Computer Communications 34 (2011) 1033–1041
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Orthogonal signaling-based queue status investigation method in IEEE 802.11 q Jae-Hoon Ko, Soonmok Kwon, Cheeha Kim ⇑ Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH), 790-784 San 31, Hyoja-Dong, Nam-Gu, Pohang, Gyungbuk, Republic of Korea
a r t i c l e
i n f o
Article history: Received 2 December 2009 Received in revised form 13 October 2010 Accepted 26 November 2010 Available online 3 December 2010 Keywords: IEEE 802.11 MAC Orthogonal signaling Queue status
a b s t r a c t IEEE 802.11 specifies four different medium access control (MAC) protocols to coordinate multiple access in a wireless local area network (WLAN). Since several tens of stations can operate in a WLAN, the performance of MAC protocols is important for overall network efficiency. It has been observed that the IEEE 802.11 MAC protocols can be improved by knowing which station has a non-empty queue, i.e., queue status. The point coordination function (PCF) can use this information to avoid polling a station that has no pending data. The HCF controlled channel access can adapt polling parameters based on queue status information, especially when scheduling a bursty and variable bit-rate traffic. Previously suggested methods are rather limited in terms of accuracy and efficiency. In this paper, we propose a novel method to investigate the queue status of multiple stations by exploiting orthogonal signaling. With synchronous transmission of orthogonal codes and symbol level signal processing, the method allows all of the associated stations to report their queue status at the same time. Challenges that can arise in the implementation of the proposed method are identified, and their solutions are suggested. The feasibility of detecting orthogonal signals is thoroughly tested on a realistic channel model. To demonstrate the performance improvement of a MAC protocol, we applied the proposed method to PCF. Both analysis and simulation show that the modified PCF significantly outperforms not only the original PCF but also other previously suggested PCF enhancements. Ó 2010 Elsevier B.V. All rights reserved.
1. Introduction Wireless local area networks (WLAN) provide wireless access to network services within a relatively small area. The IEEE 802.11 is considered the dominant technology for WLAN. Owing to its broadband capability with relatively low price, the number of 802.11 access points (AP) as well as devices equipped with wireless interfaces has sharply increased. The IEEE 802.11 is still evolving by incorporating new features, resulting in a number of enhancements over the years. IEEE 802.11 specifies four medium access control (MAC) protocols to coordinate multiple access among stations: distributed coordination function (DCF), point coordination function (PCF), enhanced distributed channel access (EDCA), and HCF controlled channel access (HCCA) [1]. In DCF, a station decides whether to access the medium based on locally observed channel status. Simultaneous access by multiple stations is avoided by random back-off before transmission. In contrast, PCF defines a contention
q This paper is based partly on preliminary works presented at IEEE LCN’09, Zürich, Switzerland, October 2009. ⇑ Corresponding author. Tel.: +82 54 279 5655; fax: +82 54 279 5699. E-mail addresses:
[email protected] (J.-H. Ko),
[email protected] (S. Kwon),
[email protected] (C. Kim).
0140-3664/$ - see front matter Ó 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.comcom.2010.11.007
free period (CFP) where stations are allowed to transmit data only when polled by an AP. EDCA and HCCA add quality of service (QoS) support to DCF and PCF, respectively. EDCA differentiates back-off time and interframe space (IFS) according to the priority of traffic. Moreover, stations with different priorities are given different transmit opportunities (TXOP) in which to send as many frames as possible. In HCCA, the characteristics of a traffic stream are described in a traffic specification (TSPEC). Using TSPEC, an AP calculates the polling interval and TXOP that best suits the needs of the traffic stream, and then polls stations accordingly in a contention-free manner. One way to improve performance of MAC protocols is to exploit the queue status of stations, i.e., whether a station has pending data in its transmission queue. PCF can utilize this information to avoid polling stations whose queues are empty [2]. It is also beneficial for HCCA to schedule bursty and variable bit-rate traffic because its time-varying transmission requirements cannot be sufficiently described by TSPEC [3,4].1 DCF and EDCA can benefit from queue status information to calculate the optimal contention window size, which minimizes packet collision probability [5,6].
1 TSPEC contains only the statistical information about a traffic stream, such as the min/average/max data rate, MSDU, burst size and delay bound.
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Several queue status investigation methods have been proposed in the literature. IEEE 802.11 specifies two fields in MAC frame for notifying of pending data packets [1]. Methods that estimate the number of contending stations (stations with non-empty queues) based on local observation are suggested for DCF [5,6]. For PCF, polling list management schemes were proposed to find out which station needs to be polled [2]. These methods are limited in terms of accuracy, time-efficiency, or the information they provide. In this paper, we introduce a novel method that investigates the queue status of all of the associated stations at the same time by utilizing orthogonal signaling. In the proposed method, the AP initiates queue status reporting by transmitting a Query frame. Upon receiving the Query, a station sends one of two unique orthogonal codes depending on its queue status. Because all of the stations send simultaneously, the AP receives the combined signal of all of the transmitted signals. From this signal, the AP detects individual signals by exploiting their uniqueness and orthogonality, obtaining each station’s queue status. The contribution of this paper is as follows. First, we introduce an efficient method to investigate queue status of multiple stations. Second, we identify the challenges in the implementation of the proposed method and suggest their solutions. Third, we thoroughly investigate the feasibility of the proposed method through simulation on a realistic channel model. Finally, taking PCF as a sample MAC protocol, we demonstrate the performance improvement leveraged by the proposed method through analysis and simulation. The remainder of the paper is organized as follows. In Section 2 previously suggested queue status investigation methods are introduced. Section 3 gives the detail of the proposed scheme, and Section 4 discusses implementation issues. In Section 5 performance evaluation is presented. Lastly, Section 6 concludes the paper.
