Massive MIMO (also known as “Large-Scale Antenna Systems”, “Very Large MIMO”, “Hyper MIMO”, “Full-Dimension MIMO” and “ARGOS”) makes a clean break with current practice through the use of a large excess of service-antennas over active terminals and time division duplex operation. Extra antennas help by focusing energy into ever-smaller regions of space to bring huge improvements in throughput and radiated energy efficiency. Other benefits of massive MIMO include the extensive use of inexpensive low-power components, reduced latency, simplification of the media access control (MAC) layer, and robustness to intentional jamming. The anticipated throughput depend on the propagation environment providing asymptotically orthogonal channels to the terminals, but so far experiments have not disclosed any limitations in this regard. While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly-joined terminals, the exploitation of extra degrees of freedom provided by the excess of service-antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios.
In this chapter we examine the capacity of a single-user wireless channel where transmitter and/or receiver have a single antenna. We will discuss capacity for channels that are both time invariant and time varying. We first look at the well-known formula for capacity of a time-invariant additive white Gaussian noise (AWGN) channel and then consider capacity of time-varying flat fading channels. We will first consider flat fading channel capacity where only the fading distribution is known at the transmitter and receiver. We will also treat capacity of frequency-selective fading channels. For time -invariant frequency-selective channels the capacity is known and is achieved with an optimal power allocation that water-fills over frequency instead of time. We will consider only discrete-time systems in this chapter.
Massive MIMO (also known as “Large-Scale Antenna Systems”, “Very Large MIMO”, “Hyper MIMO”, “Full-Dimension MIMO” and “ARGOS”) makes a clean break with current practice through the use of a large excess of service-antennas over active terminals and time division duplex operation. Extra antennas help by focusing energy into ever-smaller regions of space to bring huge improvements in throughput and radiated energy efficiency. Other benefits of massive MIMO include the extensive use of inexpensive low-power components, reduced latency, simplification of the media access control (MAC) layer, and robustness to intentional jamming. The anticipated throughput depend on the propagation environment providing asymptotically orthogonal channels to the terminals, but so far experiments have not disclosed any limitations in this regard. While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly-joined terminals, the exploitation of extra degrees of freedom provided by the excess of service-antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios.
In this chapter we examine the capacity of a single-user wireless channel where transmitter and/or receiver have a single antenna. We will discuss capacity for channels that are both time invariant and time varying. We first look at the well-known formula for capacity of a time-invariant additive white Gaussian noise (AWGN) channel and then consider capacity of time-varying flat fading channels. We will first consider flat fading channel capacity where only the fading distribution is known at the transmitter and receiver. We will also treat capacity of frequency-selective fading channels. For time -invariant frequency-selective channels the capacity is known and is achieved with an optimal power allocation that water-fills over frequency instead of time. We will consider only discrete-time systems in this chapter.
Professor Mark Beach's presentation (without videos) on the University of Bristol's Massive MIMO activities as given at the IET's 'Towards 5G Mobile Technology – Vision to Reality' event, January 25th 2017.
All of us have lofty expectations for 5G wireless technology.
Massive growth in demand for mobile data...
Massive growth in the number of connected devices...
Massive change in data transfer rates and latency...
Massive explosion in the diversity of mobile applications...
Massive....Massive....Massive....this word is frequently used like never before.
Delivering all these expectations depends on the evolution of existing technologies and revolution in new technologies.
One such revolutionary change is the use of massive multiple-input/multiple-output (MIMO) antenna systems in 5G for different frequency ranges.
Interested to understand and learn what mMIMO means?!
If yes, here is some massive theoretical information on Massive MIMO.
Introduction to basics of wireless networks such as
• Radio waves & wireless signal encoding techniques
• Wireless networking issues & constraints
• Wireless internetworking devices
orthogonal frequency division multiplexing(OFDM)
its orthogonal frequency multiplexing topic basicallly in digital signal processing , network signal and system , it also helpful in engineering course either electrical or electronics and communication engineering.
Graded-index Polymer Multimode Waveguides for 100 Gb/s Board-level Data Trans...Jian Chen
We report enhanced graded-index multimode polymer waveguides with >70GHz×m for MMF launch and >200GHz×m for restricted launch, indicating the capability of on-board waveguide transmission of >100 Gb/s. Simulations using the measured refractive index profile agree well with the experiments.
