Showing papers on "MIMO published in 2018"
Book•
03 Jan 2018TL;DR: This monograph summarizes many years of research insights in a clear and self-contained way and providest the reader with the necessary knowledge and mathematical toolsto carry out independent research in this area.
Abstract: Massive multiple-input multiple-output MIMO is one of themost promising technologies for the next generation of wirelesscommunication networks because it has the potential to providegame-changing improvements in spectral efficiency SE and energyefficiency EE. This monograph summarizes many years ofresearch insights in a clear and self-contained way and providesthe reader with the necessary knowledge and mathematical toolsto carry out independent research in this area. Starting froma rigorous definition of Massive MIMO, the monograph coversthe important aspects of channel estimation, SE, EE, hardwareefficiency HE, and various practical deployment considerations.From the beginning, a very general, yet tractable, canonical systemmodel with spatial channel correlation is introduced. This modelis used to realistically assess the SE and EE, and is later extendedto also include the impact of hardware impairments. Owing tothis rigorous modeling approach, a lot of classic "wisdom" aboutMassive MIMO, based on too simplistic system models, is shownto be questionable.
1,352 citations
TL;DR: The potential of data transmission in a system with a massive number of radiating and sensing elements, thought of as a contiguous surface of electromagnetically active material, is considered as a large intelligent surface (LIS), which is a newly proposed concept and conceptually goes beyond contemporary massive MIMO technology.
Abstract: In this paper, we consider the potential of data transmission in a system with a massive number of radiating and sensing elements, thought of as a contiguous surface of electromagnetically active material. We refer to this as a large intelligent surface (LIS), which is a newly proposed concept and conceptually goes beyond contemporary massive MIMO technology. First, we consider capacities of single-antenna autonomous terminals communicating to the LIS where the entire surface is used as a receiving antenna array in a perfect line-of-sight propagation environment. Under the condition that the surface area is sufficiently large, the received signal after a matched-filtering operation can be closely approximated by a sinc-function-like intersymbol interference channel. Second, we analyze a normalized capacity measured per unit surface, for a fixed transmit power per volume unit with different terminal deployments. As terminal density increases, the limit of the normalized capacity [nats/s/Hz/volume-unit] achieved when wavelength $\lambda$ approaches zero is equal to half of the transmit power per volume unit divided by the noise spatial power spectral density. Third, we show that the number of independent signal dimensions that can be harvested per meter deployed surface is $2/\lambda$ for one-dimensional terminal deployment, and $\pi /\lambda ^2$ per square meter for two- and three-dimensional terminal deployments. Finally, we consider implementations of the LIS in the form of a grid of conventional antenna elements, and show that the sampling lattice that minimizes the surface area and simultaneously obtains one independent signal dimension for every spent antenna is the hexagonal lattice.
712 citations
TL;DR: It is shown that in the asymptotic massive multiple-input multiple-output regime, both the missed device detection and the false alarm probabilities for activity detection can always be made to go to zero by utilizing compressed sensing techniques that exploit sparsity in the user activity pattern.
Abstract: This two-part paper considers an uplink massive device communication scenario in which a large number of devices are connected to a base station (BS), but user traffic is sporadic so that in any given coherence interval, only a subset of users is active. The objective is to quantify the cost of active user detection and channel estimation and to characterize the overall achievable rate of a grant-free two-phase access scheme in which device activity detection and channel estimation are performed jointly using pilot sequences in the first phase and data is transmitted in the second phase. In order to accommodate a large number of simultaneously transmitting devices, this paper studies an asymptotic regime where the BS is equipped with a massive number of antennas. The main contributions of Part I of this paper are as follows. First, we note that as a consequence of having a large pool of potentially active devices but limited coherence time, the pilot sequences cannot all be orthogonal. However, despite the nonorthogonality, this paper shows that in the asymptotic massive multiple-input multiple-output regime, both the missed device detection and the false alarm probabilities for activity detection can always be made to go to zero by utilizing compressed sensing techniques that exploit sparsity in the user activity pattern. Part II of this paper further characterizes the achievable rates using the proposed scheme and quantifies the cost of using nonorthogonal pilot sequences for channel estimation in achievable rates.
594 citations
TL;DR: Simulation results corroborate that the proposed deep learning based scheme can achieve better performance in terms of the DOA estimation and the channel estimation compared with conventional methods, and the proposed scheme is well investigated by extensive simulation in various cases for testing its robustness.
