Why does 802.11 use rate adaptation

In this article, head-orientated refers to when the x -direction of the accelerometer points towards the first car, the y -direction towards the right side of the train when the user faces the first car, and the z -direction towards the floor. The tilted orientation refers to when the accelerometer is randomly placed. For instance, we put the accelerometer in our bags, or hold it at different orientations. The dotted lines divide the actual four movement phases.

The vibration level is lowest in STP phase, and when the train starts to move DEP phase , the vibration level immediately elevates. The amplitude of vibration reaches its maximum in CRU phase, and decreases again when the train approaches to the station ARR phase. The vibration of acceleration turns stable when the train fully stops at the station. The experimental results in both the head-orientated scenario and the tilted scenario are shown to be similar in the MC-MRT.

However, in Figure 5c , we notice that there is a rise in acceleration in the x -axis during DEP phase the red circle and a decline when the train approaches to the station the green rectangle. We can see the acceleration in x -axis increases when the train starts to move, and decreases to about 0 when the train enters CRU phase, which means the train is moving with a steady speed.

Finally, the acceleration is back to around 0 when the train fully stops in the station. Though the acceleration pattern in ARR phase is not as obvious as that in DEP phase, it is still feasible to exploit the information to estimate the movement phases. As shown in the circled areas of Figure 5d , although the acceleration patterns are less noticeable, they can still be observed from x -axis and z -axis. The main reason is that acceleration information scatters into two or three axes.

This results in a mitigation of the signal intensity. These observations infer that we can not estimate the phases solely by relying on information from one specific axis. From the observation above, we conceive that the information from the acceleration should be classified into two parts: vibration and movement.

These information reside in the high frequency and low frequency of the acceleration, respectively. In the next section, we illustrate the design of our estimation scheme and how it extracts information from both parts to enhance the estimation accuracy. In this section, we describe the system design and the implementation of AARA. The details of the two components, movement phase estimation and rate adaptation scheme, are respectively introduced in the following two subsections.

The estimation mechanism has the following properties:. However, as described previously, we notice that different transportation systems have different moving patterns. The high frequency indicates the short-term vibration of the train, whereas the low frequency shows its long-term movement. In order to estimate the movement phases based on these two characteristics that apply to different MRT systems, we define those two indicators of acceleration as M-Acc and V-Acc.

The size of a sliding window for computing the variance of the difference between the measurement and gravitation is set to be 20 data points. Since the sampling rate of accelerometer is about 0. As for M-Acc, we use Use Use Figure 6 to illustrate the relationship between the acceleration components.

Acceleration components of the accelerometer in different phases. In Figure 6d , the orientation of the accelerometer is changed.

In other words, the unexpected estimation error will not propagate because the calibration of the estimator will be reset when the train stops at the next station. Figure 3a,b show the two indicators when a train passes through the stations in either MRT system. In our design, we utilize a two-stage estimation method to determine the movement phase. Before introducing the two-stage integrated decision function, we present the behavior of the acceleration in either MRT system.

Hence, these unpredictable shakings of M-Acc in the CRU phase will cause the estimation mechanism misjudge the shakings as the signal of deceleration of the train, and thus misestimate the ARR phase too early. The V-Acc is helpful in this problem. It is clear that the V-Acc is higher when the train is running.

While the V-Acc is high, the train must be between the stations and not entering the station yet. The proposed two-stage integrated decision function is able to estimate the movement phases precisely. Note that the vibration level is very low in STP phase in both of the MRT systems, so we are unable to distinguish which MRT system it is when the passenger just gets on-board.

The decision function mainly utilize the M-Acc to predict the phases while V-Acc is used to improve estimation accuracy. We present the details and the parameters of the two-stage decision function in Figure 7 and Table 2. The experimental results are presented in the following section. In this section, we describe how we exploit the phase estimation to enhance the rate adaptation in AARA.

Its performance has been extensively tested and verified. Since it is the most widely used rate adaptation scheme, we integrate it into our phase estimation to boost the transmission performance.

The concept of our rate adaptation scheme is simple and novel. Since traditional rate adaptation algorithms are designed for general purposes, it has no auxiliary information about the channel quality in general situations. In other words, the proposed mechanism is a two-tier design. In ARR phase, when the train is just approaching to the station, there is no history record yet, so SampleRate chooses the highest rate to transmit, e.

