Large Multiple-input Multiple-output Research Articles

Potential Impact of 5G Technology on Various Aspects of Technology Development

The introduction of fifth-generation (5G) wireless systems marks an important milestone in the development of wireless communications. This development has the potential to revolutionize many industries and improve our daily lives. Our research paper aims to come up with an inclusive overview of the prospects and developments in 5G wireless systems. It provides an in-depth look at key issues, trends, and testing directions that will shape 5G network development and deployment in the years to come. Our research papers cover a wide range of topics, including networking, spectrum usage, device-to-device communication, massive MIMO, edge computing, security, and 5G technologies. We review the present state of research and ongoing projects, providing insight into the future of 5G wireless systems and their impact on society. As experts, we guarantee that our content will be effective, unique, engaging, and tailored to your specific needs and requirements. The demand for quick, more dependable wireless connections continues to increase, making the development and implementation of fifth-generation (5G) wireless networks more difficult. In this article, we provide a comprehensive overview of ongoing and future research in 5G wireless technology, highlighting key challenges and solutions. We are investigating the multiple input multiple output (MIMO) field where no antenna can be used to improve spectral efficiency and coverage. We examine the optimization of antenna configurations, beamforming strategies, and resource allocation strategies to maximize the advantage of large MIMO when addressing network devices and systems. Additionally, we are exploring the potential of millimeter wave (mm-Wave) communications to provide maximum data and bandwidth, and the integration of shared networks (HetNets) to improve service and capacity. We will also touch on the emergence of edge computing leading to poor quality applications and reduced network collaboration in 5G networks, and the importance of network connectivity that allows the physical network infrastructure to be divided into several sessions with different characteristics. By solving problems in these areas, we strive to unlock the full potential of 5G networks to transform the industry and pave the way for advanced and intelligent connectivity in the future.

Time domain turbo equalization based on vector approximate message passing for multiple-input multiple-output underwater acoustic communications.

This paper proposes a high-performance receiver for underwater acoustic communications based on time reversal processing for multiple-input multiple-output (MIMO) systems. The receiver employs the vector approximate message passing (VAMP) algorithm as a soft equalizer in turbo equalization. By performing self-iteration between the inner soft slicer and the inner soft equalizer, the VAMP algorithm achieves near-optimal performance. Furthermore, an iterative channel-estimation-based soft successive interference cancellation method is incorporated to suppress co-channel interference in the MIMO system. Additionally, the introduction of passive time reversal technology can combine multiple channels into a single channel, which greatly reduces the computational complexity of the MIMO system, especially for large MIMO systems. The effectiveness of the proposed receiver is verified using experimental data collected in Songhua Lake, China in 2019. The results demonstrate that the proposed receiver significantly reduces the complexity of the traditional parallel-VAMP receiver without sacrificing performance and outperforms other receivers of the same type. Moreover, our experimental results also verify that the VAMP-turbo outperforms the generalized approximate message passing (GAMP)-turbo in terms of bit error rate and convergence performance.

Emerging MIMO Technologies for 6G Networks.

The demand for wireless connectivity has grown exponentially over the last years. By 2030 there should be around 17 billion of mobile-connected devices, with monthly data traffic in the order of thousands of exabytes. Although the Fifth Generation (5G) communications systems present far more features than Fourth Generation (4G) systems, they will not be able to serve this growing demand and the requirements of innovative use cases. Therefore, Sixth Generation (6G) Networks are expected to support such massive connectivity and guarantee an increase in performance and quality of service for all users. To deal with such requirements, several technical issues need to be addressed, including novel multiple-antenna technologies. Then, this survey gives a concise review of the main emerging Multiple-Input Multiple-Output (MIMO) technologies for 6G Networks such as massive MIMO (mMIMO), extremely large MIMO (XL-MIMO), Intelligent Reflecting Surfaces (IRS), and Cell-Free mMIMO (CF-mMIMO). Moreover, we present a discussion on how some of the expected key performance indicators (KPIs) of some novel 6G Network use cases can be met with the development of each MIMO technology.

