Showing papers on "MIMO-OFDM published in 2014"
TL;DR: 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.
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.
6,184 citations
TL;DR: This paper addresses the potential impact of pilot contamination caused by the use of non-orthogonal pilot sequences by users in adjacent cells, and analyzes the energy efficiency and degrees of freedom provided by massive MIMO systems to enable efficient single-carrier transmission.
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.
2,046 citations
TL;DR: The flexible nature of GFDM makes this waveform a suitable candidate for future 5G networks, and its main characteristics are analyzed.
Abstract: Cellular systems of the fourth generation (4G) have been optimized to provide high data rates and reliable coverage to mobile users. Cellular systems of the next generation will face more diverse application requirements: the demand for higher data rates exceeds 4G capabilities; battery-driven communication sensors need ultra-low power consumption; and control applications require very short response times. We envision a unified physical layer waveform, referred to as generalized frequency division multiplexing (GFDM), to address these requirements. In this paper, we analyze the main characteristics of the proposed waveform and highlight relevant features. After introducing the principles of GFDM, this paper contributes to the following areas: 1) the means for engineering the waveform's spectral properties; 2) analytical analysis of symbol error performance over different channel models; 3) concepts for MIMO-GFDM to achieve diversity; 4) preamble-based synchronization that preserves the excellent spectral properties of the waveform; 5) bit error rate performance for channel coded GFDM transmission using iterative receivers; 6) relevant application scenarios and suitable GFDM parameterizations; and 7) GFDM proof-of-concept and implementation aspects of the prototype using hardware platforms available today. In summary, the flexible nature of GFDM makes this waveform a suitable candidate for future 5G networks.
809 citations
TL;DR: 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.
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.
613 citations
TL;DR: Practical open-loop and closed-loop training frameworks are proposed that offer 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.
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.
464 citations
TL;DR: 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.
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
446 citations
TL;DR: In this paper, the problem of user grouping for two different objectives, namely, maximizing spatial multiplexing and maximizing total received power, was formulated in a graph-theoretic framework.
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.
380 citations
TL;DR: This work proposes a new approximate matrix inversion algorithm relying on a Neumann series expansion, which substantially reduces the complexity of linear data detection in single-carrier frequency-division multiple access (SC-FDMA)-based large-scale MIMO systems.
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.
363 citations
01 Dec 2014
TL;DR: The design goals of the testbed are detailed, the signaling and system architecture are discussed, and initial measured results for a uplink Massive MIMO over-the-air transmission from four single-antenna UEs to 100 BS antennas are shown.
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.
311 citations
TL;DR: This survey will first review traditional channel estimation approaches based on channel frequency response (CFR) and Parametric model (PM)-based channel estimation, which is particularly suitable for sparse channels, will be also investigated in this survey.
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.
260 citations
TL;DR: The proposed message passing algorithms can achieve a near-optimal performance while the complexity is decreased by tens of times for a 64 × 64 MIMO system and exhibit desirable tradeoffs between performance and complexity for a low-dimensional MIMo system.
Abstract: One of the challenges in the design of large-scale multiuser MIMO-OFDM systems is developing low-complexity detection algorithms. To achieve this goal, we leverage message passing algorithms over the factor graph that represents the multiuser MIMO-OFDM systems and approximate the original discrete messages with continuous Gaussian messages through the use of the minimum Kullback-Leibler (KL) divergence criterion. Several signal processing techniques are then proposed to achieve near-optimal performance at low complexity. First, the principle of expectation propagation is employed to compute the approximate Gaussian messages, where the symbol belief is approximated by a Gaussian distribution and then the approximate message is calculated from the Gaussian approximate belief. In addition, the approximate symbol belief can be computed by the a posteriori probabilities fed back from channel decoders, which reduces the complexity dramatically. Second, the first-order approximation of the message is utilized to further simplify the message updating, leading to an algorithm that is equivalent to the AMP algorithm proposed by Donoho Finally, the message updating is further simplified using the central-limit theorem. Compared with the single tree search sphere decoder (STS-SD) and the iterative (turbo) minimum mean-square error based soft interference cancellation (MMSE-SIC) in the literature through extensive simulations, the proposed message passing algorithms can achieve a near-optimal performance while the complexity is decreased by tens of times for a 64 × 64 MIMO system. In addition, it is shown that the proposed message passing algorithms exhibit desirable tradeoffs between performance and complexity for a low-dimensional MIMO system.
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TL;DR: 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-scale signal detection, precoding and LDPC code designs.
