Performance Of Massive MIMO Research Articles

Deep learning based hybrid precoder for optimal power allocation to improve the performance of massive MIMO

Deep learning based hybrid precoder for optimal power allocation to improve the performance of massive MIMO

Performance of Massive MIMO Aided by Reflective Intelligent Surfaces

Performance of Massive MIMO Aided by Reflective Intelligent Surfaces

Uplink Performance of Hardware-Impaired Cell-Free Massive MIMO With Multi-Antenna Users and Superimposed Pilots

Uplink Performance of Hardware-Impaired Cell-Free Massive MIMO With Multi-Antenna Users and Superimposed Pilots

New look at secure performance of massive MIMO with low-resolution DACs

Since the requirement of secure transmission for massive multiple-input–multiple-output (mIMO) is high, we consider a secure massive MIMO system in presence of multiple digital to analog converters (DACs). We deal with complicated performance analysis of the proposed system including a base station, a legitimate user, and an eavesdropper while these nodes are equipped with multiple antennas. In particular, we intend to present the closed-form expression for the ergodic secrecy rate of the system and also calculate the optimal power allocation for better performance gain. The simulation analysis shows that quantization bits, transmit signal to noise ratio at the base station, and power allocation plays a major role in enhancing the performance of the system.

Downlink Performance of CF Massive MIMO Under Wireless-Based Fronthaul Network

This paper investigates the downlink (DL) performance of cell-free (CF) massive multiple-input multiple-output (mMIMO) systems under a wireless mMIMO-based fronthaul network operation. Particularly, we consider multiple edge-cloud processors (ECP)s serving access points (AP)s using one of three possible fronthaul network operations, namely, microwave, millimeter wave (mmWave), or hybrid microwave/mmWave. Under each fronthaul network operation, we analyze the achievable DL data rates for two different microwave-based operations of the access link (APs-users), namely, distributed and centralized operations, while assuming APs with/without decoding capabilities. In the distributed operation, APs are responsible for performing both channel estimation and DL data precoding tasks, whereas ECPs are the responsible entities for carrying out such tasks in the centralized counterpart. Our results show that the integration between the centralized access link operation and the hybrid-based fronthaul network provides the highest DL data rates when APs are empowered with decoding capabilities. However, integrating the distributed access link operation with the microwave-based fronthaul network achieves ultimate performance when APs are not supported with decoding capabilities. Interestingly, we reveal that APs with low fronthaul capacities dominantly control the preferred network configuration in-terms of the densities and the number of deployed antennas for both APs and ECPs.

Uplink Performance of Cell-Free Massive MIMO With Multi-Antenna Users Over Jointly-Correlated Rayleigh Fading Channels

In this paper, we investigate a cell-free massive MIMO system with both access points (APs) and user equipments (UEs) equipped with multiple antennas over jointly-correlated Rayleigh fading channels. We study four uplink implementations, from fully centralized processing to fully distributed processing, and derive their achievable spectral efficiency (SE) expressions with minimum mean-squared error successive interference cancellation (MMSE-SIC) detectors and arbitrary combining schemes. Furthermore, the global and local MMSE combining schemes are derived based on full and local channel state information (CSI) obtained under pilot contamination, which can maximize the achievable SE for the fully centralized and distributed implementation, respectively. We study a two-layer decoding implementation with an arbitrary combining scheme in the first layer and optimal large-scale fading decoding (LSFD) in the second layer. Besides, we compute novel closed-form SE expressions for the two-layer decoding implementation with maximum ratio (MR) combining. In the numerical results, we compare the SE performance for different implementation levels, combining schemes, and channel models. It is important to note that increasing the number of antennas per UE may degrade the SE performance.

Performance and design of uplink multiuser massive MIMO with low-resolution ADCs

Performance and design of uplink multiuser massive MIMO with low-resolution ADCs

On the Theoretical Analysis of Network-Wide Massive MIMO Performance and Pilot Contamination

In this paper, we theoretically analyse the uplink (UL) and downlink (DL) performance of massive multiple-input and multiple-output (mMIMO) networks, in term of coverage probability, cell spectral efficiency and network area spectral efficiency, using stochastic geometry. A sophisticated but yet practical system model is considered, taking into account a path loss model differentiating line-of-sight and non-line-of-sight transmissions, an idle mode capability at the base stations and a finite user density. Our analysis pays particular attention to the existence of a finite number of UL pilots for channel estimation and the effect of pilot contamination. We study for the first time the joint impact of the number of UL pilot sequences, the user density, and the base-station density on the pilot contamination issue in a mMIMO network, which in turn characterizes the DL and UL network performance. Moreover, using the proposed framework, we investigate two different scheduling problems—UE and pilot scheduling—, to find the optimal simultaneously scheduled UE density per time-frequency resource as well as the optimal UL pilot number to maximise the spectral efficiency.

