstrate the proposed algorithm has good robustness.
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算法1 基于次梯度的鲁棒资源分配算法 1. 初始化系统参数$M$, $N$, ${P_{\rm{c}}}$, $r_n^{\min }$, $I_m^{{\rm{th}}}$, $p_{n,m}^{\max }$, ${\delta _{n,m}}$, $\upsilon _{n,m}^{\rm{C}}$, $\varepsilon _{n,m}^{\rm{D}}$和$\sigma _{\rm{D}}^2$； 2. 初始化外层最大迭代次数${L_{\max }}$和收敛精度${\psi _O}$，初始化能效${\theta ^0}$和传输功率$p_{n,m}^0$，外层迭代次数置零：$l \leftarrow 0$； 3. while $\quad \Big|\displaystyle\sum\nolimits_{n = 1}^N {\displaystyle\sum\nolimits_{m = 1}^M {\hat r_{n,m}^{ {\rm{D} },l} } } \Big/\left(\displaystyle\sum\nolimits_{n = 1}^N {\displaystyle\sum\nolimits_{m = 1}^M { {\alpha _{n,m} }p_{n,m}^l} } + {P_{\rm{c} } }\right)$$ - {\theta ^{l - 1} }| \le {\psi _O} $ or
$l \le {L_{\max } }$, do4. 初始化内层最大迭代次数${T_{\max }}$和内层收敛精度${\psi _I}$，内层迭代次数置零：$t \leftarrow 0$，初始化拉格朗日乘子$\varpi _m^0$, $\varphi _{n,m}^0$, $\beta _n^0$和$\lambda _m^0$；初始化步长$d_{\rm{1}}^0$, $d_2^0$, $d_3^0$和$d_4^0$； 5. while $\left| {{f^{t + 1}} - {f^t}} \right| > {\psi _I}(f = \varpi _m^{},\varphi _{n,m}^{},\beta _n^{},\lambda _m^{})$ or
$t \le {T_{\max } }$, do6. for $n = 1:1:N$ 7. for $m = 1:1:M$ 8. 根据式(24)计算最优传输功率${p_{n,m}}$； 9. 根据式(26)和式(27)更新子载波分配因子${\alpha _{n,m}}$； 10. 根据式(28)-式(31)更新拉格朗日乘子$\varphi _{n,m}^t$, $\varpi _m^t$, $\beta _n^t$和$\lambda _m^t$； 11. end for 12. end for 13. 更新内层迭代次数$t \leftarrow t + 1$； 14. end while 15. 更新外层迭代次数$l \!\leftarrow\! l \!+\! 1$和${\theta ^l} \!=\!\displaystyle\sum\nolimits_{n = 1}^N {\displaystyle\sum\nolimits_{m = 1}^M {\hat r_{n,m}^{ {\rm{D} },l - 1} } } \Big/ $$ \left(\displaystyle\sum\nolimits_{n = 1}^N {\displaystyle\sum\nolimits_{m = 1}^M { {\alpha _{n,m} }p_{n,m}^{l - 1} } } + {P_{\rm{c} } }\right)$； 16. end while 17. output $p_{n,m}^*$, $\alpha _{n,m}^*$和${\theta ^*}$. 
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