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
TL;DR: The purpose of this paper is to investigate the state of the art in channel models of massive MIMO with different antenna array configurations, which can be used for both theoretical analysis and practical evaluation.
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.
TL;DR: Fundamental and key technical issues in developing and realizing 3D multi-input multi-output technology for next generation mobile communications are discussed.
Abstract: Spectrum efficiency has long been at the center of mobile communication research, development, and operation. Today it is even more so with the explosive popularity of the mobile Internet, social networks, and smart phones that are more powerful than our desktops used to be not long ago. The discovery of spatial multiplexing via multiple antennas in the mid-1990s has brought new hope to boosting data rates regardless of the limited bandwidth. To further realize the potential of spatial multiplexing, the next leap will be accounting for the three-dimensional real world in which electromagnetic waves propagate. In this article we discuss fundamentals and key technical issues in developing and realizing 3D multi-input multi-output technology for next generation mobile communications.
09 Mar 2014
TL;DR: Dense SDM transmission of 20-WDM multi-carrier PDM-32QAM signals over a 40-km 12-core × 3-mode fiber with 247.9-b/s/Hz spectral efficiency is demonstrated.
Abstract: We demonstrate dense SDM transmission of 20-WDM multi-carrier PDM-32QAM signals over a 40-km 12-core × 3-mode fiber with 247.9-b/s/Hz spectral efficiency. Parallel MIMO equalization enables 21-ns DMD compensation with 61 TDE taps per subcarrier.
13 Oct 2014
TL;DR: This paper gives a brief overview of MIMO radar waveforms, which are classified into four categories: (1) time division multiple access (TDMA), (2) frequency division multipleAccess (FDMA),(3) Doppler division multiple Access (DDMA), and (4) code division multipleaccess (CDMA).
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.
TL;DR: In this article, the authors present a concatenation rule for the accumulation of MD along a multisection link, including physical origins, models, and regimes of weak and strong coupling.
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.
TL;DR: In this article, an energy-efficiency optimized power allocation (EEOPA) algorithm is proposed to improve the energy efficiency of MIMO-OFDM mobile multimedia communication systems, where all subchannels are classified by their channel characteristics.
Abstract: It is widely recognized that, in addition to the quality-of-service (QoS), energy efficiency is also a key parameter in designing and evaluating mobile multimedia communication systems, which has catalyzed great interest in recent literature. In this paper, an energy-efficiency model is first proposed for multiple-input–multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) mobile multimedia communication systems with statistical QoS constraints. Employing the channel-matrix singular value decomposition (SVD) method, all subchannels are classified by their channel characteristics. Furthermore, the multichannel joint optimization problem in conventional MIMO-OFDM communication systems is transformed into a multitarget single-channel optimization problem by grouping all subchannels. Therefore, a closed-form solution of the energy-efficiency optimization is derived for MIMO-OFDM mobile multimedia communication systems. As a consequence, an energy-efficiency optimized power allocation (EEOPA) algorithm is proposed to improve the energy efficiency of MIMO-OFDM mobile multimedia communication systems. Simulation comparisons validate that the proposed EEOPA algorithm can guarantee the required QoS with high energy efficiency in MIMO-OFDM mobile multimedia communication systems.
24 Apr 2014
TL;DR: The distinct operation and properties of massive MIMO enable practical resource-efficient load-balancing methods with near-optimal performance on the problem of balancing the load across networks with massive M IMO base-stations.
Abstract: Massive MIMO is expected to play a key role in coping with the predicted mobile-data traffic explosion. Indeed, in combination with small cells and TDD operation, it promises large throughputs per unit area with low latency. In this paper we focus on the problem of balancing the load across networks with massive MIMO base-stations (BSs). The need for load balancing arises from variations in the user population density and is more pronounced in small cells due to the large variability in coverage area. We consider methods for load balancing over networks with small and large massive MIMO BSs. As we show, the distinct operation and properties of massive MIMO enable practical resource-efficient load-balancing methods with near-optimal performance.
TL;DR: In this article, two adaptive algorithms for MIMO frequency-domain equalization (FDE) were proposed: least mean squares (LMS) and recursive least squares (RLS).
