Showing papers on "Multi-user MIMO published in 2014"

Massive MIMO for next generation wireless systems

Abstract: 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. 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 extensive use of inexpensive low-power components, reduced latency, simplification of the MAC layer, and robustness against intentional jamming. The anticipated throughput depends 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 article presents an overview of the massive MIMO concept and contemporary research on the topic.

An Overview of Massive MIMO: Benefits and Challenges

Abstract: 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.

MIMO Precoding and Combining Solutions for Millimeter-Wave Systems

Abstract: Millimeter-wave communication is one way to alleviate the spectrum gridlock at lower frequencies while simultaneously providing high-bandwidth communication channels. MmWave makes use of MIMO through large antenna arrays at both the base station and the mobile station to provide sufficient received signal power. This article explains how beamforming and precoding are different in MIMO mmWave systems than in their lower-frequency counterparts, due to different hardware constraints and channel characteristics. Two potential architectures are reviewed: hybrid analog/digital precoding/combining and combining with low-resolution analog- to-digital converters. The potential gains and design challenges for these strategies are discussed, and future research directions are highlighted.

Millimeter-wave massive MIMO: the next wireless revolution?

Abstract: The combination of millimeter-wave communications, arrays with a massive number of antennas, and small cell geometries is a symbiotic convergence of technologies that has the potential to dramatically improve wireless access and throughput. This article outlines the benefits, challenges, and potential solutions associated with cellular networks that incorporate these technologies.

Mimo for millimeter-wave wireless communications: beamforming, spatial multiplexing, or both?

Abstract: The use of mmWave frequencies for wireless communications offers channel bandwidths far greater than previously available, while enabling dozens or even hundreds of antenna elements to be used at the user equipment, base stations, and access points. To date, MIMO techniques, such as spatial multiplexing, beamforming, and diversity, have been widely deployed in lower-frequency systems such as IEEE 802.11n/ac (wireless local area networks) and 3GPP LTE 4G cellphone standards. Given the tiny wavelengths associated with mmWave, coupled with differences in the propagation and antennas used, it is unclear how well spatial multiplexing with multiple streams will be suited to future mmWave mobile communications. This tutorial explores the fundamental issues involved in selecting the best communications approaches for mmWave frequencies, and provides insights, challenges, and appropriate uses of each MIMO technique based on early knowledge of the mmWave propagation environment.

Power Scaling of Uplink Massive MIMO Systems With Arbitrary-Rank Channel Means

Abstract: This paper investigates the uplink achievable rates of massive multiple-input multiple-output (MIMO) antenna systems in Ricean fading channels, using maximal-ratio combining (MRC) and zero-forcing (ZF) receivers, assuming perfect and imperfect channel state information (CSI). In contrast to previous relevant works, the fast fading MIMO channel matrix is assumed to have an arbitrary-rank deterministic component as well as a Rayleigh-distributed random component. We derive tractable expressions for the achievable uplink rate in the large-antenna limit, along with approximating results that hold for any finite number of antennas. Based on these analytical results, we obtain the scaling law that the users' transmit power should satisfy, while maintaining a desirable quality of service. In particular, it is found that regardless of the Ricean K-factor, in the case of perfect CSI, the approximations converge to the same constant value as the exact results, as the number of base station antennas,, grows large, while the transmit power of each user can be scaled down proportionally to. If CSI is estimated with uncertainty, the same result holds true but only when the Ricean K-factor is non-zero. Otherwise, if the channel experiences Rayleigh fading, we can only cut the transmit power of each user proportionally to 1 root M. In addition, we show that with an increasing Ricean K-factor, the uplink rates will converge to fixed values for both MRC and ZF receivers.

Downlink Training Techniques for FDD Massive MIMO Systems: Open-Loop and Closed-Loop Training With Memory

