Massive Multi-input Multi-output Research Articles

Energy efficiency enhancement in millimetre-wave MIMO-NOMA using three layer user grouping and adaptive power allocation algorithm

Massive multi-input multi-output (MIMO) is realized as the principal technology in the emerging fifth generation communication network system. Hybrid structure uplink communication is considered for the MIMO Non-orthogonal multiple access (MIMO-NOMA) system’s beam forming and power efficiency improvement through the novel three-layer user grouping. In the three-layer user grouping, the K-means algorithm is adopted in the initial layer for grouping users among different clusters and rectifying clustering errors in the third layer. The second layer used the agglomerative nesting (AGNES) algorithm for merging smaller clusters based on the channel correlation and angles of arrival similarity. The beam selection is carried out to minimize the intrusion of defined beam elements and to overcome beam overlapping problems. The non-convex optimization of the power allocating problem is modified as a convex problem by introducing a Quadratic transform (QT) to minimize each user’s data rate requirement. The algorithm of coati optimization is proposed to iteratively optimize the power allocation problem. The simulation results show that our proposed methodology goes beyond the existing schemes in terms of energy efficiency beyond the maximum power and achievable sum rate can be achieved.

A new pilot assignment scheme for mitigating pilot contamination in uplink massive multi-input–multi-output (MIMO) systems

Pilot contamination is a serious issue in massive multi–input–multi–output systems which significantly degrades system performance. In this paper, we investigate a new pilot assignment scheme by integrating two-dimensional genetic algorithm with Tabu-Search algorithm (TS) to mitigate the pilot contamination problem. Firstly, we design a two-dimensional genetic algorithm equipped with elitism strategy as a principal algorithm for solving the pilot assignment problem; then, aiming to enhance the convergence speed of the genetic algorithm to the ideal optimal solution, we integrate TS with the genetic algorithm. This integrated pilot assignment scheme, henceforth designated as GATS-PA, is found to be powerful in mitigating the pilot contamination problem. Numerical simulation results verify that the proposed pilot assignment scheme is very close to the ideal optimal solution with few numbers of iterations and outperforms existing methods in terms of enhancing the average uplink rate per user over a wide range of simulation parameters.

2D Beam Domain Statistical CSI Estimation for Massive MIMO Uplink

In this paper, we investigate the beam domain statistical channel state information (CSI) estimation for the two-dimensional (2D) beam-based statistical channel model (BSCM) in massive multi-input multi-output (MIMO) systems. The problem is to estimate the beam domain channel power matrices (BDCPMs) based on multiple received pilot signals. A received signal model showing the relation between the statistical properties of the received pilot signals and the BDCPMs is derived. On the basis of the received signal model, we formulate an optimization problem with the Kullback-Leibler (KL) divergence. By solving the optimization problem, a novel method to estimate the statistical CSI without the estimates of instantaneous CSI is proposed. We further reduce the complexity of the proposed method by utilizing the circulant structures of particular matrices in the algorithm. We also showed the generality of the proposed method by introducing another application, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., estimation of the angle domain channel power matrix. Simulation results show that the proposed method has good convergence and can obtain sparse BDCPMs. Compared with the regularized multiple measurement vector focal underdetermined system solver (RM-FOCUSS) algorithm, the proposed algorithm obtains overall more accurate statistical CSI with much lower complexity and brings significant performance gains when used in channel estimation.

