Massive Multi-input Multi-output Systems Research Articles

Fast Localizing for Anonymous UAVs Oriented Toward Polarized Massive MIMO Systems

The topic of anonymous unmanned aerial vehicle (UAV) localizing based on angle estimation has been frequently discussed in the past few years. However, the existing methodologies are inefficient in a massive sensor arrays scenario. To avoid such drawback, a cooperative three-dimensional (3D) positioning methodology is introduced. The critical idea of the proposed localizing method is to estimate the two-dimensional angle of the anonymous UAV via a polarized massive multi-input multi-output (MIMO) system. To reduce the computational burden and explore the nature of the multi-dimensional data, a tensor compressive sampling (TCS) framework is proposed. Moreover, a closed-form estimation strategy is developed for 2D direction finding. Our framework is shown to be more efficient than the existing algorithm in terms of hardware/software complexity. Besides, it is suitable for a polarized MIMO system with an arbitrary array geometry. Several simulation examples are provided to show its improvement of the new methodology.

Beamforming Design in Short-Packet Transmission for URLLC in Cell-Free Massive MIMO System

Low latency, high reliability, and high data rate are three performance indicators of Ultra-reliable and low latency communication (URLLC) systems, but they are not able to be met simultaneously. Based on the determined latency and reliability requirements, this paper investigates the problem of the weighted sum rate (WSR) in cell-free massive multi-input multi-output (MIMO) system with multiple access points (APs). User-centric access point selection is considered to further reduce the computational complexity for AP used with acceptable performance loss. The transmitting beamforming is designed to mitigate inter-user interference so as to maximize the WSR under the backhaul and transmitting power constraints. Since the proposed problem is non-convex, two algorithms are proposed to solve the problem under the general SINR regime and the high SINR regime, respectively. For the former, we first approximate the objective function with the difference of convex (DC) programming and reformulate the problem into a semi-definite relaxation (SDR) form. Afterward, auxiliary variables are introduced in accordance with the successive convex approximation (SCA) method to further transform the nonconvex problem into a convex one. In the end, the sub-optimal WSR is obtained by iteratively solving a final convex problem. For the latter, the well-known WMMSE method is applied to address the WSR problem. Numerical results demonstrate the efficiency of our proposed algorithm, e.g., the maximum performance gain of the proposed algorithm is about 25% compared to the classical MMSE method. And the user-centric approach achieves 8 times computational complexity reduction with 4% performance loss.

LSTM-GRU Model-Based Channel Prediction for One-Bit Massive MIMO System

In this paper, we propose a deep learning (DL) model to estimate the channel matrix for a massive multi-input multi-output (MIMO) system with a one-bit analog-to-digital converter (ADC). In the literature, various DL methods were proposed to estimate the channel matrix; however, they were limited to a small range of noise levels. We are driven to break the data appropriately so that the estimation performance improves with the signal-to-noise ratio (SNR) because the primary premise is that the DL model should perform better in the case of higher SNR. We suggested breaking the received signal into sequences to consider the entire system as multiple sub-systems, where each antenna serves the same number of users considered as a sub-system. Consequently, the model trained the data received by each sub-system sequentially. However, the conventional back-propagation with extensive training, because of the large number of base station (BS) antennas, causes a vanishing gradient, leading to training failure. Hence, we combine long short-term memory (LSTM) with gated recurrent units (GRU) to accelerate the training speed of the network under the hypothesis that LSTM effectively processes MIMO data and overcomes the vanishing gradient problem, whereas GRU accelerates the convergence rate. The results show that our model outperforms other methods at different SNRs and various numbers of BS antennas.

Multi-modal fusion for millimeter-wave communication systems: A spatio-temporal enabled approach

In millimeter-wave (mmWave) massive multi-input multi-output (MIMO) systems, beam selection can enhance channel capacity and reduce error rate. However, existing beam selection methods for MIMO systems rely on traditional optimization techniques, which may not be feasible for real-time data transmission. Hence, this paper proposes a novel beam prediction approach via multi-modal fusion and spatio-temporal-enabled features. Specifically, the proposed method can improve MIMO system performance by integrating information from multiple perspectives, such as temporal, spatial, and frequency domains. Moreover, the proposed approach is based on a deep learning framework utilizing 3-dimensional convolutional neural networks (3DCNNs) and transformer module to capture spatio-temporal data correlations. Extensive simulation results show that the proposed mechanism outperforms massive benchmarks in terms of beam prediction accuracy from 2.63% to 11%.

