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Providing qualityofservice for frequencyaware WiFi using OFDMbased variablelength Bloom filters
EURASIP Journal on Wireless Communications and Networking volume 2014, Article number: 152 (2014)
Abstract
Modern WiFi networks are trending towards using a wider channel bandwidth to achieve high physical layer data rate. The wide channel band experiences fluctuations across the different frequencies, causing diversity in the frequency domain. Frequencyaware WiFi protocols exploit this frequency diversity and consequently achieve high wireless capacity. However, most of the existing works have not considered qualityofservice (QoS) issues. In this paper, we present a new WiFi protocol called QoSFi, that provides QoS for the mobile users in the frequency aware WiFi network. QoSFi dynamically assigns orthogonal frequency division multiplexing (OFDM) subchannels for heterogeneous mobile users to meet the QoS demands. To achieve this goal, we apply an OFDMbased variablelength Bloom filter (VBF) that synergistically integrates the channel quality estimation and QoS channel coordination. To the best of our knowledge, this is the first work that employs QoS at the frequency domain for WiFi networks. We study the impact of variablelength signatures in the aspect of throughput maximization and meeting the QoS requirements and further develop a decentralized QoSaware channelallocation algorithm that achieves suboptimal performance. Our USRP/GNURadiobased experiments and tracedriven simulations show that QoSFi provides up to 1.39 × and 1.29 × throughput improvements compared to the legacy EDCA and wellknown Knopp and Humblet’s and round robin (K&H/RR) scheduling, respectively in the QoSregimes.
1 Introduction
The recent development of communication technologies has enabled WiFi networks to achieve high physical data rates. A consistent theme in WiFi networks to advance the wireless capacity has been wider channel bandwidths as well as more antennas. The signals transmitted over a wide frequency band, however, experience independent fluctuations across different frequencies at a time instant. This phenomenon is generally called frequencyselective fading[1]. The modern wireless systems such as WiMAX [2] and 3GPP LTE [3] actually exploit this frequency diversity as another dimension of multiuser scheduling, by using the wellknown orthogonal frequency division multiple access (OFDMA) technique. Here, the base station (BS) allocates each subchannel to the mobile user who has better channel quality, consequently achieving near the Shannon’s maximum capacity of wireless channels. Further, the BS can easily employ qualityofservice (QoS) strategies to the scheduler since the mobile users are tightly synchronized with the BS.
Several WiFi protocols have been proposed [4–7] to harness the frequency diversity with a similar trend towards the use of wider channels. However, the WiFi network renders several new challenges. First, it requires finegrained channel estimation which incurs high channel estimation overhead [2–6, 8, 9]. Channel estimation in WiFi depends on training sequences (dedicated pilots) or RTS/CTSlike probing frames, which consumes time and frequency resource. Second, the channel access requests from stations (STAs) should be coordinated, further inducing corresponding protocol overhead [4–6, 10]. Third, modern WiFi networks host diverse applications which render various QoS demands; some require small latency while others need large throughput. It is challenging to be both aware of the frequency diversity and meet these QoS demands for realtime applications in WiFi networks.
Several approaches have been proposed in WiFi networks to achieve the frequency diversity awareness. Some work mainly focus on harnessing frequency diversity with additional channel estimation costs [4–6, 10], while others improve protocol efficiency (e.g., reducing channel contention cost) by operating at the frequency domain [11–13]. Our previous work [7] proposes the WiFi protocol that achieves the above objectives by using an OFDMbased Bloom filter. These schemes generally achieve high wireless capacity via frequency diversity awareness. However, none of the aforementioned schemes support the prioritized channel access of STAs for realtime traffic, thus cannot be applied to QoSsupported WiFi networks.
In this paper, we present QoSFi, a WiFi PHY/MAC protocol that exploits frequency diversity to satisfy the diverse QoS requirements of heterogeneous realtime mobile users. QoSFi dynamically assigns OFDM subchannels to heterogeneous realtime mobile users depending on their QoS requirements. To achieve this goal, we apply an OFDMbased variablelength Bloom filter (VBF) [14] that synergistically integrates two operations: (i) the channel quality estimation for exploiting frequency diversity and (ii) the frequencyaware channel coordination to meet the QoS requirements. To the best of our knowledge, this is the first work that provides QoS in the frequency domain for frequencyaware WiFi networks.
