scholarly journals Effective Beamforming Technique Amid Optimal Value for Wireless Communication

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1869 ◽  
Author(s):  
Zahid Hussain Qaisar ◽  
Muhamamd Irfan ◽  
Tariq Ali ◽  
Ashfaq Ahmad ◽  
Ghulam Ali ◽  
...  
Keyword(s):  
Massive Mimo ◽  
Power Efficiency ◽  
Complete Solution ◽  
Low Complexity ◽  
Base Station ◽  
Base Stations ◽  
Small Cells ◽  
Beam Forming ◽  
Structural Constraints ◽  
Economic Implications

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.

scholarly journals Performance measurement of small-cells deployed under a heterogeneous network: an analysis of the co-existence of small-cells with the macro‑cell in 5G NR

2021 ◽  
Author(s):  
Mobasshir Mahbub ◽  
Bobby Barua
Keyword(s):  
Mobile Networks ◽  
Heterogeneous Network ◽  
Power Efficiency ◽  
Base Station ◽  
Small Cell ◽  
Base Stations ◽  
Small Cells ◽  
Macro Cell ◽  
Received Power ◽  
Implementation Costs

Abstract Advancements of cellular networks such as 4G and 5G proposed the collaboration of small-cell technologies in mobile networks and constructed a heterogeneous network (HetNet) for collaborative connectivity. There are many benefits of small-cell-based collective communication such as the increase of device capability in indoor/outdoor locations, enhancement of wireless coverage, improved signal efficiency, lower implementation costs of gNB (Next-generation Base Station introduced in 5G), etc. The integration of small-cells by deploying low-power BSs (base stations) in conventional macro-gNBs was investigated as a convenient and economical way of raising the potentials of a cellular network with high demand from consumers. The fusion of small-cells with macro-cells offers increased coverage and capacity for heterogeneous networks. Therefore, the research aimed to realize the performance of a small-cell deployed under a macro-cell in a two-tier heterogeneous network. The research first modified the reference equation for measuring the received power by introducing the transmitter and receiver gain. The paper then measured the SINR, throughput, spectral efficiency, and power efficiency for both downlink and uplink by empirical simulation. The research further enlisted the notable outcomes after examining the simulation results and discussed some relevant research scopes in the concluding sections of the paper.

scholarly journals Utility-Based Wireless Routing Algorithm for Massive MIMO Heterogeneous Networks

2020 ◽  
Vol 10 (20) ◽  
pp. 7261
Author(s):  
Wei Zhao ◽  
Wen-Hsing Kuo
Keyword(s):  
Heterogeneous Networks ◽  
Massive Mimo ◽  
Routing Algorithm ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Base Stations ◽  
Small Cells ◽  
Multihop Wireless ◽  
Wireless Backhaul ◽  
Input Multiple Output

With the development of 5G communication, massive multiple input multiple output (MIMO) technology is getting more and more attention. Massive MIMO uses a large amount of simultaneous transmitting and receiving antennas to reduce power consumption and raise the level of transmission quality. Meanwhile, the diversification of user equipment (UE) in the 5G environment also makes heterogeneous networks (HetNets) more prevalent. HetNets allow UE of different network standards to access small cells, while the base stations of small cells access a macro base station (BS) to form a multihop wireless heterogeneous backhaul network. However, how to effectively combine these two technologies by efficiently allocating the antennas of each BS during the route construction process of heterogeneous wireless backhaul networks is still an important issue that is yet to be solved. In this paper, we propose an algorithm called preallocated sequential routing (PSR). Based on the links’ channel conditions and the available antennas and location of BSs, it builds a wireless heterogeneous network backhaul topology and adjusts each link’s transmitting and receiving antennas to maximize total utility. Simulation results showed that the proposed algorithm significantly improved the overall utility and the utility of the outer area of heterogeneous networks.

scholarly journals Interference Cancellation Based Spectrum Sharing for Massive MIMO Communication Systems

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3584
Author(s):  
Milembolo Miantezila Junior ◽  
Bin Guo ◽  
Chenjie Zhang ◽  
Xuemei Bai
Keyword(s):  
Interference Cancellation ◽  
Communication Systems ◽  
Spectrum Sharing ◽  
Null Space ◽  
Base Station ◽  
Base Stations ◽  
Small Cells ◽  
Data Traffic ◽  
New Approach ◽  
Lte Advanced

