Download Digital Wireless Communications Course Informations

1
Wireless Communications
Overview
Wireless History
2
 Ancient Systems: Smoke Signals, Carrier Pigeons, …
 Radio invented in the 1880s by Marconi
 Many sophisticated military radio systems were
developed during and after WW2
 Cellular has enjoyed exponential growth since 1988,
with almost 5 billion users worldwide today
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Ignited the wireless revolution
Voice, data, and multimedia becoming ubiquitous
Use in third world countries growing rapidly
 Wifi also enjoying tremendous success and growth
 Wide area networks (e.g. Wimax) and short-range
systems other than Bluetooth (e.g. UWB) less successful
Future Wireless Networks
3
Ubiquitous Communication Among People and Devices
Next-generation Cellular
Wireless Internet Access
Wireless Multimedia
Sensor Networks
Smart Homes/Spaces
Automated Highways
In-Body Networks
All this and more …
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Challenges

Network/Radio Challenges
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Gbps data rates with “no” errors
Energy efficiency
Scarce/bifurcated spectrum
Reliability and coverage
Heterogeneous networks
Seamless internetwork handoff
Device/SoC Challenges
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Performance
Complexity
Size, Power, Cost
High frequencies/mmWave
Multiple Antennas
Multiradio Integration
Coexistance
5
GShort-Range
AdHoc
BT
Cellular
Radio
GPS
Cog
Mem
WiFi
CPU
mmW
Software-Defined (SD) Radio:
Is this the solution to the device challenges?
BT
Cellular
FM/XM
A/D
GPS
DVB-H
A/D
Apps
Processor
WLAN
A/D
Media
Processor
Wimax
A/D
DSP
 Multiband antennas and wideband A/Ds span BW of desired signals
 DSP programmed to process desired signal based on carrier frequency, signal
shape, etc.
 Avoid specialized HW
Today, this is not cost, size, or power efficient
Compressed sensing may be a solution for sparse signals
Careful what you wish for…
Source: FCC
Source: Unstrung Pyramid Research 2010
Growth in mobile data, massive spectrum deficit and stagnant revenues
require technical and political breakthroughs for ongoing success of cellular
6
Scarce Wireless Spectrum
$$$
and Expensive
7
On the Horizon:
“The Internet of Things”
Number of Connected Objects Expected to Reach 50bn by 2020
Are we at the Shannon
limit of the Physical Layer?
We are at the Shannon Limit
 “The wireless industry has reached the theoretical limit of
how fast networks can go” K. Fitcher, Connected Planet
 “We’re 99% of the way” to the “barrier known as Shannon’s
limit,” D. Warren, GSM Association Sr. Dir. of Tech.
Shannon was wrong, there is no limit
 “There is no theoretical maximum to the amount of data
that can be carried by a radio channel” M. Gass, 802.11
Wireless Networks: The Definitive Guide