2. Related works A queue status investigation method must provide accurate information in a time-efficient way. Previously suggested methods are limited in this regard. The IEEE 802.11 specifies two fields in the MAC frame for reporting queue status: More Data and Queue Size. The More Data field is set when the station has non-empty queue. The Queue Size field contains the total size of the queued data in a multiple of 256 octets. Several suggested HCCA scheduling algorithms utilize this field to dynamically adapt a TXOP or polling interval [3,4,7,8]. The main weakness of using these fields is that only the frame sender can report its queue status. To derive the optimal contention window size for DCF, [5] estimates the number of contending stations by observing the previous contention window size, and idle and busy slot numbers. Similarly, [6] uses the effective time required for one successful packet transmission. These methods do not provide the IDs of the contenders, and may not provide accurate results because of the random nature of DCF. PCF can be very inefficient if a small portion of stations has data to send because the AP blindly polls every station. To determine which station needs to be polled, signaling and probability-based polling list management schemes were suggested. The simultaneous transmit response polling (STRP) [9] is a signaling-based method. STRP specifies an idle station (station with an empty queue) in the Poll frame. While the polled station transmits data, the idle station transmits a weaker and longer jam signal if its queue is not empty. Because of the capture effect, the AP can correctly receive data and then detect the jam signal. However, it is hard to exploit the capture effect in practice because of near–far effects and fading [2]. Another signaling-based method is
randomly addressed polling (RAP) [10]. In RAP, all active stations randomly choose their addresses from a common pool, and then transmit them using CDMA or FDMA. This procedure is repeated in several stages, and the AP selects the stage where the most different random addresses are received. The AP then starts polling these addresses. RAP suffers sharp performance degradation as the number of active stations increases because their random addresses collide with high probability. Learning automata-based polling protocol (LEAP) [11] is a probability-based method that increases the polling probability of a station if the station responds with data. Otherwise, the polling probability is decreased. The amount of increase and decrease in the probability controls the tradeoff between the convergence speed and adaptivity to traffic loads. LEAP requires a considerable time for the polling probability to converge to the station’s actual active ratio. Hence, it is difficult to promptly react to a radical change in traffic loads. Another probability-based method, cyclic shift and station removal polling (CSSR) [12], simply refrains from polling a station for the next k polling cycles if the station is found empty. Obviously, the performance of CSSR is highly dependent on the value of k. It is very hard, if not impossible, to determine a value for k that suits a wide range of network statuses. Similar to the proposed method, there are several other methods that combine orthogonal signaling with MAC to improve performance. FDMA provides orthogonal signaling, but it is known to be less efficient than OFDMA. Thus, we do not discuss FDMAbased MAC here. Fallah et al. suggest that stations contend for data transmission in CFP using CSMA [13]. Contention is done on multiple subcarriers of OFDMA to reduce collision. Perez et al. propose a method that accepts polling requests from stations using OFDMA, and then polls those stations [14]. These OFDMA-based methods require the Fourier transform whose computation complexity is relatively high. Specifically, the fast Fourier transform has complexity of O(NlogN) [15]. N is the number of subcarriers, which is the maximum number of simultaneously transmitting stations. In contrast, the proposed scheme has lower complexity O(N) (Section 3). Moreover, OFDMA necessitates strict synchronization to maintain orthogonality among subcarriers so that a complex mechanism such as those proposed in [16] is required. Another form of orthogonal signaling is provided by TDMA. One of the most representative hybrid MAC protocols that combines TDMA with CSMA is Z-MAC [17]. While medium access basically follows CSMA, each station owns a timeslot in which the station has higher priority. Because of CSMA’s probabilistic nature, Z-MAC can hardly provide differentiated QoS for various traffics. Yang and Sikdar analyze a polling-based TDMA protocol [18], in which timeslots for data transmission are dynamically assigned according to polling results at the beginning of a cycle. In essence, this operation is not different from PCF except that the contention period does not exist. Hence, the method has the same problem as PCF, which is that it wastes time and energy by polling empty stations.
