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Future wireless networksSoftware-defined and low-
complexity radiosAdvanced design of cellular
systemsWireless network convergence
and SDNAd-hoc and sensor networksCognitive and software-defined
radiosEnergy Constraints in Wireless
SystemsControl over WirelessNeuroscience applications of
wireless
EE360
EE360
EE360
EE360
Challenges
Network ChallengesScarce spectrumDemanding applicationsReliabilityUbiquitous coverageSeamless indoor/outdoor operation
Device ChallengesSize, Power, CostMIMO in SiliconMultiradio Integration Coexistance
Cellular
AppsProcessor
BT
MediaProcessor
GPS
WLAN
Wimax
DVB-H
FM/XM
Software-Defined Radio
Multiband antennas and wideband A/Ds span BW of desired signals
The DSP is programmed to process the desired signal based on carrier frequency, signal shape, etc.
Avoids specialized hardware
Cellular
AppsProcessor
BT
MediaProcessor
GPS
WLAN
Wimax
DVB-H
FM/XM A/D
A/D
DSPA/D
A/D
Compressed sensing may be a solution for sparse signals
Today, this is not cost, size, or power efficient
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 sparsityThis 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 radiosOnly 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?
Sparse state space: e.g reduced-dimension network control
Sparse users: e.g. reduced-dimension multiuser detection
To exploit sparsity, we need to find communication systems where it exists
Sparse samples: e.g. sub-Nyquist sampling
Sparse signals: e.g. white-space detection
Sparse Samples
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
h(t)
SamplingMechanism
(rate fs)
New Channel
Capacity under Sub-Nyquist SamplingTheorem 1:
Theorem 2:Bank of Modulator+FilterSingle Branch Filter Bank
Theorem 3:
Optimal among all time-preserving nonuniform sampling techniques of rate fs (ISIT’12; Arxiv)
zzzzzzzzzz
)(ts ][ny
)(1 ts
)(tsi
)(tsm
)( smTnt
)( smTnt
)( smTnt
][1 ny
][nyi
][nym
equals
zzzzzzzzzz
Selects the m branches with m highest SNRExample (Bank of 2 branches)
Joint Optimization of Input and Filter Bank
highest SNR
2nd highest SNR
low SNR
skffX 2
fX
skffX
skffX
)( skffH
)( fH
)( skffH
)( skffN
)( fN
)( skffN
)( skffS
)( fS
)( skffS
)2( skffH
)2( skffN )2( skffS
low SNR
fY1
fY2
Capacity monotonic in fs
How does this translate to practical modulation and coding schemes
Reduced-Dimension Network Design
Random State Evolution
Samplingand
Inference
StochasticControl
Resource Management
Reduced-Dimension
Representation
Spectral ReuseDue to its scarcity, spectrum
is reused
BS
In licensed bands
Cellular, Wimax Wifi, BT, UWB,…
and unlicensed bands
Reuse introduces interference
Careful what you wish for…
Growth in mobile data, massive spectrum deficit and stagnant revenues require technical and political breakthroughs for ongoing success of cellular
Are we at the Shannon limit of the Physical Layer?
Time-varying channels with memory/feedback.
We don’t know the Shannon capacity of most wireless channels
Channels with interference or relays.Uplink and downlink channels with
frequency reuse, i.e. cellular systems.Channels with delay/energy/$$$ constraints.
Rethinking “Cells” in Cellular
Traditional cellular design “interference-limited”MIMO/multiuser detection can remove interferenceCooperating BSs form a MIMO array: what is a cell?Relays change cell shape and boundariesDistributed antennas move BS towards cell boundarySmall cells create a cell within a cellMobile cooperation via relaying, virtual MIMO, analog network
coding.
SmallCell
Relay
DAS
Coop MIMO
How should cellularsystems be designed?
Will gains in practice bebig or incremental; incapacity or coverage?
Are small cells the solution to increase cellular system capacity?
Yes, with reuse one and adaptive techniques (Alouini/Goldsmith 1999)
A=.25D2p Area Spectral Efficiency
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
Future systems require Self-Organization (SON) and WiFi Offload
10x Lower COST/Mbps
10x CAPACITY Improvement
Near 100%COVERAGE
(more with WiFi Offload)
MACRO: solving initial coverage issue, existing networkPICO: solving street, enterprise & home coverage/capacity issue
Macrocell Picocell Femtocell
Today’s architecture• 3M Macrocells serving 5 billion users• Anticipated 1M small cells per year
SON Premise and Architecture
Node Installation
Initial Measurements
Self Optimization
SelfHealing
Self Configurati
on
Measurement
SON Server
SoNServer
Macrocell BS
Mobile GatewayOr Cloud
Small cell BS
X2
X2X2
X2
IP Network
SWAgent
SON is part of 3GPP/LTE standard
LTE.11
Convergence of Cellular and WiFi
- Seamless handoff between networks- Load-balancing of air interface and backbone- Carrier-grade performance on both networks
Wireless networks are everywhere, yet…
- Connectivity is fragmented- Capacity is limited (spectrum crunch and interference)- Roaming between networks is ad hoc
TV White Space &Cognitive Radio
SDWN Architecture
WiFi APFemto Cell
Pico Cell Cognitive Radio
Freq.Allocation
PowerContr
ol
SelfHealing
ICIC QoSOpt.