Abstract: The recent concept of massive multiple-input multiple-output (MIMO) can significantly improve the capacity of the communication network, and it has been regarded as a promising technology for the next-generation wireless communications. However, the fundamental challenge of existing massive MIMO systems is that high computational complexity and complicated spatial structures bring great difficulties to exploit the characteristics of the channel and sparsity of these multi-antennas systems. To address this problem, in this paper, we focus on channel estimation and direction-of-arrival (DOA) estimation, and a novel framework that integrates the massive MIMO into deep learning is proposed. To realize end-to-end performance, a deep neural network (DNN) is employed to conduct offline learning and online learning procedures, which is effective to learn the statistics of the wireless channel and the spatial structures in the angle domain. Concretely, the DNN is first trained by simulated data in different channel conditions with the aids of the offline learning, and then corresponding output data can be obtained based on current input data during online learning process. In order to realize super-resolution channel estimation and DOA estimation, two algorithms based on the deep learning are developed, in which the DOA can be estimated in the angle domain without additional complexity directly. Furthermore, simulation results corroborate that the proposed deep learning based scheme can achieve better performance in terms of the DOA estimation and the channel estimation compared with conventional methods, and the proposed scheme is well investigated by extensive simulation in various cases for testing its robustness.
577 citations
TL;DR: In this article, a deep learning-based CSI sensing and recovery mechanism is proposed to learn to effectively use channel structure from training samples, which can recover CSI with significantly improved reconstruction quality compared with existing compressive sensing-based methods.
Abstract: In frequency division duplex mode, the downlink channel state information (CSI) should be sent to the base station through feedback links so that the potential gains of a massive multiple-input multiple-output can be exhibited. However, such a transmission is hindered by excessive feedback overhead. In this letter, we use deep learning technology to develop CsiNet, a novel CSI sensing and recovery mechanism that learns to effectively use channel structure from training samples. CsiNet learns a transformation from CSI to a near-optimal number of representations (or codewords) and an inverse transformation from codewords to CSI. We perform experiments to demonstrate that CsiNet can recover CSI with significantly improved reconstruction quality compared with existing compressive sensing (CS)-based methods. Even at excessively low compression regions where CS-based methods cannot work, CsiNet retains effective beamforming gain.
513 citations
TL;DR: The suitability of hybrid beamforming methods, both, existing and proposed till first quarter of 2017, are explored, and the exciting future challenges in this domain are identified.
Abstract: The increasing wireless data traffic demands have driven the need to explore suitable spectrum regions for meeting the projected requirements. In the light of this, millimeter wave (mmWave) communication has received considerable attention from the research community. Typically, in fifth generation (5G) wireless networks, mmWave massive multiple-input multiple-output (MIMO) communications is realized by the hybrid transceivers which combine high dimensional analog phase shifters and power amplifiers with lower-dimensional digital signal processing units. This hybrid beamforming design reduces the cost and power consumption which is aligned with an energy-efficient design vision of 5G. In this paper, we track the progress in hybrid beamforming for massive MIMO communications in the context of system models of the hybrid transceivers’ structures, the digital and analog beamforming matrices with the possible antenna configuration scenarios and the hybrid beamforming in heterogeneous wireless networks. We extend the scope of the discussion by including resource management issues in hybrid beamforming. We explore the suitability of hybrid beamforming methods, both, existing and proposed till first quarter of 2017, and identify the exciting future challenges in this domain.
505 citations
01 Mar 2018
TL;DR: This work considers the cell-free massive multiple-input multiple-output (MIMO) downlink, where a very large number of distributed multiple-antenna access points (APs) serve many single-ant antenna users in the same time-frequency resource, and derives a closed-form expression for the spectral efficiency taking into account the effects of channel estimation errors and power control.
Abstract: We consider the cell-free massive multiple-input multiple-output (MIMO) downlink, where a very large number of distributed multiple-antenna access points (APs) serve many single-antenna users in the same time-frequency resource. A simple (distributed) conjugate beamforming scheme is applied at each AP via the use of local channel state information (CSI). This CSI is acquired through time-division duplex operation and the reception of uplink training signals transmitted by the users. We derive a closed-form expression for the spectral efficiency taking into account the effects of channel estimation errors and power control. This closed-form result enables us to analyze the effects of backhaul power consumption, the number of APs, and the number of antennas per AP on the total energy efficiency, as well as, to design an optimal power allocation algorithm. The optimal power allocation algorithm aims at maximizing the total energy efficiency, subject to a per-user spectral efficiency constraint and a per-AP power constraint. Compared with the equal power control, our proposed power allocation scheme can double the total energy efficiency. Furthermore, we propose AP selections schemes, in which each user chooses a subset of APs, to reduce the power consumption caused by the backhaul links. With our proposed AP selection schemes, the total energy efficiency increases significantly, especially for large numbers of APs. Moreover, under a requirement of good quality-of-service for all users, cell-free massive MIMO outperforms the colocated counterpart in terms of energy efficiency.
