1. Advanced Topics in Wireless
Wireless Communications
Advanced Topics in Wireless

2. Topics Future wireless networks
Software-defined and low-complexity radios
Advanced design of cellular systems
Wireless network convergence and SDN
Ad-hoc and sensor networks
Cognitive and software-defined radios
Energy Constraints in Wireless Systems
Control over Wireless
Neuroscience applications of wireless

3. Challenges Network Challenges Device Challenges Scarce spectrum
Demanding applications
Reliability
Ubiquitous coverage
Seamless indoor/outdoor operation
Device Challenges
Size, Power, Cost
MIMO in Silicon
Multiradio Integration
Coexistance
Cellular
Apps
Processor BT
Media
GPS
WLAN
Wimax
DVB-H
FM/XM

4. Software-Defined Radio
Cellular
Apps
Processor BT
Media
GPS
WLAN
Wimax
DVB-H
FM/XM
A/D
A/D
DSP
A/D
A/D
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
Today, this is not cost, size, or power efficient
Compressed sensing may be a solution for sparse signals

5. 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.

6. 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.

7. 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?

8. 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

9. Sparse Samples For a given sampling mechanism (i.e. a “new” channel)
(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

10. Capacity under Sub-Nyquist Sampling
Theorem 1:
Theorem 2:
Bank of Modulator+FilterSingle Branch  Filter Bank
Theorem 3:
Optimal among all time-preserving nonuniform sampling techniques of rate fs (ISIT’12; Arxiv)
zzzzzzzzzz
equals

11. Joint Optimization of Input and Filter Bank
Selects the m branches with m highest SNR
Example (Bank of 2 branches)
low
SNR
highest SNR
Capacity monotonic in fs
2nd highest
SNR
low SNR
How does this translate to practical modulation and coding schemes

12. Reduced-Dimension Network Design
Random State Evolution
Reduced-Dimension
Representation
Resource Management
Stochastic
Control
Sampling
and
Inference

13. Scarce Wireless Spectrum
$$$

14. Are we at the Shannon limit of the Physical Layer?
We don’t know the Shannon capacity of most wireless channels
Time-varying channels with memory/feedback.
Channels with interference or relays.
Uplink and downlink channels with frequency reuse, i.e. cellular systems.
Channels with delay/energy/$$$ constraints.

15. Cooperative Techniques in Cellular
Femtocells
Many open problems
for next-gen systems
Relay
Will gains in practice be
big or incremental; in
capacity or coverage?
Network MIMO: Cooperating BSs form a MIMO array
Downlink and uplink are multiuser MIMO channels
Can treat “interference” as known signal (DPC) or noise
Can cluster cells and cooperate between clusters
Can also install low-complexity relays and/or Femtocells
Mobiles can cooperate via relaying, virtual MIMO, conferencing, analog network coding, …

16. Rethinking “Cells” in Cellular
How should cellular
systems be designed?
Coop
MIMO
Small
Cell
Relay
Will gains in practice be
big or incremental; in
capacity or coverage?
DAS
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 relaying, virtual MIMO, analog network coding.

17. 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.

18. 10x CAPACITY Improvement
The Future Cellular Network: Hierarchical Architecture
10x Lower COST/Mbps
10x CAPACITY Improvement
Near 100% COVERAGE
(more with WiFi Offload)
Today’s architecture
3M Macrocells serving 5 billion users
Anticipated 1M small cells per year
MACRO: solving initial coverage issue, existing network
PICO: solving street, enterprise & home coverage/capacity issue
Macrocell
Picocell
Femtocell
Future systems require Self-Organization (SON) and WiFi Offload

19. SON Premise and Architecture
Mobile Gateway
Or Cloud
Node Installation
Initial Measurements
Self Optimization
Self Healing
Self Configuration
Measurement
SON
Server
SoN Server
IP Network X2 X2 SW
Agent X2 X2
SON is part of 3GPP/LTE standard
Small cell BS
Macrocell BS

20. Convergence of Cellular and WiFi
LTE.11
Seamless handoff between networks
Load-balancing of air interface and backbone
Carrier-grade performance on both networks

21. 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

22. SDWN Architecture App layer SW layer UNIFIED CONTROL PLANE
Video
Security
Vehicular
Networks
M2M
App layer
Health
Freq.
Allocation
Power
Control
Self
Healing
ICIC
QoS
Opt. CS
Threshold
SW layer
UNIFIED CONTROL PLANE
Commodity HW
WiFi AP
Femto Cell
Pico Cell
Cognitive Radio

23. Cognitive Radio Paradigms
Knowledge
and
Complexity
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

24. Underlay Systems
Cognitive radios determine the interference their transmission causes to noncognitive nodes
Transmit if interference below a given threshold
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 IP CR
NCR
NCR
支撑系统

25. 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
混合系统

26. 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
RX1 CR
上层系统
RX2
NCR

27. Performance Gains from Cognitive Encoding
outer bound
our scheme
prior schemes
Only the CR
transmits

28. Ad-Hoc Networks Peer-to-peer communications.
No backbone infrastructure.
Routing can be multihop.
Topology is dynamic.
Fully connected with different link SINRs
-No backbone: nodes must self-configure into a network.
-In principle all nodes can communicate with all other nodes, but multihop routing can reduce the interference associated with direct transmission.
-Topology dynamic since nodes move around and link characteristics change.
-Applications: appliances and entertainment units in the home, community networks that bypass the Internet. Military networks for robust flexible easily-deployed network (every soldier is a node).