Low complexity hybrid layered tabu‐likelihood ascent search for large MIMO detection with perfect and estimated channel state information

AbstractIn this work, we proposed a low‐complexity hybrid layered tabu‐likelihood ascent search (LTLAS) algorithm for large multiple‐input multiple‐output (MIMO) system. The conventional layered tabu search (LTS) approach involves many partial reactive tabu searches (RTSs), and each RTS requires an initialization and searching phase. In the proposed algorithm, we restricted the upper limit of the number of RTS operations. Once RTS operations exceed the limit, RTS will be replaced by low‐complexity likelihood ascent search (LAS) operations. The block‐based detection approach is considered to maintain a higher signal‐to‐noise ratio (SNR) detection performance. An efficient precomputation technique is derived, which can suppress redundant computations. The simulation results show that the bit error rate (BER) performance of the proposed detection method is close to the conventional LTS method. The complexity analysis shows that the proposed method has significantly lower computational complexity than conventional methods. Also, the proposed method can reduce almost 50% of real operations to achieve a BER of 10−3.

Decomposition of uplink multi-user OTFS channel with partial CSI feedback

This work studies system design methods for the uplink multi-user orthogonal time–frequency space (OTFS) channel which forms a virtual multiple-input multiple-output (MIMO) communication system. For such system, the received signal contains interferences in the space, frequency, and time domain at the same time. To reduce the computational complexity, this work proposes to decompose the original large MIMO channel into parallel small sub-channels in the following order: first to decompose in the space domain, then to decompose in the time domain, thereby reducing the computational complexity. To help achieve channel decomposition, the proposed method requires the transmitter to perform precoding that needs channel state information (CSI) feedback. However, the proposed method only needs partial CSI feedback including delay and Doppler shift, so the feedback burden is small. Simulation results of the bit error rate (BER) performance verify that the proposed channel decomposition method is effective.

Gibbs Sampling Detection for Large MIMO and MTC Uplinks with Adaptive Modulation.

Wireless networks beyond 5G will mostly be serving myriads of sensors and other machine-type communications (MTC), with each device having different requirements in respect to latency, error rate, energy consumption, spectral efficiency or other specifications. Multiple-input multiple-output (MIMO) systems remain a central technology towards 6G, and in cases where massive antenna arrays or cell-free networks are not possible to deploy and only moderately large antenna arrays are allowed, the detection problem at the base-station cannot rely on zero-forcing or matched filters and more complex detection schemes have to be used. The main challenge is to find low complexity, hardware feasible methods that are able to attain near optimal performance. Randomized algorithms based on Gibbs sampling (GS) were proven to perform very close to the optimal detection, even for moderately large antenna arrays, while yielding an acceptable number of operations. However, their performance is highly dependent on the chosen “temperature” parameter (TP). In this paper, we propose and study an optimized variant of the GS method, denoted by triple mixed GS, and where three distinct values for the TP are considered. The method exhibits faster convergence rates than the existing ones in the literature, hence requiring fewer iterations to achieve a target bit error rate. The proposed detector is suitable for symmetric large MIMO systems, however the proposed fixed complexity detector is highly suitable to spectrally efficient adaptively modulated MIMO (AM-MIMO) systems where different types of devices upload information at different bit rates or have different requirements regarding spectral efficiency. The proposed receiver is shown to attain quasi-optimal performance in both scenarios.