497 citations
TL;DR: The preliminary outcomes of extensive research on mmWave massive MIMO are presented and emerging trends together with their respective benefits, challenges, and proposed solutions are highlighted to point out current trends, evolving research issues and future directions on this technology.
Abstract: Several enabling technologies are being explored for the fifth-generation (5G) mobile system era. The aim is to evolve a cellular network that remarkably pushes forward the limits of legacy mobile systems across all dimensions of performance metrics. One dominant technology that consistently features in the list of the 5G enablers is the millimeter-wave (mmWave) massive multiple-input-multiple-output (massive MIMO) system. It shows potentials to significantly raise user throughput, enhance spectral and energy efficiencies and increase the capacity of mobile networks using the joint capabilities of the huge available bandwidth in the mmWave frequency bands and high multiplexing gains achievable with massive antenna arrays. In this survey, we present the preliminary outcomes of extensive research on mmWave massive MIMO (as research on this subject is still in the exploratory phase) and highlight emerging trends together with their respective benefits, challenges, and proposed solutions. The survey spans broad areas in the field of wireless communications, and the objective is to point out current trends, evolving research issues and future directions on mmWave massive MIMO as a technology that will open up new frontiers of services and applications for next-generation cellular networks.
491 citations
TL;DR: This work focuses on a dual-functional multi-input-multi-output (MIMO) radar-communication system, where a single transmitter with multiple antennas communicates with downlink cellular users and detects radar targets simultaneously and proposes a branch-and-bound algorithm that obtains a globally optimal solution.
Abstract: We focus on a dual-functional multi-input-multi-output (MIMO) radar-communication (RadCom) system, where a single transmitter with multiple antennas communicates with downlink cellular users and detects radar targets simultaneously. Several design criteria are considered for minimizing the downlink multiuser interference. First, we consider both omnidirectional and directional beampattern design problems, where the closed-form globally optimal solutions are obtained. Based on the derived waveforms, we further consider weighted optimizations targeting a flexible tradeoff between radar and communications performance and introduce low-complexity algorithms. Moreover, to address the more practical constant modulus waveform design problem, we propose a branch-and-bound algorithm that obtains a globally optimal solution, and derive its worst-case complexity as function of the maximum iteration number. Finally, we assess the effectiveness of the proposed waveform design approaches via numerical results.
487 citations
TL;DR: Numerical results show that the shared deployment outperforms the separated case significantly, and the proposed weighted optimizations achieve a similar performance to the original optimizations, despite their significantly lower computational complexity.
Abstract: Beamforming techniques are proposed for a joint multi-input-multi-output (MIMO) radar-communication (RadCom) system, where a single device acts as radar and a communication base station (BS) by simultaneously communicating with downlink users and detecting radar targets. Two operational options are considered, where we first split the antennas into two groups, one for radar and the other for communication. Under this deployment, the radar signal is designed to fall into the null-space of the downlink channel. The communication beamformer is optimized such that the beampattern obtained matches the radar’s beampattern while satisfying the communication performance requirements. To reduce the optimizations’ constraints, we consider a second operational option, where all the antennas transmit a joint waveform that is shared by both radar and communications. In this case, we formulate an appropriate probing beampattern, while guaranteeing the performance of the downlink communications. By incorporating the SINR constraints into objective functions as penalty terms, we further simplify the original beamforming designs to weighted optimizations, and solve them by efficient manifold algorithms. Numerical results show that the shared deployment outperforms the separated case significantly, and the proposed weighted optimizations achieve a similar performance to the original optimizations, despite their significantly lower computational complexity.
458 citations
TL;DR: In this paper, the authors show that with multicell MMSE precoding/combining and a tiny amount of spatial channel correlation or large-scale fading variations over the array, the capacity increases without bound as the number of antennas increases, even under pilot contamination.
