Asilomar 2011

Spectrum Leasing via Cooperative
Opportunistic Routing in Distributed Ad Hoc
Networks: Optimal and Heuristic Policies

Cristiano Tapparello
Davide Chiarotto, Michele Rossi, Osvaldo
Simeone and Michele Zorzi

04/11/11

Outline

¨  Cognitive radio
¨  Spectrum leasing and opportunistic routing
¨  Optimal policies
¨  Heuristic policies
¨  Numerical Results
¨  Conclusions

Cristiano Tapparello – Asilomar 2011 04/11/11

Cognitive Radio
¨  Coexistence
of:

q  primary
network:
licensed

q  secondary
network:
unlicensed

¨  Common
model:

q  primary
users
are
oblivious
to
the
presence
of
secondary
users

q  secondary
users
sense
the
radio
environment
in
search
of

spectrum
holes

q  Performance
limited
by
sensing
errors

¨  Spectrum
leasing:

q  primary
users
lease
part
of
the
(licensed)
spectrum
to

secondary
users
in
exchange
for
appropriate
remunera?on


Spectrum leasing via
cooperation [Simeone08]
¨  Remuneration is offered by the secondary users in the
form of cooperation (relaying)
¨  Secondary users accept to cooperate if leased
enough spectral resources to satisfy QoS requirements


Opportunistic routing

Po Pd

¨  Leverages broadcast nature of wireless channel
¨  Next hop selected in an adaptive manner, based on:
q  current channel conditions (relay availability) [Larsson01]
q  distance to destination [Zorzi03, Morris04]
q  delay and congestion criteria [Javidi09]


Spectrum leasing via
opportunistic cooperative routing
= primary
= secondary
Po Pd

¨  Spectrum leasing with remuneration offered by secondary
network via forwarding
¨  Secondary nodes act as potential next hops
¨  Secondary nodes accept to cooperate if QoS requirements are
satisfied

Optimization goal

Po Pd

¨  Find
op?mal
next-‐hop
selec?on
policies:

¤  Primary
end-‐to-‐end
throughput

¤  Primary
energy
consump?on

¨  Spectrum
leasing
subject
to
secondary
QoS

requirements


System model
T = RP [ RS [ {Po , Pd }

Po Pd

¨  Primary Network: RP [ {Po , Pd }
¨  Secondary Network: RS
¨  Nodes are static and arbitrarily placed
¨  Nodes work in half-duplex mode, spatial reuse is not allowed
¨  ⇠P = EP /N0 : average received SNR between Po and Pd
¨  RP , RS : primary, secondary transmission rates [bits/s/Hz]


Problem formulation

Po a Pd

x
y

¨  State of the system x ✓ T : subset of nodes that have
correctly decoded the primary packet
¨  Action a 2 x : node selected to act as next-hop transmitter
¨  Next state y ◆ x : subset of nodes that have correctly
decoded the packet after the transmission from a 2 x

Transition probabilities

da,n
Po a Pd

x
y

¨  Depend on outage probabilities
¤  Primary transmitter:
✓ RP
◆
2 1
pout (da,n ) = 1 exp ⌘
⇠P da,n

Transition probabilities (2)
a

Po Pd

x
y

¨  Depend on outage probabilities
¤  Secondary transmitter uses superposition coding
n  Superposition of primary and secondary packet
n  Decoding based on two decoders in parallel: 1) treat undesired
packet as noise; 2) successive interference cancellation

Secondary QoS requirement
a
RS
dS
Po Pd
pout  ✏S
x
y

¨  Secondary network QoS requirement Q = (dS , RS , ✏S )
¤  Transmission rate RS
¤  Maximum outage probability ✏S for a receiver located
no further than dS


Transition costs
a

Po Pd

x
y

¨  Each transition entails a cost:
c(x, a, y) = ↵cThr (x, a, y) + (1 ↵)cE (x, a, y)
¨  Primary end-to-end throughput cost:
cThr (x, a, y) = 1, 8 a 2 x