Abstract: Long-haul mode-division multiplexing (MDM) employs adaptive multi-input multi-output (MIMO) equalization to compensate for modal crosstalk and modal dispersion. MDM systems must typically use MIMO frequency-domain equalization (FDE) to minimize computational complexity, in contrast to polarization-division-multiplexed systems in single-mode fiber, where time-domain equalization (TDE) has low complexity and is often employed to compensate for polarization effects. We study two adaptive algorithms for MIMO FDE: least mean squares (LMS) and recursive least squares (RLS). We analyze tradeoffs between computational complexity, cyclic prefix efficiency, adaptation time and output symbol-error ratio (SER), and the impact of channel group delay spread and fast Fourier transform (FFT) block length on these. Using FDE, computational complexity increases sublinearly with the number of modes, in contrast to TDE. Adaptation to an initially unknown fiber can be achieved in ~3-5 μs using RLS or ~15-25 μs using LMS in fibers supporting 6-30 modes. As compared to LMS, RLS achieves faster adaptation, higher cyclic prefix efficiency, lower SER, and greater tolerance to mode-dependent loss, but at the cost of higher complexity per FFT block. To ensure low computational complexity and fast adaptation in an MDM system, a low overall group delay spread is required. This is achieved here by a family of graded-index graded depressed-cladding fibers in which the uncoupled group delay spread decreases with an increasing number of modes, in concert with strong mode coupling.
TL;DR: In this paper, a 100-Gb/s MIMO visible laser light communication (VLLC) system employing vertical-cavity surface-emitting lasers with 16-quadrature amplitude modulation (QAM) orthogonal frequency-division multiplexing (OFDM) modulating signals is proposed and experimentally demonstrated.
Abstract: A 100-Gb/s multiple-input multiple-output (MIMO) visible laser light communication (VLLC) system employing vertical-cavity surface-emitting lasers with 16-quadrature amplitude modulation (QAM) orthogonal frequency-division multiplexing (OFDM) modulating signals is proposed and experimentally demonstrated. The transmission capacity of systems is significantly increased by space-division-multiplexing scheme. With the aid of low-noise amplifier and equalizer at the receiving site, good bit error rate performance and clear constellation map are obtained for each optical channel. A system of eight 16-QAM-OFDM channels over 5-m free-space links with a total data rate of 100 Gb/s (12.5 Gb/s/channel × 8 channels) is successfully achieved. Such a proposed MIMO VLLC system is shown to be a prominent one not only presents its convenience in free-space optical communication, but also reveals its potential for the real implementation.
Patent•
16 Sep 2014
TL;DR: In this paper, the components, systems, and methods for reducing location-based interference in distributed antenna systems operating in multiple-input, multiple-output (MIMO) configuration are disclosed.
Abstract: Components, systems, and methods for reducing location-based interference in distributed antenna systems operating in multiple-input, multiple-output (MIMO) configuration are disclosed. Interference is defined as issues with received MIMO communications signals that can cause a MIMO algorithm to not be able to solve a channel matrix for MIMO communications signals received by MIMO receivers in client devices. These issues may be caused by lack of spatial (i.e., phase) separation in the received MIMO communications signals. Thus, to provide phase separation of received MIMO communication signals, multiple MIMO transmitters are each configured to employ multiple transmitter antennas, which are each configured to transmit in different polarization states. In certain embodiments, one of the MIMO communications signals is phase shifted in one of the polarization states to provide phase separation between received MIMO communication signals. In other embodiments, multiple transmitter antennas in a MIMO transmitter can be offset to provide phase separation.
24 Apr 2014
TL;DR: This paper establishes that SM has significant signal-to-noise (SNR) advantage over conventional modulation in large-scale multiuser (multiple-input multiple-output) MIMO systems, and proposes two novel algorithms for detection of large- scale SM-MIMO signals at the BS, one based on message passing and the other based on local search.
Abstract: Spatial modulation (SM) is attractive for multi-antenna wireless communications. SM uses multiple transmit antenna elements but only one transmit radio frequency (RF) chain. In SM, in addition to the information bits conveyed through conventional modulation symbols (e.g., QAM), the index of the active transmit antenna also conveys information bits. In this paper, we establish that SM has significant signal-to-noise (SNR) advantage over conventional modulation in large-scale multiuser (multiple-input multiple-output) MIMO systems. Our new contribution in this paper addresses the key issue of large-dimension signal processing at the base station (BS) receiver (e.g., signal detection) in large-scale multiuser SM-MIMO systems, where each user is equipped with multiple transmit antennas (e.g., 2 or 4 antennas) but only one transmit RF chain, and the BS is equipped with tens to hundreds of (e.g., 128) receive antennas. Specifically, we propose two novel algorithms for detection of large-scale SM-MIMO signals at the BS; one is based on message passing and the other is based on local search. The proposed algorithms achieve very good performance and scale well. For the same spectral efficiency, multiuser SM-MIMO outperforms conventional multiuser MIMO (recently being referred to as massive MIMO) by several dBs. The SNR advantage of SM-MIMO over massive MIMO can be attributed to: (i) because of the spatial index bits, SM-MIMO can use a lower-order QAM alphabet compared to that in massive MIMO to achieve the same spectral efficiency, and (ii) for the same spectral efficiency and QAM size, massive MIMO will need more spatial streams per user which leads to increased spatial interference.