Professor Mark Beach's presentation (without videos) on the University of Bristol's Massive MIMO activities as given at the IET's 'Towards 5G Mobile Technology – Vision to Reality' event, January 25th 2017.
All of us have lofty expectations for 5G wireless technology.
Massive growth in demand for mobile data...
Massive growth in the number of connected devices...
Massive change in data transfer rates and latency...
Massive explosion in the diversity of mobile applications...
Massive....Massive....Massive....this word is frequently used like never before.
Delivering all these expectations depends on the evolution of existing technologies and revolution in new technologies.
One such revolutionary change is the use of massive multiple-input/multiple-output (MIMO) antenna systems in 5G for different frequency ranges.
Interested to understand and learn what mMIMO means?!
If yes, here is some massive theoretical information on Massive MIMO.
Introduction to basics of wireless networks such as
• Radio waves & wireless signal encoding techniques
• Wireless networking issues & constraints
• Wireless internetworking devices
orthogonal frequency division multiplexing(OFDM)
its orthogonal frequency multiplexing topic basicallly in digital signal processing , network signal and system , it also helpful in engineering course either electrical or electronics and communication engineering.
Graded-index Polymer Multimode Waveguides for 100 Gb/s Board-level Data Trans...Jian Chen
We report enhanced graded-index multimode polymer waveguides with >70GHz×m for MMF launch and >200GHz×m for restricted launch, indicating the capability of on-board waveguide transmission of >100 Gb/s. Simulations using the measured refractive index profile agree well with the experiments.
C cf radio propagation theory and propagation modelsTempus Telcosys
The radio propagation theory is an important lesson in the radio communication curriculum. This lesson answers the following questions:
How are radio waves transmitted from one antenna to the other antenna?
What features does the radio wave have during the propagation? Which factors affect the propagation distance?
What fruits are achieved by predecessors in the radio wave propagation theory? How to apply the theory to practice?
Chapter 1 Radio Propagation Theory
Chapter 2 Radio Propagation Environment
Chapter 3 Radio Propagation Models
To meet the demands of high speed required by mobile communication of past generations ,one solution is to increase the number of antennas to the show and the reception of the wireless link this is called MIMO (Multiple input ,Multiple output )technology .however ,the integration of multiple antennas on the same PCB is delicate because of the small volume that require some applications and electromagnetic antenna between the coupling ,phenomena that we cannot neglect them .indeed a strong isolation between them has been reached to reduce fading of the signal caused by the electromagnetic antenna reached to reduce fading of the signal caused by the electromagnetic coupling and maximize the overall gain .in this article we are interested then integration on the same printed circuit of eight antennas MIMO are not operation in the same frequency band .the first antenna of this last work at 2.4GHz .other antennas have resonance frequency folling each with 20MHz offset this device is characterized by its original form that keeps is highly isolated antennas from the point of view electromagnetic coupling
DESIGN AND OPTIMIZATION A CIRCULAR SHAPE NETWORK ANTENNA MICRO STRIP FOR SOME...ijcseit
To meet the demands of high speed required by mobile communication of past generations ,one solution is
to increase the number of antennas to the show and the reception of the wireless link this is called MIMO
(Multiple input ,Multiple output )technology .however ,the integration of multiple antennas on the same
PCB is delicate because of the small volume that require some applications and electromagnetic antenna
between the coupling ,phenomena that we cannot neglect them .indeed a strong isolation between them has
been reached to reduce fading of the signal caused by the electromagnetic antenna reached to reduce
fading of the signal caused by the electromagnetic coupling and maximize the overall gain .in this article
we are interested then integration on the same printed circuit of eight antennas MIMO are not operation in
the same frequency band .the first antenna of this last work at 2.4GHz .other antennas have resonance
frequency folling each with 20MHz offset this device is characterized by its original form that keeps is
highly isolated antennas from the point of view electromagnetic coupling
To meet the demands of high speed required by mobile communication of past generations ,one solution is
to increase the number of antennas to the show and the reception of the wireless link this is called MIMO
(Multiple input ,Multiple output )technology .however ,the integration of multiple antennas on the same
PCB is delicate because of the small volume that require some applications and electromagnetic antenna
between the coupling ,phenomena that we cannot neglect them .indeed a strong isolation between them has
been reached to reduce fading of the signal caused by the electromagnetic antenna reached to reduce
fading of the signal caused by the electromagnetic coupling and maximize the overall gain .in this article
we are interested then integration on the same printed circuit of eight antennas MIMO are not operation in
the same frequency band .the first antenna of this last work at 2.4GHz .other antennas have resonance
frequency folling each with 20MHz offset this device is characterized by its original form that keeps is
highly isolated antennas from the point of view electromagnetic coupling
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...