Transition costs (2)
a

Po Pd

x
y

¨  Each transition entails a cost:
c(x, a, y) = ↵cThr (x, a, y) + (1 ↵)cE (x, a, y)
¨  Primary energy cost:
(
1 when a 2 RP [ {Po }
cE (x, a, y) =
0 when a 2 RS

Optimal Routing Policies
¨  Starting state s and final state f
¨  Goal: minimize the total expected discounted cost
"+1 #
def
X
k
J(s) = E⇡ c(x, a, y) x0 = s , 0 < <1
k=0
¨  The problem is an instance of stochastic routing
[Lott06]
¨  The optimal policy is an index policy
¤  global ranking of the nodes
¨  A centralized algorithm and its distributed
implementation is provided in [Lott06]

Heuristic Routing Policies
¨  The algorithm in [Lott06] requires full knowledge of the
network topology and has time complexity O(|T |2 )
¨  We propose two low-complexity heuristic policies that
only require local topology information
¨  Suitable for a distributed implementation
¨  Relay selection is made on-line by the current
transmitter at each hop, based on local interactions
¨  Both policies uses a primary energy budget K to control
the trade-off between the primary energy consumption
and the end-to-end throughput
¤  maximum number of primary relays that can be used
¤  stored within the packet
¤  decremented when a new primary relay is selected

K-Closer
K>0

Po a Pd

K=0

¨  Nodes are ranked according to their distance from
the destination Pd
¨  Budget K to control primary energy consumption


K-Closer (2)

✔  Pros:
¤  Fully distributed and easy to implement
¤  Limited amount of information (ACK and NACK) and
overhead (residual energy budget K)
✗  Cons:
¤  The primary energy budget K potentially limits the
available multiuser diversity
¤  Can potentially select a relay with a small number of
neighbors in its proximity


K-One Step Look Ahead
(K-OSLA)
¨  K-OSLA improves K-Closer using an expected
geographical advancement metric
¨  Each node a knows its distance from the destination a
and shares it with other nodes
¨  Each node a builds the order sets:

¤  B(a) = n1 , n2 , . . . , n|B(a)| , with
 ni+1  a , for i = 1, . . . , |B(a)| 1
ni
¤  B S (a) ✓ B(a) , which only contains secondary nodes
¨  ga,n = denotes the geographical advancement
a n
of a toward Pd provided by a relay n

K-OSLA (2)
¨  Expected geographical advancement metrics:
|B(a)| |B(a)|
X Y
¤  ga = ga,ni [1 pout (a, ni )] pout (a, nj )
i=1 j=i+1

|BS (a)| |BS (a)|
X Y
S
¤  ga = ga,mi [1 pout (a, mi )] pout (a, mj )
i=1 j=i+1

¨  At each hop, the possible relays are ranked according to
the metrics:
¤  Ga,n = ga,n + gn when K > 1
¤  Ga,n = ga,n + gn when K = 1 and a 2 RS
¤  GS = ga,n + gn otherwise
a,n
S


K-OSLA (3)

✔  Pros:
¤  Fullydistributed and easy to implement
¤  Prevents the packet to be forwarded towards
connectivity holes
✗  Cons:
¤  Requires exchange of additional information
S
¤  Additional pre-computation for gn and gn

¤  The energy budget K potentially limits the available
multiuser diversity


Numerical Results:
Optimal vs Heuristics
Optimal
0.6 K-OSLA
α=1 K-Closer
Optimal, No SL
0.5 K-OSLA, No SL
Throughput [bits/s/Hz]

K-Closer, No SL
K=8
0.4

0.3

α=0
0.2
K=0
0.1
0 2 4 6 8 10 12
Primary Energy [dB]

Numerical Results: Impact of
Secondary network density
NS = 12 K-OSLA, K=8
0.6 K-Closer, K=8
Optimal, =1