3. Orthogonal signaling-based queue status investigation We assume that every station has a bit sequence called transmission request (TR). This bit sequence is unique to a station so that it can be used as an ID of the station thereafter. Assignment of TRs to stations will be discussed in Section 4.1. Fig. 1 shows the message exchange sequence of queue status investigation and subsequent polling. Note that we assume a polling-based MAC protocol as an example MAC protocol in the figure. If an other MAC protocol is assumed, the polling part will be different. In the proposed method, an AP transmits a Query frame to let stations report their queue status. A Query can be a separate control frame or it can opportunistically piggyback on any forward
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Fig. 1. Message exchange sequence of the proposed queue status investigation method and subsequent polling.
link frame such as an Ack or Beacon frame to reduce overhead. In response, a station waits for Tdelay and then transmits its TR if it has pending data. Otherwise, the station sends the bit-wise inverse of its TR, which is called Negative Transmission Request (NTR). Because all stations transmit signals at the same time, these signals are combined at the AP. From this combined signal, the AP detects individual TR and NTR signals, determining each station’s queue status. When to initiate queue status reporting and how to exploit the results is determined by the MAC scheduling algorithm running at the AP. In addition, the choice between TR and NTR depends on the policy of a station. For example, a station with a non-empty queue may send NTR because the pending data has lower priority. Detecting TRs from the received signal is the most important step of the proposed method. To do this, the received signal is analyzed at a symbol level, instead of being demodulated to a bit sequence. If the signal is demodulated, information about the constituent signals would be lost. In addition, TRs are chosen such that they have high auto-correlation and low cross-correlation. High auto-correlation helps to detect a TR in the presence of noise, and low cross-correlation makes it easy to distinguish a TR from other TRs. It is known that the orthogonal codes and the pseudonoise codes employed in CDMA systems have these properties [19]. A major difference between these two types of codes is that the orthogonal codes have lower cross-correlation if the codes are synchronously transmitted, while the pseudonoise codes have lower cross-correlation when they are asynchronously sent. Because the proposed method prescribes that all stations send TR/ NTRs simultaneously, we choose orthogonal codes as TRs. Specifically, we considered Walsh codes and orthogonal Gold codes [19]. Both codes show zero cross-correlation when perfectly aligned. In addition, it is easy to generate a set of 2n different codes for any positive integer n, whose lengths are also 2n. Hence, the number of supportable stations can be increased by using longer codes. An NTR is generated by bit-wise inverting the corresponding TR. For example, if a TR is [1 1 1 1], then its NTR is [1 1 1 1].2 Because only the sign is changed, orthogonality with other TR/NTRs is preserved, i.e., cross-correlation is zero. Also, the cross-correlation between a TR and its NTR becomes the negative of the auto-correlation of the TR. Given the properties of TRs, detection is done by calculating the cross-correlation between a TR and the received signal. The details are as follows. Say that the TR of a station i is represented as an array of L symbols ui[k], 1 6 k 6 L. Accordingly, its NTR is (1) ui[k]. P P The received signal r[k] is then denoted as j2A uj ½k m2I um ½k, where A is the set of stations that transmitted TRs and I the set of stations that sent NTRs. The cross-correlation between the station i’s TR and the received signal is:
Ci ¼
L X
ui ½kr½k
k¼1
¼
¼
L X k¼1
ui ½k
8 L P > > > ui ½kui ½k ¼ L
> > ui ½kui ½k ¼ L if i 2 I : 1
;
k¼1
2 We assume the BPSK modulation, representing bit 1 as symbol 1, and bit 0 as symbol 1.
Fig. 2. Example of TR/NTR signal combination and detection.
P since Lk¼1 ui ½kuj ½k is zero if i – j because of orthogonality.3 The AP concludes that TR is transmitted if Ci is positive. Otherwise, NTRi is presumed to be sent. Fig. 2 shows an example of signal combination and TR detection, using 4-bit Walsh codes as TRs. Stations a, b, and c transmit their TRs, whereas station d sends its NTR. These signals are combined and received by the AP. The AP calculates the correlation between the received signal and each TR, resulting in +4 if the TR has actually been transmitted, or 4 if the NTR has been transmitted. Note that the detection process is computationally simple. Let us suppose that N stations are associated with the AP and the length of TR is also N (we assume that N is a power of 2). Detecting a TR requires N multiplications and 1 addition, assuming a multioperand adder. In a naive approach, such operations are needed for detecting every TR, resulting in the total complexity of O(N2). However, because TR is composed of 1 and 1 only, multiplication boils down to simply changing the sign of the received symbols. Moreover, the multiplication results can be reused in detecting other TRs. Hence, detecting all N TRs requires N multiplications and N additions in total. Consequently, the total complexity is O(N). The proposed method is in part similar to the direct sequence spread spectrum (DSSS) mechanism of CDMA systems in that orthogonality is achieved by using a special set of codes. However, it should be noted that the proposed scheme does not change signal bandwidth. In DSSS, each data bit is multiplied by an orthogonal code that runs at a much higher rate, producing a wider-band signal [19]. In contrast, the proposed scheme transmits orthogonal codes at the same rate as other control frames. Because the bandwidth remains unchanged, special hardware such as wideband antenna or wideband bandpass filters are not required. Moreover, code acquisition and tracking procedures are not necessary either because the AP always knows which codes are in use and when they are transmitted.
4. Implementation issues In this section, several implementation challenges are discussed. If not properly dealt with, these challenges could significantly degrade not only the efficiency but also the effectiveness of the proposed method. 4.1. Management of TR Because the number of TRs is finite, they should be assigned carefully and retrieved for reuse. An AP can assign a TR to a station when it is associated/reassociated by including the TR in the Association/Reassociation Response management frame. Similarly, the AP can retrieve a TR when its owner sends a Disassociation management frame. Because a station can leave the network without notifying the AP, the AP retrieves a TR on any indication that its owner is no longer associated. For example, if no successful data 3 ‘⁄’ represents the complex conjugate, which can be omitted because all of the TR/ NTR symbol values are real.
