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Motivation for project
Opportunistic spectrum access through auctioning has been evaluated through:◦ analytical analysis – highly simplified model required◦ Simulation – physical model limitation
Little implementation work due to lack of suitable hardware
Previous work prone to unrealistic assumptions◦ For example Interference patterns are dynamic◦ Assumed that interfering neighbors and hence
resulting conflict graph is an arbitrary problem
Proposal Part of a larger Google 1 year project to develop a
prototype of an operational online spectrum auction system◦ General challenge: What sort of wireless test bed could
be used to test an online spectrum bidding◦ Challenge 1: Conflict graphs are dynamic due to signals
suffering from unpredictable fading◦ Challenge 2: Defining a information rich bidding
language◦ Challenge 3: Accountability – can this be done with a
distributed auction model – how should the spectrum be partitioned
◦ Challenge 4: Enforcement: How do you ensure winning bidder receives service they paid for
Previous work This work builds on the following spectrum
auction research◦ General framework for wireless Spectrum
Auctions (Ghandi et al): Expressive bidding language - Piecewise linear price-quantity (PLPQ), linearizing the interference constraints
◦ Collaboration and fairness in opportunistic spectrum access (Zheng et al) : sequential heuristic for good graph colouring
◦ Dynamic Property Rights Spectrum Access (Ileri et al) : User acceptance models
Previous work Test beds
◦ ORBIT (20x20) grid test bed at Rutgers University ◦ Kansei (15x14) grid test bed at Ohio state University◦ CSIR Massive mesh lab (7x7) grid test bed in South Africa
Cognitive radio platforms◦ Berkeley Emulation Engine 2 (BEE2): generic, multi-
purpose, FPGA based, emulation platform◦ GNU Radios: Reconfigurable software-defined radio
systems , Universal Software Radio Peripheral v2 (USRP2) available
◦ Atheros 802.11 hardware with openHAL and Click platform also allow you to break out of 802.11 specification
Background Operators will sell bandwidth on a time basis
(e.g. 1Mbps for 1 hour for $x ) Bidding system is being tested in 2.4 GHz
ISM band but it is mimicking a licensed frequency band system with some interference.
3 non-overlapping channels available but with clock rate modification 11 non-overlapping channels could be auctioned
Assumes out of band communication between operators and broker
Challenge 1: Building a test bed for online spectrum auctions
Propose CSIR wireless grid as a good solution for spectrum bidding experiments
7x7 grid of 49 wireless nodes using 802.11 a/b/g radios
Each node network boots off a central server
Makes use of 30dB attenuators on radios to achieve limited range down to 1 or 2 hops in small space
Can nominate nodes as operators or users
Complete remote control of experiments possible
Challenge 2: Building a conflict graph with neighbor sensing
Planned network involves engineered solutions to minimize interference between radio transmitters
Unplanned network relies on some peer sensing to understand interference conditions.◦ If GPS position and power was known – problem becomes
more trivial – assume position is not known◦ Even if position was known, radio propagation is still not
easily predicted Propose that all bidders/operators repeatedly sense
existing/new neighbors and send this periodically to broker
Model is one of cooperation between equals (primary users are the operators)
Challenge 2: Sensing problem
Broker relies on neighbors sensing each other to build a conflict graph.