Effective Beamforming Technique Amid Optimal Value for Wireless Communication

In the notion of communication system resource provision specifically, beam-forming is a concept of proficient utilization of the power of transmission. Network densification and massive MIMO allows us to control the power efficiency and can be effectively distributed among different users by reducing cost. We presented a practical scenario for the performance of massive MIMO and multi-small cell system to analyze the overall performance of the system. Our work is based on the resource allocation with optimal structural constraints to maintain the cost effectiveness while considering economic implications. The base stations located far away from the users receive attenuated signals and give rise to path loss, whereas the problems of inter cell interference also arise due to transmission from a base station to others cells. The performance of the cellular system can be enhanced with the combination of massive Mimo and small cells, where we simulate and also provide an analysis on practical system with optimal and low complexity beam-forming. The proposed scenario illustrates a structure with an optimal linear transmit beamforming regarding an efficient number of parameters to not lose optimality, which is extendable to designate any specific cellular network in consideration. Our approach exploited schemes with low complexity that are facilitating in complete solution formation, and tested them in various and all possible cases and scenarios.

Capacity Scaling of Massive MIMO in Strong Spatial Correlation Regimes

This paper investigates the capacity scaling of multicell massive MIMO systems in the presence of spatially correlated fading. In particular, we focus on the strong spatial correlation regimes where the covariance matrix of each user channel vector has a rank that scales sublinearly with the number of base station antennas, as the latter grows to infinity. We also consider the case where the covariance eigenvectors corresponding to the non-zero eigenvalues span randomly selected subspaces. For this channel model, referred to as the "random sparse angular support" model, we characterize the asymptotic capacity scaling law in the limit of large number of antennas. To achieve the asymptotic capacity results, statistical spatial despreading based on the second-order channel statistics plays a pivotal role in terms of pilot decontamination and interference suppression. A remarkable result is that even when the number of users scales linearly with base station antennas, a linear growth of the capacity with respect to the number of antennas is achievable under the sparse angular support model. We note that the achievable rate lower bound based on massive MIMO "channel hardening", widely used in the massive MIMO literature, yields rather loose results in the strong spatial correlation regimes and may significantly underestimate the achievable rate of massive MIMO. This work therefore considers an alternative bounding technique which is better suited to the strong correlation regimes. In fading channels with sparse angular support, it is further shown that spatial despreading (spreading) in uplink (downlink) has a more prominent impact on the performance of massive MIMO than channel hardening.

On the Performance of Cell-Free Massive MIMO With Mixed-ADC Under Rician Fading Channels

In this letter, we introduce a mixed analog-to-digital converter (ADC) receiver architecture to cell-free massive multi-input multi-output (mMIMO) system under Rician fading channels. In particular, the mixed-ADC architecture permits some of receiver’s antennas to be implemented with economical low-resolution ADCs, while the rests are equipped with high-priced perfect ADCs. Leveraging on the additive quantization noise model (AQNM), a tight approximate uplink spectral efficiency (SE) expression with matched filtering receiver is derived, which provides us with a tool for easily quantifying the impacts of the Rician ${K}$ -factor, the quantization bit of low-resolution ADCs, and the proportion of the perfect ADCs in the mixed-ADC architecture. Numerical observations demonstrate that the mixed-ADC architecture with a small fraction of perfect ADCs has a great potential to boost the SE compared to the case with pure low-resolution ADCs. Moreover, the mixed-ADC architecture is superior to the full perfect ADCs counterpart in terms of energy efficiency (EE).