The main features of the QoSFi protocol are as follows:

QoSFi estimates the channel quality while concurrently performing the prioritized channel contention and coordination based on the OFDMbased Bloom filter. The QoSFi MAC protocol exchanges RTS/CTSlike QoScollision resolution request (QCRQ)/QoScollision resolution reply (QCRP) frames through the VBF. Multiple STAs contend for OFDM subchannels simultaneously, according to estimated subchannel quality and QoS demand. The AP estimates the uplink channel qualities by using this synthesized QCRQ frame without any further channel estimation overhead. After the AP makes a decision for the subchannel allocation based on the channel qualities and QoS demands, it broadcasts the QCRP frame to inform the STAs of the coordination result.

QoSFi supports the prioritized channel access by using variable length signatures in the VBF. The STA who participates the channel contention with a longer signature has a larger channel access probability than the STA with a shorter length signature. This mechanism resembles the EDCA in 802.11e in the aspects that, by using various arbitrary interframe spaces (AIFSes), high priority traffic has higher channel access and lower collision probabilities. We study the impact of the use of variable length signatures in our protocol by mathematical analysis and simulations. The results reveal that QoSFi maximizes the throughput while supporting the diverse QoS requirements with small amount of overhead.
In summary, this paper makes the following contributions. (i) We design and implement QoSFi, a novel WiFi PHY/MAC protocol that exploits frequency diversity and supports QoS demands, while achieving PHY/MAC efficiency. (ii) We study the impact of the length of signatures in the aspect of both service differentiation and false positive probability of the Bloom filter. We apply VBF to the QoSFi protocol to perform the prioritized channel contention and coordination. (iii) We prototype QoSFi on the universal software radio peripheral (USRP)/GNURadio platform and evaluate its performance using the detailed tracedriven simulation. Our results show that QoSFi provides up to 1.39 × and 1.29 × throughput improvements compared to the legacy EDCA and wellknown Knopp and Humblet’s and round robin (K&H/RR) scheduling, respectively, while providing the prioritized channel access in the QoS regimes.
The rest of this paper proceeds as follows. Section 2 reviews the related work, and Section 3 describes the detailed design of the QoSFi PHY/MAC protocol. Then, Section 4 presents the channel coordination algorithms for QoS provisioning. In Section 5, we provide a detailed analysis to examine the impact of the length of signatures in the VBF. Section 6 evaluates the QoSFi’s performance using our experimentation and tracedriven simulation, and finally, Section 7 concludes the paper.
2 Related work
2.1 Improving MAC efficiency
The general WiFi MAC protocols [8] employ the CSMA/CA exponential backoff algorithm, which is conducted in the time domain. In contrast, the frequency domain contention protocols [7, 11–13, 15] have been proved to be much more efficient in terms of wireless capacity. FICA [11] redesigns the PHY/MAC by using an OFDMbased finegrained channelization to attain protocol efficiency. Similar to FICA, [12] proposes Back2F that migrates the time domain backoff to the frequency domain. As the frequency domain backoff lasts for only several OFDM symbols, it reduces the contention time. REPICK [13] modifies the receiver to conduct frequency domain backoff instead of a transmitter and added a ACK piggybacking feature. These work share the similarity of using the OFDM technique to enhance the MAC protocol efficiency. Our previous work, DFi [7], exploits the frequency diversity while reducing overhead by using the Bloom filterbased channel contention and estimation. This paper further extends DFi by adding QoS provisioning functionalities that enable the prioritized channel access by leveraging the variablelength Bloom filter.
2.2 Frequency diversity
Many theoretic classic proposals exploiting frequency diversity are summarized in the wireless communication textbook [1]. Some of them are widely used by cellular systems such as mobile WiMAX [2] and 3GPP LTE [3]. Recently, there are theoretic studies that apply proportional fair packet scheduling in FDMAbased 3GPP LTE [16, 17] and CSMAbased OFDMA systems [10, 18]. These work solved the resource allocation problem by mathematical modeling while assuming that the perfect channel quality information is available via the training sequence (e.g., pilot signal). This is less practical in WiFi networks, since the AP and users do not maintain tight synchronization as in the cellular system. Therefore, we practically consider the WiFi channel estimation overhead.
Many practical measurement studies have been conducted to show the existence of frequency diversity. Among these, the most relevant to our work are measurement studies in the 2.4GHz/5GHz ISM bands [4, 6, 19, 20]. Also, in wireless local area networks (WLANs), several frequency diversityaware schemes have been proposed [4–6]. The authors of [4] observed the frequency diversity in wideband WLANs and introduced a practical rate adaptation scheme based on the effective SNR (eSNR). In FARA [5], a transmitter can send multiple packets to multiple receivers concurrently based on the OFDM technique. Thus, it does not need to consider the timesync problem arisen when multiple packets are combined at a receiver. FARA can be used at the downlink WLAN and may be viewed complementary to our work, since we mainly focus on the uplink. Finally, the authors of [6] proposed a diversityaware WLAN that uses an adaptive interleaver and a forward error correction (FEC) scheme based on persubcarrier channel state information (CSI). It adopts different domain approaches such as a persubcarrier FEC method and an interleaver and hence is orthogonal to our work.