Channel Models for Massive MIMO
1. Massive MIMO and
Channel Modeling for Millimeter Wave
Gustavo Fraidenraich
Engenharia Elétrica
Departamento de Comunicações
Unicamp
1
2. Achieving 10000x capacity
Source: IEEE Spectrum, July 2004, n. 7 2
10x
Performance
20x
Spectrum
50x
Base Stations = 10000x
Performance
Massive MIMO mmWave Densification
3. What is Massive MIMO?
T. L. Marzetta, “The case for MANY (greater than 16) antennas as the base station,” in Proc. ITA, San Diego, CA, USA, Jan. 2007.
Thomas L. Marzetta , "Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas ,” IEEE Trans. Commun. 2010.
BS
User 1
User 2
User K 3
M
M-1
1
2
4. 4
Antenna Array Gain
1 Element
1.0
0.5
0.0
-0.5
1.0
0.5
10
1.0
0.5
0.0
-0.5
10 Elements 20 Elements
-1.0 -0.5 0.0 0.5 1.0
20 Elements
-1.0 -0.5 0.0 0.5 1.0
-1.0
N=1
0.0
-0.5
-1.0
-1.0 -0.5 0.0 0.5 1.0
1.0
0.5
0.0
-0.5
-1.0
20
-1.0 -0.5 0.0 0.5 1.0
-1.0
5
2 Elements
Antenna Aperture λ / D
D
5. 5
What is Massive MIMO
Essentially multiuser MIMO with lots of base station antennas
Hundreds Tens of Users of BS antennas
A very large antenna array at each base station
A large number of users are served simultaneously
An excess of base station (BS) antennas
6. 6
Maximal Ratio Combining
Uplink
BS
User
M
*
1
2
*
h1
*
h2
hM
Σ
h2
h1
hM
7. 7
Maximal Ratio Transmission
Downlink
BS
User
M
1
2
h2
h1
hM
*
*
*
Knowledge of the Channel at the transmitter side.
Reciprocity!
h1
h2
hM
8. 8
Bit Error Probability
Maximal Ratio Combining
y = x + z
Pb = Q
2Eb
N0
⎛
⎝ ⎜
⎞
⎠ ⎟
y = [hhh!h]x + z
1 2 3M y = hx + z
h†y
MRC
M
Pb = 1
2
1− γ b
γ b + M
⎛
⎞
⎟
⎠ ⎜⎝ M −1+ k
M−1 Σ 1
k
⎛
⎝ ⎜
⎞
⎠ ⎟
k=0
2
+ 1
2
γ b
γ b + M
⎛
⎝ ⎜
⎞
⎠ ⎟
k
AWGN Channel
AWGN Channel
+Fading with
γ Diversity b = Eb
N0
9. 9
Maximal Ratio Combining
Bit Error Probability
0 5 10 15 20
1
0.1
0.01
0.001
10-4
10-5
10-6
M=1
M=2
M=8
M=50
Only Gaussian
Noise
17 dB
12. 12
System Model
h1
h2
hK
x1
x2
xK
Processing for user i
KΣ
y = xihi
i=1
+ z
*y
M
1
M
hi
* →1
hihi
1
M
* →0
hihj
13. 13
MRT Precoding
MASSIVE MIMO FOR NEXT GENERATION WIRELESS SYSTEMS
Erik G. Larsson, ISY, Linköping University, Sweden Ove Edfors, Lund University, Sweden Fredrik Tufvesson, Lund University, Sweden Thomas L. Marzetta,
Bell Labs, Alcatel-Lucent, USA
15. System Model
15
x
S3 Multipath
h n
15
Slow Fading +Shadowing
Fast Fading
16. 16
Signal-to-interference-plus-noise Ratio
SIR = β jkl
2
β jkl
2 +Gv
l≠j Σ
⎯M⎯→⎯∞→ β jkl
2
β 2
jkl
l≠j Σ
M
β 2
jkl
l≠j Σ
Gv
• Fading and noise vanish as M grows to infinity!
• SIR expression is independent of the transmitted powers.