However, the channel is poor due to the large distance at this instance, and thus the transmission success rate is low. AARA knows that the channel quality is unstable when the train enters the station, so the MS should adopt a more robust bit rate.

Therefore, instead of adopting from the high bit-rates as SampleRate does, AARA adopts from low bit-rates and uses a higher probing rate to adapt to the changing channel. When the train stops, the wireless channel turns stable, so the MS should immediately switch to higher rates to maximize the throughput. However, due to the consecutive failure of probing high rate in the previous phase, SampleRate still considers the channel quality poor, thereby using the lower rate to transmit. Besides, once the MS reaches to its optimal bit-rate, the extra probing packets should be reduced, so the MS can stay using the optimal bit-rate most of the time.

When the train starts moving, the channel quality degrades promptly as the train speeds up, so the MS should sense the unstable environment and automatically adapt to the channel again. The following experimental result shows that AARA can accurately estimate the movement and improve the throughput.

In order to adopt different rate adaptation strategies to every movement phase, the most critical issue is to accurately estimate the movement phases. We employ two metrics in the evaluation: recall and delay. On the other hand, we also measure the estimation delay time, which is a metric more directly related to the user.

We define a positive value of delay time to be when the beginning of the estimative phase is later than its actual time. By contrast, a negative delay denotes that the beginning of the estimative phase is earlier than it really is. The experimental results for the estimative mechanism are summarized in Tables 3 and 4.

We show the recall and delay in each phase in the two types of MRT systems. Note that the result shown in the two tables agrees with the result shown in Figure 3a,b. From Tables 3 and 4 , we observe that the total recall in the two MRT systems are Since the duration of DEP phase is relatively short, only 8. In this section, we present the result of the proposed rate adaption scheme assisted by the movement phases estimation.

Hence, the duration of the movement phases is also slightly different in the experiments conducted. However, SampleRate chooses the bit-rate with the shortest expected transmission time to adopt, i. These packets in higher bit-rate mostly fail to transmit. This not only wastes the time to send packets that are unable to be received, but also hinders the bit-rate to increase in the latter part of the ARR phase. This is because the default SampleRate will block the bit-rates with consecutive transmission failures.

Since consecutive failures often occur when SampleRate tries the bit-rate from the highest one, it cannot switch to a higher bit-rate even the channel quality is better, i. Next, we see the STP phase, the most crucial phase that dominates the overall throughput. In the beginning of STP phase, AARA adjusts to the highest bit-rate because it removes the history transmission statistics, which records the outdated channel condition in the previous phase.

On the other hand, SampleRate has no extra information to know that the history record cannot reflect the channel condition anymore. Instead, SampleRate considers that the channel quality is as bad as before; thus, it uses a relatively lower bit-rate, which then downgrades the throughput. SampleRate keeps using lower rates, mostly 11 Mbps, for about 15 s in both Figures 9b and 10b. Although SampleRate starts to adopt higher rates and increase the throughput from 20 s, it is unable to fully utilize the STP phase as the train is about to depart.

In fact, SampleRate is a sensitive rate adaptation scheme, because it is conservative to raise its bit-rate, yet it is susceptible to drop off its bit-rate. In the DEP phase, the channel quality degrades quickly. SampleRate with default parameter is able to handle the degradation of channel quality because it can quickly decrease the bit-rate. To further investigate what causes the difference between the performances of AARA and SampleRate, we also analyze the ratio of transmitted packet in each bit-rate of all four phases.

In Use Figure 11 , darker marked colors represent higher bit-rates. SampleRate uses more higher bit-rate in CRR phase, but the transmitted packets are mostly useless. This is because the number of packets in this phase is extremely few and the packets cannot be received due to the long distance. We also compute the ratio of transmitted packets in all the phases. The estimation of the phases is sequential. Because the most important phase to the overall throughput is STP phase, we focus on the estimation of STP phase start point.

If the estimated start point is delayed, AARA will clear the history record at a later point. For example, if AARA estimates the train stopping in the station at a time that is already 5 s later than it really is, then AARA clears the history record 5 s later. This means we will waste 5 s without taking the opportunity to use higher bit-rates. However, such loss will not cause disaster as it only degrades the throughput gain. So far, we focus on the idea that employing higher bit-rate to bring higher throughput when the channel quality is good.

