MIMO Architecture Research Articles

Symmetric Nested MIMO Architecture for Mixed Far-Field and Near-Field Source Localization

Symmetric Nested MIMO Architecture for Mixed Far-Field and Near-Field Source Localization

Channel estimation via compressed sampling matching pursuit for hybrid MIMO architectures in millimeter wave communication

ABSTRACT In recent times, there are considerable research efforts in utilising millimetre wave cellular system for meeting rising mobile traffic volume and escalating data rate demand. In this regard, several analytical models have been developed for obtaining Channel State Information (CSI) for the design of combiner and precoder architectures for millimetre wave communication. One prominent method that has caught the attention of most researchers is the Orthogonal Matching Pursuit (OMP) which performs poorly in noisy condition. As a result, this work introduces Compressed Sampling Matching Pursuit (CoSAMP) as alternative to OMP for obtaining CSI for the design of hybrid combiner. A comparative analysis of all the cases examined reveals that CoSAMP surpasses OMP and Least Square (LS) across SNR range of −30 dB to −10 dB, recording lowest Normalized Mean Square Error (NMSE), highest Spectral Efficiency (SE) and bit rate. For instance at −20 dB, NMSE of CoSAMP exhibits 95% and 86% gain over LS and OMP, respectively while SE and bit rate of CoSAMP are better than OMP and LS by 21% and 91%, respectively. It is therefore seen that CoSAMP is a better technique for estimating CSI and for combiner design in such condition.

Dynamic MIMO Architecture Design for Near-Field Communications

Dynamic MIMO Architecture Design for Near-Field Communications

A Cost Assessment Methodology for User-Centric Distributed Massive MIMO Architectures

A Cost Assessment Methodology for User-Centric Distributed Massive MIMO Architectures

MIC: Microwave Imaging Curtain for Dynamic and Automatic Detection of Weapons and Explosive Belts

DEXTER (detection of explosives and firearms to counter terrorism) is a project funded by NATO’s Science for Peace and Security (SPS) program with the goal of developing an integrated system capable of remotely and accurately detecting explosives and firearms in public places without impeding the flow of pedestrians. While body scanner systems in secure areas of public places are becoming more and more efficient, the attack at Brussels airport on 22 March 2016, upstream of these systems, in the middle of the crowd of passengers, demonstrated the lack of discreet and real-time security against threats of mass terrorism. The NATO-SPS international and multi-year DEXTER project aims to provide new technical and strategic solutions to fill this gap. This project is based on multi-sensor coordination and fusion, from hyperspectral remote laser to smart glasses, artificial algorithms, and suspect identification and tracking. One of these sensors is dedicated to threat detection (large weapon or explosive belt) using the clothing of pedestrians by means of an active microwave component. This project is referred to as MIC (Microwave Imaging Curtain), also supported by the French SGDSN (General Secretariat of Defense and National Security), and utilizes a radar system capable of generating 3D images in real-time to address non-checkpoint detection of explosives and firearms. The project, led by ONERA (France), is based on a radar imaging system developed by the Fraunhofer FHR institute, using a MIMO architecture with an Ultra-Wide Band waveform. Although high-resolution 3D microwave imaging is already being used in expensive body scanners to detect firearms concealed under clothing, MIC’s innovative approach lies in utilizing a high-resolution 3D imaging device that can detect larger dangerous objects carried by moving individuals at a longer range, in addition to providing discrete detection in pedestrian flow. Automatic detection and classification of these dangerous objects is carried out on 3D radar images using a deep-learning network. This paper will outline the project’s objectives and constraints, as well as the design, architecture, and performance of the final system. Additionally, it will present real-time imaging results obtained during a live demonstration in a relevant environment.