“Effectively unlimited” capacity possible via personal cells
(pcells). S. Perlman, Artemis.
What would Shannon say?
We don’t know the Shannon
capacity of most wireless channels
 Time-varying
channels.
 Channels with interference or relays.
 Cellular systems
 Ad-hoc and sensor networks
 Channels with delay/energy/$$$ constraints.
Shannon theory provides design insights
and system performance upper bounds
Current/Emerging
Wireless Systems
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Current:
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4G Cellular Systems (LTE-Advanced)
4G Wireless LANs/WiFi (802.11ac)
Satellite Systems
Bluetooth
Zigbee
WiGig
Emerging
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5G Cellular and WiFi Systems
mmWave Systems
Ad/hoc and Cognitive Radio Networks
Energy-Harvesting Systems
Much room
For innovation
Wireless networks are everywhere, yet…
TV White Space &
Cognitive Radio
- Connectivity is fragmented
- Capacity is limited (spectrum crunch
and interference)
- Roaming between networks is ad hoc
Spectral Reuse
13
Due to its scarcity, spectrum is reused
In licensed bands
and unlicensed bands
BS
Cellular, Wimax
Wifi, BT, UWB,…
Reuse introduces interference
Cellular Systems:
Reuse channels to maximize capacity
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Geographic region divided into cells
Frequency/timeslots/codes/ reused at spatially-separated locations.
Co-channel interference between same color cells.
Base stations/MTSOs coordinate handoff and control functions
Shrinking cell size increases capacity, as well as networking burden
BASE
STATION
MTSO
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Cellular Phones
Everything Wireless in One Device
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Cellular Phones
Burden
for this
performance
is on the backbone network
Everything
wireless
in one device
San Francisco
BS
BS
LTE backbone is the Internet
Internet
Nth-Gen
Cellular
Phone
System
Nth-Gen
Cellular
Paris
BS
Much better performance and reliability than today
- Gbps rates, low latency, 99% coverage indoors and out
4G/LTE Cellular
Much higher data rates than 3G (50-100 Mbps)
3G systems has 384 Kbps peak rates
Greater spectral efficiency (bits/s/Hz)
Through MIMO, adaptive techniques, “ICIC”
Flexible use of up to 100 MHz of spectrum
20 MHz spectrum allocation common
Low packet latency (<5ms).
Reduced cost-per-bit
Support for multimedia
All IP network
Rethinking “Cells” in Cellular
Small
Cell
Coop
MIMO
Relay
DAS
How should cellular
systems be designed?
Will gains in practice be
big or incremental; in
capacity or coverage?
Traditional cellular design “interference-limited”
MIMO/multiuser detection can remove interference
Cooperating BSs form a MIMO array: what is a cell?
Relays change cell shape and boundaries
Distributed antennas move BS towards cell boundary
Small cells create a cell within a cell
 Mobile cooperation via relays, virtual MIMO, network coding.
Are small cells the solution to increase
cellular system capacity?
Yes, with reuse one and adaptive techniques
(Alouini/Goldsmith 1999)
Area Spectral Efficiency
A=.25D2p
 S/I increases with reuse distance (increases link capacity).
 Tradeoff between reuse distance and link spectral efficiency (bps/Hz).
 Area Spectral Efficiency: Ae=SRi/(.25D2p) bps/Hz/Km2.
The Future Cellular Network:
Hierarchical Architecture
Today’s architecture
MACRO: solving
• 3M Macrocells serving 5 billion users
initial coverage• Anticipated 1M small cells per year
issue, existing
network
10x Lower COST/Mbps
(more
with WiFi
Offload)
PICO: solving
street, enterprise
& home
coverage/capacit
y issue
10x CAPACITY
Improvement
Macrocell Picocell
Near 100%
COVERAGE
Femtocell
Future systems require Self-Organization (SON) and WiFi Offload
SON Premise and Architecture
Mobile Gateway
Or Cloud
Node
Installation
Self
Healing
SoN
Server
Initial
Measurements
Self
Configurati
on
Measureme
nt
SON
Server
IP Network
Self
Optimization
X2
X2
 SON is part of 3GPP/LTE standard
Small cell BS
Macrocell BS
X2
X2
SW
Agent
Green” Cellular Networks
Pico/Femto
Coop
MIMO
Relay
DAS
How should cellular
systems be redesigned
for minimum energy?
Research indicates that
significant savings is possible