Cellular network operators are predicting an increase in space of more than 200 percent to carry the move and tremendous increase of total users in data traffic. The growing of investments in infrastructure such as a large number of small cells, particularly the technologies such as LTE-Advanced and 6G Technology, can assist in mitigating this challenge moderately. In this paper, we suggest a projection study in spectrum sharing of radar multi-input and multi-output, and mobile LTE multi-input multi-output communication systems near m base stations (BS). The radar multi-input multi-output and mobile LTE communication systems split different interference channels. The new approach based on radar projection signal detection has been proposed for free interference disturbance channel with radar multi-input multi-output and mobile LTE multi-input multi-output by using a new proposed interference cancellation algorithm. We chose the channel of interference with the best free channel, and the detected signal of radar was projected to null space. The goal is to remove all interferences from the radar multi-input multi-output and to cancel any disturbance sources from a chosen mobile Communication Base Station. The experimental results showed that the new approach performs very well and can optimize Spectrum Access.

scholarly journals Data Detection in Single User Massive MIMO Using Re-Transmissions

2019 ◽  
Vol 6 (1) ◽  
pp. 15-26 ◽  
Author(s):  
K. Vasudevan ◽  
K. Madhu ◽  
Shivani Singh
Keyword(s):  
Massive Mimo ◽  
Turbo Code ◽  
Additive White Gaussian Noise ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Base Station ◽  
Mimo Channel ◽  
Data Detection ◽  
Major Drawback ◽  
Single User

Background:Single user Massive Multiple Input Multiple Output (MIMO) can be used to increase the spectral efficiency since the data is transmitted simultaneously from a large number of antennas located at both the base station and mobile. It is feasible to have a large number of antennas in the mobile, in the millimeter wave frequencies. However, the major drawback of single user massive MIMO is the high complexity of data recovery at the receiver.Methods:In this work, we propose a low complexity method of data detection with the help of re-transmissions. A turbo code is used to improve the Bit-Error-Rate (BER).Results and Conclusion:Simulation results indicate a significant improvement in BER with just two re-transmissions as compared to the single transmission case. We also show that the minimum average SNR per bit required for error-free propagation over a massive MIMO channel with re-transmissions is identical to that of the Additive White Gaussian Noise (AWGN) channel, which is equal to -1.6 dB.

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

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 980 ◽  
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.

scholarly journals Optimal Low-Power Design of a Multicell Multiuser Massive MIMO System at 3.7 GHz for 5G Wireless Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Michel Matalatala ◽  
Margot Deruyck ◽  
Emmeric Tanghe ◽  
Luc Martens ◽  
Wout Joseph
Keyword(s):  
Low Power ◽  
Communication Networks ◽  
Massive Mimo ◽  
Mimo System ◽  
Low Power Design ◽  
Base Station ◽  
Base Stations ◽  
Reference Network ◽  
Power Requirement ◽  
Time Frequency

Massive MIMO techniques are expected to deliver significant performance gains for the future wireless communication networks by improving the spectral and the energy efficiencies. In this paper, we propose a method to optimize the positions, the coverage, and the energy consumption of the massive MIMO base stations within a suburban area in Ghent, Belgium, while meeting the low power requirements. The results reveal that massive MIMO provides better performances for the crowded scenario where users’ mobility is limited. With 256 antennas, a massive MIMO base station can simultaneously multiplex 18 users at the same time-frequency resource while consuming 8 times less power and providing 200 times more capacity than a 4G reference network for the same coverage. Moreover, a pilot reuse pattern of 3 is recommended in a multiuser multicell environment to obtain a good tradeoff between the high spectral efficiency and the low power requirement.

scholarly journals Enhanced Transmit-Antenna Selection Schemes for Multiuser Massive MIMO Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Byung-Jin Lee ◽  
Sang-Lim Ju ◽  
Nam-il Kim ◽  
Kyung-Seok Kim
Keyword(s):  
Computational Complexity ◽  
Radio Frequency ◽  
Spectral Efficiency ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Antenna Selection ◽  
Base Station ◽  
Base Stations ◽  
Transmit Antenna Selection ◽  
Selection Scheme