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transmission occurred for a certain time period, the AP may retrieve its TR. In addition, the transition to power save mode must also triggers TR retrieval. Medium access for these stations is either determined a priori (scheduled automatic power save delivery) or initiated by stations opportunistically (unscheduled automatic power save delivery or power saving in PCF) [1]. Thus, it is not likely that the queue status of power saving stations is of much importance to the performance of MAC protocols. State transition to/from power save mode can be detected by examining the Power Management field of a MAC frame. 4.2. Firmware modification As for stations, the most important requirement is to transmit TR/NTR signals on time, i.e., Tdelay after Query reception. Many commercial NICs do not support such timely transmission [20,21] because of high jitter in processing or queueing time. This becomes worse if the involved NICs are manufactured by different vendors. To enable punctual TR/NTR transmission, a station must record the exact reception time Tr of a Query frame. A station can record the time at which the first sample of a sample block was generated by the analog-to-digital converter (ADC) using the radio clock [22]. With the known sampling rate and the position of the Query frame in the sample block, we can extract the exact time at which the Query frame is received. Given the reception time Tr, the next step is to transmit a TR/NTR signal at Tr + Tdelay. To do this, the TR/NTR symbol block waits until the radio clock time matches the requested transmission time, and then is transmitted [22]. Because both the time-stamping and transmission scheduling are done near radio frontend, jitter in queueing and processing time at higher network stacks can be mitigated.4 It is possible to assess the achievable accuracy based on the results of [22]. The authors implemented a simple echo protocol, in which a receiver replies with a predefined delay after receiving a reference packet. The delay varied in the range from 1 ls to 100 ms. The experiment results showed that perfect time spacing was observed by a third radio device whose measurement resolution was 125 ns. 4.3. Detection of misaligned signals Signals that are sent by stations may reach the AP at different times because of differences in proximity to the AP (Fig. 3). For example, if two stations’ distances to the AP differ by 60 m, their signals arrive with 0.4 ls of difference. Also, non-line-of-sight (NLOS) propagation can introduce delay that is up to 0.2 ls in an indoor environment [23]. Such differences in arrival times cause signals to be combined with misalignment at the AP. We conducted a simulation using MATLAB to assess how much signal misalignment degrades TR detection accuracy. We measured the ratio of correctly detected signals, which is the ratio of signals that are detected as they are actually transmitted. For example, if the AP correctly detects 13 TR and 11 NTR signals out of 32 signals, we can say that the correctly detected signal ratio is 75%. Detailed simulation parameters are same as those summarized in Table 1 except the following: SNR is 10 dB; 16 out of 32 stations send TRs; signal powers are perfectly equalized (to be discussed in Section 4.4); and 32-bit Walsh codes are used as TRs. To simulate misalignment, each signal is shifted by an offset before combination. The offset is randomly chosen from 0 to M of symbol duration, where M varies in the range of 0–1. The details of the simulation 4 Clock drift can be another source of timing error. However, given that the tolerable timer accuracy of IEEE 802.11 devices is ±0.01% [1] and Tdelay is a few tens of microseconds, the effect of clock drift can be safely ignored.
Fig. 3. Signal misalignment due to differences in proximity to the AP.
Table 1 Simulation parameters for TR detection accuracy test. Parameter
Value
SNR Total number of STAs Number of TR senders Transmission rate Power equalization error TR Max. signal misalignment Max. Doppler freq. Phase diff. of delayed signal Power drop of delayed signal Number of waves for each signal Sampling rate
10 to 30 dB 32 4–28 0.5 or 1 Msps Log-N(0, 1.5) or Log-N(0, 3) dB 32-bit Walsh or O-Gold codes 0.8 ls 10 Hz 70° 8 dB 7 107/s
procedure will be illustrated in Section 5.1. The graph that is labeled ‘‘w/o avg. correlation’’ in Fig. 4 shows the results of the simulation. As misalignment exceeds 30% of symbol duration, detection accuracy decreases rapidly, showing about 28% of decrease when M = 1. To alleviate this problem, the AP calculates a series of crosscorrelations assuming that the TR and the received signal are misaligned by D, 0 6 D 6 M (Fig. 5). And then the AP takes the average cross-correlation to compare with zero. To do this, the received signal is sampled at a rate that is higher than the symbol transmission rate. This oversampling is a fundamental operation of all radio devices with digital processing.5 Because the oversampled symbols are usually decimated before demodulation, TR/NTR detection should be done right after analog-to-digital conversion. With the use of the average correlation, the detection process described in Section 3 is modified as follows. Let the oversampled symbols be r0 [j], 1 6 j 6 f L, where f is the oversampling factor. Then, the average correlation is denoted as:
E½Ci ¼
m X L 1 X u ½kr0 ½k f þ D; m þ 1 D¼0 k¼1 i
where m is f M, the maximum misalignment represented as a number of oversampled symbols. Note that this method increases the detection complexity by a factor of constant m. However, because the detection itself has very low complexity (O(N) as in Section 3), the additional complexity can be easily accommodated by modern HW. M, the ratio of the maximum misalignment to the symbol duration, should be determined in consideration of the difference in signal transmission time and propagation delay. The maximum WLAN cell radius is reported to be 67.5 m for IEEE 802.11a/b in a typical office environment [24]. Variance in propagation delay due to NLOS is about 0.2 ls [23], and error in synchronous signal transmission is less than 125 ns (Section 4.2). Consequently, we assume that the maximum difference in signal arrival time at the AP is 0.8 ls. With a transmission rate of 1 Msps and oversampling factor f of 10, M is 0.8 and m becomes 8. The graph labeled as ‘‘w/ avg. 5 In analog-to-digital conversion the sampling rate should be at least twice the signal bandwidth to avoid aliasing.
