Massive MIMO channels - measurements and models

Spatial multiplexing using Massive MIMO has been shown to have very promising properties, including large gains in spectral efficiency and several orders of magnitude lower transmit power, as compared to today's access schemes. The properties of massive MIMO have been studied mostly for theoretical channels with independent and identically distributed (i.i.d.) complex Gaussian coefficients. To efficiently evaluate massive MIMO in more realistic scenarios, we need channel models that capture important massive MIMO channel characteristics. We pursue this by analyzing measurement data from a measurement campaign in the 2.6 GHz frequency range, using a physically large array with 128 elements. Key propagation characteristics are identified from the measurements. We use the cluster-based COST 2100 MIMO channel model as a basis, and propose an extension to include those important propagation properties for massive MIMO. Statistical models of the total number of clusters, their visibility regions and visibility gains at the base station side are found based on the measurement data

Designing the Undersea Internet of Things (IoT) and Machine-to-Machine (M2M) Communications Using UnderWater Acoustic MIMO Networks

This review paper tries to assess the spectral-efficient (SE) and energy-efficient (EE) performance of underwater acoustic multiple-input multiple-output (UWA/MIMO) networks. Since UWA/MIMO networks define the cutting-edge communications platform of the future’s undersea IoT and M2M networks, the factors that influence their SE and EE performance are thoroughly examined in this paper.The contribution of this paper is three-fold. First, the performance of UWA/MIMO networks is studied with regard to appropriate transmission, SE and EE metrics. The SE and EE performance of these networks drastically depends on the used frequency band, the transmitted power, the MIMO scheme properties, the power consumption profile of the deployed UWA system equipment and the topological characteristics of MIMO configurations. In order to achieve the transition from traditional UWA single-input single-output (UWA/SISO) networks to UWA/MIMO networks, a new singular value decomposition MIMO (SVD/MIMO) module, which also permits the theoretical computation of the aforementioned transmission, SE and EE metrics in UWA networks, is first presented. Second, based on the aforementioned transmission, SE and EE metrics, a SE/EE trade-off relation is proposed in order to investigate the combined SE and EE performance of UWA/MIMO networks. On the basis of this SE/EE trade-off relation, it is first revealed that today’s UWA system equipment cannot support the further IoT broadband exploitation with satisfactory EE performance. Third, the concepts of multi-hop UWA communications and standard UWA topologies are outlined and promoted so that further SE and EE improvement can concurrently occur. These concepts are quantitatively validated by the SE and EE metrics as well as the SE/EE trade-off curves.Based on the findings of this paper, suitable transmitted power levels and better design of UWA/MIMO configurations are promoted so that: (i) SE and EE requirements can be satisfied at will; and (ii) EE-oriented high-bitrate M2M communications network design can be established.Citation: Lazaropoulos, A. G. (2016). "Designing the Undersea Internet of Things (IoT) and Machine-to-Machine (M2M) Communications Using UnderWater Acoustic MIMO Networks." Trends in Renewable Energy, 2(1), 13-50. DOI: 10.17737/tre.2016.2.1.001

Measurement Matrix Design for Compressive Sensing Based MIMO Radar

In colocated multiple-input multiple-output (MIMO) radar using compressive sensing (CS), a receive node compresses its received signal via a linear transformation, referred to as measurement matrix. The samples are subsequently forwarded to a fusion center, where an L1-optimization problem is formulated and solved for target information. CS-based MIMO radar exploits the target sparsity in the angle-Doppler-range space and thus achieves the high localization performance of traditional MIMO radar but with many fewer measurements. The measurement matrix is vital for CS recovery performance. This paper considers the design of measurement matrices that achieve an optimality criterion that depends on the coherence of the sensing matrix (CSM) and/or signal-to-interference ratio (SIR). The first approach minimizes a performance penalty that is a linear combination of CSM and the inverse SIR. The second one imposes a structure on the measurement matrix and determines the parameters involved so that the SIR is enhanced. Depending on the transmit waveforms, the second approach can significantly improve SIR, while maintaining CSM comparable to that of the Gaussian random measurement matrix (GRMM). Simulations indicate that the proposed measurement matrices can improve detection accuracy as compared to a GRMM

MIMO-OFDM Based Energy Harvesting Cooperative Communications Using Coalitional Game Algorithm

