Low-Complexity Joint Channel Estimation for Multi-User mmWave Massive MIMO Systems

For multi-user millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, the precise acquisition of channel state information (CSI) is a huge challenge. With the increase of the number of antennas at the base station (BS), the traditional channel estimation techniques encounter the problems of pilot training overhead and computational complexity increasing dramatically. In this paper, we develop a step-length optimization-based joint iterative scheme for multi-user mmWave massive MIMO systems to improve channel estimation performance. The proposed estimation algorithm provides the BS with full knowledge of all channel parameters involved in up- and down-links. Compared with existing algorithms, the proposed algorithm has higher channel estimation accuracy with low complexity. Moreover, the proposed scheme performs well even with a small number of training sequences and a large number of users. Simulation results are shown to demonstrate the performance of the proposed channel estimation algorithm.

Download Full-text

A Low Complexity Channel Estimation and Detection for Massive MIMO Using SC-FDE

Telecom ◽

10.3390/telecom1010002 ◽

2020 ◽

Vol 1 (1) ◽

pp. 3-17

Author(s):

Mário Marques da Silva ◽

Rui Dinis ◽

João Guerreiro

Keyword(s):

Channel Estimation ◽

Spectral Efficiency ◽

Massive Mimo ◽

Mimo Systems ◽

Multiple Input Multiple Output ◽

Low Complexity ◽

Data Detection ◽

Pilot Symbols ◽

Multiple Input ◽

Input Multiple Output

5G Communications will support millimeter waves (mm-Wave), alongside the conventional centimeter waves, which will enable much higher throughputs and facilitate the employment of hundreds or thousands of antenna elements, commonly referred to as massive Multiple Input–Multiple Output (MIMO) systems. This article proposes and studies an efficient low complexity receiver that jointly performs channel estimation based on superimposed pilots, and data detection, optimized for massive MIMO (m-MIMO). Superimposed pilots suppress the overheads associated with channel estimation based on conventional pilot symbols, which tends to be more demanding in the case of m-MIMO, leading to a reduction in spectral efficiency. On the other hand, MIMO systems tend to be associated with an increase of complexity and increase of signal processing, with an exponential increase with the number of transmit and receive antennas. A reduction of complexity is obtained with the use of the two proposed algorithms. These algorithms reduce the complexity but present the disadvantage that they generate a certain level of interference. In this article, we consider an iterative receiver that performs the channel estimation using superimposed pilots and data detection, while mitigating the interference associated with the proposed algorithms, leading to a performance very close to that obtained with conventional pilots, but without the corresponding loss in the spectral efficiency.

Download Full-text

A Novel Iterative Discrete Estimation Algorithm for Low-Complexity Signal Detection in Uplink Massive MIMO Systems

Electronics ◽

10.3390/electronics8090980 ◽

2019 ◽

Vol 8 (9) ◽

pp. 980 ◽

Cited By ~ 1

Author(s):

Hui Feng ◽

Xiaoqing Zhao ◽

Zhengquan Li ◽

Song Xing

Keyword(s):

Computational Complexity ◽

Massive Mimo ◽

Mimo Systems ◽

Estimation Algorithm ◽

Low Complexity ◽

Detection Algorithm ◽

Base Station ◽

Damping Factor ◽

Mimo Detection ◽

Discrete Estimation

In this paper, a novel iterative discrete estimation (IDE) algorithm, which is called the modified IDE (MIDE), is proposed to reduce the computational complexity in MIMO detection in uplink massive MIMO systems. MIDE is a revision of the alternating direction method of multipliers (ADMM)-based algorithm, in which a self-updating method is designed with the damping factor estimated and updated at each iteration based on the Euclidean distance between the iterative solutions of the IDE-based algorithm in order to accelerate the algorithm’s convergence. Compared to the existing ADMM-based detection algorithm, the overall computational complexity of the proposed MIDE algorithm is reduced from O N t 3 + O N r N t 2 to O N t 2 + O N r N t in terms of the number of complex-valued multiplications, where Ntand Nr are the number of users and the number of receiving antennas at the base station (BS), respectively. Simulation results show that the proposed MIDE algorithm performs better in terms of the bit error rate (BER) than some recently-proposed approximation algorithms in MIMO detection of uplink massive MIMO systems.