J.-H. Ko et al. / Computer Communications 34 (2011) 1033–1041
Fig. 4. Decrease in TR detection accuracy due to signal misalignment and its alleviation by using the average correlation.
Fig. 5. Calculation of the average correlation by changing the alignment of a TR to the received signal.
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cheap power control, and can compensate for large scale variations such as shadowing [25]. To use open-loop power control, a station may measure the signal strength of the Query frame that is sent at a predefined power. Based on the signal strength, the station can estimate the path loss, and then adapts its transmission power accordingly. Because the channel coherence time at 5.3 GHz is measured as 80 ms [23], it is likely that the TR/NTR signal experiences very similar pathloss to the Query frame. A more sophisticated mechanism is closed-loop power control where the AP gives stations feedback based on the received signal power [19]. The feedback can be piggybacked on any forward link frames to reduce overhead. This mechanism generally performs better than open-loop power control because the reverse link path loss is measured by the AP [26]. However, this mechanism relies on relatively frequent frame exchange between stations and the AP. Thus, we can think of using closed-loop power control for initial power equalization at the association phase, and then using open-loop power control on a frame-by-frame basis. Note that only the power of TR/NTR signals needs to be equalized.
correlation’’ in Fig. 4 shows performance improvement by using the average correlation. Another method to alleviate signal misalignment is to simply lower the TR/NTR transmission rate. If the transmission rate is reduced by half, M halves as well. Increased transmission time is compensated in part by reduced computation time. Moreover, even if the transmission rate is 0.5 Msps, transmission of 32-bit TR takes only 64 ls, which is still shorter than other MAC control frames [1].
5. Performance evaluation
4.4. Equalizing received signal power
5.1. TR detection accuracy
Signals transmitted from stations can be different in their strength because of near–far effects, multipath fading, shadow effects, noise, etc. In addition, the signal strength varies over time because channel status changes continuously. Although the orthogonality of TR/NTR signals should be preserved regardless of their strength in theory, this does not hold in practice because of noise and signal misalignment (Section 4.3). To investigate the effect of different signal strengths on TR detection accuracy, we conducted a simulation. The simulation settings are identical to Table 1 except: SNR is 10 dB; the maximum signal misalignment is 0% and 40% of symbol duration; 16 out of 32 stations send TRs; and TR is 32-bit Walsh codes. Power equalization error is simulated by multiplying each TR/NTR symbol with a coefficient drawn from a log-normal distribution. The mean of the log-normal distribution lpe is zero, while the standard deviation rpe varies from 0 to 5 dB. The simulation results show that power equalization error reduces TR detection accuracy, especially when signals are misaligned (Fig. 6). Thus, a power control mechanism is necessary. Power control mechanisms can be open-loop or closed-loop. In open-loop power control, the reverse link path loss is estimated based on the forward link signal strength, assuming that the two links are closely correlated. Although the assumption may not hold because of multipath fading, this mechanism provides fast and
The simulation is performed only on baseband signals for simplicity. Also, all signals are represented as a sequence of complex numbers (symbols). We measured two metrics: false positive ratio and false negative ratio. The former is the ratio of the falsely detected TRs to the transmitted NTRs, while the latter is the ratio of falsely detected NTRs to the transmitted TRs. Fig. 7 is a schematic diagram of the simulation procedure. First, a station generates a symbol array of its TR or NTR using BPSK modulation and the Nyquist filter [27]. Imperfect power equalization is simulated as in Section 4.4. The symbol arrays are contaminated by two-path Rayleigh fading in the channel, and they are combined with misalignment (Section 4.3). After the resultant symbol array is contaminated by AWGN, the AP receives it by applying the Nyquist filter again, and performs TR/NTR detection using the average cross-correlation (Section 4.3). The number of stations is set to 32, which is enough considering that the maximum number of stations in a practical WLAN cell is 30 [28]. We use a log-normal random variable with lpe = 0 dB and rpe = 1.5 dB for the closed-loop power equalization error [29], and with lpe = 0 dB and rpe = 3.0 dB for the open-loop power equalization error [25]. The transmission rate is either 0.5 or 1 Msps, leading to a maximum misalignment of 40% or 80% of symbol duration, respectively. Simulation parameters are summarized in Table 1.
Fig. 6. Degeneration of detection accuracy due to difference in signal strength.