Abstract: The concept of deploying a large number of antennas at the base station, often called massive multiple-input multiple-output (MIMO), has drawn considerable interest because of its potential ability to revolutionize current wireless communication systems. Most literature on massive MIMO systems assumes time division duplexing (TDD), although frequency division duplexing (FDD) dominates current cellular systems. Due to the large number of transmit antennas at the base station, currently standardized approaches would require a large percentage of the precious downlink and uplink resources in FDD massive MIMO be used for training signal transmissions and channel state information (CSI) feedback. To reduce the overhead of the downlink training phase, we propose practical open-loop and closed-loop training frameworks in this paper. We assume the base station and the user share a common set of training signals in advance. In open-loop training, the base station transmits training signals in a round-robin manner, and the user successively estimates the current channel using long-term channel statistics such as temporal and spatial correlations and previous channel estimates. In closed-loop training, the user feeds back the best training signal to be sent in the future based on channel prediction and the previously received training signals. With a small amount of feedback from the user to the base station, closed-loop training offers better performance in the data communication phase, especially when the signal-to-noise ratio is low, the number of transmit antennas is large, or prior channel estimates are not accurate at the beginning of the communication setup, all of which would be mostly beneficial for massive MIMO systems.

Modulation Formats and Waveforms for 5G Networks: Who Will Be the Heir of OFDM?: An overview of alternative modulation schemes for improved spectral efficiency

Abstract: Fifth-generation (5G) cellular communications promise to deliver the gigabit experience to mobile users, with a capacity increase of up to three orders of magnitude with respect to current long-term evolution (LTE) systems There is widespread agreement that such an ambitious goal will be realized through a combination of innovative techniques involving different network layers At the physical layer, the orthogonal frequency division multiplexing (OFDM) modulation format, along with its multiple-access strategy orthogonal frequency division multiple access (OFDMA), is not taken for granted, and several alternatives promising larger values of spectral efficiency are being considered This article provides a review of some modulation formats suited for 5G, enriched by a comparative analysis of their performance in a cellular environment, and by a discussion on their interactions with specific 5G ingredients The interaction with a massive multiple-input, multiple-output (MIMO) system is also discussed by employing real channel measurements

Joint Spatial Division and Multiplexing for mm-Wave Channels

Abstract: Massive MIMO systems are well-suited for mm-Wave communications, as large arrays can be built with reasonable form factors, and the high array gains enable reasonable coverage even for outdoor communications. One of the main obstacles for using such systems in frequency-division duplex mode, namely, the high overhead for the feedback of channel state information (CSI) to the transmitter, can be mitigated by the recently proposed joint spatial division and multiplexing (JSDM) algorithm. In this paper, we analyze the performance of this algorithm in some realistic propagation channels that take into account the partial overlap of the angular spectra from different users, as well as the sparsity of mm-Wave channels. We formulate the problem of user grouping for two different objectives, namely, maximizing spatial multiplexing and maximizing total received power in a graph-theoretic framework. As the resulting problems are numerically difficult, we proposed (sub optimum) greedy algorithms as efficient solution methods. Numerical examples show that the different algorithms may be superior in different settings. We furthermore develop a new, “degenerate” version of JSDM that only requires average CSI at the transmitter and thus greatly reduces the computational burden. Evaluations in propagation channels obtained from ray tracing results, as well as in measured outdoor channels, show that this low-complexity version performs surprisingly well in mm-Wave channels.

Large-Scale MIMO Detection for 3GPP LTE: Algorithms and FPGA Implementations

Abstract: Large-scale (or massive) multiple-input multiple-out put (MIMO) is expected to be one of the key technologies in next-generation multi-user cellular systems based on the upcoming 3GPP LTE Release 12 standard, for example. In this work, we propose-to the best of our knowledge-the first VLSI design enabling high-throughput data detection in single-carrier frequency-division multiple access (SC-FDMA)-based large-scale MIMO systems. We propose a new approximate matrix inversion algorithm relying on a Neumann series expansion, which substantially reduces the complexity of linear data detection. We analyze the associated error, and we compare its performance and complexity to those of an exact linear detector. We present corresponding VLSI architectures, which perform exact and approximate soft-output detection for large-scale MIMO systems with various antenna/user configurations. Reference implementation results for a Xilinx Virtex-7 XC7VX980T FPGA show that our designs are able to achieve more than 600 Mb/s for a 128 antenna, 8 user 3GPP LTE-based large-scale MIMO system. We finally provide a performance/complexity trade-off comparison using the presented FPGA designs, which reveals that the detector circuit of choice is determined by the ratio between BS antennas and users, as well as the desired error-rate performance.

