reliable, high capacity, multipoint, wireless information networks by matthew bromberg ph.d

23
Reliable, High Capacity, Multipoint, Wireless Information Networks by Matthew Bromberg Ph.D.

Upload: lorena-moody

Post on 31-Dec-2015

215 views

Category:

Documents


0 download

TRANSCRIPT

Reliable, High Capacity, Multipoint, Wireless Information Networks

by Matthew Bromberg Ph.D.

Problem Identification Information Networks Require High Capacity,

Reliable, Data Networking Data networks require the transfer of large data files (e.g.

medical imaging) Bandwidth in Hz is scarce and expensive. (Billions required

for nationwide footprint.) Information transfer must be reliable, especially when

human lives are at stake (e.g. military networks, emergency services, police etc.)

Wired Networks are Costly and Impractical wired infrastructure is costly to build and maintain wiring is infeasible in for mobile units and temporary

structures (e.g field hospitals) Wireless is the Only Solution for Soldiers in the Field

Soldier becomes part of wireless network Can integrate with current Land Warrior program

Problem Identification Continued

Wireless Network faces Severe Multipath unknown terrain effects urban environments indoor multipath

Wireless Network faces Degraded Propagation indoor propagation losses canyons and urban environments

Wireless Network faces Interference co-channel interference from other network nodes hostile interference from jammers interference from other, co-channel networks

Wireless Network Must be Secure must have low probability of intercept (LPI) must be secure against infiltration

Multipath Illustration Multipath: wireless signal bouncing off of

terrain buildings etc. Main path blocked causes severe reduction of

signal strength

Remote Unit

1k

2k

User in group 2

Advantages of Proposed Solution

Maximizes Network Capacity Simulations suggest more than an order of magnitude

improvement ( x 35) Exploits Multipath

Can use multipath diversity to multiply capacity Mitigates Interference

Multi-antenna array excises co-channel interference Optimizes Transmit Beamforming

Permits inexpensive remote transceivers Offers reduced interference profile for LPI increases capacity of network

Network is Optimized Locally Power control only needs local information Entire network performance is optimized

Required Transmit Power is Minimized Total network transmit power can be minimized subject to a

capacity constraint Dramatic reduction in required transmit power observed (factor of

40,000)

Network Objective Function Maximize channel capacity flows through network

Optimize maximum theoretical bit rate achievable in network Minimize transmitted power subject to capacity

constraint alternative network performance formulation can use arbitrary bit rate targets based on quality of service

QoS requirementsMaximize InformationFlow into and out ofCut Sets

Basestation

Remote

Advantages of Time Division Duplex

TDD alternates in time transmission and reception over the same frequencies

Channel on uplink nearly the same as downlink RF components shared for transmission and reception Optimal transmit weights easily obtained

Downlink Transmis-

sion

Uplink Transmis-

sion

Downlink Transmis-

sion

Uplink Transmis-

sion

Time

Freq

uen

cy

Multi-Antenna Receiver

Reverse arrows for transmitter

Exploitation of Channel Reciprocity

Channel Reciprocity asserts uplink channel response is the same (matrix transpose) as the downlink

Can be achieved in TDD networks after transmit/receive gain compensation

d1k1

d1k2

d1kQ

g1k1g1k1

g1k2g1k2

g1kQg1kQ

...

d1k G1k1k

k,k)

i2k

...

W2k

w2k1*

w2k2*

w2kQ*

d1k1

d1k^

^

d1k2^

d1kQ^

......

Channel ReciprocityH21(k; j) = H12(j; k)T (swap 1 and 2 indices above for downlink)

Remote Transmit

Base Receive

X2klink k has Q sublinks

Receiver Model

Information Theoretic Objective Function

Useful metric is mutual information

Represents maximum achievable throughput

maximize mutual information subject to power constraints

decoupled capacity metric assumes linear receiver weights easier to analyze

data processing inequality

Decoupled Capacity Achieves its Upper Bound Linear Beamforming is the best you can do for Gaussian other user interference Best receiver weights easily computed using local statistics

Reciprocity Theorem Reciprocal channels imply the Reciprocity

Theorem

D21(W,G) = D12(G*,W*)

uplink capacity equals downlink capacity transmit with conjugate of receiver weights uplink sum total power also equals downlink total power

(alternative objective function) Transmit weights are easily obtained from

receive weights. Transmit and receive weights only require local

information. (No Network God) Optimizing the receiver ’globally’ optimizes the

entire network! Network is stable and improves at every iteration.

Illustration of Reciprocity Theorem

Receive Beamformer enhances signal of interest (SOI), suppresses interferer

Transmit beamformer enhances signal of interest, offers minimal interference to other nodes in field of view

km

-1 -0.5 0 0.5 1

-

-

-

-

0.8

0.6

0.4

0.2

0

0.2

0.4

0.6

0.8

km

SOI

Inter1

Inter2

Network Optimality Using Local Information

Receiver computes optimal Wiener beamforming weights using statistics observed at receiver

Optimal transmit weights are proportional to receive weights: g = w*

Optimal power control only requires post-beamforming interference power estimate from other end of linkL (,g, ) gTlocal model of sum of xmit powers)

q Q(m) log(1 + (q)) m (capacity constraint) g = f( (gradient of total xmit power wrt target SINR)(q) = (q) *(q)/(q) (new xmit power is old power times ratio of optimal target SINR divided by achieved SINR)

gradient can be computed by simply estimating post beamformer interference at both ends of link

Locally Enabled, Globally Optimized (LEGO)