Abstract: The capacity of cellular networks can be improved by the unprecedented array gain and spatial multiplexing offered by Massive MIMO. Since its inception, the coherent interference caused by pilot contamination has been believed to create a finite capacity limit, as the number of antennas goes to infinity. In this paper, we prove that this is incorrect and an artifact from using simplistic channel models and suboptimal precoding/combining schemes. We show that with multicell MMSE precoding/combining and a tiny amount of spatial channel correlation or large-scale fading variations over the array, the capacity increases without bound as the number of antennas increases, even under pilot contamination. More precisely, the result holds when the channel covariance matrices of the contaminating users are asymptotically linearly independent, which is generally the case. If also the diagonals of the covariance matrices are linearly independent, it is sufficient to know these diagonals (and not the full covariance matrices) to achieve an unlimited asymptotic capacity.
TL;DR: In this article, a broadband channel estimation algorithm for mmWave multiple input multiple output (MIMO) systems with few-bit analog-to-digital converters (ADCs) is proposed.
Abstract: We develop a broadband channel estimation algorithm for millimeter wave (mmWave) multiple input multiple output (MIMO) systems with few-bit analog-to-digital converters (ADCs). Our methodology exploits the joint sparsity of the mmWave MIMO channel in the angle and delay domains. We formulate the estimation problem as a noisy quantized compressed-sensing problem and solve it using efficient approximate message passing (AMP) algorithms. In particular, we model the angle-delay coefficients using a Bernoulli–Gaussian-mixture distribution with unknown parameters and use the expectation-maximization forms of the generalized AMP and vector AMP algorithms to simultaneously learn the distributional parameters and compute approximately minimum mean-squared error (MSE) estimates of the channel coefficients. We design a training sequence that allows fast, fast Fourier transform based implementation of these algorithms while minimizing peak-to-average power ratio at the transmitter, making our methods scale efficiently to large numbers of antenna elements and delays. We present the results of a detailed simulation study that compares our algorithms to several benchmarks. Our study investigates the effect of SNR, training length, training type, ADC resolution, and runtime on channel estimation MSE, mutual information, and achievable rate. It shows that, in a mmWave MIMO system, the methods we propose to exploit joint angle-delay sparsity allow 1-bit ADCs to perform comparably to infinite-bit ADCs at low SNR, and 4-bit ADCs to perform comparably to infinite-bit ADCs at medium SNR.
TL;DR: A 64-channel massive multiple-input multiple-output (MIMO) transceiver with a fully digital beamforming (DBF) architecture for fifth-generation millimeter-wave communications is presented in this paper.
Abstract: A 64-channel massive multiple-input multiple-output (MIMO) transceiver with a fully digital beamforming (DBF) architecture for fifth-generation millimeter-wave communications is presented in this paper. The DBF-based massive MIMO transceiver is operated at 28-GHz band with a 500-MHz signal bandwidth and the time division duplex mode. The antenna elements are arranged as a 2-D array, which has 16 columns (horizontal direction) and 4 rows (vertical direction) for a better beamforming resolution in the horizontal plane. To achieve half-wavelength element spacing in the horizontal direction, a new sectorial transceiver array design with a bent substrate-integrated waveguide is proposed. The measured results show that an excellent RF performance is achieved. The system performance is tested with the over-the-air technique to verify the feasibility of the proposed DBF-based massive MIMO transceiver for high data rate millimeter-wave communications. Using the beam-tracking technique and two streams of QAM-64 signals, the proposed millimeter-wave MIMO transceiver can achieve a steady 5.3-Gb/s throughput for a single user in fast mobile environments. In the multiple-user MIMO scenario, by delivering 20 noncoherent data streams to eight four-channel user terminals, it achieves a downlink peak data rate of 50.73 Gb/s with the spectral efficiency of 101.5 b/s/Hz.
TL;DR: In this paper, the authors proposed a divide-and-next-largest-difference-based user pairing algorithm to distribute the capacity gain among the NOMA clusters in a controlled manner.
Abstract: This article presents advances in resource allocation for downlink non-orthogonal multiple access (NOMA) systems, focusing on user pairing and power allocation algorithms. The former pairs the users to obtain high capacity gain by exploiting the channel gain difference between the users, while the latter allocates power to users in each cluster to balance system throughput and user fairness. Additionally, the article introduces the concept of cluster fairness and proposes the divide-and-next-largest-difference-based user pairing algorithm to distribute the capacity gain among the NOMA clusters in a controlled manner. Furthermore, performance comparison between multiple-input multiple-output NOMA (MIMO-NOMA) and MIMO orthogonal multiple access (MIMO-OMA) is conducted when users have pre-defined quality of service. Simulation results are presented, which validate the advantages of NOMA over OMA. Finally, the article provides avenues for further research on resource allocation for downlink NOMA.