We also need to study that what if the channel quality is not sufficient to employ high bit-rate even when the channel quality is already the best. In this section, we evaluate the effect of channel quality as well. We design three scenarios of the experiments where the MS is placed at different positions in the car while keeping the BS in the same position on the platform. A total of three different MS-to-BS distances are measured near, medium, far , as shown in Figure The distance of MS and BS correlates with the channel quality.

In practice, the theory of lower band can be constructed by building Singer difference sets [ 35 ]. Next, the Singer difference sets theorem is presented, which is demonstrated by [ 36 ]. Theorem 5. Let be a prime power. Then, there is a - difference set under , which is called a Singer difference set. Because and , approximates to the lower bound. Definition 6. Given an integer and a quorum in a quorum system under , we define. Definition 7.

Theorem 8. The cyclic quorum system meets the rotation closure feature. Firstly, we construct a channel adaptation system using quorum system. Without loss of generality, we presume to build a channel adaptation system , where each transceiver pair can meet on different channels.

An illustration of channel adaptation system system with is shown in Figure 7. In Figure 7 , a channel adaptation system was built using the quorum system over the limited universal set. The elements in are the channels that the transceivers can work on. If the transceiver pair chooses one quorum in , then they can rendezvous on three channels. Next, we introduce the algorithm to build the.

Without loss of generality, we presume that each consists of fragments, and every fragment consists of timeslots. Therefore, the duration of every is. Particularly, we presume that and , and is the rendezvous channel set. Then, the creation step is as follows.

Then, the following quorums are built as follows: 2 We build using the following steps by the quorum. We should pay attention to the rest of fragment; the timeslot index ought to be the modulo over for building the above formula.

The four channel adaptation schedules, namely, are the elements in the collection of , which has a duration of. The channel adaptation schedules in are demonstrated in Figure 8. We construct a channel adaptation schedule in using a quorum in.

Therefore, we get. The period of every channel adaptation schedule in is , and. Owing to the intersection feature of , in each duration of , and rendezvous at least times on a particular channel. As shown in Figure 8 , the transceiver pairs choose any two channel adaptation schedules in ; then they can rendezvous at least 3 times in each duration of.

The pseudocode for QCA is presented in Algorithm 2. Next, we validate the performance of RaCA under a variety of network environments scenarios. The aggregated throughput is the sum of data rates achieved by all the mobile stations in the network. The SampleLite is a latest research that provides substantial gain relative to Minstrel-HT in terms of goodput performance. The network topology used in our simulation consists of a fixed AP and multiple mobile terminals.

A point-to-point link collects the AP to a local area network. Figure 9 shows our node simulation environment. The traffic generator produces a constant traffic rate of 2 Mbps, and each data packet has bytes. The application layer sends a total of 1, packets.

In the simulations, two mobile scenarios are considered, and the mobile speed of terminal is uniformly distributed between 1 and 10, 10 and 20, respectively. Also, we simulate two types of network density, mobile stations and mobile stations, to analyze the effect of interference level on rate adaptation schemes.

All the simulation results are the average of 5 runs. The specific configuration parameters of simulations are listed in Table 4. We first simulate two static network scenarios, mobile stations and mobile stations, to show the effect of interference on throughput performance.

Figure 10 gives the simulation results. Minstrel-HT performs worst in all scenarios because it reduces the number of spatial streams to lower bit rate if the channel is to be too lossy regardless of the causes leading to packet transmission failures. Particularly, in dense network scenario, Minstrel-HT aggravates the effect of interference due to longer transmission time and increases the contention level and likelihood of collisions. Although SampleLite achieves better performance compared to Minstrel-HT, it suffers significant performance degradation because it ignores the causes of transmission failures and reads the RSSI at the transmitter side.

Impact of Mobility TCP. Figure 11 shows the aggregated throughput achieved by different mobility scenarios. As shown in the figure, both schemes suffer significant performance degradation when node moves faster. Minstrel-HT performs poorly because it reduces the number of spatial streams to lower bit rate quickly once transmission fails.