Minimize energy at both the mobile and base station via
 New Infrastuctures: cell size, BS placement, DAS, Picos, relays
 New Protocols: Cell Zooming, Coop MIMO, RRM, Scheduling,
Sleeping, Relaying
 Low-Power (Green) Radios: Radio Architectures, Modulation,
coding, MIMO
Why Green, why now?
 The energy consumption of cellular networks is
growing rapidly with increasing data rates and
numbers of users
 Operators are experiencing increasing and volatile
costs of energy to run their networks
 There is a push for “green” innovation in most sectors
of information and communication technology (ICT)
 There is a wave of consortia and government
programs focused on green wireless
Wifi Networks
Multimedia Everywhere, Without Wires
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Wifi is today’s small cell
802.11ac
• Streaming
video
• Gbps data rates
• High reliability
• Coverage inside and out
Wireless HDTV
and Gaming
Wireless Local Area
Networks (WLANs)
01011011
0101
1011
Internet
Access
Point
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WLANs connect “local” computers (100m range)
Breaks data into packets
Channel access is shared (random access + backoff)
Backbone Internet provides best-effort service
• Poor performance in some apps (e.g. video)
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Evolution of WLAN
Single Link from MAC layer to upper layer > 500 Mbps;
Multi-user communication >1 Gbps.
802.11ac is based on 802.11n enhanced：
OFDM/OFDMA、MIMO、SDMA、FEC、MAC
layer optimization、Interference Coordination、
Cognitive Radio and Carrier Aggregation
IMT-Advanced 20-40 MHz ==> 1 Gbps
2.4/5GHz, 20 - 40MHz
2.4GHz，20 MHz
2.4/5GHz, 20MHz
> 54 Mbps
11/54 Mbps
600 Mbps
Wireless LAN Standards
802.11b (Old – 1990s)
 Standard for 2.4GHz ISM band (80 MHz)
 Direct sequence spread spectrum (DSSS)
 Speeds of 11 Mbps, approx. 500 ft range
802.11a/g (Middle Age– mid-late 1990s)
 Standard for 5GHz band (300 MHz)/also 2.4GHz
 OFDM in 20 MHz with adaptive rate/codes
 Speeds of 54 Mbps, approx. 100-200 ft range
Many
WLAN
cards
have
all 3
(a/b/g/n)
802.11n/ac(current)
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Standard in 2.4 GHz and 5 GHz band
Adaptive OFDM /MIMO in 20/40/80/160 MHz
Antennas: 2-4, up to 8
Speeds up to 600Mbps / 10 Gbps), approx. 200 ft range
Other advances in packetization, antenna use, multiuser MIMO
Why does WiFi performance suck?
Carrier Sense Multiple Access:
if another WiFi signal
detected, random backoff
Collision Detection: if collision
detected, resend
 The WiFi standard lacks good mechanisms to mitigate
interference, especially in dense AP deployments
 Multiple access protocol (CSMA/CD) from 1970s
 Static channel assignment, power levels, and carrier sensing
thresholds
 In such deployments WiFi systems exhibit poor spectrum reuse and
significant contention among APs and clients
 Result is low throughput and a poor user experience
Why not use SoN for WiFi?
SoN
Controller
- Channel Selection
- Power Control
- etc.
 SoN-for-WiFi: dynamic self-organization network
software to manage of WiFi APs.
 Allows for capacity/coverage/interference mitigation
tradeoffs.
 Also provides network analytics and planning.
In fact, why not use SoN for all wireless networks?
Software-Defined Wireless Networks
TV White Space &
Cognitive Radio
mmWave networks
Vehicle networks
Software-Defined Network Virtualization
Wireless Network Virtualization Layer
Network virtualization combines hardware and
software network resources and functionality into a
single, software-based virtual network
Software-Defined Wireless
Network (SDWN) Architecture
Video
Freq.
Allocation
Vehicular
Networks
Security
Power
Control
Self
Healing
ICIC
App layer
M2M
QoS
Opt.
Health
CS
Threshold
SW layer
UNIFIED CONTROL PLANE
HW Layer
WiFi
Cellular
mmWave
Cognitive
Radio
WiGig and mmWave
WiGig
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Standard operating in 60 GHz band
Data rates of 7-25 Gbps
Bandwidth of around 10 GHz (unregulated)
Range of around 10m (can be extended)
Uses/extends 802.11 MAC Layer
Applications include PC peripherals and displays for HDTVs, monitors &
projectors
mmWave
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Couples 60GHz with massive MIMO and better MAC
