This document presents a term project on 3D beamforming for 5G networks. It outlines the motivation for 3D beamforming to meet 5G goals and reduce interference. The objectives are to analyze narrow beam formation with different array geometries and track user movement. Results show narrow beams formed with linear, planar and cylindrical arrays using windowing. Future work includes incorporating 3D channel models and Coordinated Multi-Point features with 3D beamforming.

1. ECSE610 Winter 2016 Term-Project Presentation,
15 April 2016, McGill University
3D BEAMFORMING
syed.hussain3@mail.mcgill.ca

2. 2
Outline:
 INTRODUCTION
 5G
 MOTIVATION
 OBJECTIVE
 SCOPE
 LITERATURE SURVEY
 RESULTS
 CONCLUSION

3. 3
Introduction:
 5G – (LTE-A)
 Multi Gigabit Wireless communication to meet the enormous multi-media traffic
growth.
 To Achieve 5G Goal
 underutilized 60 GHz spectrum is considered with Large Number of Antennas i.e.
Massive MIMO.
 Beamforming
 key performance enhancer of cellular systems
if large Antennas are used.
 manipulation of Transmit aperture to increase SNR
and reduce Interference. NARROW BEAM

4. 4
Introduction:
 Steer and Focus the Transmit Beam.
 aperture weighting (a choice of Window type
e.g. Hanning, Hamming, Taylor window, Dolph-chebychev).
 window is used to control side-lobes , grating lobes.
 number of Antennas are Inversely proportional
to Beam width (Narrow Beam).
nulls
side lobes
Main lobe
Illustration Of Beam Pattern
21/φ

5. 5
5G (Service Vision)
5G
Peak
Data-Rate
Cell-Edge
Data Rate
Cell Spectral
Efficiency
LatencyMobility
Upto
100 Gbps
Upto
1000 Mbps
Upto
10 bps/Hz
Upto
1 ms
Upto
1000 km/h

6. 6
5G
 Key Enabling Technologies
 mm-wave higher Bandwidth
 Advanced Carrier aggregation
 Advanced Modulation and coding (Higher Modulation, FQAM, Advanced channel coding)
 Large scale antennas (3D Beamforming, Massive MIMO)
 Advanced Multiple Access
 Cognitive Radio
 Device-to-Device Communication
 Co-ordinated Multi-Point (CoMP)

7. 7
Motivation:
 Till LTE….
 Beamforming is done in horizontal plane
 Tilt is adjusted either Manually/Remotely
 Challenges for 5G.
 Excess Inter-user Interference in case of multiple narrow beams from multiple
eNodeB antennas.
 coverage in UMa scenario(high-rise buildings) and near the cell edge.
 Throughout and capacity is limited.
 These challenges can be achieved utilizing the elevation domain results in 3D
Beamforming.
 Weights are applied on elements of a port account for both horizontal and
vertical beam.

8. 8
Objective:
 Issues highlight In Term-Project
 Analyze the challenges and performance of forming a Narrow Beam at predefined
Azimuth and Elevation.
 Considering Array Geometries (like Linear, Planar & Cylindrical) for narrow beam
and propose their suitability for different environment.
 Track the user movement with known user locations in both horizontal and
vertical direction to mimic the Uma scenario.
 Under this scenario, explore the HO possibilities using CoMP feature of
LTE-A.
 Considering the 3D channel model to account for better realization of 3D
Beamforming.
Planar Array Linear Array
Cylindrical Array

9. 9
Scope:
 Scope is limited to simulations only.
 Assumptions
 Antennas are AAS.
 User-locations are known.
 LOS
 Adaptation of 3D channel model.
X


(x,y,z)
Z
Y
Azimuth
Elevation
Source
3D CHANNEL MODEL
Demonstration Of Active Antenna Array

10. 10
Literature survey : (Previous work)
 In Ref [1],
 Lab and field trial measurements has been performed using AAS in a single cell
 Results indicate high performance of 3D beamforming
 Algorithm for beamformer weights through UE feedback.
 For future work, CoMP and network MIMO algorithm will be use together in
multi cell setting.
 In Ref [2],
 Algorithm for reduced inter-user interference through e-tilt assignment and multi-
user selection algorithm.
 Low-complexity user-scheduling algorithm provides enhanced capacity.
 In Ref [3],
 A scheme for cell sectorization is proposed
 Sectorization is dynamic , such that it uniformly distributed traffic load in a cell.
 Sectorization boundaries are function of EL & Az.