TL;DR: This letter proposes a parametric sparse multiple input multiple output (MIMO)-OFDM channel estimation scheme based on the finite rate of innovation (FRI) theory, whereby super-resolution estimates of path delays with arbitrary values can be achieved.
Abstract: This letter proposes a parametric sparse multiple input multiple output (MIMO)-OFDM channel estimation scheme based on the finite rate of innovation (FRI) theory, whereby super-resolution estimates of path delays with arbitrary values can be achieved. Meanwhile, both the spatial and temporal correlations of wireless MIMO channels are exploited to improve the accuracy of the channel estimation. For outdoor communication scenarios, where wireless channels are sparse in nature, path delays of different transmit-receive antenna pairs share a common sparse pattern due to the spatial correlation of MIMO channels. Meanwhile, the channel sparse pattern is nearly unchanged during several adjacent OFDM symbols due to the temporal correlation of MIMO channels. By simultaneously exploiting those MIMO channel characteristics, the proposed scheme performs better than existing state-of-the-art schemes. Furthermore, by joint processing of signals associated with different antennas, the pilot overhead can be reduced under the framework of the FRI theory.
TL;DR: This work considers the problem of reconfigurable antenna state selection in a single user MIMO system and proposes an adaptive state selection technique when the channels corresponding to all the states are not directly observable and shows that the proposed technique maximizes long term link performance with reduced channel training frequency.
Abstract: Reconfigurable antennas are capable of dynamically re-shaping their radiation patterns in response to the needs of a wireless link or a network. In order to utilize the benefits of reconfigurable antennas, selecting an optimal antenna state for communication is essential and depends on the availability of full channel state information for all the available antenna states. We consider the problem of reconfigurable antenna state selection in a single user MIMO system. We first formulate the state selection as a multi-armed bandit problem that aims to optimize arbitrary link quality metrics. We then show that by using online learning under a multi-armed bandit framework, a sequential decision policy can be employed to learn optimal antenna states without instantaneous full CSI and without a priori knowledge of wireless channel statistics. Our objective is to devise an adaptive state selection technique when the channels corresponding to all the states are not directly observable and compare our results against the case of a known model or genie with full information. We evaluate the performance of the proposed antenna state selection technique by identifying key link quality metrics and using measured channels in a 2 × 2 MIMO OFDM system. We show that the proposed technique maximizes long term link performance with reduced channel training frequency.
04 Dec 2014
TL;DR: Self-equalization, a property of FBMC in massive MIMO that is introduced in this paper, has the impact of reducing complexity, sensitivity to carrier frequency offset, peak-to-average power ratio, system latency, and increasing bandwidth efficiency.
Abstract: This paper introduces filter bank multicarrier (FBMC) as a potential candidate in the application of massive MIMO communication. It also points out the advantages of FBMC over OFDM (orthogonal frequency division multiplexing) in the application of massive MIMO. The absence of cyclic prefix in FBMC increases the bandwidth efficiency. In addition, FBMC allows carrier aggregation straightforwardly. Self-equalization, a property of FBMC in massive MIMO that is introduced in this paper, has the impact of reducing (i) complexity; (ii) sensitivity to carrier frequency offset (CFO); (iii) peak-to-average power ratio (PAPR); (iv) system latency; and (v) increasing bandwidth efficiency. The numerical results that corroborate these claims are presented.
01 Dec 2014
TL;DR: This work highlights deployment and implementation strategies for massive MIMO in the context of 5G indoor small cell scenarios and indicates how to integrate large-scale arrays in future 5G networks.