Promises long-range communication w/Gbps data rates
Hardware, propagation and system design challenges
Much research on this topic today
Wimax (802.16)
Wide area wireless network standard
System architecture similar to cellular
Hopes to compete with cellular
OFDM/MIMO is core link technology
Operates in 2.5 and 3.5 MHz bands
Different for different countries, 5.8 also used.
Bandwidth is 3.5-10 MHz
Fixed (802.16d) vs. Mobile (802.16e) Wimax
Fixed: 75 Mbps max, up to 50 mile cell radius
Mobile: 15 Mbps max, up to 1-2 mile cell radius
34
Satellite Systems
 Cover very large areas
 Different orbit heights
 GEOs (39000 Km) versus LEOs (2000 Km)
• http://www.globalcomsatphone.com/globalcom/leo_geo.html
 Optimized for one-way transmission
 Radio (XM, Sirius) and movie (SatTV, DVB/S) broadcasts
 Most two-way systems struggling or bankrupt
 Global Positioning System (GPS) use growing
 Satellite signals used to pinpoint location
 Popular in cell phones, PDAs, and navigation devices
35
Paging Systems
Broad coverage for short messaging
Message broadcast from all base stations
Simple terminals
Optimized for 1-way transmission
Answer-back hard
Overtaken by cellular
36
Bluetooth
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Cable replacement RF technology (low cost)
Short range (10m, extendable to 100m)
2.4 GHz band (crowded)
1 Data (700 Kbps) and 3 voice channels, up to 3 Mbps
Widely supported by telecommunications, PC, and consumer
electronics companies
 Few applications beyond cable replacement
 Bluetooth 4.0, June 30, 2010
 Classic Bluetooth
 Bluetooth high speed (based on WiFi)
 Bluetooth low energy
 http://en.wikipedia.org/wiki/Bluetooth
8C32810.61-Cimini-7/98
37
Ultrawideband Radio (UWB)
38
UWB is an impulse radio: sends pulses of tens of
picoseconds(10-12) to nanoseconds (10-9)
Duty cycle of only a fraction of a percent
A carrier is not necessarily needed
Uses a lot of bandwidth (GHz)
High data rates, up to 500 Mbps
7.5 Ghz of “free spectrum” in the U.S. (underlay)
Multipath highly resolvable: good and bad
Limited commercial success to date
IEEE 802.15.4 / ZigBee Radios
39
Low-Rate WPAN
Data rates of 20, 40, 250 Kbps
Support for large mesh networking or star clusters
Support for low latency devices
CSMA-CA channel access
Very low power consumption
Frequency of operation in ISM bands
Focus is primarily on low power sensor networks
40
Tradeoffs
802.11n
3G
Rate
802.11g/a
Power
802.11b
UWB
Bluetooth
ZigBee
Range
Spectrum Regulation
41
Spectrum a scarce public resource, hence allocated
Spectral Allocation in US controlled by FCC
(commercial) or OSM (defense)
FCC auctions spectral blocks for set applications.
Some spectrum set aside for universal use
Worldwide spectrum controlled by ITU-R
Regulation is a necessary evil.
in multiple cognitive radio Innovations in regulation
being considered worldwide paradigms
Standards
Interacting systems require standardization
Companies want their systems adopted as standard
Alternatively try for de-facto standards
Standards determined by TIA/CTIA in US
IEEE standards often adopted
Process fraught with inefficiencies and conflicts
Worldwide standards determined by ITU-T
In Europe, ETSI is equivalent of IEEE
Emerging Systems*
• Software-defined and low-complexity
radios
• Cognitive radio networks
• Ad hoc/mesh wireless networks
• Sensor networks
• Distributed control networks
• The smart grid
• Biomedical networks
Compressed Sensing
Basic premise is that signals with some sparse structure
can be sampled below their Nyquist rate
Signal can be perfectly reconstructed from these samples
by exploiting signal sparsity
 This significantly reduces the burden on the front-end A/D
converter, as well as the DSP and storage
Might be key enabler for SD and low-energy radios
 Only for incoming signals “sparse” in time, freq., space, etc.
Reduced-Dimension Communication
System Design
 Compressed sensing ideas have found widespread
application in signal processing and other areas.
 Basic premise of CS: exploit sparsity to
approximate a high-dimensional system/signal in
a few dimensions.
 Can sparsity be exploited to reduce the
complexity of communication system design?
Sparsity: where art thou?
To exploit sparsity, we need to find
communication systems where it exists

