Massive MIMO for Next Generation Wireless Systems

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 constantmodulus 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.

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 constantmodulus 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.

We consider the problem of uplink signal detection in scalable cell-free mMIMO (CF-mMIMO) systems subject to limited fronthaul link capacity and highly correlated channel conditions. Unlike centralized MIMO systems, in which all receive antennas are placed at a central access point (CAP), in the CF-mMIMO architecture the CAP serving a given area also uses information (i.e. channel estimates and receive signals) collected by a set of surrounding access points (APs). For such a scenario, two new robust receivers are designed, which can combat the effects of limited fronthaul capacity by leveraging knowledge of the heteroscedastic covariance of the resulting effective noise. The first receiver, which has a higher complexity but yields the best performance, is based on an expectation propagation (EP) approach, while the second employs the effective noise heteroscedastic covariance in a generalized least squares (GLS) variation of the maximum likelihood (ML) detection problem. Simulation results confirm the efficacy of both proposed receivers, which are further employed to empirically study the optimum distribution of antennas among the CAP and APs. INDEX TERMS Massive MIMO, cell-free MIMO, limited feedback, robust receiver design.

This is a tutorial on current techniques that use a huge number of antennas in intelligent reflecting surfaces (IRS), large intelligent surfaces (LIS), and radio stripes (RS), highlighting the similarities, differences, advantages, and drawbacks. A comparison between IRS, LIS, and RS is performed in terms of the implementation and capabilities, in the form of a tutorial. We begin by introducing the IRS, LIS, and RS as promising technologies for 6 G wireless technology. Then, we will look at how the three notions are applied in wireless networks. We discuss various performance indicators and methodologies for characterizing and improving the performance of IRS, LIS, and RS-assisted wireless networks. We cover rate maximization, power consumption reduction, and cost implementation concerns in order to take advantage of the performance increase. Furthermore, we extend the discussion to some cases of emerging use. In the description of the three concepts, IRS-assisted communication was ...

Cloud radio access network (C-RAN) systems consisting of remote radio heads (RRHs) densely distributed in a coverage area and connected by optical fibers to a cloud infrastructure with large computational capabilities, have the potential to meet the ambitious objectives of next generation mobile networks. Actual implementations of C-RANs tackle fundamental technical and economic challenges. In this article, we present an end-to-end (E2E) solution for practically implementable C-RANs by providing innovative solutions to key issues such as the design of cost-effective hardware and powereffective signals for RRHs, efficient design and distribution of data and control traffic for coordinated communications and conception of a flexible and elastic architecture supporting dynamic allocation of both the densely distributed RRHs and the centralized processing resources in the cloud to create virtual base-stations (BSs). More specifically, we propose a novel antenna array architecture called load-controlled parasitic antenna array (LCPAA) where multiple antennas are fed by a single RF chain. Energy and spectral-efficient modulation as well as signaling schemes that are easy to implement are also provided. Additionally, the design presented for the fronthaul (FH) enables flexibility and elasticity in resource allocation to support BS virtualization. A layered-design of information control for the proposed E2E solution is presented. The feasibility and effectiveness of such LCPAA-enabled C-RAN system setup has been validated through an over-the-air (OTA) demonstration.

The fast-moving world relies on intelligent connected networks to support the numerous applications of the expanded Internet-of-Things (IoT). The evolving communication requirements of this connected world require a new sixth generation (6G) radio to enable intelligent interaction with the massive number of connected objects. The energy management of billions of connected devices supporting massive Internet-of-Things (IoT) applications is the main challenge. These IoT devices and connected nodes are energy limited, and hence, energy-aware solutions are needed to enable seamless information flow between these communicating nodes. This paper presents an intelligent network solution for improved energy efficiency in a 6G-enabled expanded IoT network. A cell-free massive multiple input multiple output (mMIMO) technology is utilized for maximum energy efficiency with optimum network resource allocation. A practical power consumption model is proposed for the designed network topology whi...

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 constantmodulus 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.

Large-scale massive MIMO network deployments can provide higher spectral efficiency and better coverage for future communication systems like 5G. Due to the large number of antennas at the base station, the system achieves stable channel quality and spatially separable channels to the different users. In this paper, linear, planar, circular and cylindrical arrays are used in the evaluation of a large-scale multi-cell massive MIMO network. The system-level performance is predicted using two different kinds of channel models. First, a ray-based deterministic tool is utilized in a real North American city environment. Second, an independent and identically distributed (i.i.d.) Rayleigh fading channel model is considered, as often used in previously published massive MIMO studies. The analysis is conducted in a 16-macrocell network with outdoor and randomly distributed users. It is shown that the array configuration has a large impact on the throughput statistics. Although the systemlevel performance with i.i.d. Rayleigh fading can be close to the deterministic prediction in some situations (e.g., with large linear arrays), significant differences are noticed when considering other types of arrays.

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.

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.
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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.

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.