Can only create an approximate solution◦ If AP1 and AP2 don’t hear each other they may still
interfere (hidden terminal)
AP1
AP2U1
U2
AP1
AP2
U1U2
Challenge 2: Sensing problem
Conservative approach: Operators drop their power level by 3dB (1/2 distance) if they didn’t see each other
Users TX can still interfere but CSMA takes care of this◦ Best solution is cellular like coordinated approach – but this is
best we can do with random access MAC◦ Interference is minimized for typical asymmetric Internet traffic
AP1 AP2
sd
Challenge 2: Sensing problem – scan options
Operate Scan
Bid
Operate Scan
vid
Operate
Bid
Scan
Bid
LicensedData channels
Bidding Back channel
Scan radio
OperateOperate
Bid
Scan
Bid
Scan
Bidding Back channel
LicensedData channels
Bidding Back channel
LicensedData channels
STA
GG
ER
ED
SC
AN
DED
ICATED
SC
AN
IN-B
AN
DS
CA
N
Challenge 2: Sensing problem – scan interval
For dedicated and staggered scan, scan as often as possible
Use maximum power possible for sending beacons For in-band scan, should be as conservative as possible to
minimize interruption to data channel But can’t just take 1 sample – this might be in a fading dip Proportional approach
◦ Pick weakest signal - if signal is strong only scan twice in operating interval, If signal is weak increase scan rate
RSS
I
Min operate time
PTHRESH
Challenge 3: Bidding language – the system
Broker (SPS) Operator
Solve conflict graph to optimize profit
Request channel
Advertise (channels, bid start price) Bid generatorScan usersProfit check
Bid (num channels. $/chan)
Neighbor ScannerNeighbors (num neighbors, list)
Reject/Accept Bid
Channel allocation (channel list, pwr diff)Setup operatorTo channel and
power level – offer service
Advertise channels/start price
The click framework (2)
ToDeviceQueue
FromDevice Classifier
Process A
Process B
Process C
Queue
Queue
ToDevice
ToDevice
Bidder (Operator ) click state machine
RequestService ToDeviceQueue
ScanNeighbors
ClassifierFromDevice
BuildOperator
BidGenerateScanUsersProfitCheck
Wait (x sec)
Wait (BidT sec)
ToDeviceQueue
ToDeviceQueue
System starts here BID_REJECT
AD_CH
CH_ALLOC
SCAN_RES
BID_DECLARE
REQ_SERViCE
Broker click state machine
ToDeviceQueue
ClassifierFromDevice
ToDeviceQueue
BID_REJECT
AD_CH
CH_ALLOC
SCAN_RES
BID_DECLARE
ToDeviceQueue
SolveConflict
REQ_SERViCE
AdvertiseService
Challenge 3: Bidding language – the packets bid system Packet header
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ | Packet Length | BID Version | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ | Originator ID | Packet Sequence Number | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ | Message Type |# Messsages | Message Size | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ | | : MESSAGE 1 : | | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ | | : MESSAGE 2 : | | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
Channel advertise Message
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ | Number of channels | Reserved | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ | Starting bid price | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ | Bid allocation time | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
Type: Single message, Direction: Operator->Broker (Unicast)
Number of channels= Number of channels broker has availableStarting bid price = Starting bid price per channel per unit of time.Bid allocation time = minimum period for channel allocation
Size with header: 24 bytesRate: 1 bid message per bid allocation time
Scan results Message
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+| Neighbor ID | Signal level |+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
Type: multi message, Direction: Operator ->Broker (unicast)
Neighbor ID = Unique ID of neighbor visible from this nodeSignal level = Level of measured signal for neighbor in RSSI
Size with header: 12 bytes + Number of neighbors * 4Rate: 1 scan results message per bid allocation time
Channel allocate Message+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+| Operator ID | Channel |+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+| Allocation time |+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ | Winning Price |+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ | Power adjustment | Reserved |+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
Type: Multiple message, Direction: Broker->Operator (broadcast)
Operator ID= Unique ID of operator being allocated channelChannel= Channel allocated to operatorAllocation time = Period of time operator can use this channel (seconds)Winning price =Price to pay for this channel allocation Power adjustment = Amount by which current power should be decreased (dBm)
Size with header: 12 + 16 * (number of allocated channels) bytesRate: 1 channel advertise message per bid allocation time
Scan Neighbors element All neighbors have same prefix and with unique ID for
SSID Scanning in WiFi is built in functionality
◦ Looks for broadcast beacons containing SSID (Service Set Identifier)
◦ Also Reports signal strength of nodes◦ In the future the beacon generating algorithm can be overridden
A staggered scan is implemented and scanning only happens during the scanning period using a one second scan interval
History is recorded and averaged Simplification – only report primary user nodes with pre-
defined SSID prefix – ignoring interference from secondary users
ScanNeighbors
[1] Z HENG , H., AND P ENG , C. Collaboration and fairness in opportunistic spectrum access. In Proc. 40th annual IEEE International Conference on Communications (June 2005).
Solve conflict graph element Takes neighbor signal strength measurements
and operator bids as inputs All signals below a threshold (PTHRESH) are
ignored Solves the conflict graph for PTHRESH using
sequential heuristic [1] which is a form of greedy algorithm◦ When signal strengths are asymmetrical on a link – the
largest of the two is used to solve the conflict graph Single channel allocation for now – multi
channel allocation later
SolveConflict
Bid generation element(1)
A = Acceptance probability of user Pi = User Price M = Number of channels Q = Minimum Profit N = Number of users
Broker (SPS) Operator
Starting bid Smin (given by SPS)
Max bid Smax (bound by min profit)
HIG
H R
ISK
HIG
H P
RO
FIT
LOW
RIS
KLO
WER
PR
OFIT
Actual bidIn this range
M
QPAS
N
ii
1
max
A and Q are chosen but how do we find the Pi ‘s and M
BidGenerateScanUsersProfitCheck
Bid generation element(2)
1
A
P
3 Mbps
2 Mbps
1 Mbps
80% acceptance Model user acceptance as a simple linear response to rate and price
How do we know what rate to choose?