29. 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.
“We must, indeed, all hang together or, most assuredly,
we shall all hang separately,” Benjamin Franklin

30. 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.

31. General Relay Strategies
TX1
TX2
relay
RX2
RX1 X1 X2
Y3=X1+X2+Z3
Y4=X1+X2+X3+Z4
Y5=X1+X2+X3+Z5
X3= f(Y3)
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

32. Beneficial to forward both interference and message
For large powers, this strategy approaches capacity

33. Wireless Sensor Networks Data Collection and Distributed Control
Smart homes/buildings
Smart structures
Search and rescue
Homeland security
Event detection
Battlefield surveillance
Energy is the driving constraint
Data flows to centralized location
Low per-node rates but tens to thousands of nodes
Intelligence is in the network rather than in the devices

34. Energy-Constrained Nodes
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.
All the sophisticated high-performance communication techniques developed since WW2 may need to be thrown out the window.
By cooperating, nodes can save energy

35. 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
Shannon
Limit Pb
Uncoded
Coded
SNR
Coding
Gain

36. Green Codes (for short distances)
Is Shannon-capacity still a good metric for system design?

37. Coding for minimum total power
Is Shannon-capacity still a good metric for system design?
Computational
Nodes
On-chip
interconnects
Extends early work of El Gamal et. al.’84 and Thompson’80

38. 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

39. “Green” Cellular Networks
Pico/Femto
How should cellular
systems be redesigned
for minimum energy?
Coop
MIMO
Relay
DAS
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

40. 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

41. Distributed Control over Wireless Links
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

42. The Smart Grid: Fusion of Sensing, Control, Communications
carbonmetrics.eu

43. 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

44. Applications in Health, Biomedicine and Neuroscience
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
Doctor-on-a-chip
Wireless
Network
Recovery from
Nerve Damage

45. 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
Microarrays make use of the DNA characteristic that is can hybridize. Meaning – that A sticks to T and G sticks to C.
How does microarrays work:
A microarray is a chip with a matrix. In each square in the matrix there are many probes that can stick to a specific messenger RNA (that represents an expressed gene). The matrix in the chip represents all human genes, for example.
A tissue (ex. Tumor tissue) is taken, the messenger RNA is extracted from it, labeled and then hybridized onto the microarray chip. If a gene is expressed in the tissue (that is, if there is messenger RNA from that gene), then it sticks to the probes in the square that represent it on the chip, according to the quantity of mRNA that were in the tissue sampled. Then the chip is scanned and sent to image analysis to get the intensity of the binding, and to any additional data analysis.
Gene Signatures
Cell-type
Proportion
Cell Types

46. Looks like CDMA “despreading”
Many gene expression deconvolution algorithms exist  Shen-Orr et al., Nature methods  known “C” and “k”  Lu et al. , PNAS  known “G” and “k”  Vennet et al., Bioinformatics  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

47. Pathways through the brainC D E
𝐼 𝑋 𝑛 → 𝑌 𝑛 =𝐻 𝑌 𝑛 − 𝑖=1 𝑛 𝐻 𝑌 𝑖 | 𝑌 𝑖−1 , 𝑋 𝑖
Direct information (DI) inference
Neuron layout B A C D E
𝐼 𝑋 𝑛 → 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
𝐼 𝑋 𝑛 → 𝑌 𝑛 =𝐻 𝑌 𝑛 − 𝑖=1 𝑛 𝐻 𝑌 𝑖 | 𝑌 𝑖−1 , 𝑋 𝑖−𝐷−𝑁 𝑖−𝐷
Constrained DI inference B A C D E

48. 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

49. Neuron Communication Basics
Neurons communicate via electrical pulses (action potentials or AP)
Dendrite inputs from other neurons generate an AP if sufficiently large
Weighting on each branch (Diversity?)
AP amplified by “Nodes of Ranier” if pulse exceeds given threshold
Node distance maximized subject to re-amplification constraint
AP propagates from axon terminal to other dendrites
Excitatory or inhibatory
神经元通信通过电脉冲 树突
Neuron

50. Conjecture: Brains designed to minimize energy of communication
Brain consumes 20% of total body energy via APs
APs don’t propagate unless pulse “large”
Suppose optimize weighting of correlated dendrite branch inputs to minimize energy subject to SNR?
Experimental
Results from
literature
Analytical
results with
log-normal
correlation of
synaptic inputs
推理：
Maybe not significant; but our tools have wide application to neuroscience

51. The End ～ Thank U！