Low-Complexity Massive MIMO Detectors Under Spatial Correlation and Channel Error Estimates

The performance, complexity and effectiveness of various massive MIMO (M-MIMO) detectors are analyzed operating under highly spatial correlated uniform planar arrays (UPA) channels. In such context, M-MIMO systems present a considerable performance degradation and also, in some cases, an increased complexity. Considering this challenging, but realistic practical scenario, various sub-optimal M-MIMO detection structures are evaluated in terms of complexity and bit error rate (BER) performance trade-off. Specifically, the successive interference cancellation, lattice reduction (LR) and likelihood ascent search (LAS) schemes, as well as a hybrid version combining such structures with conventional linear MIMO detection techniques are comparatively investigated, aiming to improve performance. Hence, to provide a comprehensive analysis, we consider the number of antennas varying in a wide range (from conventional to massive MIMO condition), as well as low and high modulation orders, aiming to verify the potential of each MIMO detection technique according to its performance–complexity trade-off. We have also studied the correlation effect when both transmit and receiver sides are equipped with UPA antenna configurations. The BER performance is verified under different conditions, varying the array configurations, combining the detection techniques, increasing the number of antennas and/or the modulation order, especially aiming a near M-MIMO condition, i.e. up to $$64\times 64$$ and $$121\times 121$$ antennas has been considered. The aggregated LAS technique has demonstrated good performance in scenarios with high number of antennas, while LR and OSIC operates better in high correlated arrangements.

On the Performance of Cell-Free Massive MIMO With Low-Resolution ADCs

This paper considers an uplink cell-free massive multi-input multi-output (mMIMO) system with multi-antenna access points (APs) and users, assuming low-resolution analog-to-digital converter (ADC) architecture is employed at the APs. Leveraging on the additive quantization noise model (AQNM), we derive a tight approximate expression for uplink spectral efficiency (SE). This trackable finding provides us with a tool for easily quantifying the impacts of the number of antenna arrays and the number of quantization bit of low-resolution ADCs. We find 5-bit is required in a cell-free mMIMO with low-resolution ADCs to achieve the same SE as a cell-free mMIMO with full-precision ADCs. Besides, when the number of antennas of the user is small, deploying more antennas at the users can boost the sum SE. Then, to further highlight the potential of low-resolution ADCs architecture, we also investigate the tradeoff between the SE and energy efficiency (EE) with design issues surrounding the quantization bit of low-resolution ADCs and the number of antenna arrays. The resulting observations reveal that by choosing a proper quantization bit, the cell-free mMIMO with low-resolution ADCs has the capability to enjoy a better SE-EE tradeoff compared to the perfect ADCs counterpart.

An Efficient Precoding Method for Improved Downlink Massive MIMO System

This paper proposes an efficient precoding method based on Neumann series (NS) to improve error performances for already developed diagonal NS (DNS) in massive MIMO system. The conventional DNS has very low complexity compared to a zero-forcing (ZF) and any other NS based ZF precoding methods such as tri-diagonal NS (TNS) and eigenvalue based NS. However, the conventional DNS has very poor error performance in highly correlated massive MIMO system and performance degradations are severe as the used modulation is large. For an efficient precoding, diagonal plus columns NS (DCNS)-1, DCNS- $k$ , and hybrid DCNS-1 and DCNS- $U$ are proposed, and these precoding schemes are adaptively used according to the number of total diagonal dominant active users. The DCNS-1 is applied to fully diagonal dominant users and DCNS- $k$ is applied to non diagonal dominant users, and finally, hybrid DCNS-1 and DCNS- $U$ is applied to partially diagonal dominant systems. For improved DCNS- $k$ , $k$ active users which cause the largest post interference power are selected by simple calculations. The simulation results show that bit error rate (BER) performance for the proposed scheme is very higher than the conventional DNS and is nearly approximated to the BER performance for the conventional optimal ZF with very low complexity.

On the Performance of Backhaul Constrained Cell-Free Massive MIMO with Linear Receivers

On the Performance of Backhaul Constrained Cell-Free Massive MIMO with Linear Receivers

3D MIMO for 5G NR: Several Observations from 32 to Massive 256 Antennas Based on Channel Measurement

By placing active antennas in a 2D grid at a BS, 3D MIMO is considered as a promising and practical technique for 5G New Radio (NR). So far, 3D MIMO studies reported are mostly done with antenna elements from 32 up to 128 in the limited scenario at one frequency. To gain further insights into the 3D massive MIMO channel and performance, field measurements from 32 to 256 antenna elements at the transmitter and 16 antenna elements at the receiver are performed in three typical deployment scenarios, including outdoor to indoor, urban microcell, and urban macrocell at both 3.5 and 6 GHz frequencies with 200 MHz bandwidth. Based on the extracted channel information from measured data, power angle spectrum, root mean square angle spread, channel capacity, and eigenvalue spread have been studied. Several observations, including 3D MIMO channel spatial dispersive properties and multi-user performance varying with antenna number, scenario, and frequency are given. These findings can provide valuable experimental insights for efficient utilization of 3D MIMO with massive antenna elements.