A flexible 100-antenna testbed for Massive MIMO

Abstract: Massive multiple-input multiple-output (MIMO) is one of the main candidates to be included in the fifth generation (5G) cellular systems. For further system development it is desirable to have real-time testbeds showing possibilities and limitations of the technology. In this paper we describe the Lund University Massive MIMO testbed — LuMaMi. It is a flexible testbed where the base station operates with up to 100 coherent radio-frequency transceiver chains based on software radio technology. Orthogonal Frequency Division Multiplex (OFDM) based signaling is used for each of the 10 simultaneous users served in the 20 MHz bandwidth. Real time MIMO precoding and decoding is distributed across 50 Xilinx Kintex-7 FPGAs with PCI-Express interconnects. The unique features of this system are: (i) high throughput processing of 384 Gbps of real time baseband data in both the transmit and receive directions, (ii) low-latency architecture with channel estimate to precoder turnaround of less than 500 micro seconds, and (iii) a flexible extension up to 128 antennas. We detail the design goals of the testbed, discuss the signaling and system architecture, and show initial measured results for a uplink Massive MIMO over-the-air transmission from four single-antenna UEs to 100 BS antennas.

Channel Estimation for OFDM

Abstract: Orthogonal frequency division multiplexing (OFDM) has been widely adopted in modern wireless communication systems due to its robustness against the frequency selectivity of wireless channels. For coherent detection, channel estimation is essential for receiver design. Channel estimation is also necessary for diversity combining or interference suppression where there are multiple receive antennas. In this paper, we will present a survey on channel estimation for OFDM. This survey will first review traditional channel estimation approaches based on channel frequency response (CFR). Parametric model (PM)-based channel estimation, which is particularly suitable for sparse channels, will be also investigated in this survey. Following the success of turbo codes and low-density parity check (LDPC) codes, iterative processing has been widely adopted in the design of receivers, and iterative channel estimation has received a lot of attention since that time. Iterative channel estimation will be emphasized in this survey as the emerging iterative receiver improves system performance significantly. The combination of multiple-input multiple-output (MIMO) and OFDM has been widely accepted in modern communication systems, and channel estimation in MIMO-OFDM systems will also be addressed in this survey. Open issues and future work are discussed at the end of this paper.

Low-Complexity End-to-End Performance Optimization in MIMO Full-Duplex Relay Systems

Abstract: In this paper, we deal with the deployment of full-duplex relaying in amplify-and-forward (AF) cooperative networks with multiple-antenna terminals. In contrast to previous studies, which focus on the spatial mitigation of the loopback interference (LI) at the relay node, a joint precoding/decoding design that maximizes the end-to-end (e2e) performance is investigated. The proposed precoding incorporates rank-1 zero-forcing (ZF) LI suppression at the relay node and is derived in closed-form by solving appropriate optimization problems. In order to further reduce system complexity, the antenna selection (AS) problem for full-duplex AF cooperative systems is discussed. We investigate different AS schemes to select a single transmit antenna at both the source and the relay, as well as a single receive antenna at both the relay and the destination. To facilitate comparison, exact outage probability expressions and asymptotic approximations of the proposed AS schemes are provided. In order to overcome zero-diversity effects associated with the AS operation, a simple power allocation scheme at the relay node is also investigated and its optimal value is analytically derived. Numerical and simulation results show that the joint ZF-based precoding significantly improves e2e performance, while AS schemes are efficient solutions for scenarios with strict computational constraints.

Energy Beamforming with One-Bit Feedback

Abstract: Multi-antenna or multiple-input multiple-output (MIMO) techniques are appealing to enhance the transmission efficiency and range for radio frequency (RF) signal enabled wireless energy transfer (WET). In order to reap the energy beamforming gain in MIMO WET, acquiring the channel state information (CSI) at the energy transmitter (ET) is an essential task. This task is particularly challenging, since existing channel training and feedback methods used for communication receivers may not be implementable at the energy receiver (ER) due to its hardware limitation. To tackle this problem, we consider in this paper a multiuser MIMO WET system, and propose a new channel learning method that requires only one feedback bit from each ER to the ET per feedback interval. Specifically, each feedback bit indicates the increase or decrease of the harvested energy by each ER in the present as compared to the previous intervals, which can be measured without changing the existing structure of the ER. Based on such feedback information, the ET adjusts transmit beamforming in subsequent training intervals and at the same time obtains improved estimates of the MIMO channels to different ERs by applying an optimization technique called analytic center cutting plane method (ACCPM). For the proposed ACCPM based channel learning algorithm, we analyze its worst-case convergence, from which it is revealed that the algorithm is able to estimate multiuser MIMO channels simultaneously without reducing the analytic convergence speed. Also, we provide extensive simulations to show its performances in terms of both convergence speed and energy transfer efficiency.

