Post on 03-Jan-2016
Optimal Selection of Power Saving Classes in IEEE 802.16e
Lei Kong, Danny H.K. Tsang
Department of Electronic and Computer Engineering Hong Kong University of Science and Technology
IEEE Wireless Communication & Network Conference ( WCNC 2007 )
Outline
Introduction Proposed Algorithm
– System model– Cost metric and Delay metric
– Policy Optimization
Simulation Conclusion
Introduction
According to the mobility extension,– IEEE 802.16e defines the sleep mode operation for the power
saving which is one of the most important features for MSSs to extend their
lifetime.
In order to support different service connections– IEEE 802.16e offers several sleep mode types, called Power
Saving Classes (PSCs)
Introduction – 802.16e sleep mode operations
Power Saving Class of Type I
Power Saving Class of Type II
Power Saving Class of Type III
…
TS_init (Initial sleep window)
2 x TS_init TL
4 x TS_init TS_max
Incoming packet
TL TS
Incoming packets
…
Incoming packets Incoming packetsIncoming packets
TS
normal operation
sleep windows
listening windows
MOB_TRF-IND
Relative Work
Sleep mode in IEEE 802.16e has been generally recognized as effective in discontinuous reception.
However, their model only applies for PSCs of type I and does not capture the new characteristics of PSCs of type II.
Relative Work
Lei Kong and Danny H. K. Tsang, “Performance Study of Power Saving Classes of Type I and II in IEEE 802.16e”, To appear in the Proc. of of the 31st IEEE Conference on Local Computer Networks, Tampa, Florida, US, November. 2006.
– The original models have captured the inherent properties of two PSCs accurately and also show the energy delay trade-offs on different PSCs
Motivation
The static timeout policy may solve the problem of when to perform the sleep mode switching, but it cannot resolve the issue on PSCs selection.
Goal
Our main goal is to find the optimal selection for PSC of types that achieves the minimum energy cost or traffic delay under different traffic requirements.
Assumptions
We assume that packet arrival rate λ ( packets/frame ).
We also denote μ ( packets/frame ) as the service rate between BS and MSS .
In PSC of type I, we assume the energy for device to switch on and off in TL is negligible
Assumptions
In PSC of type II, we assume that MSS would remain in sleep mode if the arriving packets from the pervious sleep interval is less than or equal to the maximum number of packets that it could transmit during TL.
TL TS
Incoming packets
System model
Definitions– I = { SN, SI, SII } representing normal mode, sleep mode of type I an
d type II– A = { s_N, s_I, s_II } with the intuitive meaning of ”switching to no
rmal mode”, ”switching to type I” and ”switching to type II”, respectively
– SI (k) represent the multi-sleep state of type I, where 0 ≤ k ≤ w and w is the final sleep stage
– Pk is the probability that there is packet arrival at BS in sleep stage SI (k)
– Q is the transition probability that PSC of type II to normal mode– Ri is a decision epoch in state i
Examples s_N
Stay On Normal mode
The next decision takes place when the system endures a busy period and becomes idle again.
Examples s_I
Switch to PSC I
The next decision takes place when the system endures normal mode and becomes sleep mode again.
Examples s_II
Switch to PSC II
The next decision takes place when the system endures normal mode and becomes sleep mode again.
Cost metric and Delay metric
Definition – τi (a) = the expected time duration until the next decision epoch if
action a is chosen in state i.– c i (a) = the expected power consumption incurred until the next d
ecision epoch if action a is chosen in state i.– d i(a) = the expected packet delay if action a is chosen in the prese
nt state i
Goal
Cost metric and Delay metric
Definitions– Denote PB, PI , PS, PL as the power consumption level
of busy period B, idle period I, sleep interval and listen interval TL, respectively.
– E [B], E [I] is means busy period duration and idle period duration
– S is the random variable of service time for each packet and E [S] = 1 /μ
Cost and Delayin Normal mode
Expected time duration for normal mode
Expected power consumption for normal mode
The mean waiting time for the packet – E[W] is the mean waiting time for the packet arrival du
ring the busy period. – ρ=λ/μ is traffic intensity– E[R] = E[S2]/(2E[S]) is the residual processing time– According to Pollaczek-Khintchine(PK-) mean value f
ormula [5], E[W] is
Cost and Delayin Normal mode
Cost and Delayin PSC I
Packet arrival Probability for PSC I– Pk is the probability that there is packet arrival at BS in sleep stag
e SI (k)
Cost and Delayin PSC I
The Vacation time and busy period for PSC I– Vk is the vacation time (i.e. the sleep window size plus the listen i
nterval) at stage k
– is the mean time of exceptional busy period to transmit previously buffered traffic accumulated in sleep stage k and can b
e derived from the following equation:
Cost and Delayin PSC I
Expected time duration until vacations k – is the expected duration until the next
decision epoch after sleeping for k vacations
Cost and Delayin PSC I
Expected Power consumption until vacations k – is the total expected energy consumption when
system wakes up after k vacation cycles and becomes idle.– ε i = PS2iT0 + PLTL is the power consumption at the sleep stage i
Cost and Delayin PSC I
Expected Power consumption for PSC I– Esw denotes the power consumption in switch-on and s
witch-off the transceiver at physical layer.
Cost and Delayin PSC I
Expected packet delay for PSC I– is the total expected packet delay when
system wakes up after k vacation cycles.
ρ=λ/μ is traffic intensity
Cost and Delayin PSC II
Packet arrival Probability for PSC II– VII is the vacation time for PSC of type II (i.e. the sleep window
size plus the listen interval)
– d is maximum number of packets that it could transmit during TL
– (1 − Q) is the transition probability that MSS keeps in state SII
Policy Optimization
Let xi(a) are the expected number of times that the system is in state i and command a is issued.
We define in our model that every decision epoch only tak
es place as soon as MSS becomes idle in SN.
In other words, xi(a) = 0, a ∀ and i {S∈ I, SII}.
Policy Optimization
We formulate two probabilistic constrained optimization problems: – power optimization under delay constraint
maximal expected delay with a upper bound δ.
Policy Optimization
– delay optimization under power constraint
meaning that the battery life time would be extendedσ times in the long run