5G and IoT Challenges to Antennas and Wireless Systems

Transcription

1 5G and IoT Challenges to Antennas and Wireless Systems Prof Y. Jay Guo FTSE FIEEE FIET Director, Global Big Data Technologies Centre Distinguished Professor University of Technology Sydney (UTS)

2 Outline UTS:GBDTC Key 5G Challenges and PHY Technologies Base Station Antennas Massive Antenna Arrays Reconfigurable Antennas In-Band Full Duplex Conclusions 2

3 UTS Modern and Cool No. 1 young university in Australia. Ranked #14 in the world. Fast growing by attracting talents Global Big Data Technologies Centre uts.edu.au

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5 Big data, especially non-transactional data, often needs to be: Acquired remotely Transmitted reliably Processed efficiently Stored effectively Shared safely Exploited fully Big Data Technologies Used with due care of privacy Ø BIG DATA Technologies from data acquisition to decision support Global Big Data Technologies Centre uts.edu.au

6 Big Data Technologies The problem space Internet of Things (IoT) and 5G Sensors (Wireless and optical) Networking (Planning and Deployment) Communications (Spectrum, bandwidth & connectivity) Data storage, privacy and security Data analytics, machine learning and decision support bdt.uts.edu.au

7 GBDTC Structure Centre Director Centre Co - Director Centre Manager Mobile Sensing & Communications Big Visual Data Analytics IoT Communications & Networking mmwave& THz Systems Surveillance Lab 5G & IoT Lab UAV Communication Lab Computer Vision and Pattern Recognition Lab Software Defined Networks Lab Electromagnetic Informatics Multimedia and Data Analytics Lab Network Security Lab Machine Learning Lab bdt.uts.edu.au

8 Electromagnetic Informatics Lab Distinguished Professor Y. Jay Guo Distinguished Professor Richard Ziolkowski Distinguished Visiting Professor Trevor Bird Adjunct Professor Bevan Jones (former CTO of Argus Technology) Dr Peiyuan Qin, Senior Lecturer Dr Can Ding, Lecturer 3 postdocs A number of PhD students 8

9 Antenna EI Lab ØReconfigurable antennas ØReconfigurable antenna arrays ØReconfigurable reflectarrays & transmit arrays ØReconfigurable tightly coupled arrays ØReconfigurable leaky-wave antennas ØReconfigurable conformal antennas ØMultiband base station antennas Ø Integrated antenna systems ØElectrically small antennas ØMeta-material inspired antennas ØReconfigurable mm-wave frontends using HTS devices 9

10 10 Key 5G Challenges and PHY Technologies

11 Key 5G Challenges Massive System Capacity: To support large number of devices with various bandwidth requirements. High Data Rates: Achieve Data rates of 100Mbps to 10Gbps for different scenarios. Very Low Latency (1ms) and Ultra-High Reliability: To enable the integration of mission-critical application and services. Ultra-efficient Device and Network Energy Efficiency

12 5G PHY Technologies Higher Frequency: 10G - 50GHz, GHz Wider bandwidths: 500MHz to 3GHz (below 50 GHz) New PHY technologies: e.g. GFDM, FBMC, UFMC, BFDM, NOMA Massive MIMO antennas In-Band Full Duplex.

13 13 Base Station Antennas

14 Background A station antenna is required to provide a full coverage of a geographic area. This is usually realized by 3 vertical high gain arrays with each array covering a sector.

15 Challenging Specifications Impedance Matching: Frequency bands: 698 MHz 960 MHz (31.6%) or/and 1710 MHz to 2690 MHz (44.5%) VSWR: < 1.5 Horizontal Beamwidth: 3dB Beamwidth: 60 o ± 5 o 10dB Beamwidth: < 110 o Polarization: ± 45 o Polarization Isolation: > 25dB Cross Polarization level: < o < -10 ±60 o Front Back Ration: > 25 db Vertical 3dB beamwidth: < -15degree SideLobe Level: < -18dB

16 Common Base Station Antennas The Challenge is Miniaturization! 16

17 Multiband Antennas Meta-surface placed beneath the antenna to suppress surface wave and to lower the antenna height. Meta-surfaces placed between the antenna elements to improve isolation. Programmed meta-surface wall

18 Antennas for Massive MIMO Likely at mm-wave frequencies Power handling requirement for each element reduced High level integration required to meet the overall cost requirement Antennas + filters + duplexers + Multi-disciplinary efforts Potentially change the industry landscape 18

19 19 Massive Antenna Arrays

20 Massive Antenna Arrays Massive Array For very large antenna arrays, beamforming gains are so large that inter-cell and inter-stream interference can be very low So, massive MIMO can deliver very high data rate and improve link reliability, coverage and power efficiency Implementation Issues Cost in RF Cost in packaging Cost in signal processing Page 20