A Realistic Radar Ray Tracing Simulator for Large MIMO-Arrays in Automotive Environments

With the rise of high resolution multiple input multiple output (MIMO) systems, radar became an important sensor in the development of Advanced Driver Assistance Systems (ADAS) and autonomous driving applications. Autonomous driving will rely strongly on artificial intelligence. Since most modern classification algorithms are based on neural networks, they require huge amounts of data to perform well, especially in unexpected traffic situations. Radar sensor simulation can potentially produce a great variety of training data for machine learning algorithms, which makes it an important cornerstone in the development of ADAS. Furthermore, with radar simulators, different antenna configurations and various edge cases can be simulated. In this work, a versatile ray tracing toolchain based on the shoot and bouncing rays (SBR) approach is presented. The program is able to simulate complex urban environments including realistic clutter, by utilizing simplistic reflection models. The program does not only produce realistic radar images, but also generates camera-like images using the same materials. Furthermore, this work deals with the adaption of the SBR method to radar sensors with an arbitrary number of transmit (TX)- and receive (RX) antennas, which enables the simulation of large MIMO arrays. A novel performance optimization approach is proposed for large numbers of TX antennas, which reduces the runtime dramatically. The quality of the simulation is verified by measuring a complex and realistic scenario with a high resolution automotive MIMO radar. Also, a study of the effect on quality and runtime is being investigated for various optimization approaches, including the proposed method.

Application of Deep Learning to Sphere Decoding for Large MIMO Systems

Although the sphere decoder (SD) is a powerful detector for multiple-input multiple-output (MIMO) systems, it has become computationally prohibitive in massive MIMO systems, where a large number of antennas are employed. To overcome this challenge, we propose fast deep learning (DL)-aided SD (FDL-SD) and fast DL-aided $K$-best SD (KSD, FDL-KSD) algorithms. Therein, the major application of DL is to generate a highly reliable initial candidate to accelerate the search in SD and KSD in conjunction with candidate/layer ordering and early rejection. Compared to existing DL-aided SD schemes, our proposed schemes are more advantageous in both offline training and online application phases. Specifically, unlike existing DL-aided SD schemes, they do not require performing the conventional SD in the training phase. For a $24 \times 24$ MIMO system with QPSK, the proposed FDL-SD achieves a complexity reduction of more than $90\%$ without any performance loss compared to conventional SD schemes. For a $32 \times 32$ MIMO system with QPSK, the proposed FDL-KSD only requires $K = 32$ to attain the performance of the conventional KSD with $K=256$, where $K$ is the number of survival paths in KSD. This implies a dramatic improvement in the performance--complexity tradeoff of the proposed FDL-KSD scheme.

Height Measurement with Meter Wave Polarimetric MIMO Radar: Signal Model and MUSIC-like Algorithm

Inview of the advantages of polarization diversity and waveform diversity, polarimetric multiple-input multiple-output (MIMO) radar, which combines the two advantages, is a subject worthy of in-depth study. Height measurement is one of the key problems to be solved for meter wave radar. Therefore, this paper focuses on height measurement with meter wave polarimetric MIMO radar. First, we derive the height measurement signal model for flat ground reflection combined with the two diversity techniques. Then, we properly classify the signal model and modify it to make it suitable for the classical super-resolution height measurement algorithms, including the generalized multiple signal classification(MUSIC) algorithm and steering vector synthesis (SVS) MUSIC algorithm. The split beam question in SVS-MUSIC is found and the resolution is provided. The proposed algorithms are also extended to long dipoles and large loop MIMO radar. Moreover, the computational complexity of the two algorithms and the derivation of Cramer Rao bound (CRB) for polarimetric MIMO radar are given. These methods do not require decoherence. The innovation of this paper is to provide a height measurement signal model for meter wave polarimetric MIMO radar along with its effective solution. The simulation results prove the accuracy of this approach.

Early Neighbor Rejection-Aided Tabu Search Detection for Large MIMO Systems

In this study, a low complexity tabu search (TS) algorithm for multiple-input multiple-output (MIMO) systems is proposed. To reduce the computational complexity of the TS algorithm, early neighbor rejection (ENR) and layer ordering schemes are employed. In the proposed ENR-aided TS (ENR-TS) algorithm, the least promising k neighbors are excluded from the neighbor set in each layer, which reduces the computational complexity of neighbor examination in each TS iteration. For efficient computation of the neighbors’ metrics, the ENR scheme can be incorporated into QR decomposition-aided TS (ENR-QR-TS). To further reduce the complexity and improve the performance of the ENR-QR-TS scheme, a layer ordering scheme is employed. The layer ordering scheme determines the order in which layers are detected based on their expected metrics, which reduces the risk of excluding likely neighbors in early layers. The simulation results show that the ENR-TS achieves nearly the same performance as the conventional TS while providing up to 82% complexity reduction.