A Four-cell Division Method for Pilot Sequence Design to Reduce Pilot Pollution in Massive MIMO systems

A method for pilot sequence design with the Zadoff-Chu (ZC) sequence as a base sequence is proposed to address the problem of pilot pollution in massive multi-input multi-output (MIMO) systems. Instead of the odd-length ZC sequence currently in use, the ZC sequence of even length serves as the basis of the pilot. It has the advantage of a very small cross-correlation coefficient under the premise that the sum of cyclic displacement is odd. The results of the four-cell division method are compared between the even-length and odd-length ZC pilot sequences. The MATLAB simulation shows that the signal-to-noise ratio and the channel estimation mean square error of the designed even-length ZC pilot sequence are better than those of the existing odd-length ZC pilot sequence in the case of four-cell division.

Integrated Sensing and Communications With Joint Beam-Squint and Beam-Split for mmWave/THz Massive MIMO

Integrated sensing and communications (ISAC) has attracted tremendous attention for the future 6G wireless communications systems. To improve the transmission rates and sensing accuracy, massive multi-input multi-output (MIMO) technique is leveraged with large transmission bandwidth in millimeter wave (mmWave)/terahertz (THz) band. However, the growing size of antenna array and transmission bandwidth results in the beam-squint effect, which hampers the performance of communications. Moreover, the time overhead of the traditional sensing algorithm is prohibitively high for practical systems. In this paper, instead of alleviating the beam-squint effect, we take advantage of joint beam-squint and beam-split effect and propose a novel integrated sensing and communications scheme for massive MIMO system. Specifically, with the beam-squint effect, the base station (BS) utilizes the true-time-delay (TTD) lines to steer the beams of different OFDM subcarriers towards distributive directions simultaneously. Different users then feedback their respective subcarrier frequency with the maximum array gain to BS, based on which BS could calculate the directions of the users. Moreover, by selecting sub-array with the inter-antenna spacing larger than half-wavelength, the beam-split effect can be introduced and exploited to expand the sensing range. The proposed sensing method operates over frequency-domain, and the intended sensing range is covered by all the subcarriers simultaneously, which significantly reduces the time overhead compared to the conventional sensing scheme. Simulation results have demonstrated the effectiveness as well as the superior performance of the proposed ISAC scheme.

Compute-and-forward transmission scheme in cell-free massive MIMO systems

Cell-free massive multi-input-multi-output (MIMO) systems comprise a large number of distributed access points (APs) to serve a small number of user equipments (UEs). In this paper, compute-and-forward (CF) is investigated for uplink in cell-free massive MIMO. We propose a novel reverse CF precoding scheme for downlink transmission for cell-free massive MIMO. The APs send the linear equations of messages over a multiple access channel, and the UEs recover messages without interferences. The max–min power allocation is investigated to provide uniformly good service to all UEs. Numeral results demonstrate the performance improvement provided by the proposed reverse CF precoder against zero-forcing, conjugate beamforming, and linear minimum mean-square error precoder.

Joint Channel Estimation and Mixed-ADCs Allocation for Massive MIMO via Deep Learning

Millimeter wave (mmWave) multi-user massive multi-input multi-output (MIMO) is a promising technique for the next generation communication systems. However, the hardware cost and power consumption grow significantly as the number of radio frequency (RF) components increases, which hampers the deployment of practical massive MIMO systems. To address this issue and further facilitate the commercialization of massive MIMO, mixed analog-to-digital converters (ADCs) architecture has been considered, where parts of conventionally assumed full-resolution ADCs are replaced by one-bit ADCs. In this paper, we first propose a deep learning-based (DL) joint pilot design and channel estimation method for mixed-ADCs mmWave massive MIMO. Specifically, we devise a pilot design neural network whose weights directly represent the optimized pilots, and develop a Runge-Kutta model-driven densely connected network as the channel estimator. Instead of randomly assigning the mixed-ADCs, we then design a novel antenna selection network for mixed-ADCs allocation to further improve the channel estimation accuracy. Moreover, we adopt an autoencoder-inspired end-to-end architecture to jointly optimize the pilot design, channel estimation and mixed-ADCs allocation networks. Simulation results show that the proposed DL-based methods have advantages over the traditional channel estimators as well as the state-of-the-art networks.