Massive multiple-input multiple-output (MIMO) systems are a core technology designed to achieve the performance objectives defined for 5G wireless communications. They achieve high spectral efficiency, reliability, and diversity gain. However, the many radio frequency chains required in base stations equipped with a high number of transmit antennas imply high hardware costs and computational complexity. Therefore, in this paper, we investigate the use of a transmit-antenna selection scheme, with which the number of required radio frequency chains in the base station can be reduced. This paper proposes two efficient transmit-antenna selection (TAS) schemes designed to consider a trade-off between performance and computational complexity in massive MIMO systems. The spectral efficiency and computational complexity of the proposed schemes are analyzed and compared with existing TAS schemes, showing that the proposed algorithms increase the TAS performance and can be used in practical systems. Additionally, the obtained results enable a better understanding of how TAS affects massive MIMO systems.

scholarly journals Optimizing Power by Combining Small Cells with a Massive MIMO for Maximum Energy Efficiency under Two Path Loss Models

2019 ◽  
Author(s):  
Ch. Aravind Kumar
Keyword(s):  
Energy Efficiency ◽  
Massive Mimo ◽  
Path Loss ◽  
Base Station ◽  
Design Criterion ◽  
Small Cell ◽  
Small Cells ◽  
Total Power ◽  
Efficient System ◽  
Loss Models

Currently, we are proceeding towards the 5th generationof wireless networks. The 5G is expected to utilize verywide bandwidth and would have the greater capacity comparedto 4G. Therefore, the energy efficiency is one of the prominentroles in the design criterion. To achieve the maximum energyefficiency, the typical approaches are to increase the throughputor to decrease the power consumption. Recently, the approachto combine the small cell access points to Massive MIMO hasbeen proved as one efficient way to achieve the maximum energyefficiency. In this work, the approach to combine two typeof network, namely Small Cell Access Point (SCA) and BaseStation (BS) with Massive MIMO, is considered for designing anenergy efficient system. We investigate further this approach byextending the simulation to cover both 3GPP LTE as well as IMT2020 environment with two different carrier frequencies underthree scenarios. The performance is measured and comparedwith the existing work in terms of total power consumptionper subcarrier of both the Base Station (BS) and Small CellAccess Points (SCAs). It is apparent that our proposed methodprovides the higher information rate at the same total powerper sub carrier than the existing work. Additionally, generalspeaking, the 3GPP path loss model provides the lower totalpower consumption per sub carrier at BS than IMT-2020, whileboth path loss models almost has no impact on total powerconsumption at SCAs, regardless of carrier frequency and thenumber of antennas adopted in Massive MIMO at BS.

scholarly journals Transmit and Receive Antenna Selection Based Resource Allocation for Self-Backhaul 5G Massive MIMO HetNets

2021 ◽  
Author(s):  
Farah Akif ◽  
Aqdas Malik ◽  
Ijaz Qureshi ◽  
Ayesha Abassi
Keyword(s):  
Network Performance ◽  
Service Providers ◽  
Antenna Selection ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Base Stations ◽  
Small Cells ◽  
User Association ◽  
Transmit Antenna Selection ◽  
Coverage Area

With the advancement in wireless communication technology, the ease of accessibility and increasing coverage area is a major challenge for service providers. Network densification through Small cell Base Stations (SBS) integration in Heterogeneous Networks (HetNets) promises to improve network performance for cell edge users. Since providing wired backhaul for small cells is not cost effective or practical, the third-Generation Partnership Project (3GPP) has developed architecture for self-backhaul known as Integrated Access and Backhaul (IAB) for Fifth Generation (5G). This allows for Main Base Station (MBS) resources to be shared between SBS and MBS users. However, fair and efficient division of MBS resources remains a problem to be addressed. We develop a novel transmit antenna selection/partitioning technique for taking advantage of IAB 5G standard for Massive Multiple Input Multiple Output (MIMO) HetNets. Transmit antenna resources are divided among access for MBS users and for providing wireless backhaul for SBS. We develop A Genetic Algorithm (GA) based Transmit Antenna Selection (TAS) scheme and compare with random selection, eigenvalue-based selection and bandwidth portioning. Our analysis show that GA based TAS has the ability to converge to an optimum antenna subset providing better rate coverage. Furthermore, we also signify the performance of TAS based partitioning over bandwidth partitioning and also show user association can also be controlled using number of antennas reserved for access or backhaul.

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

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 301
Author(s):  
Jianhe Du ◽  
Jiaqi Li ◽  
Jing He ◽  
Yalin Guan ◽  
Heyun Lin
Keyword(s):  
Channel Estimation ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Estimation Algorithm ◽  
Step Length ◽  
Low Complexity ◽  
Base Station ◽  
Estimation Accuracy ◽  
Input Multiple Output

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.

Sign in / Sign up

Export Citation Format

Share Document