SE Analysis and GEE Optimization of Hardware-Impaired Multi-Cell Massive MIMO Systems With Rician Fading and Phase Shifts

We consider a practical hardware-impaired multi-cell massive multi-input multi-output (mMIMO) system with spatially-correlated Rician fading channels, whose line-of-sight component experiences a random phase shift. The multi-cell mMIMO system employs two-layer large-scale fading decoding (LSFD) to mitigate the interference due to pilot contamination. We consider a dynamic analog-to-digital converter (ADC) architecture at the base stations (BS), which enables us to independently vary the resolution of ADC connected to its each antenna, and thus properly choose them to achieve a high spectral efficiency (SE). Both BS and user equipments (UEs) are also equipped with low cost radio frequency chains, which introduce additional hardware impairments. We first derive a closed-form SE expression for this system, and then optimize the UEs uplink data power and LSFD vectors to maximize the global energy efficiency metric. The proposed optimization, which is practically implementable due to its closed-form updates, is derived by combining the quadratic and Dinkelbach transforms, and generalized Rayleigh quotient. We investigate the ADC resolution and hardware impairments values for which LSFD is highly effective, and the values for which its effectiveness reduces.

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.

Massive MIMO-OFDM Transmission Without Cellular Networks Using Frequency - Selective Fading Channels

Abstract: For cell-free massive multi-input multi-output (CF-m-MIMO) across frequency-selective fading channels, this system introduces and determines the effectiveness of the orthogonal frequency-division multiplexing (OFDM)-based multi-carrier transmission. The CF-m-MIMO-OFDM system can accommodate a substantial user base and is flexible enough to offer a range of data rates for usage in a variety of contexts. With its scalability and flexibility, the CF-m-MIMO-OFDM transmission network can serve a large number of users at variable data rates. It is proposed to beamform in the frequency-domain conjugate, to choose a pilot, and to allocate resources differently for each user. User-specific resource allocation, pilot selection, and frequency-domain conjugate beamforming are all suggested. The CF-m-MIMO-OFDM system can support a large number of users and can offer varying data rates to accommodate a wide range of applications.

Wireless Information and Power Transfer Enabled Massive MIMO Multi-Way Relaying

This work considers a multi-way massive multi-input multi-output (mMIMO) relaying system wherein several wireless information and power transfer capable users, first harvest energy, and then exchange information via a relay. The mMIMO relaying system is practically modelled by considering spatially-correlated relay-user channels, and by using a non-linear energy harvesting (EH) model. A closed-form spectral efficiency expression is derived for this system, which is then used to jointly allocate the relay and users transmit powers, and the charging time to optimize the weighted sum energy efficiency (WSEE) metric. The spatially-correlated channel and the non-linear EH model not only couple the optimization variables, but also introduce nested scalar fractional optimization functions. This makes the WSEE metric non-convex. The proposed solution first uses the existing quadratic transformation (QT) to decouple the scalar fractional forms. It then applies novel results, which are developed by extending the QT framework, to decouple the optimization variables. The proposed WSEE optimization algorithm is shown to provide close-to-optimal WSEE. It is also shown that channel spatial correlation increases the amount of energy harvested by the users, but reduces their WSEE.

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.

An energy-efficient resource allocation strategy in massive MIMO-enabled vehicular edge computing networks

The vehicular edge computing (VEC) is a new paradigm that allows vehicles to offload computational tasks to base stations (BSs) with edge servers for computing. In general, the VEC paradigm uses the 5G for wireless communications, where the massive multi-input multi-output (MIMO) technique will be used. However, considering in the VEC environment with many vehicles, the energy consumption of BS may be very large. In this paper, we study the energy optimization problem for the massive MIMO-based VEC network. Aiming at reducing the relevant BS energy consumption, we first propose a joint optimization problem of computation resource allocation, beam allocation and vehicle grouping scheme. Since the original problem is hard to be solved directly, we try to split the original problem into two subproblems and then design a heuristic algorithm to solve them. Simulation results show that our proposed algorithm efficiently reduces the BS energy consumption compared to other schemes.

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.