A novel blind spatial-based random access to pilots (BSB-RAP) in overloaded massive MIMO systems

In this study, to increase the success rate of active user admission in overloaded massive multi-input multi-output (MIMO) systems, a new spatially based random access to pilots (RAP) is proposed to assign orthogonal pilots to the users requesting network access. Therefore, by increasing the acceptance rate of users in a cell, this approach reduces the training overhead and waste of resources. In the massive MIMO for crowd scenarios, the main issue is the limited number of available orthogonal pilots employed by the users in the channel estimation process. This novel approach as spatially based random access enables us to have more connected users during every coherence interval (CI) despite the mentioned limitation. Intrinsic angular domain sparsity of massive MIMO channels and the sporadic traffic of users can help us obtain the spatial features of active UEs in a blind continuous compressed sensing (CCS) approach. Proposed approach is to use a continuous compressed sensing technique based on a prior optimization that provides users’ angle of arrival (AoA) and an innovative space-based RAP protocol to assign orthogonal pilots to active users in coherent transmission. Unlike the previous works, this strategy does not need to limit the number of users to the number of available orthogonal pilots due to the employed spatial degrees of freedom.

Dynamic Beam-Based Random Access Scheme for M2M Communications in Massive MIMO Systems

Internet of things, supported by machine-to-machine (M2M) communications, is one of the most important applications for the 6th generation (6G) systems. A major challenge facing by 6G is enabling a massive number of M2M devices to access networks in a timely manner. Therefore, this paper exploits the spatial selectivity of massive multi-input multi-output (MIMO) to reduce the collision issue when massive M2M devices initiate random access simultaneously. In particular, a beam-based random access protocol is first proposed to make efficient use of the limited uplink resources for massive M2M devices. To address the non-uniform distribution of M2M devices in the space and time dimensions, an Markov decision process (MDP) problem with the objective of minimizing the average access delay is then formulated. Next, we present a dynamic beam-based access scheme based on the double deep Q network (DDQN) algorithm to solve the optimal policy. Finally, simulations are conducted to demonstrate the effectiveness of the proposed scheme including the model training and random access performance.

A New Approach to Pilot Contamination in Massive MIMO Systems for 5G Communication Networks with Butterfly Optimization Algorithm

Communication technologies, in particular fifth-generation (5G) mobile phones, are currently developing around the world. Massive multi-input-multi-output (MIMO) systems are usually used in this type of communication network so that a large number of users are able to connect to the network and use its services. Massive MIMO systems are a good solution for 5G communication networks leveraging antennas to improve communication performance. To increase output communication power in 5G networks, the problem was optimized with the butterfly optimization algorithm (BOA). According to the simulation results, the proposed method outperformed other similar communication methods, such as the Random, SPRS, WGC PD and SPRS+WGC PD algorithms in terms of the signal-to-interference-plus-noise ratio (SINR) and rate of connection of users to the network.

MGGAN-Based Hybrid Beamforming Design for Massive MIMO Systems Against Rank-Deficient Channels

The design of hybrid beamforming (HBF) is one of the key issues in millimeter wave (mmWave) and terahertz (THz) massive multi-input multi-output (MIMO) communications. In particular, only rank-deficient channel state information (CSI) can be acquired in hybrid MIMO architecture, which enhances design challenges. However, this practical problem of rank-deficient channels has been avoided by most of the existing studies on HBF. In this letter, we propose a new deep learning (DL) network model called Multi-Generator Generative Adversarial Network (MGGAN) to accomplish the design of HBF against rank-deficient channels. Specifically, MGGAN includes three generators for recovering the rank-deficient channels, achieving the analog and the digital beamforming matrices, respectively. In addition, a spectrum efficiency (SE) module is introduced to take system SE as the optimization goal of the network. Simulation results show the superiority of our proposed MGGAN model in SE performance over several DL network architectures and classical HBF algorithms. Moreover, it is more robust against the rank-deficiency of hybrid architecture MIMO channels.

Geometry-Based Stochastic Model and Statistical Characteristic Analysis of Cell-Free Massive MIMO Channels

