基于遲滯噪聲混沌神經(jīng)網(wǎng)絡(luò)的導(dǎo)頻分配
doi: 10.11999/JEIT190748
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重慶郵電大學(xué)通信與信息工程學(xué)院 重慶 400065
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移動(dòng)通信技術(shù)重慶市重點(diǎn)實(shí)驗(yàn)室 重慶 400065
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移動(dòng)通信教育部工程研究中心 重慶 400065
Hysteretic Noisy Chaotic Neural Networks Based Pilot Assignment
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School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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Chongqing Key Laboratory of Mobile Communications Technology, Chongqing 400065, China
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Engineering Research Center of Mobile Communications, Ministry of Education, Chongqing 400065, China
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摘要: 在多小區(qū)大規(guī)模多輸入多輸出(MIMO)系統(tǒng)中,導(dǎo)頻污染已經(jīng)成為制約整個(gè)系統(tǒng)的瓶頸。合理地使用導(dǎo)頻資源能減輕導(dǎo)頻污染的影響,為了尋找使邊緣用戶和容量最大的導(dǎo)頻分配方式,該文首次提出了基于遲滯噪聲混沌神經(jīng)網(wǎng)絡(luò)(HNCNN)的導(dǎo)頻分配方案。遲滯噪聲混沌神經(jīng)網(wǎng)絡(luò)作為良好的優(yōu)化工具,其優(yōu)化能力與所設(shè)計(jì)的能量函數(shù)相關(guān)。該方案結(jié)合導(dǎo)頻資源使用的特點(diǎn)以及最大化邊緣用戶和容量的計(jì)算方式,設(shè)計(jì)了新的能量函數(shù)。仿真結(jié)果表明,網(wǎng)絡(luò)能在一定迭代次數(shù)后收斂到較優(yōu)的導(dǎo)頻分配方式。與其它文獻(xiàn)方案相比,采用以HNCNN為框架求取導(dǎo)頻分配方式,可以更有效減輕導(dǎo)頻污染的影響,使系統(tǒng)性能得到改善。
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關(guān)鍵詞:
- 大規(guī)模多輸入多輸出 /
- 導(dǎo)頻污染 /
- 導(dǎo)頻分配 /
- 遲滯噪聲混沌神經(jīng)網(wǎng)絡(luò)
Abstract: In multi-cell massive Multiple Input Multiple Output (MIMO) systems, pilot contamination has become the bottleneck which restricts the performance of the whole system, so the reasonable usage of pilot resources can mitigate the pilot contamination of the system. In order to find the pilot allocation method that maximizes the total transmission capacity of edge users, a pilot allocation scheme based on Hysteretic Noise Chaotic Neural Network (HNCNN) is proposed for the first time. HNCNN is a famous optimization tool, and its optimization ability is related to the designed energy function. This scheme combines the characteristics of pilot resource usage and the calculation method of maximizing the total transmission capacity of edge users to design a new energy function. The simulation results show that the proposed network can converge to a better pilot allocation mode after a certain number of iterations. Compared with other literature pilot allocation solution, the pilot allocation method based on HNCNN can further reduce the influence of pilot contamination and improve the system performance. -
表 1 大規(guī)模MIMO參數(shù)
參數(shù) 取值 小區(qū)數(shù)$L$ 7 每小區(qū)天線數(shù) 256 小區(qū)半徑 1.6 km 小區(qū)服務(wù)數(shù) 12 上下行發(fā)射功率 15 dBm, 43 dBm 路徑衰落指數(shù)$\alpha $ 3.8 用戶分組門限F 0.33 對(duì)數(shù)陰影衰落 8 dB 下載: 導(dǎo)出CSV
表 2 HNCNN仿真分析
場(chǎng)景 隨機(jī)平均$\Sigma $ 算法 有效次數(shù) 平均$\varSigma$ 提升比例(%) 1 58.38 HNCNN-C 981/1000 78.73 34.86 HNCNN-A 1000/1000 79.90 36.86 2 65.43 HNCNN-C 982/1000 84.98 29.88 HNCNN-A 990/1000 85.86 31.22 3 78.26 HNCNN-C 964/1000 97.07 24.04 HNCNN-A 982/1000 97.49 24.57 4 54.47 HNCNN-C 982/1000 65.65 20.53 HNCNN-A 1000/1000 67.13 23.24 5 59.04 HNCNN-C 946/1000 82.20 39.23 HNCNN-A 958/1000 83.66 41.70 6 62.81 HNCNN-C 984/1000 73.14 16.45 HNCNN-A 993/1000 74.80 19.09 下載: 導(dǎo)出CSV
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