Download Full-text

A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments

Sensors ◽

10.3390/s19010164 ◽

2019 ◽

Vol 19 (1) ◽

pp. 164 ◽

Cited By ~ 11

Author(s):

Zahra Mokhtari ◽

Maryam Sabbaghian ◽

Rui Dinis

Keyword(s):

Massive Mimo ◽

Orthogonal Frequency Division Multiplexing ◽

Mimo Systems ◽

Low Cost ◽

Multiple Input Multiple Output ◽

Base Station ◽

Aging Effects ◽

Hardware Impairments ◽

Single Carrier ◽

Input Multiple Output

Massive multiple input multiple output (MIMO) technology is one of the promising technologies for fifth generation (5G) cellular communications. In this technology, each cell has a base station (BS) with a large number of antennas, allowing the simultaneous use of the same resources (e.g., frequency and/or time slots) by multiple users of a cell. Therefore, massive MIMO systems can bring very high spectral and power efficiencies. However, this technology faces some important issues that need to be addressed. One of these issues is the performance degradation due to hardware impairments, since low-cost RF chains need to be employed. Another issue is the channel estimation and channel aging effects, especially in fast mobility environments. In this paper we will perform a comprehensive study on these two issues considering two of the most promising candidate waveforms for massive MIMO systems: Orthogonal frequency division multiplexing (OFDM) and single-carrier frequency domain processing (SC-FDP). The studies and the results show that hardware impairments and inaccurate channel knowledge can degrade the performance of massive MIMO systems extensively. However, using suitable low complex estimation and compensation techniques and also selecting a suitable waveform can reduce these effects.

Download Full-text

Compressive Sensing Based Channel Estimation for Massive MIMO Communication Systems

Wireless Communications and Mobile Computing ◽

10.1155/2019/6374764 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15 ◽

Cited By ~ 3

Author(s):

Athar Waseem ◽

Aqdas Naveed ◽

Sardar Ali ◽

Muhammad Arshad ◽

Haris Anis ◽

...

Keyword(s):

Channel Estimation ◽

Compressive Sensing ◽

Massive Mimo ◽

Communication Systems ◽

Mimo Systems ◽

Multiple Input Multiple Output ◽

Sampling Theorem ◽

Base Station ◽

Mimo Channels ◽

Pilot Design

Massive multiple-input multiple-output (MIMO) is believed to be a key technology to get 1000x data rates in wireless communication systems. Massive MIMO occupies a large number of antennas at the base station (BS) to serve multiple users at the same time. It has appeared as a promising technique to realize high-throughput green wireless communications. Massive MIMO exploits the higher degree of spatial freedom, to extensively improve the capacity and energy efficiency of the system. Thus, massive MIMO systems have been broadly accepted as an important enabling technology for 5th Generation (5G) systems. In massive MIMO systems, a precise acquisition of the channel state information (CSI) is needed for beamforming, signal detection, resource allocation, etc. Yet, having large antennas at the BS, users have to estimate channels linked with hundreds of transmit antennas. Consequently, pilot overhead gets prohibitively high. Hence, realizing the correct channel estimation with the reasonable pilot overhead has become a challenging issue, particularly for frequency division duplex (FDD) in massive MIMO systems. In this paper, by taking advantage of spatial and temporal common sparsity of massive MIMO channels in delay domain, nonorthogonal pilot design and channel estimation schemes are proposed under the frame work of structured compressive sensing (SCS) theory that considerably reduces the pilot overheads for massive MIMO FDD systems. The proposed pilot design is fundamentally different from conventional orthogonal pilot designs based on Nyquist sampling theorem. Finally, simulations have been performed to verify the performance of the proposed schemes. Compared to its conventional counterparts with fewer pilots overhead, the proposed schemes improve the performance of the system.

Download Full-text

Massive MIMO, mmWave and mmWave-Massive MIMO Communications: Performance Assessment with Beamforming Techniques

10.21203/rs.3.rs-69959/v1 ◽

2020 ◽

Author(s):

Tewelgn Kebede Engda ◽

Yihenew Wondie ◽

Johannes Steinbrunn

Keyword(s):