Cell-free massive multi-input multi-output (MIMO) systems exhibit many characteristics that differ from those of traditional centralized massive MIMO systems, and there are still research gaps in the modeling of cell-free massive MIMO channels. In this paper, a geometry-based stochastic model (GBSM) that combines the double-ring model and hemisphere model to comprehensively consider the distribution of scatterers in the environment was proposed for cell-free massive MIMO channels. Combined with the line-of-sight (LoS) path, single scattering path, and double scattering path components, the channel matrix between the access points (APs) and the user was derived. The proposed model fully considers geometric parameters such as the arrival/departure direction, elevation angle, time delay, and distance, which can accurately characterize the channel. Then, we proved that the traditional channel model, standard block-fading model, and spatial basis expansion model (SBEM) adopted in a cell-free massive MIMO system could not describe the nonstationarity in the space, time, and frequency domains, whereas the proposed GBSM could. Statistical characteristics of the channel were analyzed, including the space cross-correlation function (CCF), time autocorrelation function (ACF), Doppler power spectral density (PSD), level crossing rate (LCR), and average fade duration (AFD). Then, we investigated the proposed model by simulating the space CCF, time ACF, Doppler PSD, LCR, and AFD under the conditions of two different scatterer densities. Through simulations and analyses, some new features of cell-free massive MIMO channels were identified, providing a theoretical basis for in-depth research on cell-free massive MIMO systems. Finally, the measurement-based scenario and the WINNER II channel model are compared to demonstrate that the GBSM is more practical to characterize real cell-free massive MIMO channels.

Compressive Channel Estimation Based on the Deep Denoising Network in an IRS-Enhanced Massive MIMO System.

Integrating large intelligent reflecting surfaces (IRS) into a millimeter-wave (mmWave) massive multi-input-multi-output (MIMO) technique has been a promising approach to enhance the performance of the wireless communication system with the channel state information (CSI). Most existing work assume that ideal channel estimation can be obtained, but the proposed high-dimensional cascaded MIMO channels and passive reflectors pose a great challenge to these methods. To address the abovementioned problems, we proposed a new method for the reduction of training overhead in IRS with a partial ON/OFF model and an optimizing strategy for pilot design approach. The energy consumption of large-scale antenna arrays and the pilot overhead in the training phase of signal transmission are greatly reduced. Besides, we proposed an improved deep residual shrinkage denoising network, which possesses better denoising performance with a soft thresholding model. The channel data can be denoised by deep learning methods, which greatly improve the accuracy of channel estimation. Simulation results demonstrate that the superiority of the proposed network over prior solutions.

Generalized Superimposed Training Scheme in Cell-Free Massive MIMO Systems

Regular pilots in a massive multi-input multi-output (MIMO) system with large number of users suffer from the pilot contamination effect due to limited training time. In this paper, for a cell-free massive MIMO system, we have proposed a generalized superimposed pilot (GSP) scheme, where the available number of pilots are equal to the coherence time slots, and the transmitting data symbols are spread over the coherence time with the help of simple precoding. Further, in order to keep the system scalable, a low complexity and distributed time processing approach is employed, and the corresponding rate components are analyzed. It is shown that with careful design of precoding matrix and number of data symbols, the GSP symbols can provide much better channel estimation and data detection performance, as compared to the regular pilot scheme and the conventional superimposed scheme. These results have been verified via simulations. It is also inferred that centralized processing in cell free system improves the data detection performance than localized processing. Iterative data detection at the central node also improves the MSE of data estimates. The pilot contamination effect, is significantly reduced due to availability of larger number of pilots, as compared to regular pilots transmission.

Multigroup Multicast Downlink Cell-Free Massive MIMO Systems With Multiantenna Users and Low-Resolution ADCs/DACs

In this article, the effects of multiantenna users and low-resolution analog-to-digital converters/digital-to-analog converters (ADCs/DACs) on multigroup multicast downlink cell-free massive multi-input multi-output (MIMO) systems are explored. Leveraging on the distributed conjugate beamforming receiver and the generalized additive quantization noise model, we first derive a closed-form expression for the achievable sum spectral efficiency (SE). The resultant expression enables us to precisely quantify the effects of key parameters, such as the transmit power of access points (APs), the numbers of groups, users’ antennas, APs’ antennas, and quantization bits. Due to the joint influence of channel hardening property, multiantenna gain, and intergroup interference, the research becomes sophisticated particularly. The result shows that when the sum-SE is small, the multicast is better than the unicast and vice versa. Typically, it reveals that the full-resolution DAC can be replaced by 5-bit low-resolution DAC to achieve the same performance, and the additional antenna at users can effectively enhance the system performance. Then, according to a generated energy cost model, the formula of the energy efficiency (EE) is obtained. When the DAC quantization is equal to 2-bit, it can achieve the maximum sum EE. Finally, the simulation results are provided to verify the derived results.