Design and simulation of MIMO and massive MIMO for 5G mobile communication system

This study concentrates on the investigation of signal detection by using Zero Forcing (ZF), Minimum Mean Square Error (MMSE) and Beam Forming (BF), for a MIMO and massive MIMO system. Outcomes show that ZF is the modest method for signal detection, though BF gives better Bit Error Rate (BER) performance with some constraints in implementation, but MMSE implementation complexity is reduced by avoiding the matrix inversion in the receiver while sustaining the optimal performance as compared to other conventional methods. For several antennas at the base location, it is too difficult to implement the weight matrix for ZF, still, it is appropriate for BF with the benefit of decent digital signal algorithms. Performance of MIMO and massive MIMO using equalisation is investigated to get the throughput of the system (BER vs. SNR). Results indicate a significant improvement in BER for 16x16 as compared to 4x4, 8x1 and 8x8 antennas at the base station. The other parameters like capacity and Signal Interference Noise Ratio (SINR) are likewise examined for massive MIMO system.

The Rayleigh Fading Channel Prediction via Deep Learning

This paper presents a multi‐time channel prediction system based on backpropagation (BP) neural network with multi‐hidden layers, which can predict channel information effectively and benefit for massive MIMO performance, power control, and artificial noise physical layer security scheme design. Meanwhile, an early stopping strategy to avoid the overfitting of BP neural network is introduced. By comparing the predicted normalized mean square error (NMSE), the simulation results show that the performances of the proposed scheme are extremely improved. Moreover, a sparse channel sample construction method is proposed, which saves system resources effectively without weakening performances.

Uplink Performance of Single-Carrier Receiver in Massive MIMO With Pilot Contamination

We investigate the uplink (UL) performance of multicell massive MIMO (maMIMO) communication systems in frequency-selective fading channels, when the channel estimates are corrupted by pilot contamination, and the receiver employs maximum-ratio combining (MRC). Specifically, two fundamental schemes (receivers/waveforms) are considered: the time-reversal MRC (TRMRC) under single-carrier, and the conventional frequency-domain MRC (FDMRC) for orthogonal frequency-division multiplexing. We first derive approximate analytical expressions for the UL signal-to-interference-plus-noise ratio (SINR) performance of maMIMO systems, deploying both TRMRC and FDMRC. Then, lower bounds for the achievable rates of both systems are presented, and the derived expressions are validated by means of Monte-Carlo simulations. The performance of the investigated systems are compared regarding the variation of several parameters, such as the number of base station (BS) antennas M, number of users for each cell K, UL transmit power ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">u</sub> , and the number of taps of the time-dispersive channel L. Our results demonstrate that the attained SINR performance of both schemes are very similar, but, in terms of achievable rates, TRMRC in general outperforms FDMRC by not requiring cyclic prefix transmission. On the other hand, our computational complexity analysis demonstrates that the TRMRC scheme demands somewhat higher processing resources (O(LMK)) than FDMRC (O(MK)). Thus, we propose a reduced complexity implementation for the TRMRC receiver, by employing the fast convolution with overlap-and-add technique, which is able to achieve the same result of TRMRC with a complexity of O(log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (2L)MK). For a practical maMIMO setup, the complexity reduction is about 50%. Finally, based on the derived expressions, we propose an iterative way to obtain the number of users that is able to maximize the sum rate of the cell, and investigate its behavior with the variation of different parameters, such as number of antennas, data, and pilot transmit power, as well as different frequency and pilot reuse schemes.

Channel Rectification and Signal Estimation Based on EIV Model in Massive MIMO System

The performance of Massive MIMO is severely limited by channel estimation error, which is caused by pilot contamination and channel aging. In this paper, we propose an estimation algorithm based on the weighted total least-squares method with errors-in-variables (EIV) model to alleviate the influence of pilot contamination and channel aging. Then, a channel rectification method has been investigated to diminish the inaccuracy of channel estimation. Comparing with the traditional methods, it not only helps to make the signal estimation more accurate, but also provides opportunities to correct the channel model with estimation error and update the aged channel statement information. Simulations are provided to verify the efficacy of this method.