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Outage Performance and Optimal Design of MIMO-NOMA Enhanced Small Cell Networks with Imperfect Channel-State Information

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摘要 This paper focuses on boosting the performance of small cell networks(SCNs)by integrating multiple-input multiple-output(MIMO)and nonorthogonal multiple access(NOMA)in consideration of imperfect channel-state information(CSI).The estimation error and the spatial randomness of base stations(BSs)are characterized by using Kronecker model and Poisson point process(PPP),respectively.The outage probabilities of MIMO-NOMA enhanced SCNs are first derived in closed-form by taking into account two grouping policies,including random grouping and distance-based grouping.It is revealed that the average outage probabilities are irrelevant to the intensity of BSs in the interference-limited regime,while the outage performance deteriorates if the intensity is sufficiently low.Besides,as the channel uncertainty lessens,the asymptotic analyses manifest that the target rates must be restricted up to a bound to achieve an arbitrarily low outage probability in the absence of the inter-cell interference.Moreover,highly correlated estimation error ameliorates the outage performance under a low quality of CSI,otherwise it behaves oppositely.Afterwards,the goodput is maximized by choosing appropriate precoding matrix,receiver filters and transmission rates.In the end,the numerical results verify our analysis and corroborate the superiority of our proposed algorithm.
出处 《China Communications》 SCIE CSCD 2021年第10期107-128,共22页 中国通信(英文版)
基金 supported in part by the National Key Research and Development Program of China under Grant 2017YFE0120600 in part by National Natural Science Foundation of China under Grants 61801192,62171200,and 61801246 in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2019A1515012136 in part by Natural Science Foundation of Anhui Province under Grant 1808085MF164 in part by the Science and Technology Planning Project of Guangdong Province under Grants 2018B010114002 and 2019B010137006 in part by the Science and Technology Development Fund,Macao SAR(File no.0036/2019/A1 and File no.SKL-IOTSC2021-2023) in part by the Hong Kong Presidents Advisory Committee on Research and Development(PACRD)under Project No.2020/1.6 in part by Qinglan Project of University of Jiangsu Province in part by the Research Committee of University of Macao under Grant MYRG2018-00156-FST in part by 2018 Guangzhou Leading Innovation Team Program(China)under Grant 201909010006。
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