Optical fiber-based distributed radio frequency (rf) antenna systems supporting multiple-input, multiple-output (mimo) configurations, and related components and methods

Abstract: Optical fiber-based distributed antenna systems that support multiple-input, multiple-output (MIMO) antenna configurations and communications. Embodiments disclosed herein include optical fiber-based distributed antenna system that can be flexibly configured to support or not support MIMO communications configurations. In one embodiment, first and second MIMO communication paths are shared on the same optical fiber using frequency conversion to avoid interference issues, wherein the second communication path is provide to a remote extension unit to remote antenna unit. In another embodiment, the optical fiber-based distributed antenna systems may be configured to allow to provide MIMO communication configurations with existing components. Existing capacity of system components are employed to create second communication paths for MIMO configurations, thereby reducing overall capacity, but allowing avoidance of frequency conversion components and remote extension units.

Systems and methods for a configuration web service to provide configuration of a wireless power transmitter within a wireless power transmission system

Abstract: A system and method for a configuration web service to provide configuration of a wireless power transmitter within a wireless power transmission system is disclosed. The wireless power transmitter configuration network may include at least one wireless power transmitter connected to an energy power source and at least one computer device which may communicate with each other through wireless or wired network connections, where each wireless power transmitter may include a distributed system database coupled to web service software. The operator/user may browse the specific URL or IP address associated with the configuration web page, which the wireless power transmitter may host and render, to specify the wireless power transmitter's configuration information. The configuration web service may allow users to perform configurations with and without the need of an external network service in range and regardless of the physical location of the wireless power transmitter.

Linear Precoding Based on Polynomial Expansion: Large-Scale Multi-Cell MIMO Systems

Abstract: Large-scale MIMO systems can yield a substantial improvements in spectral efficiency for future communication systems. Due to the finer spatial resolution and array gain achieved by a massive number of antennas at the base station, these systems have shown to be robust to inter-user interference and the use of linear precoding appears to be asymptotically optimal. However, from a practical point of view, most precoding schemes exhibit prohibitively high computational complexity as the system dimensions increase. For example, the near-optimal regularized zero forcing (RZF) precoding requires the inversion of a large matrix. To solve this issue, we propose in this paper to approximate the matrix inverse by a truncated polynomial expansion (TPE), where the polynomial coefficients are optimized to maximize the system performance. This technique has been recently applied in single cell scenarios and it was shown that a small number of coefficients is sufficient to reach performance similar to that of RZF, while it was not possible to surpass RZF. In a realistic multi-cell scenario involving large-scale multi-user MIMO systems, the optimization of RZF precoding has, thus far, not been feasible. This is mainly attributed to the high complexity of the scenario and the non-linear impact of the necessary regularizing parameters. On the other hand, the scalar coefficients in TPE precoding give hope for possible throughput optimization. To this end, we exploit random matrix theory to derive a deterministic expression of the asymptotic signal-to-interference-and-noise ratio for each user based on channel statistics. We also provide an optimization algorithm to approximate the coefficients that maximize the network-wide weighted max-min fairness. The optimization weights can be used to mimic the user throughput distribution of RZF precoding. Using simulations, we compare the network throughput of the proposed TPE precoding with that of the suboptimal RZF scheme and show that our scheme can achieve higher throughput using a TPE order of only 5.

Designing multi-user MIMO for energy efficiency: When is massive MIMO the answer?

Abstract: Assume that a multi-user multiple-input multiple-output (MIMO) communication system must be designed to cover a given area with maximal energy efficiency (bits/Joule). What are the optimal values for the number of antennas, active users, and transmit power? By using a new model that describes how these three parameters affect the total energy efficiency of the system, this work provides closed-form expressions for their optimal values and interactions. In sharp contrast to common belief, the transmit power is found to increase (not decrease) with the number of antennas. This implies that energy efficient systems can operate at high signal-to-noise ratio (SNR) regimes in which the use of interference-suppressing precoding schemes is essential. Numerical results show that the maximal energy efficiency is achieved by a massive MIMO setup wherein hundreds of antennas are deployed to serve relatively many users using interference-suppressing regularized zero-forcing precoding.