Compute Weightsw2 = Rx

2x2

-1 Rx2s

Estimate Transfer Powerh2 =| w2

H Rx2 s /Rss |2 /1

Estimate Interference Poweri2 = Ry2 y2

- 1 h2 Rss

Set gradient: g( k)= i2 (k) i1 (k)/h2

Optimize local model

= arg min L (,g, )1 =i1 /h2 =i2 /h

Base Station (User 2 Node)

Subscriber Unit (User 1 Node)

Compute Weightsw1 = Rx1 x1

-1Rx

1s

Estimate Transfer Power

h2 =| w1H Rx1 s /Rss |2 /2

Estimate Interference Poweri1 = Ry1y

1 - 2 h2 Rss

i1

Interfering SUs

Interfering BSs

Patented Technique

Computations can be concentrated at basestation

Convergence to Theoretical Maximum Capacity

Simulation Parameters: 19 Cells, 1 km radius, 1800 MHz, Hata cost 231 path loss model,

Rayleigh fading, statistically independent antennas, 128 sample block processing, non-blind max-SINR beamform weights, 4 antennas at base, 2 antennas at remote. 1 remote in network.

Rapid convergence to theoretical maximum capacity

0 5 10 15 20 25 300

510

1520

25

Bits

per

Sam

ple

Reverse Link Capacity

0 5 10 15 20 25 305

10

15

20

25

Bits

per

Sam

ple

Forward Link Capacity

Iteration

Max Capacity

Max Capacity

Convergence Example 19 Cell network 1 remote per

cell (per band) Each remote

has links to 2 basestations

8 antennas at each basestation

2 antennas at each remote

2 independent channels

-5 -4 -3 -2 -1 0 1 2 3 4 5

-4

-3

-2

-1

0

1

2

3

4

km

km

LEGO Convergence Easily achieves 5 bps per Hz (could have achieved a lot more) Converges in 15 iterations (40 msec or so) Node transmit power < 20 dBm (Well under unlicensed band

spec.)

0 10 20 30 40 50 60 70 800

10

20

30

40

50

dB

m

Forward Link Xmit Gains

0 10 20 30 40 50 60 70 80-5

0

5

10

15

20

Iterations

dB

m

Reverse Link Xmit Gains

0 10 20 30 40 50 60 70 800

5

10

15

Bit

s p

er

Sa

mp

le

Reverse Link Capacity

0 10 20 30 40 50 60 70 800

5

10

15

20

Bit

s p

er

Sa

mp

le

Forward Link Capacity

Iteration

5 bps/Hz = 10 bits/samp * 50 ksamps/100kHz

Minimize power subject tocapacity constraint metric

Multiplying Capacity by Exploiting Diversity

7 Cell Network.(Reuse pattern of 3).

Max Xmit pow. = 15 dBm.

Equal number of antennas per base and remote.

Number of antennas varied.

LEGO Exploits Multipath, vs Single Path Transmission, Conventional Power Management

-5 -4 -3 -2 -1 0 1 2 3 4 5

-4

-3

-2

-1

0

1

2

3

4

km

km

0 2 4 6 8 10 120

5

10

15

20

25

Number of antennas

Bits

pe

r S

amp

leCapacity vs Num. antennas

LEGO

Single Mode Equal Power

Channel rank allowed to growLEGO never uses more than 5 modes

Multiplying Capacity by Optimal Power Management

19 cell network. Number of users per cell varied.

Maximum achievable worst-case capacity plotted.

0 1 2 3 4 51

2

3

4

5

6

7

8

9

10

Ca

pac

ity, B

its P

er

Sa

mp

le

Users Per Cell (1/Reuse)

Channel Capacity vs. Cell Capacity

LEGO

Const. Power Single antenna

2.5 × capacity of standard power management and 35 × an algorithm that does not exploit reciprocity

Multiplying Capacity Using MultiPoint Networks

4 Cell Network Max Xmit Power 53 dBm Star and Ad-Hoc

Topologies 8 antennas at base, 2 at

each remote

km

-2 -1 0 1 2

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

km-2 -1 0 1 2

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

km

km

0

5

10

15

20

25

30

35B

its

Per

Sam

ple

Star Topology

Ad-Hoc Topology

65% capacity increase

(100% Asymptotic increase)

Increased connectivity multiplies capacity.

Minimizing Transmit Power Experiment Setup

5 Antennas at each base station * 2 Antennas at each remote unit 3 Basestations, 6 Remotes, 2 links per remote LEGO power control, vs Standard vs 1 antenna comparison Transmit power varied, max remote bit rate plotted 6 independent (50 kHz) frequency

channels

-2.5 -2 -1.5 -1 -0.5 0 0.5 1-2.5

-2

-1.5

-1

-0.5

0

0.5

km

km

Standard power control:Constant link transmit powerand constant link receive power at basestation. (similar to CDMA)

1 Antenna case can only use a single link at each remote, and FDMA for co-channel interference

LEGO is 40,000times better.

Reducing Required Transmit Power

To achieve 8.6 bps per Hz requires 25.3 dB or 339 times more power for Standard Power Management

To achieve2.9 bps per Hzrequires 46.1dBor 41 thousandtimes morepower for thesingle antennacase.

Cost of poweramplifiersincreases by thepower squared. -20 -10 0 10 20 30 40 50

0

2

4

6

8

10

12

14

16

18

20

dBm

bps

per

Hz

LEGO

Standard

Single Antenna

Compared to LEGO Performance

COTS Implementation

LEGO permits network operation in the presence of co-channel interference

Network could operate in unlicensed band, at 2.4 GHz 5 GHz and 900 MHz are other possibilities

A large amount of commercial off the shelf hardware (COTS) exists for the unlicensed bands.

Hardware costs can be kept down, following the philosophy of the Army’s Land Warrior program.