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首页> 外文期刊>IEEE Transactions on Vehicular Technology >Sparse Vector Coding Aided Ultra-Reliable and Low-Latency Communications in Multi-User Massive MIMO Systems
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Sparse Vector Coding Aided Ultra-Reliable and Low-Latency Communications in Multi-User Massive MIMO Systems

机译:稀疏的矢量编码辅助超可靠和低延迟通信在多用户大型MIMO系统中

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摘要

Ultra-reliable and low-latency communication (URLLC) has been recognized as a key service to support delay-sensitive applications for next generation wireless systems. A critical challenge is how to support multiple users with ultra reliability in the short packet transmission. In this paper, we propose a sparse vector coding (SVC)-aided URLLC multi-user transmission scheme by employing the massive multiple-input multiple-output (MIMO) technique. To reliably acquire the support information, we formulate the SVC decoding problem in multi-user massive MIMO systems as the problem to find out nonzero positions of a sparse vector. Then, the support recovery problem is transformed into a series of sub-problems by decoupling the support identification of each user, where the successive interference cancellation based matching pursuit (SIC-MP) algorithm is proposed to estimate the support sequentially. In addition, the user sorting scheme is proposed to alleviate the inter-user interference in the SVC decoding. The complexity of the proposed SIC-MP algorithm is only linear with the system parameters.
机译:超可靠和低延迟通信(URLLC)被识别为关键服务,以支持下一代无线系统的延时敏感应用。关键挑战是如何在短数据包传输中支持具有超可靠性的多个用户。在本文中,我们提出了一种稀疏的向量编码(SVC) - 通过采用大量多输入多输出(MIMO)技术来提出稀疏的矢量编码(SVC) - 求解URLLC多用户传输方案。为了可靠地获取支持信息,我们在多用户大规模MIMO系统中制定SVC解码问题作为发现稀疏向量的非零位置的问题。然后,通过解耦每个用户的支持识别,将支持恢复问题转换为一系列子问题,其中提出了基于连续的干扰取消(SiC-MP)算法来顺序估计支撑件。另外,提出了用户分类方案来缓解SVC解码中的用户间干扰。所提出的SIC-MP算法的复杂性仅具有系统参数的线性。

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