Optimal Data Detection and Signal Estimation in Systems With Input Noise

Practical systems often suffer from hardware impairments that already appear during signal generation. Despite the limiting effect of such input-noise impairments on signal processing systems, they are routinely ignored in the literature. In this paper, we propose an algorithm for data detection and signal estimation, referred to as Approximate Message Passing with Input noise (AMPI), which takes into account input-noise impairments. To demonstrate the efficacy of AMPI, we investigate two applications: Data detection in large multiple-input multiple output (MIMO) wireless systems and sparse signal recovery in compressive sensing. For both applications, we provide precise conditions in the large-system limit for which AMPI achieves optimal performance. We furthermore use simulations to demonstrate that AMPI achieves near-optimal performance at low complexity in realistic, finite-dimensional systems.

Groupwise Neighbor Examination for Tabu Search Detection in Large MIMO Systems

In the conventional tabu search (TS) detection algorithm for multiple-input multiple-output (MIMO) systems, the metrics of all neighboring vectors are computed to determine the best one to move to. This strategy requires high computational complexity, especially in large MIMO systems with high-order modulation schemes such as 16- and 64-QAM signaling. This paper proposes a novel reduced-complexity TS detection algorithm called neighbor-grouped TS (NG-TS), which divides the neighbors into groups and finds the best neighbor by using a simplified cost function. Furthermore, based on the complexity analysis of NG-TS, we propose a channel ordering scheme that further reduces its complexity. Simulation results show that the proposed NG-TS with channel ordering can achieve up to 85% complexity reduction with respect to the conventional TS algorithm with no performance loss in both low- and higher-order modulation schemes.

MIMO Borehole Radar Imaging Based on High Degree of Freedom for Efficient Subsurface Sensing

This paper presents an efficient multiple-input multiple-output (MIMO) borehole radar imaging method based on a high degree of freedom for subsurface sensing. The variable separations between the different transmitter and receiver pairs in MIMO borehole radar are considered and introduced as imaging coefficients into the expanded unified MIMO sample set, which gives the MIMO sample set desired characteristics of a high degree of freedom. By exploiting the high degree of freedom in the unified MIMO expanded sample set, the sample interpolation and energy migration of target reflections can be done in once efficient imaging processing for all transmitters of the MIMO radar system, and finally, accurate target image with low sidelobe level (SL) can be delivered. The computational cost of the proposed method will barely increase with the number of transmitters in the MIMO radar survey, which enables the proposed method to handle MIMO borehole radar imaging in an accurate and efficient manner. The imaging properties of the proposed method are proven to be superior to the conventional imaging methods in SL and computational cost, which is suitable for large MIMO borehole imaging surveys in subsurface sensing applications.

QR-Decomposition-Aided Tabu Search Detection for Large MIMO Systems

In the conventional tabu search (TS) detection algorithm for multiple-input multiple-output (MIMO) systems, the cost metrics of all neighboring vectors are computed to determine the best neighbor. This can require an excessively high computational complexity, especially in large MIMO systems because the number of neighboring vectors and the dimension per vector are large. In this study, we propose an improved TS algorithm based on the QR decomposition of the channel matrix (QR-TS), which allows for finding the best neighbor with a significantly lower complexity compared with the conventional TS algorithm. Specifically, QR-TS does not compute all metrics by early rejecting unpromising neighbors, which reduces the computational load of TS without causing any performance loss. To further optimize the QR-TS algorithm, we investigate novel ordering schemes, namely the transmit-ordering (Tx-ordering) and receive-ordering (Rx-ordering), which can considerably reduce the complexity of QR-TS. Simulation results show that QR-TS reduces the complexity approximately by a factor of two compared with the conventional TS. Furthermore, when both Tx-ordering and Rx-ordering are applied, QR-TS requires approximately $60\%\text{ -- }90\%$ less complexity compared with the conventional TS scheme. The proposed algorithms are suitable for both low-order and high-order modulation, and can achieve a significant complexity reduction compared to the Schnorr–Euchner and $K\text{-}$ best sphere decoders in large MIMO systems.