MIMO Is All You Need：A Strong Multi-in-Multi-Out Baseline for Video Prediction

The mainstream of the existing approaches for video prediction builds up their models based on a Single-In-Single-Out (SISO) architecture, which takes the current frame as input to predict the next frame in a recursive manner. This way often leads to severe performance degradation when they try to extrapolate a longer period of future, thus limiting the practical use of the prediction model. Alternatively, a Multi-In-Multi-Out (MIMO) architecture that outputs all the future frames at one shot naturally breaks the recursive manner and therefore prevents error accumulation. However, only a few MIMO models for video prediction are proposed and they only achieve inferior performance due to the date. The real strength of the MIMO model in this area is not well noticed and is largely under-explored. Motivated by that, we conduct a comprehensive investigation in this paper to thoroughly exploit how far a simple MIMO architecture can go. Surprisingly, our empirical studies reveal that a simple MIMO model can outperform the state-of-the-art work with a large margin much more than expected, especially in dealing with long-term error accumulation. After exploring a number of ways and designs, we propose a new MIMO architecture based on extending the pure Transformer with local spatio-temporal blocks and a new multi-output decoder, namely MIMO-VP, to establish a new standard in video prediction. We evaluate our model in four highly competitive benchmarks. Extensive experiments show that our model wins 1st place on all the benchmarks with remarkable performance gains and surpasses the best SISO model in all aspects including efficiency, quantity, and quality. A dramatic error reduction is achieved when predicting 10 frames on Moving MNIST and Weather datasets respectively. We believe our model can serve as a new baseline to facilitate the future research of video prediction tasks. The code will be released.

A Joint Multiservice Transmission Scheme for RSMA-Aided Cell-Free mMIMO System

In this letter, we propose a joint multi-service (MS) transmission scheme based on cell-free (CF) massive MIMO architecture. Based on the user-centric (UC) principle, we perform access point (AP) selection and form broadcast, multicast, and unicast messages at each AP according to the rate splitting multiple access (RSMA) technique, then we carry out the joint transmission of MS signals. In terms of system optimization, a novel three-step resource allocation algorithm is proposed to deal with the non-convex problem of achievable sum-rate maximization which downscales multidimensional variables to solve the one-dimensional problems of AP selection, intra-AP power ratio determination, and inter-AP power allocation in steps. We provide achievable sum-rate analyses backed by numerical results to show the superiority of the proposed scheme and optimization algorithm compared with the baselines.

An improved resource allocation architecture using swarm intelligence for mm-Wave MIMO communication architecture

An improved resource allocation architecture using swarm intelligence for mm-Wave MIMO communication architecture

An improved resource allocation architecture utilising swarm intelligence for mm-wave MIMO communication architecture

An improved resource allocation architecture utilising swarm intelligence for mm-wave MIMO communication architecture

On resource scheduling and interference mitigation in distributed massive-MIMO wireless orientations via NOMA transmission

The goal of this paper is to evaluate the performance of a proposed resource management approach that mitigates the co-channel interference (CCI) in non-orthogonal multiple access (NOMA) scenarios and maintains their enhanced spectral efficiency in distributed massive multiple input multiple output (d-massive MIMO) configurations as well. In this context, dedicated resource scheduling algorithms (RSAs) in power domain (PD) and frequency domain (FD) are studied in terms of resources’ orthogonality. Specifically, in PD case, adjusted power levels (denoted as PD-NOMA) per subcarrier and mobile terminal (MT) are assigned, while in FD case, the subcarriers are either orthogonal (FD orthogonal multiple access, FD-OMA) or non-orthogonal (FD-NOMA) to each other. The response of the d-massive MIMO system is evaluated statistically via independent Monte Carlo (MC) simulations considering a multicellular multi-user network topology and compared to typical multicellular MIMO configurations. In this framework, a simulation platform is implemented that integrates both the PD and FD RSAs. In PD, both intercell co-channel interference (Inter-CCI) and intracell co-channel interference (Intra-CCI) are modelled analytically in order to estimate the assigned power per subcarrier and MT. In the latter case (Intra-CCI), the worst-case scenario is assumed: a subcarrier can be assigned to multiple MTs (full spectral overlapping) leading to intense Intra-CCI. In FD, two subcarrier allocation approaches are considered: Pseudo-Random or maximum signal-to-noise ratio (MSNR). The simulations in both FD implementations (FD-NOMA, FD-OMA) show that, thanks to the proposed PD-NOMA scheme, each MT requires 1/4 of the maximum available power for downlink transmission. Moreover, in any of the investigated NOMA schemes, despite the intense Intra-CCI, roughly the same number of MTs as in the OMA case can access the network. Therefore, it is straightforward that, even in worst-case scenarios, the NOMA RSAs (i) are wisely exploiting the available resources; (ii) can inherently combat intense Intra-CCI and, in this way, maintain the system’s performance (number of MTs, power savings, resilience against CCI, computation complexity). Finally, it is worth noting that in contrary to typical MIMO configurations, the d-massive MIMO architecture alone can lead up to a 13.63% increase in the system’s capacity (10% maximum allowed blocking probability, 1 tier, 1 subcarrier per MT). In this case, the increased spatial separation that is achieved, along with the exploitation of NOMA RSAs, lead to a decreased CCI (both Intra- and Inter-); hence, SNR is improved and consequently the number of accepted MTs as well.