In this section we first investigate the accuracy of TR/NTR detection in a realistic channel model via simulation. Based on the results, we applied the proposed method to PCF to demonstrate performance improvement of a MAC protocol leveraged by queue status information. We choose PCF because its inefficiency mainly stems from lack of queue status information, as discussed in Section 2.
Fig. 7. Schematic diagram of the simulation procedure to evaluate TR/NTR detection accuracy.
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Fig. 10. Average false negative error ratio when rpe = 1.5 dB.
Fig. 8. Average false negative error ratio for different SNR.
Fig. 8 is the trend of the average false negative error ratio as SNR, codes, transmission rate, and rpe change. The trend of the average false positive error ratio was almost identical. The case where the transmission rate is 1 Msps and rpe is 1.5 dB is omitted for readability, since its results are more or less the same as that of 0.5 Msps and 3.0 dB. Clearly, detection accuracy improves as SNR grows, but improvement when SNR = 10 dB is marginal. The reason is that the power equalization error and signal misalignment become dominant factors. Lowering the transmission rate, which in turn leads to less misalignment, significantly reduces detection error. Also, the power equalization error severely affects the detection accuracy. Walsh codes turn out to be more robust than orthogonal Gold codes. However, given a lower transmission rate and more accurate power equalization, the two types of codes perform similarly. We use Walsh codes in the rest of the paper. Figs. 9 and 10 show the false negative ratio as the number of TR senders changes. The results of the false positive ratio are almost identical. Only the results of Walsh codes and SNR 5 10 dB are shown, and rpe is 3.0 dB in Fig. 9 and 1.5 dB in Fig. 10. The number of TR senders does not seem to affect detection accuracy. Again, it is clear that SNR, signal misalignment, and the power equalization error are major factors that determine detection accuracy.
5.2. Improvement of PCF channel utilization In this section, we analyze how much improvement of PCF is expected by employing the proposed method. The modified PCF will be called orthogonal signalling-based PCF (OSPCF) in the rest of the paper. In OSPCF, the AP piggybacks Query on a Beacon frame so that all stations report their queue status at the start of a CFP. After detecting individual TRs, the AP polls only the stations that have data to send. In this way, OSPCF can avoid polling empty stations unlike the original PCF. To highlight the performance improvement, only the uplink data transfer within CFP is considered. Performance is measured in terms of channel utilization, which is defined as the ratio of the data transmission time to the CFP duration. Note that channel utilization is equivalent to normalized throughput. Without loss of generality, we can redefine channel utilization as the ratio of the average data transmission time in CFP to the average CFP duration. Stations are modeled as M/G/1 queues. The interval between data arrival at a station is assumed to be exponentially distributed with a rate of k. Data arrivals at different stations are presumed independent events. The TR/NTR detection error is specified by the false positive error ratio Pfp and false negative error ratio Pfn.
Fig. 9. Average false negative error ratio when rpe = 3.0 dB.
We also define the true positive and true negative event, which are the opposite of the false negative and false positive event, respectively. Consequently, the true positive ratio Ptp is 1 Pfn and the true negative ratio Ptn is 1 Pfp. Each TR detection is an independent Bernoulli trial with Ptp if TR is transmitted, or with Ptn if NTR is transmitted. Thus, the result of detecting a TR does not affect detection of other TRs. A station with a non-empty queue is polled only when its TR is correctly detected by the AP. Hence, the service time is represented as T S X, where TS is the superframe length and X is a geometric random variable with probability Ptp. Accordingly, the average service time is TS/Ptp and the utilization factor q is k TS/Ptp. Table 2 describes the parameters used in the analysis. All of the frame lengths include propagation delay, IFSs, and processing time. The length of control frames is set to 200 ls, taking into account the minimum MAC frame length of 36 octets. Notice that the Query frame length TQuery is set to 0, assuming that it piggybacks on a Beacon frame. The TR/NTR signal length TTR includes Tdelay (Fig. 1), the length of the actual TR/NTR signal including preamble, and their detection time at the AP. As for Tdelay, SIFS of the IEEE 802.11 standard is sufficient because a station can process and respond to a Poll frame within SIFS according to PCF [1]. Detection time may not be longer than the MAC processing delay, which is at most 2 ls [1]. With 32-bit TR/NTR plus preamble length, we set TTR to 400 ls, which is twice the size of a normal control frame. For the remainder of this section, time t is measured relative to the start of a superframe. Unlike the standard [1], we use the TR/ NTR transmission as the start of a superframe as shown in Fig. 11. Thus, a station decides whether to send TR or NTR at t = 0, significantly simplifying the case analysis. Note that the length of a superframe remains the same as that of the original definition. According to a station’s queue status at t = 0 and the TR detection results, a station belongs to one of the following four groups in a given CFP: true positive, false positive, true negative, and false negative stations. We denote the number of stations in each group as Ntp, Nfp, Ntn, and Nfn. Clearly, they sum up to Ntot. False positive stations can be further divided into one of two subgroups: false positive stations with and without data transfer. The former denotes those stations whose TRs are falsely detected and that happen to transmit data because data has arrived by the time the stations are polled. In contrast, a false positive station without data transfer has a queue that remains empty until polled. We denote the
Table 2 Parameters for analysis of PCF channel utilization. Parameter
Description
Value
Tdata Tbeacon Tnull TQuery Tpoll TTR TS Ntot k
Data frame len. Beacon frame len. Null frame len. Query frame len. Poll frame len. TR/NTR signal len. Superframe len. Total number of stations Data arrival rate at a STA
1000 ls 200 ls 200 ls 0 200 ls 400 ls 60 ms 16 0.5–16/s
J.-H. Ko et al. / Computer Communications 34 (2011) 1033–1041
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within a superframe is kTS. Because the stations are stable systems and data transmission time is Tdata, channel utilization is:
Ntot kT S T data : E½t cfp Fig. 11. Definition of superframe used in the analysis.