21 Hybrid Antenna Array a Tradeoff between Performance and Cost Y. J. Guo, J. Bunton, V. Dyadyuk, and X. Huang, Hybrid Adaptive Antenna Array, Patent AU P, 02/02/2009J. X. Huang, Y. Jay Guo, and J. Bunton, A hybrid adaptive antenna array, IEEE Transactions on Wireless communications, Vol. 9, No. 5, pp , May X. Huang and Y. Jay Guo, Frequency-domain AoA estimation and beamforming with hybrid antenna array, IEEE Transactions on Wireless Communications, Vol. 10, No. 8, pp , August J. A. Zhang, X. Huang, V. Dyadyuk, and Y. Jay Guo, Massive hybrid antenna array for millimeter-wave cellular communications, IEEE Wireless Communications Magazine, pp , February 2015.

22 Massive Hybrid Array Architectures Each subarray is an analog array, consisting of antennas connected with tunable phase shifters in the RF chain Each subarray is connected to a baseband processor via a DAC in the transmitter or an ADC in the receiver a) Hybrid array architecture for a transmitter and receiver; b) two types of array configurations for a hybrid uniform square array: interleaved (upper) and localized (bottom) configurations. Page 22

23 Hybrid Array Solution Combining multiple antennas to form an analogue sub-array Analogue beamforming with part of the antenna array Combining multiple analogue subarrays to form a hybrid array, followed by a digital beamformer Advantages Reduces the cost of the RF devices (lower power/device), and the cost and complexity of the digital beamformer Generates high levels of transmit power for longer range operation (solid state power sources are available but at low power levels) Enables the smart antenna technology to be applied to optimize the system performance Page 23

24 24 Reconfigurable Antennas

25 From the Greeks to Playing Lego Equation to obtain the required phase distribution for the elements of a reflectarray Φ",$%&' ()* +,* -./0' 12* /3$0' 1 Reflecarray with 1Y. 1Y. Fixed Beam direction /3$5' ) 2 Initial reconfigurable reflectarray antenna by using LEGOs as the cell element. J. Guo and S. K. Barton, Phase correcting zonal reflector incorporating rings, IEEE T-AP, vol. 43, no. 4, Apr J. Guo and S. K. Barton, Phase efficiency of the reflective array antenna, IEE Proc. Micro. Antenna and Propag. Vol. 142, no. 2, Apr

26 Wideband to narrow band frequency RA P. -Y. Qin, F. Wei, Y. J. Guo, A Wideband to Narrowband Tunable Antenna Using A Reconfigurable Filter, IEEE Transactions on Antennas and Propagation, vol. 63, no. 5, pp , May

27 Dual-band Polarization RA Dual-band polarization RA among two orthogonal linear and 45 degree polarizations ØTM 10 and TM 30 modes are selected to make the antenna operate in the 2.4 GHz and 5.8 GHz bands. ØThe center of each edge of the patch is connected to ground via a PIN diode for polarization switching. ØBy switching PIN diodes, the antenna can radiate either horizontal, vertical, or 45 linear polarization in the two frequency bands. P. -Y. Qin, Y. J. Guo, C. Ding, IEEE T-AP, vol. 61, no. 11, pp , Nov

28 Multi-linear Polarization RA with shorting posts Antenna structure Patch layer and Biasing layer Shorting posts and 8 Metallic vias A ground plane (c) Side view (a) Patch layer (b) Biasing layer 28

29 Antenna Prototype Patch layer Biasing layer 29

30 Beam-steering Reconfigurable PRS Antenna v Approach to enhance the gain of Pattern RAs Ø Employing superstrate atop the pattern RAs to form PRS antenna 30

31 Beam Steering PRS Antenna Designs 1) Phased Array Source λg/4 PRS FR4 aperture-coupling-fed z Lr antenna array y θ Rogers mm microstrip patch aperture feed network ground plane network 31

32 Approaches for PRS Antennas to Realize Beam Steering 2. Employing Phase-Varying PRS Structure Uniform PRS structure Broadside Beam Non-uniform PRS structure Tilted Beam Γ Γ Γ1 Γ2 32

33 Reconfigurable PRS Structure and Biasing Biasing Pad 15 nh Inductor PIN diode V y x Part I Part II Gnd Inductive striplines 33

34 L. Y. Ji, Y. J. Guo, P. Y. Qin, S. X. Gong, and R. Mittra, A Reconfigurable Partially Reflective Surface (PRS) Antenna for Beam Steering, IEEE Transactions on Antennas and Propagation, vol. 63, no. 6, pp , Jun

35 35 In-Band Full Duplex

36 Current Half Duplex Radio Communications Self-interference is millions to billions (60-90dB) times stronger than received signal It is generally not possible for radios to receive and transmit in the same frequency band simultaneously due to the interference that results. 36

