Massive Massive Multi-input Multi-output Research Articles

An efficient multi‐probe enabled midfield over‐the‐air test method for 5G base station antenna pattern reconstruction

AbstractWith the accelerated deployment of 5G massive multi‐input multi‐output (MIMO)‐structured base stations (BSs), accurate and efficient testing of massive MIMO arrays is essential to ensure that the 5G massive MIMO antenna array can perform as expected. The increasing trend of both antenna element and array physical size makes massive MIMO antenna pattern measurement in the direct‐far‐field OTA environment unrealistic due to the signal attenuation, long measurement time, and ultra‐high test system construction cost. Therefore, the necessity of a compact, efficient, low‐complex, yet accurate OTA test method is evident, especially for 5G massive MIMO BS arrays. This paper proposes a novel multi‐probe‐enabled midfield (MF) OTA test method for 5G massive MIMO devices in compact measurement settings where antenna pattern measurement can be efficiently performed. The detailed theoretical analysis for the 5G massive MIMO array MF antenna pattern reconstruction method is presented, and the simulated validation results based on the typical 5G BS antenna arrays are provided and analysed, which demonstrate the effectiveness of the proposed MF OTA test method and can provide insights into the radio frequency parametric measurement for 5G massive MIMO devices.

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.

Estimation Error Cost Optimization of Demand Side Management Based on Massive MIMO Communication Network in Smart Grid

The estimation error cost (EEC) of smart grid under the demand side management is related to the network behavior and the accuracy of energy dispatch. In this paper, a communication and energy bidirectional link connection network (CEBCN) is proposed for decreasing the EEC of smart grid under demand side management. In the CEBCN, the network behavior is quantified to communication reliability and response validity. And the communication, implemented by massive multi-input-multi-output (MIMO), and energy dispatch can be realized, simultaneously. Also, the EEC is further decreased through a cost-priority based energy dispatch strategy proposed in this paper. Based on the CEBCN, the closed-form expressions are derived about the communication reliability and response validity. Naturally, the closed-form solution is derived for the EEC in the communication and energy transmission model, the results of which are used to investigate the EEC performance in smart grid. Numerical results validate that the reliability and validity of the proposed CEBCN and reveal the effect of various parameters on the EEC. Notably, the high EEC caused by communication instability can be reduced by at least 40% with the massive MIMO, and the cost-priority based energy dispatch strategy can further improve the EEC performance on communication infrastructure.

User group-based pilot allocation and data power optimization in cell-free massive MIMO for coexistence of UAVs and ground users

To address the pilot contamination issue in cell-free massive multi-input multi-output (MIMO) systems supporting simultaneous communication between unmanned aerial vehicles (UAVs) and ground users (GUEs), a user group-based pilot allocation algorithm is proposed. Furthermore, the system performance is enhanced by optimizing the data power. Initially, the distances between users and pilots are calculated based on the principles of maximizing spectral efficiency and utilizing the large-scale fading matrix. Ground users and UAVs are randomly divided into two groups: primary users and shared users. The contamination intensity between users is measured based on the distance between users and pilots, and shared user pairs that are allocated the same orthogonal pilots are generated. Next, a user-centric approach is employed to select a subset of access points (APs) to serve the users. The scalable fractional power optimization strategy is combined as a power optimization scheme for uplink data transmission, providing a controllable trade-off between user fairness and average spectral efficiency. Simulation results demonstrate that the user group-based pilot allocation algorithm significantly improves the system’s overall spectral efficiency, and the fractional power optimization algorithm enhances the average spectral efficiency of the system.

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.

Optimizing Antenna Topology for Massive MIMO Planar Arrays With Line-of-Sight Deterministic Channels

Utilizing the classical uniform arrays in massive multi-input multi-output (MIMO) communications will lead to rank-deficiency of channel matrix under line-of-sight (LOS) propagation scenarios, which can be further manifested as the uneven distribution of channel eigenvalues, resulting in the inability to support multi-stream data transmissions. In this paper, we are committed to addressing this problem by the topology design of non-uniform planar array (NUPA) under a generalized scene where the receiving and transmitting arrays are misaligned placed with certain placement angles and a relative rotation offset angle. Specifically, the geometric structure relationship of the arrays must be investigated to derive a general expression for the channel capacity with this NUPA deployment. By applying the singular value decomposition (SVD) method and analyzing the relationship between the eigenvalues and the misaligned angles, the impact of misaligned angles on channel capacity can be disclosed, i.e., channel capacity under specific transmission distance is related to the offset angle but irrelevant to the placement angles. The NUPA design guidelines can then be acquired by using the Kronecker product. Numerical results confirm our theoretical analysis and demonstrate the superiority of the proposed NUPA topology in promoting channel capacity.

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.