Better performance with SampleLite in both scenarios is because it relies on RSSI to choose the MCs, which will provide maximum expected throughput for packet transmission. However, through analyzing the trace data, we found that SampleLite cannot respond to the most appropriate rate, which is because of incorrect and coarse-grained property of RSSI obtaining at the transmitter side. We also conduct experiments with UDP datastreams.

Figure 12 shows that RaCA performs best in terms of UDP aggregated throughput compared to the other two schemes, up to These gains are mainly attributed to the following two aspects. On the one hand, Minstrel-HT reduces the number of spatial streams to choose lower bit rate if the channel is to be too lossy regardless of the causes leading to packet transmission failures. On the other hand, Minstrel-HT and SampleLite do not consider causes of the packet transmission failures, which will result in high packet transmission failures in intensive network scenarios, where channel communications suffer severe interferences.

Impact of Mobility UDP. Figure 13 plots the UDP aggregated throughput measured in different mobile scenarios. We see that RaCA still works better than the other two schemes in both scenarios.

In 1 10 speed scenario, we observe up to In 10 20 speed scenario, RaCA also gives significant gains, which are up to These gains are attributed to rapid response to channel condition changes. Particularly, in high speed scenarios, RaCA can converge to appropriate rate rapidly and accurately.

Next, we introduce the testbed experiments conducted in a controlled laboratory environment. Figure 14 shows the floor plan of our testbed environments. Each point from L1 to L6 in Figure 14 represents a location of stations. We generate data traffic using Atheros CSI tool [ 39 ].

For each set of experiment scenarios, we send packets with bytes per packet at the application layer every 50 milliseconds and average the results of 10 runs. Impact of Mobility. In this scenario, we generate datastreams from one station to an AP. When the station keeps static, RaCA achieves the best aggregated throughput by quick convergence to the right bit rate, as shown in Figure In mobile environment, the station keeps moving with walking speed. Thanks to the intelligent rate adaptation scheme, RaCA also provides the best throughput performance, achieving up to According to analyzing the data rates distribution measured in mobile environment, we found that RaCA dominantly uses the most stable rate compared to Minstrel-HT and SampleLite.

It also demonstrates that RaCA can respond rapidly to channel state changes. Impact of Interference. Also, we consider mobile scenarios, where three group mobile stations keep moving between L1 and L2, L3 and L4, and L5 and L6, respectively, and they send data to AP at the same time.

In these scenarios, we keep station close enough to the AP to ensure that the AP does not receive packet incorrectly because of weak signal. Figures 16 and 17 show the aggregated throughput obtained by AP at different locations in static and mobile scenarios, respectively.

RaCA shows significant throughput gains which are up to Thanks to the intelligent rate and channel adaptation scheme, RaCA can quickly switch to another channel when suffering from interferences. When mobile station moves away from AP, the station suffers from channel-errors.

RaCA can fastly converge to right bit rate and achieve much better throughput performance than the other two protocols. In mobile station scenario, AP achieves relatively lower throughput for all three protocols. In this paper, the rate adaptation problem for IEEE Otherwise, RaCA chooses another channel to work on using QCA algorithm when the communication suffers severe interference.

This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Article of the Year Award: Outstanding research contributions of , as selected by our Chief Editors. Read the winning articles. Journal overview. Special Issues. Academic Editor: Mauro Tortonesi.

Received 11 Oct Accepted 17 Mar Published 03 Apr Abstract Rate adaptation, which dynamically chooses transmission rate provided at the physical layer according to the current channel conditions, is a fundamental resource management issue in IEEE Introduction IEEE Figure 1. Figure 2. Figure 3. Figure 4. Figure 5. Figure 6. Table 1. MCS parameters for mandatory 20 MHz. Number of spatial streams is 1 GI: Guard Interval.

Table 2. Table 3. The definition of variables used in Algorithm 1. Algorithm 1. Figure 7. An illustration of with. Figure 8. A demonstration of channel adaptation system with ,. The variable in white box is selected from the set at random. Algorithm 2. Parameters Value Physical standard Table 4. Figure 9. An illustration of simulation topology, which consists of a node wireless local area network and a node local area network. Figure Figures and Tables from this paper. Citation Type.

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