• For an arbitrarily small transmitted energy- per-bit, the SIR can be
approached arbitrarily closely by employing a sufficient number of
antennas.
19. 19
Experimental Results for Massive MIMO
Lund University - Sweden
128 antennas freq. 1.2 ~ 6 GHz
10 users
National Instrument Plataform - USRP
1,2 meters
20. 20
Experimental Results for Massive MIMO
Lund University - Sweden
High speed data streaming for multiple users
10 mobile uses stream HD
video on uplink to basestation
Basestation streams 10 HD
videos on downlink to users.
21. 21
Experimental Results for Massive MIMO
Lund University
128 Antennas 128 Virtual Antenna Array
22. 22
γ = λmax −λmin
γ
4 Terminals, M=4,32, and 128 - H (4 x M)
23. 23
Experimental Results for Massive MIMO
LOS scenario with four
users co-located
NLOS scenario with four
users co-located
LOS scenario where
the four users are
well separated.
Angle of Arrival
24. 24
Experimental Results for Massive MIMO
Argos: Practical Many-Antenna Base Stations
Rice University, Bells Labs and Yale University
64 Antennas
WARP Plataform
freq. 2.4 GHz
Argos: Practical Many-Antenna Base Stations
Clayton Shepard, Hang Yu, Narendra Anand, Lin
Zhong1
Li Erran Li, Thomas Marzetta2,
Richard Yang3
26. 26
#
! !"
&
=
$ $%
h h
11 12
h h
21 22
H
11 h
22 h
21 h
12 h
MIMO Model
Mt Mr
C = min Mt ,Mr ( )log2 (1+ SNR)
if Mt ≫ Mr
C = Mr log2 (1+ SNR)
Capacity scales with the number of users
31. Channel Modeling for millimeter Wave
• Parameters
– Free Space Attenuation
– Path Loss Exponent
– AOA (Angle of Arrival) and AOD (Angle of
Departure)
– Penetration loss
32. 32
Free Space Attenuation
The equation often leads to an erroneous belief that free space attenuates an
electromagnetic wave according to its frequency.
The expression for FSPL actually encapsulates two effects:
Distance dependency Frequency dependency
of Antenna
Attenuation = PT
PR
= 4π d2 4π f 2
c2
1
G
Antenna Gain=1
33. 33
Free Space Attenuation
d=150 m
10 100 1000 104
60 GHz
d HmetersL
AttenuationHdBL
140
120
100
80
60
3 GHz
26 dB
d=3000m
A(dB) = 20log10
4π
c
df ⎛⎝ ⎜
⎞⎠ ⎟
= 20log10 (d)+ 20log10 ( f )−147.55
f - Hz
d - meters
Antenna Gain = 1
34. 34
λ −2
Free Space Attenuation
• For a fixed antenna area, the beamforming gain grows with ;
• The increase in path loss can be entirely compensated by applying beam
forming;
• In fact, the path loss can be more than compensated relative to today’s
cellular systems, with beamforming applied at both ends.
• We conclude that maintaining the same physical antenna size, mmW
propagation does not lead to any reduction in path loss relative to
current cellular frequencies.
35. Path Loss Exponent
L =10nlog10 (d)
180
135
90
45
0
n=6 - Indoor Environments
n=4 - Two Ray Model
n=2 - Free Space
n=1,5 Waveguide
1 10 100 1000
d (meters)
L (dB)
43. AOA - Angle of Arrival
1) As the frequency increases, decreases and the
therefore the resolvability of the antenna array increases.
2) As the frequency increases the angular spread decreases.
43
θ ~
λ
D
Source: David Tse book
44. 44
AOA - Angle of Arrival
28 GHz
6 main Lobes
George R. MacCartney Jr and Theodore Rappaport, "Millimeter Wave Propagation Measurements for Outdoor Urban
Mobile and Backhaul Communications in New York City,”IEEE ICC 2014.
45. George R. MacCartney Jr and Theodore Rappaport, "Millimeter Wave Propagation Measurements for Outdoor Urban
Mobile and Backhaul Communications in New York City,”IEEE ICC 2014.
45
AOA - Angle of Arrival
73 GHz
3 main Lobes
46. 46
AOA - Angle of Arrival
In order to overcome the loss in the degrees of freedom, we must
use 2D antennas.
