Multiple-input multiple-output (MIMO) technology is maturing and is being incorporated into emerging wireless broadband standards like long-term evolution (LTE) [1]. For example, the LTE standard allows for up to eight antenna ports at the base station. Basically, the more antennas the transmitter/receiver is equipped with, and the more degrees of freedom that the propagation channel can provide, the better the performance in terms of data rate or link reliability. More precisely, on a quasi static channel where a code word spans across only one time and frequency coherence interval, the reliability of a point-to-point MIMO link scales according to Prob(link outage) ` SNR-ntnr where nt and nr are the numbers of transmit and receive antennas, respectively, and signal-to-noise ratio is denoted by SNR. On a channel that varies rapidly as a function of time and frequency, and where circumstances permit coding across many channel coherence intervals, the achievable rate scales as min(nt, nr) log(1 + SNR). The gains in multiuser systems are even more impressive, because such systems offer the possibility to transmit simultaneously to several users and the flexibility to select what users to schedule for reception at any given point in time [2].

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Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays

Massive multiple-input multiple-output (MIMO) wireless communications refers to the idea equipping cellular base stations (BSs) with a very large number of antennas, and has been shown to potentially allow for orders of magnitude improvement in spectral and energy efficiency using relatively simple (linear) processing. In this paper, we present a comprehensive overview of state-of-the-art research on the topic, which has recently attracted considerable attention. We begin with an information theoretic analysis to illustrate the conjectured advantages of massive MIMO, and then we address implementation issues related to channel estimation, detection and precoding schemes. We particularly focus on the potential impact of pilot contamination caused by the use of non-orthogonal pilot sequences by users in adjacent cells. We also analyze the energy efficiency achieved by massive MIMO systems, and demonstrate how the degrees of freedom provided by massive MIMO systems enable efficient single-carrier transmission. Finally, the challenges and opportunities associated with implementing massive MIMO in future wireless communications systems are discussed.

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An Overview of Massive MIMO: Benefits and Challenges

Recent theoretical results describing the sum capacity when using multiple antennas to communicate with multiple users in a known rich scattering environment have not yet been followed with practical transmission schemes that achieve this capacity. We introduce a simple encoding algorithm that achieves near-capacity at sum rates of tens of bits/channel use. The algorithm is a variation on channel inversion that regularizes the inverse and uses a "sphere encoder" to perturb the data to reduce the power of the transmitted signal. This work is comprised of two parts. In this first part, we show that while the sum capacity grows linearly with the minimum of the number of antennas and users, the sum rate of channel inversion does not. This poor performance is due to the large spread in the singular values of the channel matrix. We introduce regularization to improve the condition of the inverse and maximize the signal-to-interference-plus-noise ratio at the receivers. Regularization enables linear growth and works especially well at low signal-to-noise ratios (SNRs), but as we show in the second part, an additional step is needed to achieve near-capacity performance at all SNRs.

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A vector-perturbation technique for near-capacity multiantenna multiuser communication-part I: channel inversion and regularization

Multiple transmit antennas in a downlink channel can provide tremendous capacity (i.e., multiplexing) gains, even when receivers have only single antennas. However, receiver and transmitter channel state information is generally required. In this correspondence, a system where each receiver has perfect channel knowledge, but the transmitter only receives quantized information regarding the channel instantiation is analyzed. The well-known zero-forcing transmission technique is considered, and simple expressions for the throughput degradation due to finite-rate feedback are derived. A key finding is that the feedback rate per mobile must be increased linearly with the signal-to-noise ratio (SNR) (in decibels) in order to achieve the full multiplexing gain. This is in sharp contrast to point-to-point multiple-input multiple-output (MIMO) systems, in which it is not necessary to increase the feedback rate as a function of the SNR