Sparse signals: e.g. white-space detection

Sparse samples: e.g. sub-Nyquist sampling

Sparse users: e.g. reduced-dimension multiuser detection

Sparse state space: e.g reduced-dimension network
control
Sparse Samples
New Channel
Sampling
Mechanism
(rate fs)
 For a given sampling mechanism (i.e. a “new” channel)
 What is the optimal input signal?
 What is the tradeoff between capacity and sampling rate?
 What known sampling methods lead to highest capacity?
 What is the optimal sampling mechanism?
 Among all possible (known and unknown) sampling schemes
Capacity under Sub-Nyquist Sampling
 Theorem 1:
 Theorem 2:
 Bank of Modulator+FilterSingle Branch  Filter Bank
t  n(mTs )
q(t)
zzzz
p(t)
zzzz
zz

y1[n]
s1 (t )
zzzzzz
s (t )
zzzz
y[n]
t  n(mTs )
equals
yi [n]
si (t )
t  n(mTs )
 Theorem 3:
sm (t )
 Optimal among all time-preserving nonuniform sampling
techniques of rate fs (ISIT’12; Arxiv)
ym [n]
Joint Optimization of Input and Filter Bank
Selects the m branches with m highest SNR
Example (Bank of 2 branches)
low
SNR
H ( f  2kfs )
X  f  2kfs 

H ( f  kfs )
highest
SNR
X  f  kfs 
highest
SNR
Xf 

H ( f  kfs )
low SNR
X  f  kfs 

N ( f  kfs ) S ( f  kfs )

H( f )
2nd
N ( f  2kfs ) S ( f  2kfs )


N( f )
S( f )

Y1  f 
Capacity monotonic in fs
Y2  f 
N ( f  kfs ) S ( f  kfs )

How does this translate to practical modulation and coding schemes
Reduced-Dimension Network
Design
Random State Evolution
Stochastic
Control
Resource Management
Reduced-Dimension
Representation
Sampling
and
Inference
Cognitive Radios
CRTx
IP
NCR
NCR
CR
MIMO Cognitive Underlay
CR
CRRx
NCRTx
NCRRx
Cognitive Overlay
Cognitive radios support new users in existing
crowded spectrum without degrading licensed users
 Utilize advanced communication and DSP techniques
 Coupled with novel spectrum allocation policies
Multiple paradigms
 (MIMO) Underlay (interference below a threshold)
 Interweave finds/uses unused time/freq/space slots
 Overlay (overhears/relays primary message while cancelling
interference it causes to cognitive receiver)
Cognitive Radio Multiple Paradigms
52
Underlay
Cognitive radios constrained to cause minimal
interference to noncognitive radios
Interweave
Cognitive radios find and exploit spectral holes to
avoid interfering with noncognitive radios
Overlay
Cognitive radios overhear and enhance noncognitive
radio transmissions
Knowledge
and
Complexity
Underlay Systems
 Cognitive radios determine the interference their
transmission causes to noncognitive nodes
 Transmit if interference below a given threshold
IP
NCR
NCR
CR
CR
 The interference constraint may be met
 Via wideband signalling to maintain interference below the
noise floor (spread spectrum or UWB)
 Via multiple antennas and beamforming
Interweave Systems
 Measurements indicate that even crowded spectrum is not
used across all time, space, and frequencies
 Original motivation for “cognitive” radios (Mitola’00)
 These holes can be used for communication
 Interweave CRs periodically monitor spectrum for holes
 Hole location must be agreed upon between TX and RX
 Hole is then used for opportunistic communication with minimal
interference to noncognitive users
Overlay Systems
Cognitive user has knowledge of other user’s
message and/or encoding strategy
Used to help noncognitive transmission
Used to presubtract noncognitive interference
CR
NCR
RX1
RX2
Performance Gains
from Cognitive Encoding
outer bound
our scheme
prior schemes
Only the CR
transmits
Ad-Hoc/Mesh Networks
Outdoor Mesh
Indoor Mesh
ce
57
Ad-Hoc Networks
Peer-to-peer communications
 No backbone infrastructure or centralized control
Routing can be multihop.
Topology is dynamic.
Fully connected with different link SINRs
Open questions
 Fundamental capacity region
 Resource allocation (power, rate, spectrum, etc.)
 Routing
Cooperation in
Wireless Networks
Many possible cooperation strategies:
 Virtual MIMO, relaying (DF, CF, AF), one-shot/iterative
conferencing, and network coding
 Nodes can use orthogonal or non-orthogonal channels.
 Many practice and theoretical challenges
 New full duplex relays can be exploited
Design Issues
60
Ad-hoc networks provide a flexible network
infrastructure for many emerging applications.
The capacity of such networks is generally unknown.
Transmission, access, and routing strategies for adhoc networks are generally ad-hoc.
Crosslayer design critical and very challenging.
Energy constraints impose interesting design
tradeoffs for communication and networking.
General Relay Strategies
TX1
RX1
Y4=X1+X2+X3+Z4
X1
relay
Y3=X1+X2+Z3
TX2

