Energy-Efficiency Resource Allocation Algorithm for THz-NOMA System
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摘要: 太赫兹（Terahertz，THz）频带与非正交多址接入（Non-Orthogonal Multiple Access，NOMA）技术相结合在实现大规模连接和超高速通信方面有着突出的优势。但是目前关于下行THz-NOMA系统的资源分配问题研究还很少，现有的方案存在算法复杂度高和系统性能低等缺点。以能量效率为优化目标，研究下行THz-NOMA系统的资源分配问题：首先，为了降低用户和子信道间的匹配复杂度，将该问题等效为双边匹配（Two Side Match，TSM）问题，提出了基于TSM的匹配算法；其次，针对子信道间功率分配问题的非凸性，通过将非凸函数转化为两个凸函数的差分，迭代求解凸子问题得到该问题的解。对于子信道内用户功率分配，推导出了最优功率分配的闭式解。仿真结果表明，本文提出的子信道匹配算法比开关匹配算法复杂度更低，提出的功率分配算法比传统的功率分配算法可实现更高的系统能效。Abstract: The combination of Terahertz (THz) frequency band and Non-Orthogonal Multiple Access (NOMA) technology has outstanding advantages in realizing large-scale connection and ultra-high speed communication. However, there are few researches on resource allocation of downlink THz-NOMA system, and the existing resource allocation schemes have disadvantages of high algorithm complexity and low system performance. In order to obtain a resource allocation scheme that is balanced in both complexity and performance, this paper studies the resource allocation problem of downlink THz-NOMA system with the optimization goal of maximizing energy efficiency. Firstly, in order to reduce the complexity of matching between users and subchannels, the subchannel allocation problem is equivalent to the Two-Side Match (TSM) problem, and a low-complexity TSM subchannel allocation algorithm is proposed. Secondly, in view of the non-convexity of inter-channel power allocation problem, this paper transforms the non-convex function into the difference of two convex functions, and proposes an inter-channel power allocation algorithm based on difference convex programming. The solution of inter-channel power allocation problem is obtained by solving the convex subproblem iteratively. For user power allocation in subchannels, a closed form solution of optimal user power allocation in subchannels is derived. Simulation results show that the proposed subchannel matching algorithm is less complex than the switching matching algorithm, and the proposed power allocation algorithm can achieve higher system energy efficiency than the traditional power allocation algorithm.
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图 2 不同子信道匹配算法的系统能量效率对比
Figure 2. Comparison of system energy efficiency among different subchannel allocation algorithms
图 3 不同传输功率的系统能量效率对比
Figure 3. Comparison of system energy efficiency among different transmission power
图 4 不同用户数量的系统能量效率对比
Figure 4. Comparison of system energy efficiency among different number of users
图 5 不同
${{{P_c}} \mathord{\left/ {\vphantom {{{P_c}} {{P_s}}}} \right. } {{P_s}}}$ 时的系统能量效率对比Figure 5. Comparison of system energy efficiency with different Pc/Ps
图 6 不同迭代次数和传输功率时的能量效率对比
Figure 6. Comparison of system energy efficiency with different iterations and transmission power
表 1 模型参数
Table 1. Parameters for models
Radius of SBS/m N0/(dBm·Hz−1) ${B_{\rm{w}}}$/GHz ${P_s}$/W ${P_c}$/W ${\rm{UE}}$ Number Carrier frequency/THz 10 −174 10 10 1 10 0.34 
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表 2 匹配算法复杂度对比
Table 2. Comparison of matching algorithm complexity
Algorithm name Complexity Iteration times1) DC-SA $O({N^2})$ 25 Switched matching [8] $O(2PAM{N^3})$ About $2 \times {10^4}$ Exhaustive search $O({{(2N)!} \mathord{\left/ {\vphantom {{(2N)!} {{2^N}}}} \right. } {{2^N}}})$ $1.13 \times {10^5}$ 1) M=10
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