The mobile data traffic has been exponentially growing during the last decades, which has been enabled by the densification of the network infrastructure, in terms of increased cell density (i.e., ultra-dense network (UDN)) and/or increased number of active antennas per access point (AP) (i.e., massive multiple-input multiple-output (mMIMO)). However, neither UDN nor mMIMO will meet the increasing data rate demands of the sixth generation (6G) wireless communications due to the inter-cell interference and large quality-of-service variations, respectively. Cell-free (CF) mMIMO, which combines the best aspects of UDN with mMIMO, is viewed as a key solution to this issue. In such systems, each user equipment (UE) is served by a preferred set of surrounding APs that cooperate to serve the UE in a CF approach. In this paper, we provide a survey of the state-of-the-art literature on CF mMIMO systems. As a starting point, we present the significance and challenges of improving the user-experienced data rates which motivate CF mMIMO, derive the basic properties of CF mMIMO, and provide an introduction to other technologies related to CF mMIMO. We then provide the canonical framework for CF mMIMO, where the essential details (i.e., transmission procedure and mathematical system model) are discussed. Next, we provide a deep look at the resource allocation and signal processing problems related to CF mMIMO and survey the state-of-the-art schemes and algorithms. After that, we discuss the practical issues when implementing CF mMIMO, including fronthaul limitations and hardware impairment. Potential future directions of CF mMIMO research are then highlighted. We conclude this paper with a summary of the key lessons learned in this field. The objective of this paper is to provide a starting point for anyone who wants to conduct research on CF mMIMO for future wireless networks.
翻译:在过去几十年里,移动数据流量呈指数式增长,这得益于网络基础设施的密度增加(即超临界网络(UDN))和(或)每个接入点(AP)(即大量多投入多输出量(MIMO))的活天线数量增加。然而,无论是UDN还是MMFIMO都无法满足第六代(6G)无线通信不断增长的数据率需求,这分别是由于细胞间干扰和高质服务差异造成的。无细胞(CF)MMIIMO(将UDN的最佳电流处理速度与MIMIMIMO相结合)被视为解决这一问题的关键解决方案。在这种系统中,每个用户设备都得到一组首选的AP(即大量多投入多输出量多输出量(MIMIMO)的多输出量(mMIM)数据量增加。在本文中,我们对CFMMM(MMM)系统的最新模型和深度(我们首先介绍了改善用户-历史数据处理方式的意义和挑战, 包括MICMM(M)的引入基础数据率(MFMMM)数据比率(我们随后进行相关数据)的流程,然后,我们可以提供相关数据。