Study of MIMO integration methods with fifth generation technologies

It is expected that with the general deployment of cellular 4G systems the new technologies would be required to satisfy the heightening demand for higher data transfer speeds and lower delays while retaining low deployment and maintenance costs. Multiple array technologies such as MIMO will play a key role in the forthcoming 5G and 6G systems. This paper will present several MIMO architectures and beamforming solutions for cm- and mm-wave 5G systems that work in the 20 (3-30 GHz) and 100 (30-300 GHz) spectrums. These spectrums provide much higher potential for small cell deployment with the use of large-scale antenna arrays due to the higher data capacity than that of the traditional cellular network systems. The mm-wave 5G ELA systems that will gradually replace LTE are going to have higher capacity with the use of Frequency Division Duplexing (FDD) because it enables the usage of uplink/downlink reciprocity in controlling the beamforming operation. It is, however, must be noted that MIMO technology has a key problem that consists of obtaining the necessary information needed to control the transmit and receive algorithms, the solution of which will be discussed in this paper. The array calibration errors that can cause severe performance losses are also going to be considered. The paper therefore will focus on the performance and implementation issues of different multi-user-MIMO (MU-MIMO) solutions for transmission in the cm- and mm-wave bands as well as key problems that follow the transmit MIMO processing for 5G systems. Performance evaluation of RF-based architecture and the traditional baseband transmission methodology for downlink MU-MIMO implementation as well as several transmission schemes to highlight the issues with MU-MIMO for 5G and the solutions to them will be provided.

Broadband array radar based on microwave photonic frequency multiplication and de-chirp receiving (Invited)

Microwave photonic radar enables the generation and processing of broadband radar signals, which can significantly improve the range resolution of the radar system. To improve the radar angle resolution and realize flexible beam control, combining microwave photonic radar technology with array radar technology is an inevitable development trend. Previously, the optical truth delay technology is intensively investigated to achieve squint-free beam steering in broadband phased array radars, which usually face the problems of high complexity, poor flexibility, and limited delay accuracy. In recent years, the broadband radar architecture based on microwave photonic frequency multiplication and de-chirp receiving has received extensive attention. The array radar constructed based on this technology has wide operation bandwidth while enabling real-time digital compensation and processing functions, which provides a new idea for the development of broadband array radars. In this paper, the research progress of the broadband array radar based on microwave photonic frequency multiplication and de-chirp processing was reviewed. After expounding the transceiver mechanism of microwave photonic broadband radar, the method for constructing broadband phased array radar and the performance of digital beam scanning and imaging were introduced. Then, the radar array was extended to MIMO architecture. The broadband microwave photonic MIMO radar based on optical wavelength division multiplexing technology was introduced and its performance in target detection and imaging was analyzed.

Cell-Free and User-Centric Massive MIMO Architectures for Reliable Communications in Indoor Factory Environments

Factory automation is one of the use cases for 5G-and-beyond mobile networks where strict requirements in terms of latency, availability and reliability are required. In this paper, we investigate the potentials of massive MIMO in delivering those promises for industrial automation. Namely, communications between actuators (ACs) and Access Points (APs) inside an industrial scenario is considered and different transmission modes are compared: joint transmission (JT) where the distributed antennas are used to communicate with each AC, cell-free transmission (CFT) where all the ACs are served by all APs, single AP transmission (SAT) where each AC is served by only one AP, and user-centric transmission (UCT) where each AC is served by a subset of APs. A power control strategy, aimed at maximizing the minimum signal-to-interference plus noise ratio (SINR), is also introduced. Numerical results, shown in terms of downlink SINR and achievable rate, evaluated using the final block length capacity formula (FBLC), demonstrate that the use of distributed antenna setting and of power control bring substantial performance improvements in terms of reliability and latency.