TL;DR: This paper provides a systematic review of the mutual coupling in multiple-input multiple-output (MIMO) systems, including the effects on performances of MIMO systems and various decoupling techniques.
Abstract: This paper provides a systematic review of the mutual coupling in multiple-input multiple-output (MIMO) systems, including the effects on performances of MIMO systems and various decoupling techniques. The mutual coupling changes the antenna characteristics in an array, and therefore, degrades the system performance of the MIMO system and causes the spectral regrowth. Although the system performance can be partially improved by calibrating out the mutual coupling in the digital domain, it is more effective to use decoupling techniques (from the antenna point) to overcome the mutual coupling effects. Some popular decoupling techniques for MIMO systems (especially for massive MIMO base station antennas) are also presented.
TL;DR: In this paper, a unified framework of geometry-based stochastic models for the 5G wireless communication systems is proposed, which aims at capturing small-scale fading channel characteristics of key 5G communication scenarios, such as massive MIMO, high-speed train, vehicle-to-vehicle, and millimeter wave communications.
Abstract: A novel unified framework of geometry-based stochastic models for the fifth generation (5G) wireless communication systems is proposed in this paper. The proposed general 5G channel model aims at capturing small-scale fading channel characteristics of key 5G communication scenarios, such as massive multiple-input multiple-output, high-speed train, vehicle-to-vehicle, and millimeter wave communications. It is a 3-D non-stationary channel model based on the WINNER II and Saleh-Valenzuela channel models considering array-time cluster evolution. Moreover, it can easily be reduced to various simplified channel models by properly adjusting model parameters. Statistical properties of the proposed general 5G small-scale fading channel model are investigated to demonstrate its capability of capturing channel characteristics of various scenarios, with excellent fitting to some corresponding channel measurements.
TL;DR: In this paper, the authors evaluate the channel hardening and favorable propagation properties of a realistic stochastic access point (AP) deployment in CF massive MIMO networks and show that channel hardness only appears in special cases, for example, when the pathloss exponent is small.
Abstract: Cell-free (CF) massive multiple-input multiple-output (MIMO) is an alternative topology for future wireless networks, where a large number of single-antenna access points (APs) are distributed over the coverage area. There are no cells but all users are jointly served by the APs using network MIMO methods. Prior works have claimed that the CF massive MIMO inherits the basic properties of cellular massive MIMO, namely, channel hardening and favorable propagation. In this paper, we evaluate if one can rely on these properties when having a realistic stochastic AP deployment. Our results show that channel hardening only appears in special cases, for example, when the pathloss exponent is small. However, by using 5–10 antennas per AP, instead of one, we can substantially improve the hardening. Only spatially well-separated users will exhibit favorable propagation, but when adding more antennas and/or reducing the pathloss exponent, it becomes more likely for favorable propagation to occur. The conclusion is that we cannot rely on the channel hardening and the favorable propagation when analyzing and designing the CF massive MIMO networks, but we need to use achievable rate expressions and resource allocation schemes that work well also in the absence of these properties. Some options are reviewed in this paper.
TL;DR: In this article, the authors proposed a non-coherent transmission scheme for mMTC and specifically for grant-free random access, which leverages elements from the approximate message passing (AMP) algorithm.
Abstract: A key challenge of massive MTC (mMTC), is the joint detection of device activity and decoding of data. The sparse characteristics of mMTC makes compressed sensing (CS) approaches a promising solution to the device detection problem. However, utilizing CS-based approaches for device detection along with channel estimation, and using the acquired estimates for coherent data transmission is suboptimal, especially when the goal is to convey only a few bits of data. First, we focus on the coherent transmission and demonstrate that it is possible to obtain more accurate channel state information by combining conventional estimators with CS-based techniques. Moreover, we illustrate that even simple power control techniques can enhance the device detection performance in mMTC setups. Second, we devise a new non-coherent transmission scheme for mMTC and specifically for grant-free random access. We design an algorithm that jointly detects device activity along with embedded information bits. The approach leverages elements from the approximate message passing (AMP) algorithm, and exploits the structured sparsity introduced by the non-coherent transmission scheme. Our analysis reveals that the proposed approach has superior performance compared with application of the original AMP approach.