Mobile Networks ◽

Massive Mimo ◽

Performance Metrics ◽

Mimo Systems ◽

Multiple Input Multiple Output ◽

Low Complexity ◽

Fifth Generation ◽

Emerging Trends ◽

Multiple Input ◽

Input Multiple Output

Abstract A considerable amount of enabling technologies are being explored in the era of fifth generation (5G) mobile system. The dream is to build a wireless network that substantially improves the existing mobile networks in all performance metrics. To address this 5G design targets, massive MIMO (multiple input multiple output) and mmWave (millimeter wave) communication are also candidate technologies. Luckily, in many respects these two technologies share a symbiotic integration. Accordingly, a logical step is to integrate mmWave communications and massive MIMO to form mmWave-massive MIMO which substantially increases user throughput, improve spectral and energy efficiencies, increase the capacity of mobile networks and achieve high multiplexing gains. Thus, this work analyses the concepts, performances, comparison and discussion of these technologies called: massive MIMO, mmWave Communications and mmWave-massive MIMO systems jointly. Besides, outcomes of extensive researches, emerging trends together with their respective benefits, challenges, proposed solutions and their comparative analysis is addressed. The performance of hybrid analog-digital beamforming architecture with a fully digital and analog beamforming techniques are also analyzed. Analytical and simulation results show that the low-complexity hybrid analog-digital precoding achieves all round comparable precoding gains for mmWave-Massive MIMO technology.

Download Full-text

Dynamically Subarray-Connected Hybrid Precoding Scheme for Multiuser Millimeter-Wave Massive MIMO Systems

Wireless Communications and Mobile Computing ◽

10.1155/2021/5528522 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Guangyan Liao ◽

Feng Zhao

Keyword(s):

Millimeter Wave ◽

Massive Mimo ◽

Mean Squared Error ◽

Mimo Systems ◽

Multiple Input Multiple Output ◽

Base Station ◽

Hybrid Precoding ◽

Minimum Mean Squared Error ◽

Specific Subset ◽

Input Multiple Output

Hybrid precoding is widely used in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, most prior work on hybrid precoding focused on the fully connected hybrid architectures and the subconnected but fixed architectures in which each radio frequency (RF) chain is connected to a specific subset of the antennas. The limited work shows that dynamic subarray architectures address the tradeoff between achievable spectral efficiency and energy efficiency of mmWave massive MIMO systems. Nevertheless, in the multiuser hybrid precoding systems, the existing dynamic subarray schemes ignore the fairness of users and the problem of user selection. In this paper, we propose a novel multiuser hybrid precoding scheme for dynamic subarray architectures. Firstly, we select a multiuser set among all users according to the analog effective channel information of the base station (BS) and then design the subset of the antennas to each RF by the fairness antenna-partitioning algorithm. Finally, the optimal analog precoding vector is designed according to each subarray, and the digital precoding is designed by the minimum mean-squared error (MMSE) criterion. The simulation results show that the performance advantages of the proposed multiuser hybrid precoding scheme for dynamic subarray architectures.

Download Full-text

Grouping-Based Channel Estimation and Tracking for Millimeter Wave Massive MIMO Systems

Wireless Communications and Mobile Computing ◽

10.1155/2021/2922359 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Rui Yin ◽

Xin Zhou ◽

Wei Qi ◽

Celimuge Wu ◽

Yunlong Cai

Keyword(s):

Computational Complexity ◽

Channel Estimation ◽

Millimeter Wave ◽

Massive Mimo ◽

Mimo Systems ◽

Mimo System ◽

Closed Form Solution ◽

Network Capacity ◽

Base Station ◽

Estimation Accuracy

Although the millimeter wave (mmWave) massive multiple-input and multiple-output (MIMO) system can potentially boost the network capacity for future communications, the pilot overhead of the system in practice will greatly increase, which causes a significant decrease in system performance. In this paper, we propose a novel grouping-based channel estimation and tracking approach to reduce the pilot overhead and computational complexity while improving the estimation accuracy. Specifically, we design a low-complexity iterative channel estimation and tracking algorithm by fully exploiting the sparsity of mmWave massive MIMO channels, where the signal eigenvectors are estimated and tracked based on the received signals at the base station (BS). With the recovered signal eigenvectors, the celebrated multiple-signal classification (MUSIC) algorithm can be employed to estimate the direction of arrival (DoA) angles and the path amplitude for the user terminals (UTs). To improve the estimation accuracy and accelerate the tracking speed, we develop a closed-form solution for updating the step-size in the proposed iterative algorithm. Furthermore, a grouping method is proposed to reduce the number of sharing pilots in the scenario of multiple UTs to shorten the pilot overhead. The computational complexity of the proposed algorithm is analyzed. Simulation results are provided to verify the effectiveness of the proposed schemes in terms of the estimation accuracy, tracking speed, and overhead reduction.