CSThreshol
d
UNIFIED CONTROL PLANE
Commodity HW
SW layer
App layerVideo SecurityVehicularNetworks HealthM2M
Cognitive Radio Paradigms
UnderlayCognitive radios constrained to
cause minimal interference to noncognitive radios
InterweaveCognitive radios find and exploit
spectral holes to avoid interfering with noncognitive radios
OverlayCognitive radios overhear and
enhance noncognitive radio transmissions
Knowledge
andComplexi
ty
Underlay Systems Cognitive radios determine the
interference their transmission causes to noncognitive nodesTransmit if interference below a given
threshold
The interference constraint may be metVia wideband signalling to maintain
interference below the noise floor (spread spectrum or UWB)
Via multiple antennas and beamforming
NCR
IP
NCRCR CR
Interweave Systems Measurements indicate that even
crowded spectrum is not used across all time, space, and frequenciesOriginal motivation for “cognitive” radios
(Mitola’00)
These holes can be used for communicationInterweave CRs periodically monitor
spectrum for holesHole location must be agreed upon between
TX and RXHole 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 strategyUsed to help noncognitive
transmissionUsed to presubtract noncognitive
interferenceRX1
RX2NCR
CR
Ad-Hoc Networks
Peer-to-peer communications. No backbone infrastructure. Routing can be multihop. Topology is dynamic. Fully connected with different link
SINRs
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 challengesNew full duplex relays can be exploited
Design Issues
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 ad-hoc networks are generally ad-hoc.
Crosslayer design critical and very challenging.
Energy constraints impose interesting design tradeoffs for communication and networking.
General Relay Strategies
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
TX1
TX2
relay
RX2
RX1X1
X2
Y3=X1+X2+Z3
Y4=X1+X2+X3+Z4
Y5=X1+X2+X3+Z5
X3= f(Y3)
Beneficial to forward bothinterference and message
• For large powers, this strategy approaches capacity
Wireless Sensor NetworksData Collection and Distributed Control
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
• Smart homes/buildings• Smart structures• Search and rescue• Homeland security• Event detection• Battlefield surveillance
Energy-Constrained Nodes
Each node can only send a finite number of bits.Transmit energy minimized by maximizing
bit timeCircuit energy consumption increases with
bit timeIntroduces 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
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
Uncoded
Coding Gain
SNR
Pb
ShannonLimit
Coding for minimum total power
Is Shannon-capacity still a good metric for system design?
Extends early work of El Gamal et. al.’84 and Thompson’80
ComputationalNodes On-chip
interconnects
Fundamental area-time-performance tradeoffs
For encoding/decoding “good” codes,
Stay away from capacity!Close to capacity we have
Large chip-area, more time/power
Area occupied by wires Encoding/decoding clock cycles
Regular LDPCs closer to bound than capacity-approaching LDPCs!Need novel code designs with short wires, good performance
Green” Cellular Networks
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
Pico/Femto
Relay
DAS
Coop MIMO
How should cellularsystems be redesignedfor minimum energy?
Research indicates thatsignificant savings is possible
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
Distributed Control over Wireless
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
Automated Vehicles - Cars - Airplanes/UAVs - Insect flyers
: Many design challenges
Much progress in some areas
Smart meteringGreen buildings and structuresOptimization Demand responseModeling and simulationIncentives and economics
But many of the hard researchquestions have not yet been asked
Current work: sensor placement, fault Detection and control with sparse sensors
Applications in Health, Biomedicine and
Neuroscience
Recovery fromNerve Damage
Doctor-on-a-chip
WirelessNetwork
Neuro/Bioscience- EKG signal
reception/modeling- Brain information theory- Nerve network
(re)configuration- Implants to
monitor/generate signals- In-brain sensor networks
Body-Area Networks
scan
Gene Expression Profiling
tumor tissue
RNA extraction labeling hybridization
70 genes
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-typeProportion
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
B A
C D
E
𝐼 ( 𝑋𝑛 →𝑌 𝑛)=𝐻 (𝑌 𝑛) −∑𝑖=1
𝑛
𝐻 (𝑌 𝑖∨𝑌 𝑖− 1 , 𝑋 𝑖)
Direct information (DI) inference
𝐼 ( 𝑋𝑛 →𝑌 𝑛)=𝐻 (𝑌 𝑛) −∑𝑖=1
𝑛
𝐻 (𝑌 𝑖∨𝑌 𝑖− 1 , 𝑋 𝑖− 𝐷− 𝑁𝑖− 𝐷 )
Constrained DI inference
B A
C D
E
Neuron layoutB A
C D
E
𝐼 ( 𝑋𝑛 →𝑌 𝑛)=𝐻 (𝑌 𝑛) −∑𝑖=1
𝑛
𝐻 (𝑌 𝑖∨𝑌 𝑖− 1 , 𝑋 𝑖− 𝐷 )
DI inference with delay lower bound
B A
C D
E
Pathways through the brain
Other Applications of Communications and Signal Processing to Brain Science
EpilepsyEpileptic 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
ParkinsonsCreates 20 MHz noise in brain
region120 MHz square wave injection
mitigates the symptoms