Probabilistic distribution learning algorithm based transmit antenna selection and precoding for millimeter wave massive MIMO systems

In modern day communication systems, the massive MIMO architecture plays a pivotal role in enhancing the spatial multiplexing gain, but vice versa the system energy efficiency is compromised. Consequently, resource allocation in-terms of antenna selection becomes inevitable to increase energy efficiency without having any obvious effect or compromising the system spectral efficiency. Optimal antenna selection can be performed using exhaustive search. However, for a massive MIMO architecture, exhaustive search is not a feasible option due to the exponential growth in computational complexity with an increase in the number of antennas. We have proposed a computationally efficient and optimum algorithm based on the probability distribution learning for transmit antenna selection. An estimation of the distribution algorithm is a learning algorithm which learns from the probability distribution of best possible solutions. The proposed solution is computationally efficient and can obtain an optimum solution for the real time antenna selection problem. Since precoding and beamforming are also considered essential techniques to combat path loss incurred due to high frequency communications, so after antenna selection, successive interference cancellation algorithm is adopted for precoding with selected antennas. Simulation results verify that the proposed joint antenna selection and precoding solution is computationally efficient and near optimal in terms of spectral efficiency with respect to exhaustive search scheme. Furthermore, the energy efficiency of the system is also optimized by the proposed algorithm, resulting in performance enhancement of massive MIMO systems.

Efficient Frequency Scaling Algorithm for Short-Range 3-D Holographic Imaging Based on a Scanning MIMO Array

Millimeter-wave (MMW) holographic imaging technology is widely used in plenty of short-range applications like security and medical diagnosis. When combining with multiple-input-multiple-output (MIMO) array, such a technology can acquire precise reconstruction with wider field of view and higher dynamic range. However, to focus the higher dimensional data set obtained from MIMO architecture, the complicated iteration or interpolation employed by the previous state-of-the-art focusing techniques prevents the real-time operation of such an imaging system under a general computation power. It is more economical to increase the operational speed by improving the algorithm efficiency. Hence, a novel fast imaging algorithm that uses multistatic frequency scaling technique is proposed in this article for achieving real-time 3-D imaging on a 1-D MIMO scanning system. Only fast Fourier transform (FFT)/inverse FFT (IFFT) and multiplications are employed in the algorithm, which can be easily implemented. Compared with the previous state-of-the-art techniques, the proposed algorithm has the lower computation complexity. Practical experiments with self-developed MMW MIMO scanning radar prove the accuracy and efficiency of the algorithm. On a common laptop without any acceleration technology, the proposed algorithm requires less than one tenth of the time as required by the previous state-of-the-art techniques.

A Novel Application of Pattern Search Algorithm for Efficient Estimation of Channel State Information in MIMO Network

A novel intelligent computation mechanism is proposed for the estimation of channel state information in MIMO architecture. In this computing technique, the global optimizer Pattern Search algorithm is used to find the accurate channel state information which is based on coefficients of the channel matrix. The pattern search algorithm is exploited for the square channel matrix which consists of an equal number of array elements at both transmitter and receiver end of the communication system. Rayleigh fading channel modeling is explored in this work for evaluating the response of received signal over channel matrix of different dimensions. Also, two cases of measured white Gaussian noise are applied for evaluating the trend of algorithm which are without noise and 20 dB of noise. A Mean Square Error based evaluation function is formulated for assessing the true response of channel coefficients with the estimated one. This error function is effective enough to reach the best coefficient with only one symbol. The efficiency and performance of the pattern search algorithm are examined with different types of analysis like accuracy, convergence, and computational complexity on the bases of statistical parameters. A sufficient number of absolute Monte-Carlo simulations are practiced for the extensive statistical study of the proposed algorithm.

