基于稀疏感知有序干擾消除的大規(guī)模機器類通信系統(tǒng)多用戶檢測

申濱; 吳和彪; 趙書鋒; 崔太平

doi:10.11999/JEIT190994

基于稀疏感知有序干擾消除的大規(guī)模機器類通信系統(tǒng)多用戶檢測

doi: 10.11999/JEIT190994

重慶郵電大學(xué)通信與信息工程學(xué)院重慶 400065

基金項目: 國家重大研發(fā)計劃(2017YFE0118900)，歐盟H2020項目(734798)

詳細信息

作者簡介:
申濱：男，1978年生，教授，研究方向為認知無線電、大規(guī)模MIMO等

吳和彪：男，1994年生，碩士生，研究方向為大規(guī)模機器類系統(tǒng)多用戶檢測

趙書鋒：男，1991年生，碩士生，研究方向為大規(guī)模MIMO系統(tǒng)信號檢測

崔太平：男，1981年生，講師，研究方向為認知無線電、車聯(lián)網(wǎng)

通訊作者:
申濱　shenbin@cqupt.edu.cn

中圖分類號: TN929.5
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2019-12-13
- 修回日期: 2020-06-23
- 網(wǎng)絡(luò)出版日期: 2020-07-18
- 刊出日期: 2020-12-08

Sparsity-aware Ordered Successive Interference Cancellation Based Multi-user Detection for Uplink mMTC

School of Communications and Information Engineering, Chongqing University of Posts andTelecommunications, Chongqing 400065, China

Funds: The National Key R&D Program of China (2017YFE0118900), The EU H2020 Project (734798)

摘要

摘要:
在大規(guī)模機器類通信(mMTC)系統(tǒng)中，以用戶活躍性為先驗信息，接收機可以基于稀疏感知最大后驗概率(S-MAP)準則來檢測多用戶信號。為了降低S-MAP檢測的計算復(fù)雜度，基于干擾消除的思想，該文提出一種改進的活躍性感知有序正交三角分解(IA-SQRD)算法，以適用于mMTC系統(tǒng)上行鏈路多用戶信號檢測。IA-SQRD算法將傳統(tǒng)的活躍性感知有序正交三角分解(A-SQRD)算法的最終解作為初始解，并額外增加迭代干擾消除操作，以進一步提高檢測性能。此外，利用與改進A-SQRD算法相似的思路，該文對稀疏感知串行干擾消除(SA-SIC)、有序正交三角分解(SQRD)及數(shù)據(jù)相關(guān)的排序和正則化(DDS)算法亦進行了改進設(shè)計，分別獲得了相應(yīng)的改進型算法，即ISA-SIC、I-SQRD及I-DDS算法。仿真結(jié)果表明：相對于A-SQRD算法，在未顯著增加計算復(fù)雜度的情況下，在系統(tǒng)誤比特率(BER)為
\begin{document}$2.5 \times {10^{ - 2}}$\end{document}
時，該文所提IA-SQRD算法可取得3 dB性能增益；并且，對于不同的活躍概率或擴頻序列長度等參數(shù)配置下的mMTC系統(tǒng)，IA-SQRD算法相對于其它算法均表現(xiàn)出更優(yōu)良的多用戶檢測性能。
- 多用戶檢測 /
- 大規(guī)模機器類通信 /
- 最大后驗概率 /
- 串行干擾消除
Abstract:
In massive Machine-Type Communication (mMTC) systems, when the user activity is exploited as a priori information for the receiver, the Sparsity-aware Maximum A Posteriori probability (S-MAP) criterion can be used to recover the sparse multi-user vectors over the uplink mMTC systems. In order to reduce the computational complexity of S-MAP detection, based on interference cancellation mechanism, an Improved Activity-aware Sorted QR Decomposition (IA-SQRD) algorithm is proposed in this paper. The IA-SQRD algorithm utilizes the final solution of the A-SQRD algorithm as the initial solution and the iterative interference cancellation operation is performed to improve further the detection performance. Following the same philosophy in improving the A-SQRD algorithm, the conventional Sparsity-Aware Successive Interference Cancellation (SA-SIC), Sorted QR Decomposition (SQRD), and Data-Dependent Sorting and regularization (DDS) algorithms are modified to enhance the performance, respectively. Simulation results verify that compared with the A-SQRD algorithm, a 3 dB gain is achieved by the proposed IA-SQRD algorithm when the Bit Error Rate (BER) is
\begin{document}$2.5 \times {10^{ - 2}}$\end{document}
, without significantly increasing the computational complexity. In addition, given different system configurations in terms of active probability and the length of spread spectrum sequence, the proposed IA-SQRD also exhibits better performance than that of the other algorithms mentioned in this paper.
- Multi-user detection /
- Massive Machine-Type Communication (mMTC) /
- Maximum A Posteriori (MAP) probability /
- Successive interference cancellation

