基于改進(jìn)稀疏度自適應(yīng)匹配算法的免授權(quán)非正交多址接入上行傳輸多用戶(hù)檢測(cè)
doi: 10.11999/JEIT190505
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重慶郵電大學(xué)通信與信息工程學(xué)院 重慶 400065
基金項(xiàng)目: 重慶市科技重大主題專(zhuān)項(xiàng)重點(diǎn)示范項(xiàng)目(cstc2018jszx-cyztzxX0035)，重慶市教委科學(xué)技術(shù)研究項(xiàng)目(KJQN201800642)
Multi-User Detection Based on Sparsity Adaptive Matching Pursuit Compressive Sensing for Uplink Grant-free Non-Orthogonal Multiple Access
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School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Funds: The Chongqing of Science and Technology Bureau, (cstc2018jszx-cyztzxX0035), The Project of Science and Technology Research Program of Chongqing Education Commission (KJQN201800642)
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摘要: 免授權(quán)非正交多址接入技術(shù)(NOMA)結(jié)合多用戶(hù)檢測(cè)技術(shù)(MUD)，能夠滿(mǎn)足大規(guī)模機(jī)器通信(mMTC)場(chǎng)景中的大連接量、低信令開(kāi)銷(xiāo)和低時(shí)延傳輸?shù)刃枨?。在基于壓縮感知(CS)的MUD算法中，活躍用戶(hù)數(shù)往往作為已知信息，而實(shí)際通信系統(tǒng)中很難準(zhǔn)確估計(jì)?；诖耍撐奶岢鲆环N改進(jìn)稀疏度自適應(yīng)匹配的多用戶(hù)算法(MSAMP-MUD)。該算法首先利用廣義Dice系數(shù)匹配準(zhǔn)則選擇與殘差最匹配的原子，更新用戶(hù)支撐集；當(dāng)殘差能量接近噪聲能量時(shí)，終止迭代，從而獲得最終支持集；否則，采取上述準(zhǔn)則更新用戶(hù)支撐集，提高支撐集中活躍用戶(hù)數(shù)估計(jì)精度。在迭代過(guò)程中，根據(jù)最近兩次殘差能量之比，選取不同的迭代步長(zhǎng)，以降低檢測(cè)迭代次數(shù)。仿真結(jié)果表明，所提算法與傳統(tǒng)基于CS的MUD算法相比，誤碼率降低約9%，迭代次數(shù)減少約10%。
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關(guān)鍵詞:
- 大規(guī)模機(jī)器通信 /
- 免授權(quán) /
- 非正交多址 /
- 壓縮感知 /
- 多用戶(hù)檢測(cè)
Abstract: Grant-free Non-Orthogonal Multiple Access (NOMA) combined with Multi-User Detection (MUD) technology can meet the requirements of large connection volume, low signaling overhead and low latency transmission in massive Machine Type Communications (mMTC) scenarios. In the MUD algorithm based on Compressed Sensing (CS), the number of active users is often used as known information, but it is difficult to accurately estimate in the actual communication system. Based on this, this paper proposes a multi-user algorithm (Modified Sparsity Adaptive Matching Pursuit MUD, MSAMP-MUP) to improve the adaptive matching of sparsity. Firstly, the algorithm uses the generalized Dice coefficient matching criterion to select the atom that best matches the residual, and updates the user support set. When the residual energy is close to the noise energy, the iteration is terminated to obtain the final support set; Otherwise, the above criteria are used to update the user support set, and the estimation accuracy of the active users in the support set is improved. In the iteration process, different iteration steps are selected according to the ratio of the last two residual energies, so as to reduce the number of detection iterations. The simulation results show that, compared with the traditional CS-based MUD algorithm, the proposed algorithm reduces the bit error rate by about 9% and the number of iterations by about 10%. -
表 1 系統(tǒng)仿真主要參數(shù)
參數(shù) 參數(shù)值 系統(tǒng)用戶(hù)數(shù)$K$ 200 子載波數(shù)$N$ 100 時(shí)隙數(shù)$J$ 7 閾值${\varepsilon _1}$ 1.2 調(diào)制方式 QPSK 過(guò)載率$\lambda $ 200% 擴(kuò)頻矩陣 Toeplitz矩陣 
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