Reducing the Codeword Search Complexity of FDD Moderately Large MIMO Beamforming Systems

It is of great interest to develop larger scale multiple-input multiple-output (MIMO) technology based on frequency division duplexing (FDD) for backward compatibility with existing standards and already deployed cellular networks. One hindrance to the FDD approach is the complexity of the codeword search performed by the receiver, which is known to scale with the number of transmit antennas $N_{t}$ in a system. The codeword search complexity does not scale linearly and soon becomes infeasible even for the not-so-massive MIMO systems expected in the very near future. We address this problem by proposing a natural sequence-based orthogonal codebook and an intra-array interference magnitude (IIM) framework for MIMO beamforming based on quantized equal gain transmission that altogether tremendously simplifies the codeword search problem. We also present an assessment on the quality of the codeword selected by the proposed framework that explains it’s near optimal performance, and, provide detailed codeword search complexity quantification. Finally, we provide simulation analyses of beamforming gain under time selective fading channels and an FDD MIMO system transmit delay model. Our analyses indicate that despite significantly reducing complexity, the proposed IIM framework executed by already commercialized processors is capable of approaching the ideal performance of an static system even in the presence of time-selectivity inherent to high-speed mobile communications. Our analyses also quantify the impact of feedback delay on average beamforming gain of moderately large transmit arrays.

A near maximum likelihood performance modified firefly algorithm for large MIMO detection

Abstract To meet the ever-growing demand for high data rates, employing a large number of antennas at both the transmitter and receiver is a necessity for future advanced wireless systems. Multiple-input multiple-output (MIMO) systems, which are equipped with multiple antennas, provide high data rates with high spectral efficiency. However, the design of an efficient, robust and non-erroneous detection algorithm is a huge challenge in large MIMO systems. In this paper, a stochastic bio-inspired meta-heuristic algorithm is proposed for large MIMO detection. The proposed algorithm is motivated by the bioluminescence of fireflies and uses a probabilistic metric to update solutions in the search space. Robustness of the proposed algorithm is verified under channel estimation errors at the receiver. Simulation results reveal that the proposed algorithm outperforms unordered congestion control ant colony optimization, congestion control ant colony optimization, standard particle swarm optimization, binary particle swarm optimization, memetic particle swarm optimization, firefly algorithm, firefly algorithm with neighborhood attraction, minimum mean square error and successive interference cancellation based MIMO detection techniques in terms of bit error rate (BER) performance. The proposed algorithm achieves near maximum likelihood BER performance with lower computational complexity. This makes the proposed algorithm an appropriate candidate for reliable detection in future large MIMO systems.

Large MIMO Detection Schemes Based on Channel Puncturing: Performance and Complexity Analysis

A family of low-complexity detection schemes based on channel matrix puncturing targeted for large multiple-input multiple-output (MIMO) systems is proposed. It is well known that the computational cost of MIMO detection based on QR decomposition is directly proportional to the number of non-zero entries involved in back-substitution and slicing operations in the triangularized channel matrix, which can be too high for low-latency applications involving large MIMO dimensions. By systematically puncturing the channel to have a specific structure, it is demonstrated that the detection process can be accelerated by employing standard schemes, such as chase detection, list detection, nulling-and-cancellation detection, and sub-space detection on the transformed matrix. The performance of these schemes is characterized and analyzed mathematically, and bounds on the achievable diversity gain and probability of bit error are derived. Surprisingly, it is shown that puncturing does not negatively impact the receive diversity gain in hard-output detectors. The analysis is extended to soft-output detection when computing per-layer bit log-likelihood ratios; it is shown that significant performance gains are attainable by ordering the layer of interest to be at the root when puncturing the channel. Simulations of coded and uncoded scenarios certify that the proposed schemes scale up efficiently both in the number of antennas and constellation size, as well as in the presence of correlated channels. In particular, soft-output per-layer sub-space detection is shown to achieve a 2.5 dB signal-to-noise ratio gain at 10−4 bit error rate in 256-quadratic-amplitude modulation $16\times 16$ MIMO, while saving 77% of nulling-and-cancellation computations.

