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3D Beamforming

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The presentation is about key-feature of 5G i.e. Beamforming in Full-Dimension MIMO.

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3D Beamforming

  1. 1. ECSE610 Winter 2016 Term-Project Presentation, 15 April 2016, McGill University 3D BEAMFORMING syed.hussain3@mail.mcgill.ca
  2. 2. 2 Outline:  INTRODUCTION  5G  MOTIVATION  OBJECTIVE  SCOPE  LITERATURE SURVEY  RESULTS  CONCLUSION
  3. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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.

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