X3= f(Y3)
X2
Y5=X1+X2+X3+Z5
RX2
Can forward message and/or interference

Relay can forward all or part of the messages


Much room for innovation
Relay can forward interference

To help subtract it out
Beneficial to forward both
interference and message
•
For large powers, this strategy approaches capacity
Wireless Sensor Networks
63
Data Collection and Distributed Control
•
•
•
•
•
•




Smart homes/buildings
Smart structures
Search and rescue
Homeland security
Event detection
Battlefield surveillance
Energy (transmit and processing) is the driving constraint
Data flows to centralized location (joint compression)
Low per-node rates but tens to thousands of nodes
Intelligence is in the network rather than in the devices
Energy-Constrained Nodes
64
Each node can only send a finite number of bits.
 Transmit energy minimized by maximizing bit time
 Circuit energy consumption increases with bit time
 Introduces a delay versus energy tradeoff for each bit
Short-range networks must consider transmit, circuit,
and processing energy.
 Sophisticated techniques not necessarily energy-efficient.
 Sleep modes save energy but complicate networking.
Changes everything about the network design:
 Bit allocation must be optimized across all protocols.
 Delay vs. throughput vs. node/network lifetime tradeoffs.
 Optimization of node cooperation.
Channel Coding
 Channel coding used to reduce error probability
 Metric is typically the coding gain
 Also measured against Shannon limit
Shannon
Limit
Pb
Uncoded
 Block and Convolutional Codes
 Induce bandwidth expansion, reduce data rate
 Easy to implement; well understood (decades of research)
 Coded Modulation (Trellis/Lattice Codes; BICM)
 Joint design of code and modulation mapper
 Provides coding gain without bandwidth expansion
 Can be directly applied to adaptive modulation
 Turbo Codes (within a fraction of a dB of Shannon limit)
 Clever encoding produces pseudo “random” codes easily
 Decoder fairly complex
 Low-density parity check (LDPC) Codes