TL;DR: The key transceiver design challenges, including channel estimation, signal detector, channel information feedback and transmit precoding, are discussed and a mixed-ADC architecture is introduced as an alternative technique of improving overall system performance.
Abstract: Nowadays, mmWave MIMO systems are favorable candidates for 5G cellular systems. However, a key challenge is the high power consumption imposed by its numerous RF chains, which may be mitigated by opting for low-resolution ADCs, while tolerating a moderate performance loss. In this article, we discuss several important issues based on the most recent research on mmWave massive MIMO systems relying on low-resolution ADCs. We discuss the key transceiver design challenges, including channel estimation, signal detector, channel information feedback and transmit precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative technique of improving overall system performance. Finally, the associated challenges and potential implementations of the practical 5G mmWave massive MIMO system with ADC quantizers are discussed.
TL;DR: In this paper, the authors considered a frequency-selective mm-wave channel and proposed compressed sensing-based strategies to estimate the channel in the frequency domain, and evaluated different algorithms and computed their complexity to expose tradeoffs in complexity overhead performance as compared with those of previous approaches.
Abstract: Channel estimation is useful in millimeter wave (mm-wave) MIMO communication systems. Channel state information allows optimized designs of precoders and combiners under different metrics, such as mutual information or signal-to-interference noise ratio. At mm-wave, MIMO precoders and combiners are usually hybrid, since this architecture provides a means to trade-off power consumption and achievable rate. Channel estimation is challenging when using these architectures, however, since there is no direct access to the outputs of the different antenna elements in the array. The MIMO channel can only be observed through the analog combining network, which acts as a compression stage of the received signal. Most of the prior work on channel estimation for hybrid architectures assumes a frequency-flat mm-wave channel model. In this paper, we consider a frequency-selective mm-wave channel and propose compressed sensing-based strategies to estimate the channel in the frequency domain. We evaluate different algorithms and compute their complexity to expose tradeoffs in complexity overhead performance as compared with those of previous approaches.
TL;DR: A compact, high performance, and novel-shaped ultra-wideband (UWB) multiple-input multiple-output (MIMO) antenna with low mutual coupling with good agreement between the simulated and measured results is observed.
Abstract: A compact, high performance, and novel-shaped ultra-wideband (UWB) multiple-input multiple-output (MIMO) antenna with low mutual coupling is presented in this paper. The proposed antenna consists of two radiating elements with shared ground plane having an area of $50\times 30$ mm2. F-shaped stubs are introduced in the shared ground plane of the proposed antenna to produce high isolation between the MIMO antenna elements. The designed MIMO antenna has very low mutual coupling of (S21 7.4 dB), high multiplexing efficiency ( ${\eta }_{\mathrm {Mux}}> -3.5$ ), and high peak gain over the entire UWB frequencies. The antenna performance is studied in terms of S-Parameters, radiation properties, peak gain, diversity gain, envelop correlation coefficient, and multiplexing efficiency. A good agreement between the simulated and measured results is observed.
TL;DR: It is demonstrated that NomA with NGDPA achieves a sum rate improvement of up to 29.1% compared with NOMA with the gain ratio power allocation method in the $2\times 2$ MIMO-VLC system with three users.
Abstract: In this letter, we apply the non-orthogonal multiple access (NOMA) technique to improve the achievable sum rate of multiple-input multiple-output (MIMO)-based multi-user visible light communication (VLC) systems. To ensure efficient and low-complexity power allocation in indoor MIMO-NOMA-based VLC systems, a normalized gain difference power allocation (NGDPA) method is first proposed by exploiting users’ channel conditions. We investigate the performance of an indoor $2\times 2$ MIMO-NOMA-based multi-user VLC system through numerical simulations. The obtained results show that the achievable sum rate of the $2\times 2$ MIMO-VLC system can be significantly improved by employing NOMA with the proposed NGDPA method. It is demonstrated that NOMA with NGDPA achieves a sum rate improvement of up to 29.1% compared with NOMA with the gain ratio power allocation method in the $2\times 2$ MIMO-VLC system with three users.
TL;DR: In this article, a compact design of multiple-input multiple-output (MIMO) Antenna with dual sharply rejected notch bands for portable wireless ultrawideband (UWB) applications is presented and experimentally investigated.