MIMO Broadcast Channels With Finite-Rate Feedback

There is considerable pressure to define the key requirements of 5G, develop 5G standards, and perform technology trials as quickly as possible. Normally, these activities are best done in series but there is a desire to complete these tasks in parallel so that commercial deployments of 5G can begin by 2020. 5G will not be an incremental improvement over its predecessors; it aims to be a revolutionary leap forward in terms of data rates, latency, massive connectivity, network reliability, and energy efficiency. These capabilities are targeted at realizing high-speed connectivity, the Internet of Things, augmented virtual reality, the tactile internet, and so on. The requirements of 5G are expected to be met by new spectrum in the microwave bands (3.3-4.2 GHz), and utilizing large bandwidths available in mm-wave bands, increasing spatial degrees of freedom via large antenna arrays and 3-D MIMO, network densification, and new waveforms that provide scalability and flexibility to meet the varying demands of 5G services. Unlike the one size fits all 4G core networks, the 5G core network must be flexible and adaptable and is expected to simultaneously provide optimized support for the diverse 5G use case categories. In this paper, we provide an overview of 5G research, standardization trials, and deployment challenges. Due to the enormous scope of 5G systems, it is necessary to provide some direction in a tutorial article, and in this overview, the focus is largely user centric, rather than device centric. In addition to surveying the state of play in the area, we identify leading technologies, evaluating their strengths and weaknesses, and outline the key challenges ahead, with research test beds delivering promising performance but pre-commercial trials lagging behind the desired 5G targets.

5G : A tutorial overview of standards, trials, challenges, deployment, and practice

Recent theoretical results describing the sum-capacity when using multiple antennas to communicate with multiple users in a known rich scattering environment have not yet been followed with practical transmission schemes that achieve this capacity. We introduce a simple encoding algorithm that achieves near-capacity at sum-rates of tens of bits/channel use. The algorithm is a variation on channel inversion that regularizes the inverse and uses a "sphere encoder" to perturb the data to reduce the energy of the transmitted signal. The paper is comprised of two parts. In this second part, we show that, after the regularization of the channel inverse introduced in the first part, a certain perturbation of the data using a "sphere encoder" can be chosen to further reduce the energy of the transmitted signal. The performance difference with and without this perturbation is shown to be dramatic. With the perturbation, we achieve excellent performance at all signal-to-noise ratios. The results of both uncoded and turbo-coded simulations are presented.

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A vector-perturbation technique for near-capacity multiantenna multiuser communication-part II: perturbation

Rapid temporal variations in wireless channels pose a significant challenge for space-time modulation and coding algorithms. This letter examines the performance degradation that results when time-varying flat fading is encountered when using trained and unitary space-time modulation. Performance is characterized for a channel having a constant specular component plus a time-varying diffuse component. A first-order autoregressive (AR) model is used to characterize diffuse channel coefficients that vary from symbol to symbol, and is shown to lead to an effective signal-to-noise ratio (SNR) that decreases with time. Differential modulation is shown to have an advantage in effective SNR over trained unitary modulation at high power. Simulation results are provided to support our analysis.

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Effective SNR for space-time modulation over a time-varying Rician channel

The primary issue in downlink beamforming for wireless communications is how to balance the need for high received signal power for each user against the interference produced by the signal at other points in the network. In this paper, we describe several approaches to this problem: channel inversion, regularized channel inversion, vector modulo pre-coding, channel block diagonalization and coordinated transmit/receive beamforming. While the basic idea behind these algorithms is the same, namely the use of channel information at the transmitter to predict and then counteract the interference produced at each node in the network, each of the algorithms is based on achieving a different performance objective. We compare the various goals of the above algorithms, and detail their respective advantages and disadvantages in terms of computational complexity, required transmit power, network throughput, and assumed receiver capabilities. The results of several simulation studies are presented to quantify these comparisons.

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Downlink transmit beamforming in multi-user MIMO systems

The application of MIMO processing techniques in channels that are shared among multiple users is a relatively new problem that is increasingly important as MIMO transmission is put into practical use. In this chapter we specifically consider the multi-user downlink, where a base station with multiple antennas transmits simultaneously to more than one user. We begin with an overview of some of the multi-user MIMO transmission schemes that have been proposed up to this point, then demonstrate how they might be expected to perform by applying the algorithms to measurement data from indoor and outdoor propagation environments. Specifically, we compare the number of simultaneous users the channel will support for the two different environments, the amount of separation of the users necessary to achieve maximum throughput, and the quality of channel information available to the base station when the users are mobile. In both environments, full multi-user diversity is achieved at relatively short distances on the order of one meter. The total number of simultaneous users in outdoor environments is limited compared to uncorrelated channels due to the relatively sparse multipath structure of the channel. The distances at which channel information becomes too old to be useful to the transmitter appears to be similar for both types of channels.

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Christian B. Peel

Papers

A vector-perturbation technique for near-capacity multiantenna multiuser communication-part I: channel inversion and regularization

A vector-perturbation technique for near-capacity multiantenna multiuser communication-part II: perturbation

Effective SNR for space-time modulation over a time-varying Rician channel

Downlink transmit beamforming in multi-user MIMO systems

Performance of Multi-User Spatial Multiplexing with Measured Channel Data