State-of-the-art codes (802.11n, LTE)
Invented by Gallager in 1950s, reinvented in 1990s
Low density parity check matrix
Encoder complex; decoder uses belief propagation techniques
Coded
Coding
Gain
SNR
Green Codes (for short distances)
Is Shannon-capacity still a good metric for system design?
Coding for minimum total power
Is Shannon-capacity still a good metric for system design?
X5
B1
B2
B3
Computational
Nodes
B4
X6
X7
X8
On-chip
interconnects
Extends early work of El Gamal et. al.’84 and Thompson’80
Fundamental area-time-performance tradeoffs
 For encoding/decoding “good” codes,
X5
B1
B2
B3
B4
Area occupied by wires
Encoding/decoding clock cycles
Stay away from capacity!
Close to capacity we have
• Large chip-area, more time/power
Regular LDPCs closer to bound than capacity-approaching LDPCs!
Need novel code designs with short wires, good performance
X6
X7
X8
69
Distributed Control over Wireless
Automated Vehicles
- Cars
- Airplanes/UAVs
- Insect flyers
Interdisciplinary design approach
•
•
•
•
Control requires fast, accurate, and reliable feedback.
Wireless networks introduce delay and loss
Need reliable networks and robust controllers
Mostly open problems : Many design challenges
The Smart Grid:
Fusion of Sensing, Control, Communications
Open problems:
- Optimize sensor placement in the grid
- New designs for energy routing and distribution
- Develop control strategies to robustify the grid
- Look at electric cars for storage and path planning
carbonmetrics.eu
Much progress in some areas
Smart metering
Green buildings and structures
Optimization
Demand response
Modeling and simulation
Incentives and economics
But many of the hard research
questions have not yet been asked
Current work: sensor placement, fault
Detection and control with sparse sensors
Applications in Health,
Biomedicine and Neuroscience
Body-Area
Networks
Doctor-on-a-chip
Wireless
Network
Neuro/Bioscience
- EKG signal
reception/modeling
- Brain information theory
- Nerve network
(re)configuration
- Implants to
monitor/generate signals
-In-brain sensor networks
- SP/Comm applied to
bioscience
Recovery from
Nerve Damage
Gene Expression Profiling
70 genes
RNA
extraction
labeling
hybridization
scan
tumor
tissue
 Gene expression profiling predicts clinical outcome of
breast cancer (Van’t Veer et al., Nature 2002.)
 Immune cell infiltration into tumors  good prognosis.
 Gene expression measurements: a mix of many cell types
Gene Signatures
Cell-type
Proportion
Cell Types
Looks like CDMA “despreading”
• Many gene expression deconvolution algorithms exist
 Shen-Orr et al., Nature methods 2010  known “C” and “k”
 Lu et al. , PNAS 2003
 known “G” and “k”
 Vennet et al., Bioinformatics 2001
 known “k”
 Large databases exist where these parameters are unknown
 Can we apply signal processing methods to blindly separate gene
expression?
 We adapt techniques from hyperspectroscopy (Piper et al,
AMOS 2004) assuming “C”, “G” and “k” unknown
Beat existing techniques, even nonblind ones
Pathways through the brain
Direct information (DI) inference
Neuron layout
B
A
C
B
A
E
C
D
E
D
𝑛
𝐼 𝑋𝑛 → 𝑌𝑛 = 𝐻 𝑌𝑛 −
𝐻 𝑌𝑖 |𝑌 𝑖−1 , 𝑋 𝑖
𝑖=1
DI inference with delay lower bound
B
A
C
E
𝑛
𝐼 𝑋𝑛 → 𝑌𝑛 = 𝐻 𝑌𝑛 −
D
𝐻 𝑌𝑖 |𝑌 𝑖−1 , 𝑋 𝑖−𝐷
𝑖=1
Constrained DI inference
B
A
C
E
𝑛
D
𝑖−𝐷
𝐻 𝑌𝑖 |𝑌 𝑖−1 , 𝑋𝑖−𝐷−𝑁
𝐼 𝑋𝑛 → 𝑌𝑛 = 𝐻 𝑌𝑛 −
𝑖=1
Other Applications of Communications
and Signal Processing to Brain Science
 Epilepsy
 Epileptic fits caused by an oscillating signal moving from
one region to another.
 When enough regions oscillate, a fit occurs
 Working with epilepsy expert (MD) to understand how
signal travels between regions.
 Has sensors directly implanted in brain: can read signals
and inject them.
 Can we use drugs to block propagation or signal injection to
cancel signals
 Parkinsons
 Creates 20 MHz noise in brain region
 120 MHz square wave injection mitigates the symptoms
Main Points
77
 The wireless vision encompasses many exciting applications
 Technical challenges transcend all system design layers
 5G networks must support higher performance for some users
and extreme energy efficiency for others
 Cloud-based software to dynamically control and optimize
wireless networks needed (SDWN)
 Innovative wireless design needed for 5G cellular/WiFi,
mmWave systems, massive MIMO, and IoT connectivity
 Standards and spectral allocation heavily impact the evolution of
wireless technology