Massive MIMO Channel Measurement Data Set for Localization and Communication

Channel state information (CSI) needs to be estimated for reliable and efficient communication, however, user location information is hidden inside and can be further exploited. This article presents a detailed description of a Massive Multi-Input Multi-Output (MaMIMO) testbed and provides a set of experimental location-labeled CSI data. We first focus on the design of the hardware and software of a MaMIMO testbed for gathering multiple CSI data sets. We then show this data can be used for learning-based localization and enhanced communication research. The presented data set is made fully available to the research community. We illustrate that a CSIbased joint communication and sensing processing pipeline can be evaluated and designed based on the collected data set. Specifically, the localization output obtained by a convolutional neural network (CNN) trained on the data sets is used to schedule users to improve the spectral efficiency (SE) of the communication system. Finally, we pose promising directions for further exploiting this data set and creating more data sets.

High-Performance Low-Complexity Hierarchical Frequency Synchronization for Distributed Massive MIMO-OFDMA Systems

We propose a high-performance yet low-complexity hierarchical frequency synchronization scheme for orthogonal frequency-division multiple-access (OFDMA) aided distributed massive multi-input multi-output (MIMO) systems, where multiple carrier frequency offsets (CFOs) have to be estimated in the uplink. To solve this multi-CFO estimation problem efficiently, we classify the active antenna units (AAUs) as the master and the slaves. Then, we split the scheme into two stages. During the first stage the distributed slave AAUs are synchronized with the master AAU, while the user equipment (UE) is synchronized with the closest slave AAU during the second stage. The mean square error (MSE) performance of our scheme is better than that of the representative state-of-the-art baseline schemes, while its computational complexity is substantially lower.

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.

Design and Optimization of Hardware Impaired Multi-Cell Rician-Faded mMIMO Systems With Pilot Decontamination Precoding

We consider a hardware-impaired multi-cell Rician-faded massive multi-input multi-output (mMIMO) system with two-layer pilot decontamination precoding, also known as large-scale fading precoding (LSFP). We derive a closed-form spectral efficiency (SE) expression by assuming a flexible dynamic analog-to-digital converter (ADC)/digital-to-analog converter (DAC) architecture, and hardware-impaired radio frequency chains at the base stations (BSs) and user equipments. The dynamic ADC/DAC architecture enables us to vary the resolution of ADC/DAC connected to each BS antenna, and suitably choose them to maximize SE. We design a distortion-aware minimum mean squared error (DA-MMSE) precoder, and investigate its usage by combining it with the two-layer LSFP, and conventional single-layer precoding (SLP). We show that the DA-MMSE precoder with SLP outperforms its distortion-unaware counterpart, which is used with LSFP. We analytically show that for pure LoS channels, the LSFP reduces to SLP, and its implementation can thus be avoided. It is shown that the LSFP can tolerate high hardware impairments at the BS, but is extremely sensitive to the ADC resolution of the user. We also optimize the global energy efficiency by using a minorization-maximization based algorithm, and show its improved performance over the conventional SE optimization techniques.

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%.

Revisiting Dirty Paper Coding to Hybrid Precoding for Massive MIMO Downlink Broadcast Channel

It is well-known that dirty paper coding (DPC)-based transceiver can achieve the sum capacity of a multi-input multi-output (MIMO) broadcast channel. In this paper, we study DPC-based hybrid precoding to maximize the sum-rate of a massive MIMO (mMIMO) downlink broadcast channel, subject to the constant modulus constraint of the analog phase shifter network (PSN). Using the duality between a massive MIMO broadcast channel (mMIMO-BC) and a massive MIMO multiple access channel (mMIMO-MAC), we propose to first optimize the transmitters and the hybrid receiver for the mMIMO-MAC, before obtaining the hybrid precoder of mMIMO-BC according to the MAC-BC duality. The algorithm is also extended for mMIMO orthogonal frequency division multiplexing (OFDM) systems over broadband frequency-selectivity channels. Numerical results show that the proposed DPC-based hybrid precoding can significantly outperform the existing linear counterpart, especially in the case of numerous users. In simulations based on the 5G new radio (5G-NR) standard, the relative gain of the proposed scheme appears prominent, which suggests that the DPC can be a worthy candidate for the future generation of wireless communications.

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.

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.