scholarly journals Characterisation of Indoor Massive MIMO Channels Using Ray-Tracing: A Case Study in the 3.2–4.0 GHz 5G Band

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1250
Author(s):  
Luis Valle ◽  
Jesús R. Pérez ◽  
Rafael P. Torres
Keyword(s):  
Ray Tracing ◽  
Massive Mimo ◽  
Base Station ◽  
Ofdm System ◽  
Mimo Channels ◽  
5G Systems ◽  
Target Environment ◽  
Temporal Dispersion ◽  
Measurement Campaigns

In this paper, research results on the applicability of ray-tracing (RT) techniques to model massive MIMO (MaMi) channels are presented and discussed. The main goal is to show the possibilities that site-specific models based on rigorous RT techniques, along with measurement campaigns considered for verification or calibration purposes where appropriate, can contribute to the development and deployment of 5G systems and beyond using the MaMi technique. For this purpose, starting from the measurements and verification of the simulator in a symmetric, rectangular and accessible scenario used as the testbed, the analysis of a specific case involving channel characterisation in a large, difficult access and measurement scenario was carried out using the simulation tool. Both the measurement system and the simulations emulated the up-link in an indoor cell in the framework of a MaMi-TDD-OFDM system, considering that the base station was equipped with an array consisting of 10 × 10 antennas. The comparison of the simulations with the measurements in the testbed environment allowed us to affirm that the accuracy of the simulator was high, both for determining the parameters of temporal dispersion and frequency selectivity, and for assessing the expected capacity in a specific environment. The subsequent analysis of the target environment showed the high capacities that a MaMi system can achieve in indoor picocells with a relatively high number of simultaneously active users.

scholarly journals Compressive Sensing Based Channel Estimation for Massive MIMO Communication Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
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.

scholarly journals Data Ridden Pilots Based Estimation of Sparse Multipath Massive MIMO Channels Using Orthogonal Matching Pursuit

2019 ◽  
Vol 25 (4) ◽  
pp. 81-87 ◽  
Author(s):  
Babar Mansoor ◽  
Moazzam Islam Tiwana ◽  
Syed Junaid Nawaz ◽  
Abrar Ahmed ◽  
Abdul Haseeb ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Matching Pursuit ◽  
Minimum Mean Square Error ◽  
Research Work ◽  
Base Station ◽  
Orthogonal Matching Pursuit ◽  
Mean Square ◽  
Mimo Channels

Massive Multiple-Input Multiple-Output (MIMO) is envisioned to be a strong candidate technology for the upcoming 5th generation (5G) of wireless communication networks. This research work presents a novel Compressed Sensing (CS) and Superimposed Training (SiT) based technique for estimating the sparse uplink channels in massive MIMO systems. The proposed technique involves arithmetic addition of a periodic, but low powered training sequence with each user’s information sequence. Consequently, separately dedicated resources for the pilot symbols are not needed. Moreover, to attain the estimates of the Channel State Information (CSI) in the uplink, the sparsity exhibited by the MIMO channels is exploited by incorporating CS based Orthogonal Matching Pursuit (OMP) algorithm. For decoding the transmitted information symbols of each user, a Linear Minimum Mean Square Error (LMMSE) based equalizer is incorporated at the receiving Base Station (BS). Based on the obtained simulation results, the proposed SiT-OMP technique outperforms the existing Least Squares (SiT) channel estimation technique. The comparison is done using performance metrics of the Bit Error Rate (BER) and the Normalized Channel Mean Square Error (NCMSE).

Performance analysis of various massive MIMO detection algorithms in 5G Wireless technologies

2019 ◽  
Vol 10 ◽  
Author(s):  
Shihab Jimaa ◽  
Jawahir Al-Ali
Keyword(s):  
Massive Mimo ◽  
Spectrum Efficiency ◽  
System Capacity ◽  
Network Coverage ◽  
Mimo Detection ◽  
Infinity Norm ◽  
Detection Algorithms ◽  
Alternating Direction ◽  
5G Systems ◽  
Efficient Power

Background: The 5G will lead to a great transformation in the mobile telecommunications sector. Objective: The huge challenges being faced by wireless communications such as the increased number of users have given a chance for 5G systems to be developed and considered as an alternative solution. The 5G technology will provide a higher data rate, reduced latency, more efficient power than the previous generations, higher system capacity, and more connected devices. Method: It will offer new different technologies and enhanced versions of the existing ones, as well as new features. 5G systems are going to use massive MIMO (mMIMO), which is a promising technology in the development of these systems. Furthermore, mMIMO will increase the wireless spectrum efficiency and improve the network coverage. Result: In this paper we present a brief survey on 5G and its technologies, discuss the mMIMO technology with its features and advantages, review the mMIMO capacity and energy efficiency and also presents the recent beamforming techniques. Conclusion: Finally, simulation of adopting different mMIMO detection algorithms are presented, which shows the alternating direction method of multipliers (ADMM)-based infinity-norm (ADMIN) detector has the best performance.