Generalized Superimposed Training Scheme in IRS-Assisted Cell-Free Massive MIMO Systems

In this article, for a cell-free massive multi-input multi-output (MIMO) system assisted with intelligent reflective surface (IRS) panels, a generalized superimposed pilot (GSP) training scheme is proposed, where the available number of pilots are equal to the coherence time slots, and the transmitting data symbols are spread over the coherence time with the help of simple precoding. Further, in order to keep the system scalable, a low complexity approach is employed for processing, and the corresponding rate components are analyzed. It is shown that with careful design of precoding and number of data symbols, the GSP symbols can provide much better channel estimation and data detection performance, as compared to the regular pilot scheme and the conventional superimposed scheme. These results, verified via simulations, shows that the centralized processing improves the data detection performance than localized processing. The pilot contamination effect, is significantly reduced due to the availability of larger number of pilots, as compared to the regular pilots transmission. For four IRS panels in the system, the proposed scheme is shown to reduce the channel estimation errors by 74% approximately.

Localization and Channel Reconstruction for Extra Large RIS-Assisted Massive MIMO Systems

Reconfigurable intelligent surface (RIS) is a promising material that can passively manipulate electromagnetic waves and improve the quality of mobile communication services at a low cost. It can be made large to extend the service region and acquire the ability for localization enhancement. However, the lack of a signal processing module at the RIS makes channel estimation a difficult problem. When employing an extra large RIS, users are in the near field of the RIS and the channel shows spatial nonstationarity, making the problem more complicated. In this paper, these challenges are addressed by a low-overhead joint localization and channel reconstruction scheme proposed for extra large RIS-assisted massive multi-input multi-output systems. This scheme can accurately identify the visibility region (VR) of each user, find the user positions by exploiting the near-field characteristics, and reconstruct the channels by jointly utilizing the pilots of multiple users. Numerical results demonstrate that the identified VR covers more than 97% of the real VR, and the user localization accuracy reaches centimeter level at the millimeter-wave frequency band. A more accurate channel reconstruction result than that of existing works can also be obtained. This work verifies the great potential of RIS and is a solid step toward the integration of communication and sensing.

Pilot Design of the 1-Bit Massive MIMO in Rayleigh-Fading Channel

In the wireless communication system, the pilot is essential for channel estimation. This paper study the pilot design of the 1-bit massive multi-input multi-output system with the flat Rayleigh fading channel. This paper aims to design the pilot matrix minimizing the estimation error of the Bussgang linear minimum mean squared error (BLMMSE) channel estimator, of which the estimation error is given by a closed-form formula. An approximate BLMMSE channel estimation error is derived in the low signal-to-noise ratio (SNR) region. The lower bound of the approximate channel estimation error for all possible pilot matrices is derived. The analysis shows that the pilot matrix with pairwise orthogonal and equal-norm row vectors and equal-norm column vectors is optimal in terms of approximate channel estimation error. The hypersphere-based steepest descent (HSD) algorithm is proposed to design the pilot matrix by optimizing the performance of the BLMMSE estimator. The numerical results show that the proposed HSD algorithm has a noticeable improvement in channel estimation compared with the DFT pilot and the random pilot. The HSD algorithm is also valid for the near maximum likelihood estimator and SNR mismatch.

Sub-6 GHz band massive MIMO antenna system for variable deployment scenarios in 5G base stations

A compact massive multi input multi output (MIMO) antenna system with 1 × 4 (sector) subarray configuration operating at sub-6 GHz range for 5th generation (5G) base stations has been designed and analysed in various geometries (rectangular, triangular, and hexagonal). The capacity of the system can be increased by more than 10 times whereas the energy efficiency can be increased 100 times using the massive MIMO system (Larsson et al. in IEEE Commun Mag 52(2):186–195, 2014). A maximum of 5 sectors has been used, with each sector comprising of 1 × 4 subarray elements. Each sector consists of three layers, in which 1 × 4 patches are located on its top layer whereas it’s feeding network and ground plane are placed at the bottom layer and middle layer respectively. The entire system can operate in two modes, individual port operation and massive MIMO array operation with beam steering capabilities. The measured bandwidth of the system is 140 MHz that covers the frequencies from 3.36 to 3.50 GHz in sub-6 GHz band. The overall dimension of a unit subarray in terms of length, width and height is 280.5 × 56.1 × 2 mm3. The gain of an individual port is found to be 12.77 dBi and the overall gain of a single panel with 5 sectors arranged in rectangular form is 19.73 dBi. Mutual coupling among all the ports has been kept less than − 16 dB. The operating frequency range of the antenna array system is chosen in the range of 3.3–3.8 GHz, as this band has been allocated and targeted across the globe to enable 5G in Sub-6 GHz band.