Assume 1 channel will give you a fixed base rate B (e.g. 5 Mbps)
Assume operators oversell their bandwidth at a certain ratio r (e.g. 5:1) and never sell less than rate b (e.g. 1 Mbps)
Share this among N users using M channels
N
MBrR
..
Users #chan
R (Mbps)
1 1 5
2 1 5
10 1 2.5
25 1 1
26 2 1.92
50 2 1
Bid generation element(3)
Bid price depends on:◦ Number of users◦ Minimum profit prepared to make◦ Risk aversion of operator◦ Choice of higher or lower acceptance probability of
user ◦ Oversell ratio of operator
If Smax bid price is negative then can’t make minimum profit -> don’t bid
Results Setup 5 nodes as
operators in the grid
Configured with◦ Fixed data rate
11Mbps◦ Starting power at
max (19dBm)◦ Beacons sent on
channel 14 (clean channel)
Results (scan element)
1 9 17 25 33 41 49 57 65 73 81 8902468
101214161820
Node 14Node 17Node 44Node 74
Tme (s)
RSSI
1 9 17 25 33 41 49 57 65 73 81 8902468
101214161820
Node 12Node 17Node 44Node 74
Time (s)
RSSI
1 10 19 28 37 46 55 64 73 82 910
5
10
15
20
Node 12Node 14Node 17Node 74
Time (s)
RSSI
1 11 21 31 41 51 61 71 81 9102468
101214
Node 12Node 14Node 17Node 44
Time (s)
RSSI
Picking users(who is in range)
| 0| 7| 0| 4| 3| 1| 0|| 5| 4| 3| 2| 2| 3| 2|| 0| 5| 0| 6| 6| 1| 5||13|12| 7| 0| 6| 6| 0|| 0| 0|10|14| 7| 5| 9||19|19| 6| 2| 4| 6| 0|| X|19| 0|11| 0| 3| 0|
Users in range of Node 17
|11|18| 8|12| 6| 0| 0||17|16| 0| 6|11| 8| 9|| 0|20| 9| 7|14| 9| 7|| X|22|17| 0| 9|14| 0|| 0|20|14|13| 3|13|11||12| 0| 0| 7|11| 8| 9||17|16| 0|13|10|10|12|
Users in range of Node 14
|10|18|11| 9|11| 5| 0|| X|20| 0|10|10|10| 0|| 0|18| 0| 6| 8| 9| 6||18| 7|14|15|14| 9| 0|| 0|10|14| 8|13| 6| 9|| 0|10| 5| 0| 0| 8| 7|| 9| 0| 0| 0| 0| 5| 0|
Users in range of Node 12
| 0| 0| 0| 9|10|12| 0||15| 0|14|17|12|15| 7|| 0|16| 0|18|21| 4| 6|| 0|18| 0|X |20|17| 0|| 0|10|20|24|18| 7|14|| 0|12|18| 7| 0|18| 0|| 0| 0| 0| 0| 0|13|19|
Users in range of Node 44
Final selection of users
1 2
3
4 5
6
7
8
9
10
11
1
2
3
4
1
6
7
59
10
11
12
13
14
1
2
3
4
5
6
78
8 9 10
11
12
Operator
User
Assume user selects strongest signal
Random selection of user per operator from operator’s user list
TCP traffic source and sink started between user and operator
Test for 30 seconds and repeat test 1000 times with new random user trio each time
Final throughput results
Node 14 Node 17 Node 44 Node 74 Average0
1000
2000
3000
4000
5000
6000
1 ch 3 op pwr 192 ch 3 op pwr 192 ch 3 op pwr 153 ch 4 op pwr 193 ch 4 op pwr 15
Kbit
s/s
Conclusions A grid based test bed is a viable platform for
testing online spectrum auction systems The click framework greatly simplifies the
construction of the bidding state machine and components
Scanning should be done as often as possible but◦ Aggregate at the operator to save bandwidth◦ If inband, scan rate should be proportional to signal
strength The broker should instruct operators that can’t
see each other to reduce their transmit power by 3 dB, throughput increased by 20%
Future Finish coding bid generator based on Piecewise linear
price-quantity (PLPQ) Solve Graph multi colouring (GMC) problem for allocating
multiple channels Include multiple brokers Add ability to bid for more time slots Make use of channel width modification using clock rate
to connect real users Decentralized System could use 802.11 mesh as a
fallback data channel and back channel Make use of users for sensing Bidding can also involve multiple bid slots and TX power
dependent pricing
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