Exploiting spatial sparsity for estimating channels of hybrid MIMO systems in millimeter wave communications

Abstract: Hybrid multiple input multiple output (MIMO) systems consist of an analog beamformer with large antenna arrays followed by a digital MIMO processor. Channel estimation for hybrid MIMO systems in millimeter wave (mm-wave) communications is challenging because of the large antenna array and the low signal-to-noise ratio (SNR) before beamforming. In this paper, we propose an open-loop channel estimator for mm-wave hybrid MIMO systems exploiting the sparse nature of mm-wave channels. A sparse signal recovery problem is formulated for channel estimation and solved by the orthogonal matching pursuit (OMP) based methods. A modification of the OMP algorithm, called the multi-grid (MG) OMP, is proposed. It is shown that the MG-OMP can significantly reduce the computational load of the OMP method. A process for designing the training beams is also developed. Specifically, given the analog training beams the baseband processor for beam training is designed. Simulation results demonstrate the advantage of the OMP based methods over the conventional least squares (LS) method and the efficiency of the MG-OMP over the original OMP.

Large MIMO Systems

Abstract: Large MIMO systems, with tens to hundreds of antennas, are a promising emerging communication technology This book provides a unique overview of this technology, covering the opportunities, engineering challenges, solutions and state of the art of large MIMO test beds There is in-depth coverage of algorithms for large MIMO signal processing, based on meta-heuristics, belief propagation and Monte Carlo sampling techniques, and suited for large MIMO signal detection, precoding and LDPC code designs The book also covers the training requirement and channel estimation approaches in large-scale point-to-point and multi-user MIMO systems; spatial modulation is also included Issues like pilot contamination and base station cooperation in multi-cell operation are addressed A detailed exposition of MIMO channel models, large MIMO channel sounding measurements in the past and present, and large MIMO test beds is also presented An ideal resource for academic researchers, next generation wireless system designers and developers, and practitioners in wireless communications

Massive MIMO Channel Models: A Survey

Abstract: The exponential traffic growth of wireless communication networks gives rise to both the insufficient network capacity and excessive carbon emissions. Massive multiple-input multiple-output (MIMO) can improve the spectrum efficiency (SE) together with the energy efficiency (EE) and has been regarded as a promising technique for the next generation wireless communication networks. Channel model reflects the propagation characteristics of signals in radio environments and is very essential for evaluating the performances of wireless communication systems. The purpose of this paper is to investigate the state of the art in channel models of massive MIMO. First, the antenna array configurations are presented and classified, which directly affect the channel models and system performance. Then, measurement results are given in order to reflect the main properties of massive MIMO channels. Based on these properties, the channel models of massive MIMO are studied with different antenna array configurations, which can be used for both theoretical analysis and practical evaluation.

Enabling technologies and architectures for 5G wireless

Abstract: The proliferation of smart devices and the resulting exponential growth in data traffic has increased the need for higher capacity wireless networks. The cellular systems industry is envisioning an increase in network capacity by a factor of 1000 over the next decade to meet this traffic demand. In addition, with the emergence of Internet of Things (IoT), billions of devices will be connected and managed by wireless networks. Future networks must satisfy the above mentioned requirements with high energy efficiency and at low cost. Hence, the industry attention is now shifting towards the next set of innovations in architecture and technologies that will address capacity and service demands envisioned for 2020, which cannot be met only with the evolution of 4G systems. These innovations are expected to form the so called fifth generation wireless communications systems, or 5G. Candidate 5G solutions include i) higher densification of heterogeneous networks with massive deployment of small base stations supporting various Radio Access Technologies (RATs), ii) use of very large Multiple Input Multiple Output (MIMO) arrays, iii) use of millimeter Wave spectrum where larger wider frequency bands are available, iv) direct device to device (D2D) communication, and v) simultaneous transmission and reception, among others. In this paper, we present the main 5G technologies. We also discuss the network and device evolution towards 5G.

High SNR capacity of millimeter wave MIMO systems with one-bit quantization

Abstract: Millimeter wave (mmWave) is a viable technology for future cellular systems. With bandwidths on the order of a gigahertz, high-resolution analog-to-digital converters (ADCs) become a power consumption bottleneck. One solution is to employ very low resolution one-bit ADCs. This paper analyzes the flat fading multiple-input multiple-output (MIMO) channel with one-bit ADC. Bounds on the high signal-to-noise ratio (SNR) capacity are derived for the single-input multiple-output (SIMO) channel and the general MIMO channel. The results show how the number of paths, number of transmit antennas, and number of receive antennas impact the capacity at high SNR.