A companding approach for PAPR suppression in OFDM based massive MIMO system

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ajay Kumar Yadav ◽  
Pritam Keshari Sahoo ◽  
Yogendra Kumar Prajapati
Keyword(s):  
Massive Mimo ◽  
Orthogonal Frequency Division Multiplexing ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Average Power ◽  
Base Station ◽  
Convex Optimization Problem ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Mimo Ofdm

Abstract Orthogonal frequency division multiplexing (OFDM) based massive multiuser (MU) multiple input multiple output (MIMO) system is popularly known as high peak-to-average power ratio (PAPR) issue. The OFDM-based massive MIMO system exhibits large number of antennas at Base Station (BS) due to the use of large number of high-power amplifiers (HPA). High PAPR causes HPAs to work in a nonlinear region, and hardware cost of nonlinear HPAs are very high and also power inefficient. Hence, to tackle this problem, this manuscript suggests a novel scheme based on the joint MU precoding and PAPR minimization (PP) expressed as a convex optimization problem solved by steepest gradient descent (GD) with μ-law companding approach. Therefore, we develop a new scheme mentioned to as MU-PP-GDs with μ-law companding to minimize PAPR by compressing and enlarging of massive MIMO OFDM signals simultaneously. At CCDF = 10−3, the proposed scheme (MU-PP-GDs with μ-law companding for Iterations = 100) minimizes the PAPR to 3.70 dB which is better than that of MU-PP-GDs, (iteration = 100) as shown in simulation results.

Analysis of the Effect of Tilting Antenna on the Coverage Area of ??the 4G LTE Network (Case Study of Trenggalek District)

2019 ◽  
Vol 9 (4) ◽  
pp. 43-48
Author(s):  
Rizal Aji Istantowi
Keyword(s):  
Base Station ◽  
Base Stations ◽  
Optimal Conditions ◽  
Coverage Area ◽  
Lte Networks ◽  
Lte Network ◽  
Big Cities ◽  
4G Lte ◽  
4G Lte Networks

4G LTE networks in big cities are already well available. Meanwhile, on small to medium-sized cities, the 4G LTE network is not evenly distributed and maximized. This study chooses the variable tilting antenna to the coverage area, because in sending information from a base station using an antenna. The average RSRP value (dBm) of the existing base station in the calculation with a distance of 200 m is -122.90 dBm, a distance of 500 m is -136.79 dBm, and a distance of 1000 m -147.30 dBm. Meanwhile, in the simulation with a distance of 200 m of -108.22 dBm, a distance of 500 m of -121.81 dBm, and a distance of 1000 m of -132.69 dBm. The coverage area value of the existing base station in the calculation is 5.29%, while in the simulation it is 11.18%. The average RSRP value (dBm) at optimal conditions for calculations at a distance of 200 m is -80.13 dBm, at a distance of 500 m is -94.03 dBm and at a distance of 1000 m is -104.56 dBm. Meanwhile, the simulation at a distance of 200 m is -98.09 dBm, at a distance of 500 m is -112.79 dBm and at a distance of 1000 m is -123.31 dBm. The value of the coverage area for the calculation is 20.32%, while for the simulation it is 15.01%. The current need for base stations in Trenggalek District that has been met is 68%.

An Accurate, Robust and Low Dimensionality Deep Learning Localization Approach in DM-MIMO Systems Based on RSS

2021 ◽  
Author(s):  
Seyedeh Samira Moosavi ◽  
Paul Fortier
Keyword(s):  
Massive Mimo ◽  
Clustering Algorithm ◽  
Mean Squared Error ◽  
Mimo Systems ◽  
Principal Component ◽  
Base Station ◽  
Location Estimation ◽  
Similar Data ◽  
High Coverage ◽  
Single Antenna

Abstract Currently, localization in distributed massive MIMO (DM-MIMO) systems based on the fingerprinting (FP) approach has attracted great interest. However, this method suffers from severe multipath and signal degradation such that its accuracy is deteriorated in complex propagation environments, which results in variable received signal strength (RSS). Therefore, providing robust and accurate localization is the goal of this work. In this paper, we propose an FP-based approach to improve the accuracy of localization by reducing the noise and the dimensions of the RSS data. In the proposed approach, the fingerprints rely solely on the RSS from the single-antenna MT collected at each of the receive antenna elements of the massive MIMO base station. After creating a radio map, principal component analysis (PCA) is performed to reduce the noise and redundancy. PCA reduces the data dimension which leads to the selection of the appropriate antennas and reduces complexity. A clustering algorithm based on K-means and affinity propagation clustering (APC) is employed to divide the whole area into several regions which improves positioning precision and reduces complexity and latency. Finally, in order to have high precise localization estimation, all similar data in each cluster are modeled using a well-designed deep neural network (DNN) regression. Simulation results show that the proposed scheme improves positioning accuracy significantly. This approach has high coverage and improves average root-mean-squared error (RMSE) performance to a few meters, which is expected in 5G and beyond networks. Consequently, it also proves the superiority of the proposed method over the previous location estimation schemes.

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