47. 47
Delay Spread
The RMS delay spread is independent of frequency in the LOS scenario
Source: Dajana Cassioli, Luca Alfredo Annoni and Stefano Piersanti, “Characterization of Path Loss and Delay Spread of 60-GHz UWB Channels vs. Frequency, “ IEEE ICC 2013 - Wireless Communications
Symposium.
48. 48
Delay Spread
For NLOS, delay spread increases with the frequency and then
saturates.
49. 49
Set of measurements at 10 GHz
- Penetration loss
- AOA
- Knife edge diffraction
- Delay Spread
Prof. Matti Latva-Aho
PhD. Student Claudio F. Dias
56. Wall Penetration Loss Measurements
56
• Simple penetration loss
measurements with few
antenna locations
• Idea was to measure the
penetration by moving
antennas only fractions
of wavelength between
the measurements
57. 57
Conclusions
Benefits from the (many) excess antennas
Simplified multiuser processing (MRC and MRT)
Reduced transmit power
Thermal noise and fast fading vanish
mmW Communication
Narrow-beam communication is new to cellular
communications and poses difficulties.
Free space does not increase as frequency increases
(keeping the same effective antenna area).
Penetration loss is the new problem (on-off behavior of
the channel).
The loss of degrees of freedom, as frequency increases,
may be compensated using 2D antennas.
We need 3D channel modeling to better understand all the
physical phenomena.
58. 58
References
[1] - Mustafa Riza Akdeniz, Yuanpeng Liu, Mathew K. Samimi, Shu Sun, Student Member, IEEE, Sundeep Rangan,
Theodore S. Rappaport, and Elza Erkip, "Millimeter Wave Channel Modeling and Cellular Capacity Evaluation,”, IEEE
JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 32, NO. 6, JUNE 2014.
[2] - Millimeter Wave Cellular Ultra-Wideband Statistical Channel Model for NonLine of Sight Millimeter-Wave Urban
Channels Communications: Channel Models, Capacity Limits, Challenges and Opportunities
Prof. Ted Rappaport NYU WIRELESS, NYU Polytechnic School of Engineering, Joint work with Sundeep Rangan and Elza
Erkip.
[3] - A. F. Toledo, D. GJ Lewis, and A.M.D. Turkmani, "Radio Propagation into Buildings at 1.8 GHz”
[4] P. Nobles, and F. Halsall, "Delay Spread and Received Power Measurements within a Building at 2GHz, 5 GHz and 17
Ghz,”
[5] - Maria-Teresa Martinez-Ingles, Davy P. Gaillot, Juan Pascual-Garcia, Jose-Maria Molina-Garcia-Pardo, Martine Lienard,
and José-Víctor Rodríguez, “Deterministic and Experimental Indoor mmW Channel Modeling, “IEEE ANTENNAS AND
WIRELESS PROPAGATION LETTERS, VOL. 13, 2014 1047.
[6] -D. Cox, "Measurements of 800 MHz Radio Transmission
Into Buildings with Metallic Walls”, The Bell System Technical Journal 1983
[7] - A. F. Toledo, , Adel Turlmani, and David Parsons, "Estimating Coverage of Radio Transmission into and within
Buildings at 900, 1800, and 2300 MHz,” IEEE Personal Communications April 1998.
[8] - Hao Xu, Member, IEEE, Vikas Kukshya, Member, IEEE, and Theodore S. Rappaport, Fellow, IEEE , “Spatial and
Temporal Characteristics of 60-GHz Indoor Channels, “IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL.
20, NO. 3, APRIL 2002.
[9] - Mathew Samimi, Kevin Wang, Yaniv Azar, George N. Wong, Rimma Mayzus, Hang Zhao, Jocelyn K. Schulz, Shu Sun,
Felix Gutierrez, Jr., and Theodore S. Rappaport , 28 GHz Angle of Arrival and Angle of Departure Analysis for Outdoor
Cellular Communications using Steerable Beam Antennas in New York City, VTC 2013.
[10] - Theodore S. Rappaport, Yijun Qiao, Jonathan I. Tamir, James N. Murdock, Eshar Ben-Dor , “Cellular Broadband
Millimeter Wave Propagation and Angle of Arrival for Adaptive Beam Steering Systems (Invited Paper),”RWS 2012.
[11] - Dajana Cassioli, Luca Alfredo Annoni and Stefano Piersanti, “Characterization of Path Loss and Delay Spread of 60-
GHz UWB Channels vs. Frequency, “ IEEE ICC 2013 - Wireless Communications Symposium.