Abstract: Massive MIMO has emerged as one technology enabler for the next generation mobile communications 5G. The gains promised by massive MIMO are augured to overcome the capacity crunch in today's mobile networks and to pave the way for the ambitious targets of 5G. The challenge to realize massive MIMO for 5G is a successful and cost-efficient integration in the overall network concept. This work highlights deployment and implementation strategies for massive MIMO in the context of 5G indoor small cell scenarios. Different massive MIMO deployment scenarios are analyzed for a standard 3GPP indoor office scenario. In particular stand-alone MIMO at a single location, distributed MIMO without cooperation and network MIMO with full cooperation are investigated for varying array configurations. For the performance analysis of the different MIMO configurations the ratio of total transmit antennas to spatial streams is varied stepwise from equality to a factor of ten. For implementation of massive MIMO in 5G networks trends in beamforming techniques, mutually coupled subarrays, over the calibration procedure and estimated ADC performance in 2020 time-frame are discussed. Based on the debate the paper indicates how to integrate large-scale arrays in future 5G networks.
22 Jun 2014
TL;DR: This paper applies B-MIMO theory to develop a framework for analyzing the small cell in terms of the orthogonal beam footprints and demonstrates that the low-complexity transceivers enable 1000s of Gigabit/s aggregate rates in mm-wave small cells serving hundreds of MSs.
Abstract: Through orders-of-magnitude larger bandwidths and small wavelengths that enable high-dimensional multiple-input multiple-output (MIMO) operation, millimeter-wave (mm-wave) systems operating from 30–300 GHz provide a unique opportunity for meeting the exploding capacity demands on wireless networks. Previously, the performance of multiuser MIMO (MU-MIMO) precoders that exploit the concept of beamspace MIMO (B-MIMO) communication - multiplexing data onto orthogonal spatial beams - was explored for access points (APs) equipped with n-dimensional uniform linear arrays (ULAs). It was shown that APs using reduced complexity B-MIMO transceivers achieve near-optimal performance with complexity that tracks the number of mobile stations (MSs). In this paper we explore the application of the reduced complexity B-MIMO transceivers to APs equipped with uniform planar arrays (UPAs) serving small cells. First, we apply B-MIMO theory to develop a framework for analyzing the small cell in terms of the orthogonal beam footprints. We then examine the effect of several parameters on the system performance and demonstrate that the low-complexity transceivers enable 1000s of Gigabit/s aggregate rates in mm-wave small cells serving hundreds of MSs.
04 May 2014
TL;DR: This paper proposes - to the best of the knowledge - the first ASIC design for high-throughput data detection in single carrier frequency division multiple access (SC-FDMA)-based large-scale MIMO systems, such as systems building on future 3GPP LTE-Advanced standards.
Abstract: This paper proposes - to the best of our knowledge - the first ASIC design for high-throughput data detection in single carrier frequency division multiple access (SC-FDMA)-based large-scale MIMO systems, such as systems building on future 3GPP LTE-Advanced standards. In order to substantially reduce the complexity of linear soft-output data detection in systems having hundreds of antennas at the base station (BS), the proposed detector builds upon a truncated Neumann series expansion to compute the necessary matrix inverse at low complexity. To achieve high throughput in the 3GPP LTE-A uplink, we develop a systolic VLSI architecture including all necessary processing blocks. We present a corresponding ASIC design that achieves 3.8 Gb/s for a 128 antenna, 8 user 3GPP LTE-A based large-scale MIMO system, while occupying 11.1 mm 2 in a TSMC 45nm CMOS technology.
18 May 2014
TL;DR: Simulation results give first suitability indications for 5G for the combination of waveform and multiple access scheme and reveal that IDMA brings in significant enhancement for low rate users, and UFMC introduces additional protection to high-rate users.
Abstract: In this paper we investigate multiple access schemes and multi-carrier waveforms in the context of future 5th Generation (5G) wireless communication systems. We compare classical Frequency Division Multiple Access (FDMA) to Interleave-Division Multiple Access (IDMA) on top of two different multicarrier waveforms: Orthogonal Frequency Division Multiplexing (OFDM) and a new approach called Universal Filtered Multi-Carrier (UFMC). A relaxation of timing and frequency alignment requirements is taken into account for supporting applications like Machine Type Communications (MTC) and the Internet of Things (IoT). This paper contains a first uplink comparison scenario where traffic with Relaxed Synchronicity (RS) is embedded into synchronous traffic. Two main users of interest are either using IDMA or FDMA on top of either OFDM or UFMC modulation. Simulation results give first suitability indications for 5G for the combination of waveform and multiple access scheme. The numerical results reveal that IDMA brings in significant enhancement for low rate users, and UFMC introduces additional protection to high-rate users. Both schemes can be combined well.