Andrea Goldsmith Wireless Systems Laboratory Stanford University PIMRC September 3, 2014

Future Wireless Networks Ubiquitous Communication Among People and Devices

Next-generation Cellular Wireless Internet Access Sensor Networks Smart Homes/Spaces Automated Highways Smart Grid Body-Area Networks Internet of Things All this and more …

Future Cell Phones

Everything in one device Burden for wireless this performance is on the backbone network San Francisco

BS

BS

LTE backbone is the Internet Internet

Nth-Gen Cellular

Phone System

Nth-Gen

Paris

Cellular

BS

Much better performance and reliability than today - Gbps rates, low latency, 99% coverage indoors and out

Careful what you wish for…

Source: FCC Growth in mobile data, massive spectrum deficit and stagnant revenues require technical and political breakthroughs for ongoing success of cellular

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.

Was Shannon wrong, or now irrelevant?

 Of course not  What is the flaw in their logic  Use single-user capacity formula for comparison  Let power/bandwidth grow (asymptotically large)  Aggressive frequency reuse via small cells  Space as the final frontier: (Massive) MIMO

In fact, we don’t know the Shannon capacity of most wireless channels  Time-varying channels with no/imperfect CSI  Channels and networks with feedback  Channels with delay/energy/HW constraints.  Channels with interference or relays  Cellular systems  Ad-hoc/peer-to-peer networks

 Multicast/common information networks

Rethinking “Cells” in Cellular Small Cell

Coop MIMO

How should cellular systems be designed?

Relay

DAS

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 relaying, virtual MIMO, analog 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 issue, existing network

PICO: solving street, enterprise & home coverage/capacity issue

FEMTO: solving enterprise & home Picocell Macrocell coverage/capacity issue

10x Lower COST/Mbps

(more with WiFi Offload) 10x CAPACITY Improvement

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

IP Network Self Configuration

Measurement

SON Server

Self Optimization

X2

X2

Small cell BS Macrocell BS

X2

X2

SW Agent

Why not use SoN for all wireless networks? TV White Space & Cognitive Radio

mmWave networks

Vehicle networks

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

SDWN Challenges  Algorithmic complexity  Frequency allocation alone is NP hard  Also have MIMO, power control, CST, hierarchical

networks: NP-really-hard  Advanced optimization tools needed, including a combination of centralized and distributed control

 Hardware Interfaces (especially for WiFi)  Seamless handoff between heterogenous networks

Massive MIMO: What is it? Dozens of devices

Hundreds of BS antennas

 10x more BS antennas than today, serving many users  Increases diversity/capacity/multiuser gains of MIMO  With coherent massive MIMO, all the effects of noise and small-scale fading are removed.  Performance limited by pilot contamination

Can small cells reduce the impact of pilot contamination?

System Model  Massive MIMO TDD cellular system

 K users uniformly distributed in each cell  Uplink training with fixed set of pilot

sequences

 Copilot Distance D is fixed and

independent of cell radius R

 Pilot Contamination: Downlink SIR & capacity for k-th user in cell 1:

where βs depend on the position of the users in all cells:

Results  Users are

uniformly distributed inside the cells

 Cells are

approximated with circles

As the cell-size reduces, pilot contamination effect diminishes

Noncoherent Massive MIMO  Obtaining CSI very challenging  What can we do without CSI?  Propose low-complexity noncoherent SIMO system  Transmitter: Constellation points are power levels  Receiver: Senses only the received power  Symbol Error Rate: SERi  e  nIi ( di )  Depends on the distance “d” between adjacent

constellation points  Small “d” approximation:  Accurate for large constellations

d2 I i (d )  f ( pi )

Non-coherent optimal in scaling!  Constellation design  A minimum distance criterion provides a simple design  Achieves asymptotically vanishing error probability n = 500

700

600

500

400

300

200

100

0 0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

 Scaling Law result  As number of receiver antennas increases, scaling law of the

achievable rate without CSI is the same as that with perfect CSI:

C º Cnocsi log(n) £ Ccsi log(n)  Same scaling applies to multiple single antenna TXs with 1 RX

Minimum Distance Criterion – SER Results Analytical upper bound

Too many receive antennas are needed!

