Massive MIMO for Next Generation Wireless Systems

Distributed antenna system (DAS) is a highly diversified concept that can help lower radiated power, maximize coverage, and improve spectral efficiency. Yet, the most exciting feature of DAS is the distribution of remote radio heads (RRH) or antennas over a wide geographical area, which allows the use of less complex signal processing techniques resulting in the reduction of systems’ size and cost. These features of DAS were utilized in network MIMO, distributed MIMO, multi-cell MIMO, and distributed-input distributed-output (DIDO) systems to move away from the centralized approach as in conventional multi-user and massive MIMO systems. In this tutorial paper, we first talk about MIMO systems with their relevant technologies by categorizing them into two main approaches according to their working principles and systematic architecture; the first approach is based on a centralized (or collocated) architecture, whereas the second is based on a decentralized (or distributed) architecture. We then deep dive into all the different technologies related to these architectures one by one and explain each in detail with much more focus on the similarities and differences between them. After that, we exposition the main target of this study, which is to answer the following question "Is cell-free massive MIMO (CF-MMIMO) equivalent to the already existing and commercialized pCell technology?".  To confidently answer this question, we comprehensively and deeply study the two technologies considering the differences in their system models and look into their conceptual formulations  as well as their respective channel models. More particularly, we detail CF-MMIMO and pCell architectures while giving special attention to pCell's SDR wireless platform and stating why it is crucial for the current wireless systems. Then, we present the understandings that are acquired after careful observations and provide recommendations based upon them. In short, this study aims to focus the reader attention on the fact that all the distributed and cell-free MIMO-related technologies work on the same principle, and the way these systems are built makes them fundamentally equivalent, yet, they are displayed quite differently using different terminologies and perspectives, which can cause confusion to readers and end up misleading future researchers.
============
WISLAB (wislabi.com/solutions) offers solutions for building and deploying fully secure, cloud-based, and low-cost end-to-end 4G/5G networks along with providing consultations on helping companies reduce their networks CAPEX/OPEX cost and determine which solutions are best suited for their needs and use cases.

By exploiting large antenna arrays, massive MIMO (multiple input multiple output) systems can greatly increase spectral and energy efficiency over traditional MIMO systems. However, increasing the number of antennas at the base station (BS) makes the uplink joint channel estimation and data detection (JED) challenging in massive MIMO systems. In this paper, we consider the JED problem for massive SIMO (single input multiple output) wireless systems, which is a special case of wireless systems with large antenna arrays. We propose exact Generalized Likelihood Ratio Test (GLRT) optimal JED algorithms with low expected complexity, for both constant-modulus and nonconstant-modulus constellations. We show that, despite the large number of unknown channel coefficients, the expected computational complexity of these algorithms is polynomial in channel coherence time (T) and the number of receive antennas (N), even when the number of receive antennas grows polynomially in the channel coherence time (N = O(T 11) suffices to guarantee an expected computational complexity cubic in T and linear in N). Simulation results show that the GLRT-optimal JED algorithms achieve significant performance gains (up to 5 dB improvement in energy efficiency) with low computational complexity. Index Terms-Joint channel estimation and data detection, massive SIMO, generalized likelihood ratio test, tree search algorithm.

Multi-user MIMO offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified because every active terminal utilizes all of the time-frequency bins. However, multi-user MIMO, as originally envisioned, with roughly equal numbers of service antennas and terminals and frequency-division duplex operation, is not a scalable technology. Massive MIMO (also known as large-scale antenna systems, very large MIMO, hyper MIMO, full-dimension MIMO, and ARGOS) makes a clean break with current practice through the use of a large excess of service antennas over active terminals and time-division duplex operation.

Multiuser Multiple-Input Multiple-Output (MIMO) offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified because every active terminal utilizes all of the time-frequency bins. However, multiuser MIMO, as originally envisioned with roughly equal numbers of service-antennas and terminals and frequency division duplex operation, is not a scalable technology. Massive MIMO (also known as "Large-Scale Antenna Systems", "Very Large MIMO", "Hyper MIMO", "Full-Dimension MIMO" and "ARGOS") makes a clean break with current practice through the use of a large excess of service-antennas over active terminals and time division duplex operation. Extra antennas help by focusing energy into ever-smaller regions of space to bring huge improvements in throughput and radiated energy efficiency. Other benefits of massive MIMO include the extensive use of inexpensive low-power components, reduced latency, simplification of the media access control (MAC) layer, and robustness to intentional jamming. The anticipated throughput depend on the propagation environment providing asymptotically orthogonal channels to the terminals, but so far experiments have not disclosed any limitations in this regard. While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly-joined terminals, the exploitation of extra degrees of freedom provided by the excess of service-antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios. This paper presents an overview of the massive MIMO concept and of contemporary research on the topic. 1 Background: MultiUser MIMO Maturing MIMO, Multiple-Input Multiple Output, technology relies on multiple antennas to simultaneously transmit multiple streams of data in wireless communication systems. When MIMO is used to communicate with several terminals at the same time, we speak of multiuser MIMO. Here, we just say MU-MIMO for short.

Multiuser Multiple-Input Multiple-Output (MIMO) offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified because every active terminal utilizes all of the time-frequency bins. However, multiuser MIMO, as originally envisioned with roughly equal numbers of service-antennas and terminals and frequency division duplex operation, is not a scalable technology. Massive MIMO (also known as "Large-Scale Antenna Systems", "Very Large MIMO", "Hyper MIMO", "Full-Dimension MIMO" and "ARGOS") makes a clean break with current practice through the use of a large excess of service-antennas over active terminals and time division duplex operation. Extra antennas help by focusing energy into ever-smaller regions of space to bring huge improvements in throughput and radiated energy efficiency. Other benefits of massive MIMO include the extensive use of inexpensive low-power components, reduced latency, simplification of the media access control (MAC) layer, and robustness to intentional jamming. The anticipated throughput depend on the propagation environment providing asymptotically orthogonal channels to the terminals, but so far experiments have not disclosed any limitations in this regard. While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly-joined terminals, the exploitation of extra degrees of freedom provided by the excess of service-antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios. This paper presents an overview of the massive MIMO concept and of contemporary research on the topic. 1 Background: MultiUser MIMO Maturing MIMO, Multiple-Input Multiple Output, technology relies on multiple antennas to simultaneously transmit multiple streams of data in wireless communication systems. When MIMO is used to communicate with several terminals at the same time, we speak of multiuser MIMO. Here, we just say MU-MIMO for short.