11. 11
Literature survey : (Previous work)
 Optimal 3D beam pattern is achieved using convex optimization (max. Main lobe
s.t Side-lobes reduced)
 UE position determination algorithm is also proposed (so that BS knows UE lies
in which sector)
 In Ref [4],
 An Information-theoretic 3D channel model that incorporates elevation angle
also is proposed.
 Random parameters in channel model equation makes theoretical analysis
difficult and thus analytical channel model based on principal of maximum
entropy is also presented.
 In Ref [5],
 Some measurements has been carried out for dynamic tilt adaptation.
 Different cases, like cell splitting, UE specific and cell specific beam is
considered.
 Two scenarios, Noise-limited and Interference limited, is considered.

12. 12
Literature survey : (Previous work)
 In Ref [6],
 Current status and challenges of FD-MIMO are presented.
 Status :
 2D-AAS and 3D channel models are implemented with improve
performance.
 Challenges :
 Wide beam formation (Ant virtualization) in large antenna regime for control
signal and CQI measurement
 Frame-work of FD-MIMO for LTE/LTE-A.
 High complexity of antenna calibration.
 Feedback and codebook design in FDD.
 Accurate channel estimation with low-complexity.
 User-scheduling

13. 13
Results :
 We simulate narrow beam in Large Antenna case using Taylor window and Dolph
chebychev in Linear, Planar and Cylindrical array with assumption of AAS, known
user-location and LOS.
Narrow Beam Pattern of ULA (32×1)
Using Taylor Window (Az=0 , El=0) Using Chebychev Window (Az=45 , El=-20) User Tracking (Az=0, El=0) to
(Az=45, El=-20)

14. 14
Results :
Narrow Beam Patterns of
URA (32×32) :
Using Taylor Window (Az=0 , El=0) Using Chebychev Window (Az=45 , El=-20)
Horizontal Beam Pattern Vertical Beam Pattern
User Tracking (Az=0, El=0) to
(Az=45, El=-20) :

15. 15
Results :
Narrow Beam Patterns of Cylindrical Array (8×128)
Using Taylor Window (Az=60 , El=0) Using Chebychev Window (Az=170 , El=0) User Tracking (Az=0, El=0) to
(Az=70, El=0)

16. 16
Conclusion
 3D Beamforming is one of the key feature of 5G, as mm-wave is used so narrow beam is highly
needed and thus it becomes necessary to consider elevation domain.
 Through measurements and proposed algorithm in different research papers, it is established
that 3D Beamformer significantly enhances cellular system efficiency.
 AAS is the enabling technology of 3D Beamforming.
 Suggested future work in 3D Beamforming
 Incorporating 3D channel model for more practical realizations.
 Incorporating more features of LTE-A like CoMP, User-scheduling algorithm , network
MIMO algorithm with 3D Beamforming.

17. 17
References
[1] Koppenborg, Johannes, et al. "3D beamforming trials with an active antenna
array." Smart Antennas (WSA), 2012 International ITG Workshop on. IEEE, 2012.
[2] Zhang, Yiyan, et al. "Antenna Tilt Assignment for Three-Dimensional
Beamforming in Multiuser Systems." 2015 IEEE Global Communications
Conference (GLOBECOM). IEEE, 2015.
[3] Lee, Chang-Shen, et al. "Sectorization with beam pattern design using 3D
beamforming techniques." Signal and Information Processing Association Annual
Summit and Conference (APSIPA), 2013 Asia-Pacific. IEEE, 2013.
[4] Nadeem, Qurrat-Ul-Ain. 3D Massive MIMO Systems: Channel Modeling and
Performance Analysis. Diss. 2015.
[5] Godara, Lal C. "Application of antenna arrays to mobile communications. II. Beam-
forming and direction-of-arrival considerations." Proceedings of the IEEE 85.8
(1997): 1195-1245.
[6] Xu, Gary, et al. "Full-dimension MIMO: Status and challenges in design and
implementation." 2014 IEEE Communication Theory Workshop (CTW). 2014.