Electromagnetic Lens-Focusing Antenna Enabled Massive MIMO: Performance Improvement and Cost Reduction

Abstract: Massive multiple-input-multiple-output (MIMO) techniques have been recently advanced to tremendously improve the performance of wireless communication networks. However, the use of very large antenna arrays at the base stations brings new issues, such as the significantly increased hardware and signal processing costs. In order to reap the performance gains of massive MIMO and yet reduce its cost, this paper proposes a novel system design by integrating an electromagnetic (EM) lens with the large antenna array, termed the EM-lens enabled MIMO. The EM lens has the capability of focusing the power of an incident wave to a small area of the antenna array, whereas the location of the focal area varies with the angle of arrival (AoA) of the wave. Hence, in scenarios where the arriving signals from geographically separated users have different AoAs, the EM-lens enabled receiver provides two new benefits, namely, energy focusing and spatial interference rejection. By taking into account the effects of imperfect channel estimation via pilot-assisted training, in this paper, we analytically show that the average received signal-to-noise ratio in both the single-user and multiuser uplink transmissions can be improved by the EM-lens enabled system. Furthermore, we demonstrate that the proposed design makes it possible to considerably reduce the hardware and signal processing costs with only slight degradations in performance. To this end, two complexity/cost reduction schemes are proposed, which are small-MIMO processing with parallel receiver filtering applied over subgroups of antennas to reduce the computational complexity, and channel covariance based antenna selection to reduce the required number of radio frequency chains. Numerical results are provided to corroborate our analysis and show the great potential advantages of our proposed EM-lens enabled MIMO system for next generation cellular networks.

MIMO and beamforming solutions for 5G technology

Abstract: Multi-antenna technologies such as beamforming and Multiple-Input, Multiple-Output (MIMO) are anticipated to play a key role in “5G” systems, which are expected to be deployed in the year 2020 and beyond. With a class of 5G systems expected to be deployed in both cm-wave (3-30 GHz) and mm-wave (30-300 GHz) bands, the unique characteristics and challenges of those bands have prompted a revisiting of the design and performance tradeoffs associated with existing multi-antenna techniques in order to determine the preferred framework for deploying MIMO technology in 5G systems. In this paper, we discuss key implementation issues surrounding the deployment of transmit MIMO processing for 5G systems. We describe MIMO architectures where the transmit MIMO processing is implemented at baseband, RF, and a combination of RF and baseband (a hybrid approach). We focus on the performance and implementation issues surrounding several candidate techniques for multi-user-MIMO (MU-MIMO) transmission in the mm-wave bands.

Analysis and comparison of MIMO radar waveforms

Abstract: Choosing a proper waveform is a critical task for the implementation of multiple-input multiple-output (MIMO) radars. In addition to the general requirements for radar waveforms such as good resolution, low sidelobes, etc, MIMO radar waveforms also should possess good orthogonality. In this paper we give a brief overview of MIMO radar waveforms, which are classified into four categories: (1) time division multiple access (TDMA), (2) frequency division multiple access (FDMA), (3) Doppler division multiple access (DDMA), and (4) code division multiple access (CDMA). A special circulating MIMO waveform is also addressed The properties as well as application limitations of different waveforms are analyzed and compared. Some simulations results are also presented to illustrate the respective performance of different waveforms.

MIMO Signal Processing for Mode-Division Multiplexing: An overview of channel models and signal processing architectures

Abstract: We present the fundamentals of multiple-input, multiple-output (MIMO) signal processing for mode-division multiplexing (MDM) in multimode fiber (MMF). As an introduction, we review current long-haul optical transmission systems and how continued traffic growth motivates study of new methods to increase transmission capacity per fiber. We describe the key characteristics of MIMO channels in MMF, contrasting these with wireless MIMO channels. We review MMF channel models, the statistics derived from them, and their implications for MDM system performance and complexity. We show that optimizing performance and complexity requires management of channel parameters-particularly group delay (GD) spread and mode-dependent loss and gain-by design of transmission fibers and optical amplifiers, and by control of mode coupling along the link. We describe a family of fibers optimized for low GD spread, which decreases with an increasing number of modes. We compare the performance and complexity of candidate MIMO signal processing architectures in a representative long-haul system design, and show that programmable frequency-domain equalization (FDE) of chromatic dispersion (CD) and adaptive FDE of modal dispersion (MD) is an attractive combination. We review two major algorithms for adaptive FDE of MD-least mean squares (LMS) and recursive least squares (RLS)-and analyze their complexity, throughput efficiency, and convergence time. We demonstrate that, with careful physical link design and judicious choice of signal processing architectures, it is possible to overcome MIMO signal processing challenges in MDM systems.