0.5

0.4

0.3 NS = 0

0.2

0.1
4 5 6 7 8 9 10 11 12
Primary Energy [dB]

Numerical Results: Impact of
budget K on K-Osla
NS = 12 Optimal, = 0
0.6 K=8 Optimal, = 1
K-OSLA
0.5

0.4
NS = 2
K=0
0.3

0.2

0.1

0 2 4 6 8 10 12
Primary Energy [dB]

Conclusions
¨  Spectrum leasing solution to the problem of
coexistence of primary and secondary nodes
¨  The problem is formulated as a stochastic routing

problem to obtain optimal policies
¨  Two reduced complexity heuristics have been

proposed and they achieve close-to-optimal
performance
¨  Optimal and heuristic policies improve both primary

energy and throughput


Refences
¨  [Larsson01]: P. Larsson, “Selection Diversity Forwarding in a Multihop Packet Radio Network with
Fading Channel and Capture,” ACM SIGMOBILE Mob. Comput. Commun. Rev., vol. 5, no. 4, pp. 47–
54, Oct. 2001.
¨  [Zorzi03]: M. Zorzi and R. Rao, “Geographic random forwarding (GeRaF) for ad hoc and sensor
networks: multihop performance,” IEEE Trans. Mobile Comput., vol. 2, no. 4, pp. 337–348, Oct. 2003.
¨  [Morris04]: S. Biswas and R. Morris, “Opportunistic Routing in Multi-Hop Wireless Networks,” ACM
SIGCOMM Comput. Commun. Rev., vol. 34, no. 1, Jan. 2004.
¨  [Javidi09]: M. Naghshvar, H. Zhuang, and T. Javidi, “A General Class of Throughput Optimal Routing
Policies in Multi-hop Wireless Networks,” preprint, http://arxiv.org/pdf/0908.1273.
¨  [Chiarotto10]: D. Chiarotto, O. Simeone, M. Zorzi, ”Throughput and Energy Efficiency of Opportunistic
Routing with type-I HARQ in Linear Multihop Networks”, in Proc. of IEEE GlobeCom, Miami, FL, 6–10
Dec. 2010.
¨  [Simeone08]: O. Simeone, I. Stanojev, S. Savazzi, Y. Bar-Ness, U. Spagnolini, and R. Pickholtz,
“Spectrum Leasing to Cooperating Secondary Ad Hoc Networks,” IEEE J. Select. Areas Commun., vol.
26, no. 1, Jan. 2008.
¨  [Lott06]: C. Lott and D. Teneketzis, “Stochastic routing in ad hoc networks”, IEEE Transactions on
Automatic Control, vol. 51, no.1, Jan. 2006.
¨  [Chiarotto11]: D. Chiarotto, O. Simeone and M. Zorzi, “Spectrum leasing via cooperative
opportunistic routing”, IEEE Transactions on Wireless Communications, Vol. 10, no. 9, Sep. 2011


Optimal Routing Policies:
transition probabilities
8
>0
> (Pd 2 x or x = f ) and y 6= f
>
<1 (Pd 2 x or x = f ) and y = f
pxy (a) =
>0
> Pd 2 x, x 6= f and y = f
/
>
:
Pxy (a) Pd 2 x, x 6= f and y 6= f
/

Y Y
Pxy (a) = [1 pout (a, n)] pout (a, m)
n2T s.t. m2T s.t.
n2y,n2x
/ m2y
/


Secondary transmissions
h ⇣ ⌘i
(1) (2)
Pout,SP (da,n ) = 1 exp min HP , HP

(
(1)
1, 0   1 2 RP
HP = 2 RP 1 RP
(1 (1 )2RP )⇠ ⌘ , 1 2 < 1
S da,n

( n RS RP
o
(2) max (1 2 RS )⇠1 d ⌘ , 2⇠ d 1 , 0 <
⌘ <2 RS
HP = 2 S a,n S a,n
1, = 0 and 2 RS   1


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Asilomar 2011