HTML全文

圖 1 在[0.1 0.3]區(qū)間中隨機均勻分布的用戶活躍概率

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圖 2 BER性能對比，$64 \times 128$配置

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圖 3 BER性能對比，$128 \times 256$配置

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圖 4 不同用戶活躍概率對應(yīng)的BER性能

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圖 5 不同擴頻序列長度對應(yīng)的BER性能

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表 1 改進型活躍性感知有序正交三角分解(IA-SQRD)檢測算法

　輸入：${y}$, ${H}$, ${{A}_0}$, ${\rm{\sigma }}_w^2$,$\left\{ {{p_n}} \right\}_{n = 1}^N$

　輸出：${{\bar s}^{{T_{{\rm{iter}}}}}}$(${T_{{\rm{iter}}}}$為迭代次數(shù))

　(1) ${{\rm{\lambda }}_n} = \ln [(1 - {p_n})/({p_n}/\left| {A} \right|)]$

　(2) ${{y}_0} = [{y};{\bf{0}_N}]$, ${Q} = [{H};{{\rm{\sigma }}_w}{\rm{diag}}\left( {\sqrt {{\lambda }} } \right)]$, ${R} = {{{\textit{0}}}_{N \times N}}$, ${P} = {{I}_N}$

　(3) for $n = 1,2, \cdots ,N$ do

　(4) ${n_{\min } } = \arg {\min _{j = n,n + 1, ··· ,N} }{\left\| { {{q}_j} } \right\|^2}$

　(5) 交換${Q}$, ${R}$和${P}$中的$n$和${n_{\min }}$列

　(6) ${R_{nn}} = \left\| {{{q}_n}} \right\|$,${{q}_n} = {{q}_n}/{R_{nn}}$

　(7) for $j = n + 1, ···,N - 1,N$ do

　(8) ${R_{nj}} = {q}_n^{\rm{H}}{{q}_j}$, ${{q}_j} = {{q}_j} - {R_{nj}}{{q}_n}$

　(9) end for

　(10) end for

　(11) ${{\tilde y}_0} = {{Q}^{\rm{H}}}{{y}_0}$

　(12) for $n = N,N - 1, ··· ,1$ do

　(13) $x_n' = \left({\tilde y_{0,n} } - \displaystyle\sum\limits_{l = n + 1}^N { {R_{nl} } } {\hat x_l}\right)/{R_{nn} }$

　(14) ${\hat x_n} = {Q_{{{A}_0}}}({x_{n'}})$

　(15) end for

　(16) $\hat{ x} = \hat{ x}{{P}^{\rm{H}}}$

　(17) ${s} = \hat{ x}$

　(18) ${G} = {{H}^{\rm{H}}}{H}$, $ = {{H}^{\rm{H}}}{y}$

　(19) for $t = 1:{T_{{\rm{iter}}}}$

　(20) for $n = 1:N$
　(21) $\hat s_n^{(t)} = \hat s_n^{(t - 1)} + \dfrac{ { {b_n} - \displaystyle\sum\limits_{j = 1}^N { {G_{nj} }\hat s_j^{(t - 1)} } } }{ { {G_{nn} } } }$

　(22) $\bar s_n^{(t)} = {Q_{{{A}_0}}}(\hat s_n^{(t)})$

　(23) end for

　(24) end for

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表 2 計算復(fù)雜度比較(復(fù)數(shù)浮點運算次數(shù))

$M$	$N$	SQRD	A-SQRD	I-SQRD	IA-SQRD
$16$	$32$	$3.4 \times {10^4}$	$1.0 \times {10^5}$	$5.5 \times {10^4}$	$1.2 \times {10^5}$
$32$	$64$	$2.7 \times {10^5}$	$7.9 \times {10^5}$	$4.2 \times {10^5}$	$9.4 \times {10^5}$
$64$	$128$	$2.1 \times {10^6}$	$6.3 \times {10^6}$	$3.2 \times {10^6}$	$7.4 \times {10^6}$

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