Linear Propagation Effects in Mode-Division Multiplexing Systems

Abstract: In this paper, we review linear propagation effects in a multimode fiber (MMF) and their impact on performance and complexity in long-haul mode-division multiplexing (MDM) systems. We highlight the many similarities to wireless multi-input multioutput (MIMO) systems. Mode-dependent loss and gain (MDL), analogous to multipath fading, can reduce average channel capacity and cause outage in narrowband systems. Modal dispersion (MD), analogous to multipath delay spread, affects the complexity of MIMO equalization, but has no fundamental effect on performance. Optimal MIMO transmission uses a basis of the Schmidt modes, which may be obtained by a singular value decomposition of the MIMO channel. In the special case of a unitary channel (no MDL), an optimal basis is the set of principal modes, which are eigenvectors of a group delay operator, and are free of signal distortion to first order. We present a concatenation rule for the accumulation of MD along a multisection link. We review mode coupling in MMF, including physical origins, models, and regimes of weak and strong coupling. Strong mode coupling is a key to overcoming challenges in MDM systems. Strong coupling reduces the group delay spread from MD, minimizing the complexity of MIMO signal processing. Likewise, it reduces the variations of loss and gain from MDL, maximizing channel capacity. In the strong-coupling regime, the statistics of MD and MDL depend only on the number of modes and the variance of accumulated group delay or loss/gain, and can be derived from the eigenvalue distributions of certain Gaussian random matrices.

Multi-Branch Tomlinson-Harashima Precoding Design for MU-MIMO Systems: Theory and Algorithms

Abstract: Tomlinson-Harashima precoding (THP) is a nonlinear processing technique employed at the transmit side which is a dual to the successive interference cancelation (SIC) detection at the receive side. Like SIC detection, the performance of THP strongly depends on the ordering of the precoded symbols. The optimal ordering algorithm, however, is impractical for multiuser MIMO (MU-MIMO) systems with multiple receive antennas due to the fact that the users are geographically distributed. In this paper, we propose a multi-branch THP (MB-THP) scheme and algorithms that employ multiple transmit processing and ordering strategies along with a selection scheme to mitigate interference in MU-MIMO systems. Two types of multi-branch THP (MB-THP) structures are proposed. The first one employs a decentralized strategy with diagonal weighted filters at the receivers of the users and the second uses a diagonal weighted filter at the transmitter. The MB-MMSE-THP algorithms are also derived based on an extended system model with the aid of an LQ decomposition, which is much simpler compared to the conventional MMSE-THP algorithms. Simulation results show that a better bit error rate (BER) performance can be achieved by the proposed MB-MMSE-THP precoder with a small computational complexity increase.

Conjugate Gradient-based Soft-Output Detection and Precoding in Massive MIMO Systems

Abstract: Massive multiple-input multiple-output (MIMO) promises improved spectral efficiency, coverage, and range, compared to conventional (small-scale) MIMO wireless systems. Unfortunately, these benefits come at the cost of significantly increased computational complexity, especially for systems with realistic antenna configurations. To reduce the complexity of data detection (in the uplink) and precoding (in the downlink) in massive MIMO systems, we propose to use conjugate gradient (CG) methods. While precoding using CG is rather straightforward, soft-output minimum mean-square error (MMSE) detection requires the computation of the post-equalization signal-to-interference-and-noise-ratio (SINR). To enable CG for soft-output detection, we propose a novel way of computing the SINR directly within the CG algorithm at low complexity. We investigate the performance/complexity trade-offs associated with CG-based soft-output detection and precoding, and we compare it to exact and approximate methods. Our results reveal that the proposed method outperforms existing algorithms for massive MIMO systems with realistic antenna configurations.