On the Achievable Rate of Multiple-Input–Multiple-Output Underwater Acoustic Communications

This paper investigates the data gain brought by the multiple-input-multiple-output (MIMO) principle for underwater acoustic (UWA) communication. By considering a class of acoustic MIMO channels where each fading coefficient is modeled by a Rice distribution law with correlation factor depending on channel Doppler spread, we derive the achievable rate of such system that takes into account both channel estimation error and training sequence overhead. Model parameters are then estimated from an experiment campaign in a MIMO shallow water channel conducted in the roadstead of Brest, France. The system achievable rate is evaluated for several MIMO architectures and channel configurations and then compared against the conventional single-input-multiple-output transmission. The achievable rate gain is finally put in perspective with end-to-end data rate performance of a single-carrier MIMO transmission system experimented at sea.

Joint transmit antenna selection and precoding for millimeter wave massive MIMO systems

Millimeter wave (mmW) communication coupled with massive MIMO architecture is attaining considerable attention to overcome the huge data rate requirements. Increasing the dimensions of a massive MIMO architecture provides spatial multiplexing gains, but at the cost of decrease in hardware efficiency. To optimize the energy efficiency of massive MIMO systems, in this paper, focusing on a sub-connected hybrid architecture, we propose a joint transmit antenna selection and precoding technique. Low Complexity heuristic algorithms are used to perform the antenna selection in order to limit the number of active antennas, hence, decreasing the power consumption which subsequently increases the energy efficiency. Precoding for the selected transmit antennas is performed using successive interference cancellation (SIC) based scheme. Simulation results show that the heuristic algorithms based transmit antenna selection scheme requires less computations and its performance is close to exhaustive search algorithm. Furthermore, SIC based precoding enables to overcome serious signal attenuation of mmW systems and performs best in terms of spectral efficiency for a partially connected hybrid structure. The joint solution is optimal in-terms of computational complexity and energy efficiency, which optimizes the performance of mmW massive MIMO systems.

Hybrid Massive MIMO for Urban V2I: Sub-6 GHz vs mmWave Performance Assessment

The relevance of vehicle communications in next-generation networks entails the necessity of studying their performance in the framework of upcoming massive architectures. In this regard, a study of the wireless channel between a hybrid massive MIMO BS and a vehicular platform is proposed. Adequate metrics and methodology are provided for the assessment of such scenarios. The impact of different multi-antenna geometries and MIMO architectures in both vehicle and BS is modeled based on a numerical simulation of a realistic urban area. We compare several massive hybrid BS configurations with distinct multi-antenna vehicular rooftop solutions. This paper studies the channel eigenvalues and capacities for sub-6 GHz (3.6 GHz) and millimeter-wave (mmWave) (26 GHz) bands under different propagation conditions. The results suggest that the use of hybrid massive configurations increase the channel capacity by subdividing the massive BS into two or four logical radio channels. It is also shown that the greater available bandwidth at mmWave band can compensate the larger attenuation of radio channels in small size cells. The use of adequate metrics to measure the temporal coherence of the channel is also discussed for the beam switching update.

Analysis of UAV Communications in Cell-Free Massive MIMO Systems

We study support for unmanned aerial vehicle (UAV) communications through a cell-free massive MIMO architecture, wherein a large number of access points (APs) is deployed in place of large co-located massive MIMO arrays. We consider also a variation of the pure cell-free architecture by applying a user-centric association approach, where each user is served only from a subset of APs in the network. Under the general assumption that the propagation channel between the mobile stations, either UAVs or ground users (GUEs), and the APs follows a Ricean distribution, we derive closed form spectral efficiency lower bounds for uplink and downlink with linear minimum mean square error channel estimation. We consider several power allocation and user scheduling strategies for such a system, and, among these, also minimum-rate maximizing power allocation strategies to improve the system fairness. Our numerical results reveal that cell-free massive MIMO architecture and its low-complexity user-centric alternative may provide better performance than a traditional multi-cell massive MIMO network deployment.