number of those stations as Nfp,d and Nfp,nd, respectively, which sum up to Nfp. The CFP duration is determined by the number of stations that transmit data frames and those that send Null frames. More specifically, the length of the CFP with w true positive stations, x false positive stations with data transfer, and y false positive stations without data transfer is:
t cfp;w;x;y ¼ T Query þ T TR þ ðw þ xÞðT poll þ T data Þ þ yðT poll þ T null Þ þ T beacon :
ð2Þ
is dependent on NNE, the number of stations with non-empty queues at t = 0, and the TR detection results. With NNE = Ntp + Nfn and independency of individual TR detection, (2) becomes
Pr½Nfp;d ¼ x; Nfp;nd ¼ yjNtp ¼ w; NNE ¼ N; Pr½Ntp ¼ wjNNE ¼ N;
ð3Þ ð4Þ
Pr½Nfp ¼ x þ yjNNE ¼ N;
ð5Þ
Pr½NNE ¼ N;
ð6Þ
where N = w + z. According to the binomial distribution,
Pw ð1 P tp ÞNw ; w tp Ntot N xþy ð5Þ ¼ Pfp ð1 Pfp ÞNtot Nxy ; xþy Ntot qN ð1 qÞNtot N : ð6Þ ¼ N ð4Þ ¼
Deriving channel utilization of the original PCF is relatively simple. The service time is fixed to TS because a station gets polled once every CFP. Hence, the utilization factor q becomes k TS. A CFP is characterized only by the number of data transmitting stations Nd. Since data arrival at stations are independent events, the probability of a particular CFP with x data transfers follows binomial distribution:
Pr½Nd ¼ x ¼
Ntot x
qx ð1 qÞNtot x :
ð8Þ
Similar to (1), the length of a CFP with x data transfer is
ð1Þ
The probability of a particular CFP
Pr½Ntp ¼ w; Nfp;d ¼ x; Nfp;nd ¼ y; Nfn ¼ z;
ð7Þ
N
To derive (3), we need to consider the probability of data arrival at a false positive station before it is polled. The polling time of a station is determined by the number of data transmissions and null transmissions that occurred before. To be more precise, if l data transfers and m null transfers occurred, the polling time tp,l,m is Ttr + l(Tpoll + Tdata) + m(Tpoll + Tnull) + Tpoll. The last Tpoll accounts for the Poll frame received by the station concerned. Since the data arrival interval follows the exponential distribution, a false positive station that is polled at tp,l,m transfers data with probability 1 etp;l;m . We can then recursively express (3) by considering all of the possible polling sequences. Let f[i, j, k, l, m] denote the probability that there are j false positive stations with data transfer and k false positive stations without data transfer, given i true positive stations, and l and m stations that have transmitted data and Null frames, respectively. Because the next station can be of any type, considering all possible cases with equal probability gives,
xðT poll þ T data Þ þ ðNtot xÞðT poll þ T null Þ þ T beacon :
ð9Þ
Using (8) and (9), the average CFP period is derived, and finally the average channel utilization can be obtained similarly (7). We derived channel utilization while varying the offered load at a station. Channel utilization is then compared with the simulation results. The detail of the simulation will be described in the next section. Pfn and Pfp are both set to 0.032 based on the results in Fig. 9 (SNR = 10 dB, qpe = 3 dB, 0.5 Msps). Although the detection accuracy varies slightly as the number of TR senders changes, we use fixed values for simplicity. Note that these settings are rather conservative, given that an SNR of 10 dB is considered to be a relatively bad channel condition in a typical office environment [30]. Also, only open-loop power control is assumed, and the chosen Pfn and Pfp are the worst values among all of the TR senders. Fig. 12 shows the results. It is obvious that OSPCF significantly outperforms the original PCF for all traffic loads. In particular, when the load is low, channel utilization improves by more than 30%. The analysis and simulation results agree to a great degree, with an average error of 0.13%. 5.3. Comparison with other schemes In this section, we compare the channel utilization and queueing delay of OSPCF with other methods that are proposed to improve PCF. Namely, STRP [9], RAP [10], and LEAP [11] are compared with OSPCF. The performance of the original PCF is also measured. We used an event-driven simulator written in Java. To obtain steady-state performance, the simulator starts gathering statistics when all of the stations finish transmitting 20,000 data frames, and runs until all of the stations transmit 10,000 More Data frames. We assume perfect frame delivery except for the TR and NTR signals. Note that this rather impractical assumption is a conservative choice for OSPCF. The simulation settings are summarized in Table 3. Refer to Table 2 for parameters of OSPCF. The length of a frame includes propagation delay, IFS, and processing time. For all algorithms, the length of the Beacon, Poll, and Null frames are equal for fair comparison. The jam signal length of STRP Tjam,strp is set to 10 ls longer than
f ½i; j; k; l; m ¼ f ½i 1; j; k; l þ 1; m þ f ½i; j 1; k; l þ 1; m ð1 etp;l;m Þ þ f ½i; j; k 1; l; m þ 1 etp;l;m ; where f is zero if any of i, j and k are negative and f[0, 0, 0, l, m] = 1. Given that each polling sequence is equally likely, (3) is represented as f[w, x, y, 0, 0] divided by the number of all possible polling se wþxþy quences . wxy We can then finally derive the average CFP length E[tcfp] using (1) and (2). The average number of data frames arriving at a station
Fig. 12. Analytic comparison of channel utilization between PCF and OSPCF using simulation and analysis.