This document is the Accepted Manuscript version. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, we consider the problem of cooperative communication between relays and base station in an advanced MIMO-OFDM framework, under the assumption that the relays are supplied by electric power drawn from energy harvesting (EH) sources. In particular, we focus on the relay selection, with the goal to guarantee the required performance in terms of capacity. In order to maximize the data throughput under the EH constraint, we model the transmission scheme as a non-transferable coalition formation game, with characteristic function based on an approximated capacity expression. Then, we introduce a powerful mathematical tool inherent to coalitional game theory, namely: the Shapley value (Sv) to provide a reliable solution concept to the game. The selected relays will form a virtual dynamically-configuredMIMO network that is able to transmit data to destination using efficient space-time coding techniques. Numerical results, obtained by simulating the EH-powered cooperativeMIMO-OFDMtransmission with Algebraic Space-Time Coding (ASTC), prove that the proposed coalitional game-based relay selection allows to achieve performance very close to that obtained by the same system operated by guaranteed power supply. The proposed methodology is finally compared with some recent related state-of-the-art techniques showing clear advantages in terms of link performance and goodput.Peer reviewe

Why Does a Kronecker Model Result in Misleading Capacity Estimates?

Many recent works that study the performance of multi-input multi-output (MIMO) systems in practice assume a Kronecker model where the variances of the channel entries, upon decomposition on to the transmit and the receive eigen-bases, admit a separable form. Measurement campaigns, however, show that the Kronecker model results in poor estimates for capacity. Motivated by these observations, a channel model that does not impose a separable structure has been recently proposed and shown to fit the capacity of measured channels better. In this work, we show that this recently proposed modeling framework can be viewed as a natural consequence of channel decomposition on to its canonical coordinates, the transmit and/or the receive eigen-bases. Using tools from random matrix theory, we then establish the theoretical basis behind the Kronecker mismatch at the low- and the high-SNR extremes: 1) Sparsity of the dominant statistical degrees of freedom (DoF) in the true channel at the low-SNR extreme, and 2) Non-regularity of the sparsity structure (disparities in the distribution of the DoF across the rows and the columns) at the high-SNR extreme.Comment: 39 pages, 5 figures, under review with IEEE Trans. Inform. Theor

MIMO Interference Alignment Over Correlated Channels with Imperfect CSI

Interference alignment (IA), given uncorrelated channel components and perfect channel state information, obtains the maximum degrees of freedom in an interference channel. Little is known, however, about how the sum rate of IA behaves at finite transmit power, with imperfect channel state information, or antenna correlation. This paper provides an approximate closed-form signal-to-interference-plus-noise-ratio (SINR) expression for IA over multiple-input-multiple-output (MIMO) channels with imperfect channel state information and transmit antenna correlation. Assuming linear processing at the transmitters and zero-forcing receivers, random matrix theory tools are utilized to derive an approximation for the post-processing SINR distribution of each stream for each user. Perfect channel knowledge and i.i.d. channel coefficients constitute special cases. This SINR distribution not only allows easy calculation of useful performance metrics like sum rate and symbol error rate, but also permits a realistic comparison of IA with other transmission techniques. More specifically, IA is compared with spatial multiplexing and beamforming and it is shown that IA may not be optimal for some performance criteria.Comment: 21 pages, 7 figures, submitted to IEEE Transactions on Signal Processin

Matrix Completion in Colocated MIMO Radar: Recoverability, Bounds & Theoretical Guarantees

It was recently shown that low rank matrix completion theory can be employed for designing new sampling schemes in the context of MIMO radars, which can lead to the reduction of the high volume of data typically required for accurate target detection and estimation. Employing random samplers at each reception antenna, a partially observed version of the received data matrix is formulated at the fusion center, which, under certain conditions, can be recovered using convex optimization. This paper presents the theoretical analysis regarding the performance of matrix completion in colocated MIMO radar systems, exploiting the particular structure of the data matrix. Both Uniform Linear Arrays (ULAs) and arbitrary 2-dimensional arrays are considered for transmission and reception. Especially for the ULA case, under some mild assumptions on the directions of arrival of the targets, it is explicitly shown that the coherence of the data matrix is both asymptotically and approximately optimal with respect to the number of antennas of the arrays involved and further, the data matrix is recoverable using a subset of its entries with minimal cardinality. Sufficient conditions guaranteeing low matrix coherence and consequently satisfactory matrix completion performance are also presented, including the arbitrary 2-dimensional array case.Comment: 19 pages, 7 figures, under review in Transactions on Signal Processing (2013