Imperfect CSI‐based large MIMO systems

The authors address the problem of channel estimation and data detection in a large multiple-input multiple-output (MIMO) systems in Rayleigh fading environment. A method of channel estimation and data detection is proposed in large MIMO systems. The channel matrix is decomposed in N d /2 × N s /2 channel matrices; where N d and N s are the number of antennas at the destination and source, respectively. The pilot signals are clubbed with data symbols encoded in N d /2 × N s /2 orthogonal space-time block codes (OSTBC), and then by using the properties of null matrix, channel estimation is performed. A decoder of OSTBC data is derived by assuming the perfect channel state information (CSI) at the destination; then perfect CSI is replaced by estimated CSI in the decision metric. The expression for the moment generating function (MGF) of the received signal-to-noise ratio at the destination node is derived for the proposed scheme. Using the MGF expressions, they obtain the analytical symbol error rate for the proposed channel estimation-based decoders for general OSTBCs with M-ary phase-shift keying modulation symbols.

Multi-User Millimeter Wave MIMO With Full-Dimensional Lens Antenna Array

Millimeter wave (mmWave) communication by utilizing lens antenna arrays is a promising technique for realizing cost-effective 5G wireless systems with large MIMO (multiple-input multiple-output) but only limited radio frequency (RF) chains. This paper studies an uplink multi-user mmWave single-sided lens MIMO system, where only the base station (BS) is equipped with a full-dimensional (FD) lens antenna array with both elevation and azimuth angle resolution capabilities, and each mobile station (MS) employs the conventional uniform planar array (UPA) without the lens. By exploiting the angle-dependent energy focusing property of the lens antenna array at the BS as well as the multi-path sparsity of mmWave channels, we propose a low-complexity path division multiple access (PDMA) scheme, which enables virtually interference-free multi-user communications when the angle of arrivals (AoAs) of all MS multi-path signals are sufficiently separable at the BS. To this end, a new technique called path delay compensation is proposed at the BS to effectively transform the multi-user frequency-selective MIMO channels to parallel frequency-flat small-size MIMO channels for different MSs, for each of which the low-complexity single-carrier(SC) transmission is applied. For general scenarios with insufficient AoA separations, analog beamforming at the MSs and digital combining at the BS are jointly designed to maximize the achievable sum-rate of the MSs based on their effective MIMO channels resulting from path delay compensation. In addition, we propose a new and efficient channel estimation scheme tailored for PDMA, which requires negligible training overhead in practical mmWave systems and yet leads to comparable performance as that based on perfect channel state information (CSI).

Bit-Level Reduced Neighborhood Search for Low-Complexity Detection in Large MIMO Systems

Neighborhood search algorithms (NSAs) reduce the detection complexity in a multiple-input multiple-output (MIMO) system equipped with a large number of antennas. These algorithms iteratively search for the optimal maximal likelihood (ML) vector in a chosen neighborhood and therefore, their performance depends on the probability that the desired solution vector belongs to the neighborhood. An efficient choice of neighborhood vectors which are likely to reduce the ML cost, can in-turn reduce the complexity of the NSAs. To enable this, we propose a novel MIMO detection framework by representing the transmit symbols as a polynomial function of its constituent bits. We use this framework to propose: 1) a bit-level extension for the minimum mean squared error detector to initialize neighborhood search and 2) a metric-based selection criteria to reduce the neighborhood size. Combining the two ideas, we re-frame the NSAs, namely, likelihood ascent search and reactive tabu search, and numerically show that the proposed approach significantly reduces the complexity without affecting the bit error rate.