37 In Band Full Duplex General Concept If the self-interference can be cancelled, wireless systems can transmit and receive simultaneously over the same frequency band It offers the potential to double the spectral efficiency of current systems Beyond spectral efficiency, IBFD can also enable new capabilities, for example, collision detection while transmitting, instantaneous feedback from other terminals, 37

38 Three SI Cancellation Techniques Digital cancellation o Up to db cancellation o Noisy estimate of the self-interference channel and noisy components of the self-interferer cannot be cancelled Analog cancellation o Cancellation performance up to 60 db o All transmitter impairments can be cancelled o Relax the requirements on digital signal processing Mixed-signal cancellation: the digital TX signal is processed and converted to analog RF, where subtraction occurs. o This requires a dedicated additional up-convertor, which in practice introduces its own noise and distortion o limits its cancellation to 35 db 38

39 The Sources of Self-Interference Internal Interference Antenna coupling Near field reflection 39

40 Self-Interference Cancellation Requirements 40

41 Analog Domain Suppression Aim to suppress self-interference in the analogue receive chain before the ADC To reduce the distortion due to transmitter nonlinearity and phase noise, analogue domain suppression is better to be implemented at RF frontend as close as possible to the transmit and receive antennas Analogue domain suppression can be either channel-aware or channel-unaware. Channel-aware techniques attempt to cancel both the direct and reflected path interference, whereas channel unaware techniques can only cancel the direct path interference Weaknesses: analogue-domain signal processing can be very difficult especially for wideband reflected-path interference 41

42 Digital Domain Suppression Aim to cancel self-interference after ADC by applying sophisticated DSP techniques to the received signal The advantage of digital domain approaches is that the signal processing is relatively easy and mature The most important task for digital domain techniques is to build a discrete-time interference model to capture everything between DAC and ADC Weaknesses: the ADC dynamic range limits the interference reduction performance. Therefore, digital domain cancellation is the last resort to cancel the self-interference left over from the propagation domain and analogue domain approaches 42

43 SIC by Analog FIR Filters Implemented at RF frontend Tapping the outgoing signal as close as possible to the transmit antenna Placing the cancellation point as close as possible to the receive antenna It is channel-aware, so that both direct-path and reflected-path interference can be cancelled X. Huang and Y. Jay Guo, Radio Frequency Self-interference Cancellation with Analog Least Mean Square Loop, IEEE Trans MTT, Issue 99,

44 Existing SIC Techniques by Analog FIR Filters (1) It consists of several parallel delay lines and tunable attenuators, each providing a copy of the transmitted signal Multiple copies are combined to interpolate the self-interference However, direct interpolation of an RF signal requires very finely determined delays (comparable to the inverse of the RF carrier frequency) The tuneable attenuators also need to be dynamically determined by additional digitally implemented optimization algorithm 44

45 Existing SIC Techniques by Analog FIR Filters (2) The tapped delay lines are used together with phase shifters which provide orthogonal copies of the RF signal The delay between taps is comparable to the inverse of the signal bandwidth The tap coefficients are determined by analogue least mean square (ALMS) circuits implemented at baseband However, ideal integrators are necessary in the ALMS circuits Additional down-conversion circuits and more analogue multipliers are required 45

46 Novel SIC by ALMS Loop Tx Rx Weighting coefficients are automatically adapted by ALMS loop with simple RC circuits Implemented directly at RF not baseband We have proved that the interference suppression ratio (ISR) is determined by the loop gain (including LNA in ) and transmitted signal power (given the multiplier constants) theoretical limits X. Huang and Y. Jay Guo, Radio Frequency Self-interference Cancellation with Analog Least Mean Square Loop, IEEE Trans MTT, Issue 99, HPA 90 o Tx HPA LPF T T LPF T T LPF 90 o 90 o 90 o LPF LPF LPF LPF LPF LPF LPF T T 90 o 90 o 90 o LPF LPF Rx LNA LNA LNA Gain 2µ LNA Gain 2µ 46

47 In-depth Analysis of ALMS Loop The behaviours of the ALMS loop are also analysed at both micro and macro scales, considering signal s both cyclostationary and stationary properties 47

48 Future Research Challenges I/Q imbalance and phase noise directly impact the performance Wideband near perfectly matched antennas High performance low cost and compact circulators Physical layer algorithm design Network protocol design Fundamental performance limits. 48

49 Conclusions 5G and IoT are posing new challenges to antennas and wireless systems Majority of the new research will be at higher frequencies and on multi-band systems New solutionswill be inter-disciplinary Materials and devices Antennas and microwave/mm-wave circuits Digital signal processing and analogue systems Joint communications and sensing 49

50 Thank You!