Download Full-text

Semi-blind channel estimation based on Modified CMA and unitary scrambling for massive MIMO systems

10.21203/rs.3.rs-237287/v1 ◽

2021 ◽

Author(s):

Noura Sellami ◽

Mohamed Siala

Keyword(s):

Channel Estimation ◽

Mimo Systems ◽

Multiple Input Multiple Output ◽

Base Station ◽

Pilot Contamination ◽

Constant Modulus Algorithm ◽

Unitary Matrices ◽

Channel Estimator ◽

Blind Channel ◽

Input Multiple Output

Abstract Pilot contamination is one of the main impairments in multi-cell massive Multiple-Input Multiple-Output (MIMO) systems. In order to improve the channel estimation in this context, we propose to use a semi-blind channel estimator based on the constant modulus algorithm (CMA). We consider an enhanced version of the CMA namely the Modified CMA (MCMA) which modifies the cost function of the CMA algorithm to the sum of cost functions for real and imaginary parts. Due to pilot contamination, the channel estimator may estimate the channel of a contaminating user instead of that of the user of interest (the user for which the Base Station wants to estimate the channel and then the data). To avoid this, we propose to scramble the users sequences before transmission. We consider different methods to perform unitary scrambling based on rotating the transmitted symbols (one Dimensional (1-D) scrambling) and using unitary matrices (two-Dimensional (2-D) scrambling). At the base station, the received sequence of the user of interest is descrambled leading to a better convergence of the channel estimator. We also consider the case where the Automatic Repeat reQuest (ARQ) protocol is used. In this case, using scrambling leads to a significant gain in terms of BLock Error Rate (BLER) due to the change of the contaminating users data from one transmission to another induced by scrambling.

Download Full-text

Channel estimation for spatially/temporally correlated massive MIMO systems with one-bit ADCs

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-019-1587-x ◽

2019 ◽

Vol 2019 (1) ◽

Cited By ~ 1

Author(s):

Hwanjin Kim ◽

Junil Choi

Keyword(s):

Channel Estimation ◽

Massive Mimo ◽

Mimo Systems ◽

Multiple Input Multiple Output ◽

Temporal Correlation ◽

Quantization Noise ◽

Estimation Accuracy ◽

Single Shot ◽

Temporal Correlations ◽

Temporally Correlated

AbstractThis paper considers the channel estimation problem for massive multiple-input multiple-output (MIMO) systems that use one-bit analog-to-digital converters (ADCs). Previous channel estimation techniques for massive MIMO using one-bit ADCs are all based on single-shot estimation without exploiting the inherent temporal correlation in wireless channels. In this paper, we propose an adaptive channel estimation technique taking the spatial and temporal correlations into account for massive MIMO with one-bit ADCs. We first use the Bussgang decomposition to linearize the one-bit quantized received signals. Then, we adopt the Kalman filter to estimate the spatially and temporally correlated channels. Since the quantization noise is not Gaussian, we assume the effective noise as a Gaussian noise with the same statistics to apply the Kalman filtering. We also implement the truncated polynomial expansion-based low-complexity channel estimator with negligible performance loss. Numerical results reveal that the proposed channel estimators can improve the estimation accuracy significantly by using the spatial and temporal correlations of channels.

Download Full-text

Low-complexity beam-domain channel estimation and power allocation in hybrid architecture massive MIMO systems

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-019-1546-6 ◽

2019 ◽

Vol 2019 (1) ◽

Author(s):

Xiao Chen ◽

Zaichen Zhang ◽

Liang Wu ◽

Jian Dang

Keyword(s):

Channel Estimation ◽

Power Allocation ◽

Spectral Efficiency ◽

Communication Systems ◽

Mimo Systems ◽

Beam Steering ◽

Estimation Method ◽

Low Complexity ◽

Estimation Accuracy ◽

Hybrid Architecture

Abstract In this journal, we investigate the beam-domain channel estimation and power allocation in hybrid architecture massive multiple-input and multiple-output (MIMO) communication systems. First, we propose a low-complexity channel estimation method, which utilizes the beam steering vectors achieved from the direction-of-arrival (DOA) estimation and beam gains estimated by low-overhead pilots. Based on the estimated beam information, a purely analog precoding strategy is also designed. Then, the optimal power allocation among multiple beams is derived to maximize spectral efficiency. Finally, simulation results show that the proposed schemes can achieve high channel estimation accuracy and spectral efficiency.

Download Full-text