Fifty Years of MIMO Detection: The Road to Large-Scale MIMOs

By Shaoshi Yang, Lajos Hanzo. IEEE Communications Surveys and Tutorials, 2015

772 citations recorded by the source corpus.

The emerging massive/large-scale multiple-input multiple-output (LS-MIMO) systems that rely on very large antenna arrays have become a hot topic of wireless communications. Compared to multi-antenna aided systems being built at the time of this writing, such as the long-term evolution (LTE) based fourth generation (4G) mobile communication system which allows for up to eight antenna elements at the base station (BS), the LS-MIMO system entails an unprecedented number of antennas, say 100 or more, at the BS. The huge leap in the number of BS antennas opens the door to a new research field in communication theory, propagation and electronics, where random matrix theory begins to play a dominant role. Interestingly, LS-MIMOs also constitute a perfect example of one of the key philosophical principles of the Hegelian Dialectics, namely, that “quantitative change leads to qualitative change.” In this treatise, we provide a recital on the historic heritages and novel challenges facing LS-MIMOs from a detection perspective. First, we highlight the fundamentals of MIMO detection, including the nature of co-channel interference (CCI), the generality of the MIMO detection problem, the received signal models of both linear memoryless MIMO channels and dispersive MIMO channels exhibiting memory, as well as the complex-valued versus real-valued MIMO system models. Then, an extensive review of the representative MIMO detection methods conceived during the past 50 years (1965-2015) is presented, and relevant insights as well as lessons are inferred for the sake of designing complexity-scalable MIMO detection algorithms that are potentially applicable to LS-MIMO systems. Furthermore, we divide the LS-MIMO systems into two types, and elaborate on the distinct detection strategies suitable for each of them. The type-I LS-MIMO corresponds to the case where the number of active users is much smaller than the number of BS antennas, which is currently the mainstream definition of LS-MIMO. The type-II LS-MIMO corresponds to the case where the number of active users is comparable to the number of BS antennas. Finally, we discuss the applicability of existing MIMO detection algorithms in LS-MIMO systems, and review some of the recent advances in LS-MIMO detection.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/0c3c1a4b60a2c022e7c33bcef898d6c30526395e

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Citations in the corpus

Massive MIMO Detection Techniques: A Survey (2019, methodology): “Massive MIMO has become a hot research topic in last few years, and, hence, several survey papers related to massive MIMO systems have been published [11], [12], [13], [14], [15], [16].”
Model-Driven Deep Learning for MIMO Detection (2020, background): “To obtain the beneﬁts of MIMO [2], efﬁcient channel estimation and signal detection algorithms, which balance performance and complexity, are essential in receiver design and have arouse a series of research [3]–[7].”
Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning (2020, neutral): “RECENT years have witnessed the successful deployment of massive multiple input multiple output (MassiveMIMO) in the fifth generation (5G) wireless communication systems, as a promising approach to support massive number of users at high data rate, low latency and secure transmission simultaneously ”