Constellation Design Optimization

Rician Fading K = 0, SNR = 0 dB

Constellation Design Optimization in non-asymptotic regime  Design Criterion:  All points have same right and left error exponents  Implies all point have approximately the same SER dR,1& dL,2& dR,2&

p1+σ2&

p2+σ2&

dL,3&

dR,3&

p3+σ2&

dL,4&

p4+σ2&

Distance between adjacent points increases as power increases

 Robustness  Can incorporate uncertainty of statistics into design  All points must have minimum value of left and right error

exponent for all possible statistics in uncertainty interval

Performance The robust design is able to sustain a good performance in a mismatched channel

The robust design is not much worse than the “nominal” design

 Extensions  Optimized and robust constellation designs for multiuser uplink systems  Noncoherent phase receivers

 Comparison of noncoherent and coherent uplink designs  Noncoherent versus coherent downlink designs  Multiuser systems

Minimum number of Receive Antennas: SER= 10-4 Minimum Distance Design criterion: Significantly worse performance than the new designs.

For low constellation sizes and low uncertainty interval, robust design demonstrates better performance.

Extension to multiuser systems: currently working on constellation optimization

Spectrum innovations beyond licensed/unlicensed paradigms

Cognitive Radio Paradigms  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

Underlay MIMO Cognitive Radios (CR)  “Intelligent” radio (SU) coexists with licensed user (PU)  Uses MIMO technology for interference mitigation If the SU transmits in 𝑁𝑢𝑙𝑙 𝑯12 , interference to PU is zero

How can the SU obtain the null space to the PU?

Our approach

x(t) The whole process is only based on energy measurements

 Power Control

   

The SU-Tx intelligently choses the messages x(t) The PU-Rx notifies the PU-Tx to change its TX power (or MCS,…) Variation in the TX power of the PU-Tx is sensed by the SU-Tx The SU-Tx chooses the next message x(t) based only on the increase or decrease of the sensed power from the PU-TX.  Tracking algorithm searches “around” outdated null space, can track simultaneously with sending data.

Blind Null Space Learning: Example Rayleigh fading with maximum Doppler frequency Fd.

Tracking meets peak interference constraints 𝑃𝑋 : 𝑋-th percentile of decrease in interference

𝑁𝑡 = 2, 𝑁𝑟 = 1

Application to Cooperative Multipoint Out of Group Interference (OGI) mitigation

Software-Defined (SD) Radio: Is this the solution to the device challenges? BT Cellular

FM/XM

A/D

GPS DVB-H

Apps Processor

WLAN

Media Processor

Wimax

A/D A/D

DSP

A/D

 Wideband antennas and A/Ds span BW of desired signals  DSP programmed to process desired signal: no specialized HW

Today, this is not cost, size, or power efficient What if we sample below the Nyquist rate?

Sub-Nyquist Sampled Channels Analog Channel Message

N( f )

H( f )

Encoder

x(t )

y (t )

Decode r

Message C. Shannon

Wideband systems may preclude Nyquist-rate sampling! Sub-Nyquist sampling well explored in signal processing  Landau-rate sampling, compressed sensing, etc.  Performance metric: MSE H. Nyquist

We ask: what is the capacity-achieving subNyquist sampler and communication design

Filter Bank Sampling

t  n(mTs )

y1[n]

s1 (t )

 (t ) x(t )

h(t )

t  n(mTs )

yi [n]

si (t ) t  n(mTs )

sm (t )

ym [n]

 Theorem: Capacity of the sampled channel using a

bank of m filters with aggregate rate fs

Similar to MIMO Water-filling MIMO – Decoupling over singular values

Pre-whitening

Sub-Nyquist Sampling Optimal  “Sparse” channel model

 Capacity not monotonic

Effective Bandwidth

in fs for 1 branch  Capacity monotonic in

fs for enough branches

Sub-Nyquist Region

SuperNyquist Region

Sampling with Modulator+Filter (1 or more)  (t )

q(t)

x(t) h(t )



p(t)

y[n]

s(t )

 Theorem:  Bank of Modulator+FilterSingle Branch  Filter Bank t  n(mT ) s

q(t)

zzzz p(t) zzzz zz



y1[n]

s1 (t )

zzzz s(t ) zzzz zz

y[n]

t  n(mTs )

equals

yi [n]

si (t ) t  n(mTs )

 Theorem

sm (t )

 Optimal among all time-preserving nonuniform

sampling techniques of rate fs

ym [n]

Summary  Existing and emerging applications will require

significant breakthroughs in wireless system design  Small cells and massive MIMO are key enablers, but

pose new technical challenges  Innovative techniques for cognitive radio can help

alleviate spectrum deficits  An interdisciplinary design approach to hardware and

system design is needed to meet future challenges