Multi-user 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, 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. 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: Multi-User 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.

This thesis focuses on advanced signal processing techniques for multicarrier modulation, in particular, orthogonal frequency division multiplexing (OFDM). OFDM promises a substantial increase in data rate and robustness against the frequency selectivity of multipath channels. For coherent detection, channel estimation is essential for receiver design. In this thesis, we will present a receiver design where the channel estimator exploits the sparse nature of the physical channel. We present the most popular subspace algorithm from the array processing literature, namely root-MUSIC, recent sparse identification algorithms in the form of orthogonal matching pursuit (OMP) and basis pursuit (BP), and a hybrid method called path identification (PI) algorithm which is the main contribution of this thesis. We also compare the performance of these estimators with that of the conventional estimators such as least-squares (LS) estimator and linear minimum-mean-squares estimator (LMMSE).iii

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In this paper, we consider the potential of data-transmission in a system with a massive number of radiating and sensing elements, thought of as a contiguous surface of electromagnetically active material. We refer to this as a large intelligent surface (LIS). The "LIS" is a newly proposed concept, which conceptually goes beyond contemporary massive MIMO technology, that arises from our vision of a future where man-made structures are electronically active with integrated electronics and wireless communication making the entire environment "intelligent".We firstly consider capacities of single-antenna autonomous terminals communicating to the LIS where the entire surface is used as a receiving antenna array. Under the condition that the surfacearea is sufficiently large, the received signal after a matched-filtering (MF) operation can be closely approximated by a sinc-function-like intersymbol interference (ISI) channel. Secondly, we analyze the capacity per square meter (m 2 ) deployed surface,Ĉ, that is achievable for a fixed transmit power per volume-unit,P ; the volume-unit can be m, m 2 , and m 3 depending on the scenario under investigation. As terminal-density increases, the limit ofĈ achieved when the wavelength λ approaches zero isP /(2N 0 )[nats/s/Hz/volume-unit], where N 0 is the spatial power spectral density (PSD) of the additive white Gaussian noise (AWGN). Moreover, we also show that the number of independent signal dimensions per m deployed surface is 2/λ for one-dimensional terminal-deployment, and π/λ 2 per m 2 for two and three dimensional terminal-deployments. Thirdly, we consider implementations of the LIS in the form of a grid of conventional antenna elements and show that, the sampling lattice that minimizes the surface-area of the LIS and simultaneously obtains one signal space dimension for every spent antenna is the hexagonal lattice. Lastly, we extensively discuss the design of the state-of-the-art low-complexity channel shortening (CS) demodulator for data-transmission with the LIS.The authors are with the P 2N 0 [nats/s/Hz/volume-unit], whereP is the transmit power per volume-unit and N 0 is the spatial power spectral density (PSD) of additive white Gaussian noise (AWGN). In particular, we show

Massive MIMO, also known as very-large MIMO or large-scale antenna systems, is a new technique that potentially can offer large network capacities in multi-user scenarios. With a massive MIMO system, we consider the case where a base station equipped with a large number of antenna elements simultaneously serves multiple single-antenna users in the same time-frequency resource. So far, investigations are mostly based on theoretical channels with independent and identically distributed (i.i.d.) complex Gaussian coefficients, i.e., i.i.d. Rayleigh channels. Here, we investigate how massive MIMO performs in channels measured in real propagation environments. Channel measurements were performed at 2.6 GHz using a virtual uniform linear array (ULA) which has a physically large aperture, and a practical uniform cylindrical array (UCA) which is more compact in size, both having 128 antenna ports. Based on measurement data, we illustrate channel behavior of massive MIMO in three representative propagation conditions, and evaluate the corresponding performance. The investigation shows that the measured channels, for both array types, allow us to achieve performance close to that in i.i.d. Rayleigh channels. It is concluded that in real propagation environments we have characteristics that can allow for efficient use of massive MIMO, i.e., the theoretical advantages of this new technology can also be harvested in real channels.Index Terms-Massive MIMO, very-large MIMO, multi-user MIMO, channel measurements 1536-1276 (c)

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Abstract-Massive MIMO can greatly increase both spectral and transmit-energy efficiency. This is achieved by allowing the number of antennas and RF chains to grow very large. However, the challenges include high system complexity and hardware energy consumption. Here we investigate the possibilities to reduce the required number of RF chains, by performing antenna selection. While this approach is not a very effective strategy for theoretical independent Rayleigh fading channels, a substantial reduction in the number of RF chains can be achieved for real massive MIMO channels, without significant performance loss. We evaluate antenna selection performance on measured channels at 2.6 GHz, using a linear and a cylindrical array, both having 128 elements. Sum-rate maximization is used as the criterion for antenna selection. A selection scheme based on convex optimization is nearly optimal and used as a benchmark. The achieved sum-rate is compared with that of a very simple scheme that selects the antennas with the highest received power. The power-based scheme gives performance close to the convex optimization scheme, for the measured channels. This observation indicates a potential for significant reductions of massive MIMO implementation complexity, by reducing the number of RF chains and performing antenna selection using simple algorithms.

A new approach to low-complexity channel estimation in orthogonal-frequency division multiplexing (OFDM) systems is proposed. A lowrank approximation is applied to a linear minimum mean-squared error (LMMSE) estimator that uses the frequency correlation of the channel. By using the singular-value decomposition (SVD) an optimal low-rank estimator is derived, where performance is essentially preservedeven for low computational complexities. A fixed estimator, with nominal values for channel correlation and signalto-noise ratio (SNR), is analysed. Analytical meansquared error (MSE) and symbol-error rates (SER) are presented for a 16-QAM OFDM system.