Abstract: In this paper, a compact design of multiple-input multiple-output (MIMO) Antenna with dual sharply rejected notch bands for portable wireless ultrawideband (UWB) applications is presented and experimentally investigated. The proposed UWB MIMO Antenna has a compact size of 18 mm $\times$ 34 mm. The tapered microstrip fed slot Antenna acts as a single radiating element with inverted L-shaped slits to introduce notches at wireless local area network and the IEEE INSAT/Super-Extended C-bands. The mutual coupling of less than −22 dB is achieved over the entire operating band (2.93–20 GHz). At the center of notched band, the efficiency of the Antenna drops that indicates a good interference suppression performance. The performance of the MIMO Antenna in terms of isolation among the ports, radiation pattern, efficiency, realized gain, envelope correlation coefficient, mean effective gain, and total active reflection coefficient is studied.
26 Nov 2018
TL;DR: Numerical results show that the proposed approach can improve the performance of the iterative algorithm significantly under Rayleigh and correlated MIMO channels.
Abstract: In this paper, we propose a model-driven deep learning network for multiple-input multiple-output (MIMO) detection. The structure of the network is specially designed by unfolding the iterative algorithm. Some trainable parameters are optimized through deep learning techniques to improve the detection performance. Since the number of trainable variables of the network is equal to that of the layers, the network can be easily trained within a very short time. Furthermore, the network can handle time-varying channel with only a single training. Numerical results show that the proposed approach can improve the performance of the iterative algorithm significantly under Rayleigh and correlated MIMO channels.
TL;DR: The orthogonal-mode method is presented to mitigate the mutual coupling of the tightly arranged pairs without any external decoupling structure and provide a promising solution to compact 5G MIMO mobile phone antennas with good isolation and diversity performance.
Abstract: In this communication, the novel compact tightly arranged pairs are employed to form a $4 \times 4$ multiple-input-multiple-output (MIMO) system and an $8 \times 8$ MIMO system operating at 3.4–3.6 GHz for fifth-generation (5G) mobile phones. Each tightly arranged pair is composed of a bent monopole and an edge-fed dipole with a compact size of $7 \times 12$ mm2. The orthogonal-mode method is presented to mitigate the mutual coupling of the tightly arranged pairs without any external decoupling structure. With the help of the orthogonal mode, isolation performances across the desired band of the $4 \times 4$ MIMO system and the $8 \times 8$ MIMO system are better than 20 and 17 dB, respectively, with the elements closely spaced. The measured efficiencies are 51.7%–84.5%/49%–72.9%, and the measured envelope correlation coefficients are less than 0.06/0.07 for the $4 \times 4/ 8 \times 8$ MIMO system. The proposed MIMO systems provide a promising solution to compact 5G MIMO mobile phone antennas with good isolation and diversity performance.
TL;DR: Simulation results show that the proposed reinforcement learning-based power control scheme for the downlink NOMA transmission can significantly increase the sum data rates of users, and thus, the utilities compared with the standard Q-learning-based strategy.
Abstract: Nonorthogonal multiple access (NOMA) systems are vulnerable to jamming attacks, especially smart jammers who apply programmable and smart radio devices such as software-defined radios to flexibly control their jamming strategy according to the ongoing NOMA transmission and radio environment. In this paper, the power allocation of a base station in a NOMA system equipped with multiple antennas contending with a smart jammer is formulated as a zero-sum game, in which the base station as the leader first chooses the transmit power on multiple antennas, while a jammer as the follower selects the jamming power to interrupt the transmission of the users. A Stackelberg equilibrium of the antijamming NOMA transmission game is derived and conditions assuring its existence are provided to disclose the impact of multiple antennas and radio channel states. A reinforcement learning-based power control scheme is proposed for the downlink NOMA transmission without being aware of the jamming and radio channel parameters. The Dyna architecture that formulates a learned world model from the real antijamming transmission experience and the hotbooting technique that exploits experiences in similar scenarios to initialize the quality values are used to accelerate the learning speed of the Q-learning-based power allocation, and thus, improve the communication efficiency of the NOMA transmission in the presence of smart jammers. Simulation results show that the proposed scheme can significantly increase the sum data rates of users, and thus, the utilities compared with the standard Q-learning-based strategy.
TL;DR: The results show that the proposed antenna array can still exhibit good radiation and MIMO performances when operating under data mode and read mode conditions.