Secrecy Performance Analysis of Mixed-ADC/DAC Cell-Free Massive MIMO in the Presence of Multiple Eavesdroppers

This paper studies the secrecy performance of the cell-free massive multi-input multi-output (MIMO) system, where access points (APs) are equipped with a mixed analog-to-digital converter (ADC) and digital-to-analog converter (DAC) architecture. The distributed antenna structure is prone to multiple eavesdropping network threats. Specifically, legitimate transmissions can be affected by non-colluding and colluding eavesdroppers. Based on the above considerations, we determine the expressions of the sum achievable secrecy rate and secrecy energy efficiency (SEE) with applications of the additive quantization noise model and the conjugate performing precoding method. These provide the composite measures to analyze the effects of the mixed-ADC/DAC architecture on secrecy performance. We also propose a SEE-maximization power control (SEEMPC) scheme, which can be solved by the sequential convex approximation algorithm. In addition to verifying the effectiveness of the proposed SEEMPC algorithm, numerical simulations are carried out to evaluate the impact of some key design parameters, including the number of APs, quantization bit of coarse ADCs/DACs, ratio of ideal ADCs/DACs, and power control strategy.

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.

Robust Channel Estimation Scheme for Multi-UAV MmWave MIMO Communication with Jittering

In unmanned aerial vehicle (UAV)-assisted millimeter-wave (mmWave) communications, the communication performance is significantly degraded by UAV jitter. We formulate a UAV-assisted mmWave channel model with hybrid beamforming for the impacts of UAV jitter. Then, we derive the distribution of angle of arrivals/departures (AOAs/AODs) with random fluctuation of the UAV attitude angle. We develop an iterative reweight-based robust scheme as the super-resolution AOAs/AODs estimation method. Specifically, we introduce the partially adaptive momentum (Padam) estimation method to optimize the objective function of the jittering UAV mmWave massive multi-input multioutput (MIMO) system. Finally, compared with existing channel estimation schemes, the proposed UAV mmWave channel estimation method can achieve robust super-resolution performance in AOAs/AODs and path gains estimation with numerical results. Therefore, the proposed channel estimation scheme is very suitable for UAV mmWave massive MIMO communications with jittering.

Two Stage Beamforming in Massive MIMO: A Combinatorial Multi-Armed Bandit Based Approach

In frequency division duplex (FDD) massive multi-input multi-output (MIMO), the two-stage beamforming (TSB) using channel covariance matrices (CCM) can significantly reduce the downlink training length (DTL) and channel feedback. However, the overhead to estimate the CCM is large. In this paper, a combinatorial multi-armed bandit (CMAB) based TSB scheme is proposed without requirement of CMM. Particularly, the problem of the pre-beamforming matrix design is transformed into a CMAB problem. We consider the pre-beamforming matrix design in each slot as the arm selection in the CMAB, and convert the problem of the arm selection into a 0-1 integer linear programming problem, which can be solved by the branch-and-bound method. During the training process, the maximum likelihood method is used to detect the power of angle spectrum, and the angle range of each user is determined adaptively. We prove that the regret grows logarithmically with time, such that the proposed scheme converges towards the optimal action. Finally, simulation results demonstrate that the proposed scheme can significantly improve the spectral efficiency.

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.

Constructions of 2-D Golay Complementary Array Sets With Flexible Array Sizes for Omnidirectional Precoding in Massive MIMO

Constructing two-dimensional (2-D) Golay complementary array sets (GCASs) with flexible array sizes become an important topic due to their practical application for omnidirectional precoding in massive multi-input multi-output (MIMO) systems. In this letter, we propose two direct constructions of GCASs from 2-D extended generalized Boolean functions (EGBFs). Compared with the previous works, the presented constructions lead to more flexible array sizes for a fixed set size, which may give an extra benefit in accommodating more URA configurations. Simulation results indicate that the proposed GCASs can be employed as precoding matrices to achieve omnidirectional transmission, and behave a superior bit error rate (BER) performance over other schemes.

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.

Heterogeneous Doppler Spread-Based CSI Estimation Planning for TDD Massive MIMO

Massive multi-input multi-output (MIMO) has been recognized as a key technology for fifth generation (5G) networks. Indeed, it enables to meet the challenging requirements of increased coverage, capacity and massive connectivity. Nevertheless, its performance is limited by channel estimation overhead which scales in time division duplexing (TDD) systems with the number of active users, consequently limiting the system’s efficiency. In this paper, we address the bottleneck of channel estimation in TDD Massive MIMO through optimized lean carrier. We propose an adaptive uplink training scheme that exploits the heterogeneous Doppler spreads of the different users in order to reduce the periodicity of uplink sounding signal transmission. The idea is to enable the network to plan its uplink training decisions for long time periods while taking into consideration user mobility. To this end, we formulate a two time-scale control problem that takes into account the different rates of the wireless channel and location changes. In the fast time scale, an optimized uplink training policy is derived based on estimated user locations. In the slow time scale, positioning decisions are optimized. Simulation results show that the optimized training policies provide considerable improvement in the system efficiency even with partial location knowledge.

Average Sum Rate of D2D Underlaid Multigroup Multicast Cell-Free Massive MIMO With Multi-Antenna Users

This letter proposes a device-to-device (D2D) underlaid multigroup multicast cell-free massive multi-input multi-output (MIMO) system with low-resolution digital-to-analog converters (DACs) at distributed access points (APs) and multiple antennas at both the APs and users. The closed-form rate expressions for cell-free users (CFUEs) and D2D users (DUEs) are derived using imperfect channel state information (CSI). Finally, simulation results for the achievable average sum rate shed light on the impact of key parameters and highlight the significance of the collaborative deployment of D2D and multigroup multicast cell-free massive MIMO.

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.