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Table 3 Simulation parameter settings. Parameter
Value
Parameter
Value
Transmission rate Tjam,strp Tquery,strp Ttransmit,strp Tq/t,strp Tra,rap k
1 Msps 1010 ls 200 ls 200 ls 200 ls 1000 ls 0.2 –17
Lleap aleap Lrap Prap Tready,rap Number of stations Number of data frames
0.1 0.3 2 31 0 32 10,000/STA Fig. 14. Comparison of queueing delay with other polling list management methods.
Tdata as specified in [9]. The Ready frame length of RAP Tready,rap is set to 0 because it can piggyback on a Beacon frame. Tra,rap, the random address transmission and detection time of RAP, is five times the Poll frame as in [11] to include CDMA or FDMA overheads and multi-stage random address transmissions. The number of random addresses of RAP, Prap, is set to 32 so that it equals the number of TRs employed in OSPCF. Lrap, the number of random address transmission stages, is 2 as specified in [10]. Two parameters of LEAP, Lleap and aleap, are set to 0.1 and 0.3, respectively, to make LEAP adapt quickly to constant network loads [11]. Fig. 13 shows the results of the simulation. OSPCF performs very well for the whole range of the offered load tested. Specifically, it outperforms all other schemes when the load is relatively low (0.05 6 q 6 0.45). RAP also shows its best performance at a low load, seemingly because it rarely experiences address collision. However, after q = 0.2, RAP’s channel utilization rapidly decreases and then reaches a saturation point when q = 0.42. The saturation point is where all of the stations compete for a random address because they all have non-empty queues, resulting in excessive address collision. STRP performs slightly better than OSPCF when q > 0.5, probably because the AP always knows at least one active station. In this case, the AP can Query the queue status of an idle station while polling an active station. However, OSPCF’s channel utilization is still comparable to that of STRP when q > 0.5. In addition, STRP performance degerates into that of PCF when almost all of the stations do not have data (q 6 0.15) [9]. Despite a sharp improvement after q = 0.15, OSPCF significantly outperforms STRP until q = 0.5. LEAP yields low channel utilization over the entire range, presumably because of its probabilistic nature. Note that when the network becomes saturated, all of the tested methods except RAP exhibit similar performance because every station has a non-empty queue. In this case, the original PCF performs best because any polling list management scheme merely acts as an overhead. Fig. 14 shows the queueing delay, which is defined as the time that a packet waits in a queue until it is transmitted. In comparison, OSPCF performs very well for almost the entire range of the offered load (q 6 0.9). PCF always performs much better than others, especially when the load becomes heavier. This is because PCF does not incur upfront overhead of polling list management, and stations under a heavy load are likely to have non-empty queues.
LEAP shows rather poor performance when low-to-medium load is applied. Interestingly, LEAP’s delay becomes shortest when q is about 0.3. The reason is that the lowest polling probability of LEAP is aleap, which is set to 0.3 in the simulation. Thus, even if q is smaller than 0.3, the polling probability converges to 0.3, leading to higher queueing delay than that of q = 0.3. RAP’s queueing delay soars when it reaches its saturation point (q 0.4). In this case, packet arrivals outnumber packet transmissions, resulting in an unbounded queueing delay. STRP performs well until q 6 0.3, but its delay becomes greater than that of OSPCF when 0.4 6 q 6 0.9. When q P 0.9, STRP and LEAP perform better than OSPCF. However, such a situation rarely happens in practice. 6. Conclusions We proposed a novel method that efficiently collects queue status from multiple stations by utilizing orthogonal signaling. With synchronous transmission of orthogonal codes and symbol level signal processing, the proposed method allows all of the associated stations to report their queue status at the same time. We investigated implementation challenges including firmware modification, signal misalignment, and signal power equalization, and then suggested their solutions. The feasibility of the proposed method was thoroughly tested using a realistic channel model where AWGN and multipath fading apply. To demonstrate performance improvement achieved by the use of the proposed method, we modified PCF. Both the analysis and simulation showed that the modified PCF significantly outperforms the original PCF and other previously suggested methods in terms of channel utilization and queueing delay. Acknowledgment This research was supported by the MKE (The Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA-2010C1090–1021-0006) References
Fig. 13. Comparison of channel utilization with other polling list management methods.
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