Abstract: A 12-port antenna array operating in the long term evolution (LTE) band 42 (3400–3600 MHz), LTE band 43 (3600–3800 MHz), and LTE band 46 (5150–5925 MHz) for 5G massive multiple-input multiple-output (MIMO) applications in mobile handsets is presented. The proposed MIMO antenna is composed of three different antenna element types, namely, inverted $\pi $ -shaped antenna, longer inverted L-shaped open slot antenna, and shorter inverted L-shaped open slot antenna. In total, eight antenna elements are used for the $8 \times 8$ MIMO in LTE bands 42/43, and six antenna elements are designed for the $6 \times 6$ MIMO in LTE band 46. The proposed antenna was simulated, and a prototype was fabricated and tested. The measured results show that the LTE bands 42/43/46 are satisfied with reflection coefficient better than −6 dB, isolation lower than −12 dB, and total efficiencies of higher than 40%. In addition to that, the proposed antenna array has also shown good MIMO performances with an envelope correlation coefficient lower than 0.15, and ergodic channel capacities higher than 34 and 26.5 b/s/Hz in the LTE bands 42/43 and LTE band 46, respectively. The hand phantom effects are also investigated, and the results show that the proposed antenna array can still exhibit good radiation and MIMO performances when operating under data mode and read mode conditions.
TL;DR: A multi-band 10-antenna array working at the sub-6-GHz spectrum (LTE bands 42/43 and LTE band 46) for massive multiple-input multiple-output (MIMO) applications in future 5G smartphones is proposed.
Abstract: A multi-band 10-antenna array working at the sub-6-GHz spectrum (LTE bands 42/43 and LTE band 46) for massive multiple-input multiple-output (MIMO) applications in future 5G smartphones is proposed. To realize $10\times 10$ MIMO applications in three LTE bands, 10 T-shaped coupled-fed slot antenna elements that can excite dual resonant modes are integrated into a system circuit board. Spatial and polarization diversity techniques are implemented on these elements so that the improved isolation and mitigated coupling effects can be achieved. The proposed antenna array was manufactured and experimentally measured. Desirable antenna efficiencies of higher than 42% and 62% were measured in the low band and high band, respectively. Vital results, such as the envelope correlation coefficient, channel capacity, and mean effective gain ratio, have also been computed and analyzed. The calculated ergodic channel capacities of the $10\times 10$ MIMO system working in the LTE bands 42/43 and LTE band 46 reached up to 48 and 51.4 b/s/Hz, respectively.
TL;DR: In this paper, the authors investigated low RF-complexity technologies to solve the problem of hardware cost and power consumption in mmWave MIMO systems, and compared the performance of PAHP and LAHP in practice.
Abstract: mmWave MIMO with large antenna array has attracted considerable interest from the academic and industry communities, as it can provide larger bandwidth and higher spectrum efficiency. However, with hundreds of antennas, the number of RF chains required by mmWave MIMO is also huge, leading to unaffordable hardware cost and power consumption in practice. In this article, we investigate low RF-complexity technologies to solve this bottleneck. We first review the evolution of low RF-complexity technologies from microwave frequencies to mmWave frequencies. Then, we discuss two promising low RF-complexity technologies for mmWave MIMO systems in detail, that is PAHP and LAHP, including their principles, advantages, challenges, and recent results. We compare the performance of these two technologies to draw some insights about how they can be deployed in practice. Finally, we conclude this article and point out some future research directions in this area.
TL;DR: This paper considers the joint design of a multiple-input multiple-output (MIMO) radar with co-located antennas and a MIMO communication system, and a reduced-complexity iterative algorithm, based on iterative alternating maximization of three suitably designed subproblems, is proposed and analyzed.
Abstract: This paper considers the joint design of a multiple-input multiple-output (MIMO) radar with co-located antennas and a MIMO communication system. The degrees of freedom under the designer's control are the waveforms transmitted by the radar transmit array, the filter at the radar array and the code-book employed by the communication system to form its space-time code matrix. Two formulations of the spectrum sharing problem are proposed. First, the design problem is stated as the constrained maximization of the signal-to-interference-plus-noise ratio at the radar receiver, where interference is due to both clutter and the coexistence structure, and the constraints concern both the similarity with a standard radar waveform and the rate achievable by the communication system, on top of those on the transmit energy. The resulting problem is nonconvex, but a reduced-complexity iterative algorithm, based on iterative alternating maximization of three suitably designed subproblems, is proposed and analyzed. In addition, the constrained maximization of the communication rate is also investigated. The convergence of all the devised algorithms is guaranteed. Finally, a thorough performance assessment is presented, aimed at showing the merits of the proposed approach.