The IEEE 802.11 standard document defines two power management (PM) modes - Active mode and Power Save (PS) mode. In the Active mode, STAs stay in the Awake state and are fully powered to receive frames. On the other hand, STAs in the PS mode shall be in the Doze state and awake to listen to selected beacons. a STA in the Doze state consumes much less power than in the Awake state by turning off the RF circuitry. The switchover in between the states takes about 250µs [30]. The Lucent IEEE 802.11 WaveLAN card [22], for example, consumes 1.65 W, 1.4 W and 1.15 W in the transmit, receive and idle modes, respectively, in the Awake state. In the Doze state, it consumes 0.045 W which is much less than any mode of the Awake state.
A STA in the PS mode uses the power-save polling (PS-Poll) scheme to retrieve data buffered in AP to improve energy efficiency. However, the performance of downlink packet latency and bandwidth utilization may not be satisfactory when this scheme is implemented because of the dependency on beacon interval, access contention, and additional signaling load by PS-Poll frames. The 802.11e also includes some optional extension of power save functionality defined as Automatic Power Save Delivery (APSD). The unscheduled APSD
(U-APSD) is a distributed mechanism for STAs to decide when to awake to receive buffered frames at the AP and use triggering data frames to retrieve corresponding buffered data.
Scheduled APSD (S-APSD) is a centralized mechanism for the AP to determine some fixed intervals that STAs should periodically awake to receive the frames. These frameworks can reduce the signaling load (PS-Poll frames) and are, as stated, mainly designed for downlink usage.
There are some other centralized PM schemes [19], [20] which achieve energy saving by adopting the shortest-job-first policy to schedule transmissions. The shortest-job-first policy arranges the access orders of STAs according to their aggregate required time and STAs which finish their transmissions can switch to the Doze state. It can minimize the average waiting time after the announcement of schedule for STAs; however, the STAs of later orders tend to overhear more.
Scheduled Automatic Power-Save Delivery
To support QoS while dealing with power saving issue, U-APSD and S-APSD architectures are defined in IEEE 802.11e [2], [38]. The AP should use EDCA access method when selecting the U-APSD scheme. On the other hand, the S-APSD lays the burden of channel coordination on the AP to calculate the schedule and announce it to STAs. When S-APSD is used, depending on whether the usage is for an access category or for a TS, EDCA or HCCA is chosen as access policy, respectively. For the S-APSD, the STA firstly communicates with the AP via ADDTS request frame setting both APSD and Schedule subfields in the TS info field before getting admitted. If the requested service can be satisfied, the QAP will notify the STA of the schedule including the Service Start Time (SST) and the negotiated Service Interval (SI). In APSD, the contiguous time that a STA stays awake to receive the buffered frames from QAP is defined as Service Period (SP). STAs using S-APSD should automatically switch to the Awake state at the scheduled starting time of each SP defined
by
SST + m × SI, where m ≥ 0. (2.1)
Then they fall back to sleep till receiving the frames with the EOSP (End Of Service Period) flag being set. The service schedule can be updated after negotiation between the QAP and STA finishes. To maintain QoS guarantee, the new SST should fall into the region between the minimum SI and maximum SI after the beginning of the previous SP. Compared with the 802.11 PSM, besides QoS support, the S-APSD can also reduce signaling loads such as PS-Poll. Moreover, the number of collisions can be decreased as well.
Overlapping Aware S-APSD
Although IEEE 802.11e defines the architecture of S-APSD, its specific implementation is left as an open issue. To reduce the chance of SP overlapping, as described in [38], there could be two scheduling approaches to schedule the starting time of SPs. One is contiguous scheduling, which means that the scheduled SPs should be placed one after another. It has the advantage of simplifying the process to determine the SST for the schedule of a new traffic streams (TS). However, it often requires the SIs to be altered to satisfy certain constraint so that contiguous scheduling is possible. A necessary and sufficient condition for a group of periodic tasks defined by service intervals (SIs) and Transmission Opportunities (TXOPs) to be scheduled contiguously by an Equal-spacing-based Rate Monotonic algorithm was derived in [36]. Altering SIs may shorten the sleeping time of STAs and, as a result, cause more energy consumption to retrieve the same amount of data buffered at QAP. Besides, the unused time of scheduled SP might be wasted because it could be too short for other STAs using EDCA to transmit their frames. On the other hand, the non-contiguous scheduling does not put any restrictions on the setting of SIs. Compared with contiguous scheduling, non-contiguous scheduling allows other EDCA STAs to have higher chance to transmit their frames for the unused time of scheduled SP [38]. However, the larger degree of freedom for scheduling also increases its complexity for finding the suitable SST of a new TS.
In [38], the authors proposed a non-contiguous scheduling algorithm called Overlapping Aware S-APSD (OAS-APSD). The OAS-APSD algorithm aims at finding the SST of a new TS which achieves the least probability of SP overlapping. The pseudo-code of the OAS-APSD algorithm is shown below. To be concise, we use scheduled instants to represent the scheduled starting time of SPs. The Scheduled Events (SEs) in the OAS-APSD algorithm refer to the scheduled instants known to the QAP, for example, Beacons with period BI and already scheduled SPs with period SIs for TSs. In this algorithm, SInew represents the SI of the new TS to be scheduled.
SI2
Existing scheduled events SI1
… …
SInew time
New joining TS
… …
SInew time
New joining TS
Relative distances
Figure 2.1: Illustration of relative distance between scheduled events.
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The OAS-APSD algorithm [38].
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SST to be determined given a specific SInew
N, SST , SSTtemp, distavg, temp distavg, max distmin ← 0 temp distmin ← BI
Create empty list of SEs → ListSE
Compute LCM considering All SIs plus BI → LCM for ∀ SEs ∈ [tcurrent, tcurrent+ LCM ] do
Insertion in ListSE of SEs end for
while SSTtemp < SInew do
while SSTtemp+ SInew× N < LCM do Find prev SE and next SE in ListSE
distnext SE ← next SE − (SSTtemp+ SInew× N) distprev SE ← SSTtemp+ SInew× N − prev SE
Insertion in distances SSTtemp of distnext SE and distprev SE N → N + 1
end while
temp distmin ← Minimum of distances SSTtemp temp distavg ← Average of distances SSTtemp if temp distmin > max distmin then
max distmin ← temp distmin distavg ← temp distavg SST ← SSTtemp
else if temp distmin = max distmin then if temp distavg > distavg then
distavg ← temp distavg SST ← SSTtemp
else if temp distavg = distavg then SST ← random(SST, SSTtemp) end if
end if
SSTtemp ← SSTtemp+ precision end while
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The basic idea of the OAS-APSD algorithm is to find the optimal SST of the new TS in an interval [0, SInew] which achieves the maximum among minimum relative distances between SPs of the new TS and existing scheduled events. Here, the relative distances between SPs of the new TS and existing scheduled events are defined as the distances from every SP of the new TS to its closest previous and next existing scheduled events, as illustrated in Fig. 2.1. Note that there are only a finite number of possibilities for the SST because there is a maximum precision used by the 802.11e specification. To determine the optimal SST, relative distances are calculated for an interval of duration LCM, the least common multiple of the SIs, including SInew. If there is a tie, then the one with maximum average relative distance is selected. In case there is still a tie based on average relative distance, it is broken arbitrarily. Clearly, the computational complexity of the OAS-APSD algorithm is large for large values of LCM. This could make the algorithm infeasible for real systems and the complexity issue is listed as one of the major future works in [38]. Therefore, it motivates our work to design low complexity scheduling algorithms for S-APSD [44] [46] which will be presented in the following chapters.