2.3 QoSprovisioning
QoS provisioning in wireless networks has been widely studied from different perspectives. We classify them into two categories: centralized approaches and decentralized approaches. The centralized approaches generally involve admission control and scheduling. For example, the main idea of resource allocation in [21] is to compute the reserved bandwidth for each user in terms of the average delay. In [22], the authors proposed a powerful concept termed effective capacity, which enables to analyze the statistical delay. IEEE 802.11e [23] standard defines EDCA, a decentralized QoS support protocol. Unlike centralized QoS provisioning schemes, the EDCA mechanism does not require a dedicated central entity that computes computationally complex scheduling and/or admission control algorithms. As a result, the EDCA mechanism only supports a certain level of QoS differentiation as opposed to the centralized approaches that provides QoS guarantees. Our QoSFi protocol incorporates two features from each type of the QoS provisioning mechanism: (i) the EDCAlike decentralized feature for being applied in WiFi networks and (ii) the admission control algorithm that enhances QoS provision functionality using the effective capacity concept [22].
2.4 Bloom filter
The Bloom filter is a spaceefficient data structure. Here, the space applies to the network resources, which usually directly affects the protocol efficiency. The price paid for this space efficiency is the probabilistic ambiguity inherent to the Bloom filter: it tells us that the element either definitely is not in the set or may be in the set. This space efficiency enables the Bloom filter to be utilized in many networkrelated applications, some representative examples are summarized in [24]. Our previous work [7] shows that the OFDMbased Bloom filter implementation is practically feasible in real WiFi networks. It mainly deals with the ambiguity problem raised from the use of Bloom filter and addresses the problem. There are many Bloom filter variants, including variable length Bloom filter, counting Bloom filter, deletable Bloom filter, etc [25]. Among them, in this paper, we apply the variable length Bloom filter [14] to augment the QoS provisioning functionality.
There are other methods that provide the similar functions, i.e., simultaneous channel coordination and estimation. For example, one can use pseudorandom noise sequence (PN) [26] or ZadoffChu sequence [27] (ZC  one of the constant amplitude zero autocorrelation waveform, a.k.a., CAZAC) for the similar goal of QoSFi. Though the sequencebased method can provide more accurate channel estimation, generally, it incurs larger overhead. While superimposing multiple sequences can mitigate the overhead, it decreases channel estimation accuracy. In addition, the sequencebased channel estimation generally suffers from the socalled ‘estimation error floor’ problem, where the error does not depend on channel noise itself but the length of sequences. Finally, the Bloom filter can also produce more accurate channel estimation, by using the cross correlation technique like PN sequences. However, it naturally incurs larger overhead compared to Bloom filter (as QCRQ/QCRP is implemented with an OFDM symbol) and suffers from the same problems mentioned so far.
3 QoSFi protocol design
In this section, we first present the overview of the proposed protocol and then give details of the PHY/MAC design.
3.1 Protocol overview
QoSFi is an OFDMbased QoSaware PHY/MAC protocol that allows different WiFi STAs to access several narrower orthogonal subchannels with differentiated channel access priority in a wideband WiFi. Figure 1 illustrates the basic channel access scheme for QoSFi. In QoSFi, a wideband WiFi band is divided into several orthogonal subchannels and each of them is used as a channel access unit. QoSFi STAs select multiple candidate subchannels and contend for the use of the selected subchannels based on their traffic demands and QoS requirements. The channel access for each subchannel is coordinated through RTS/CTSlike QCRQ/QCRP frames (Sections 3.2.2 and 3.2.3). If the medium is idle for the period of distributed interface space (DIFS), QoSFi STAs transmit QCRQ frames simultaneously on their selected subchannels, where each STA selects candidate subchannels likely to have good channel quality. QCRQ frames transmitted on each subchannel contain the transmission information of the corresponding STAs with a binary bit sequence called a signature.
Similar to the 802.11 EDCA, the de facto QoS MAC protocol for WiFi, QoSFi defines four levels of priority for channel access by assigning four types of signatures, each of which corresponds to an access category (Section 3.2.1).
Thus, each STA has four different signatures and conveys one of them to the QoSAP through QCRQ frames according to QoS requirements and the corresponding access categories (Section 3.2.1). Upon the receipt of QCRQs sent from multiple STAs, the QoSFi AP estimates the uplink channel quality of the STAs (Section 3.2.4) and performs frequencyaware subchannel allocation to the STAs based on a predefined channel allocation policy, for example, maximizing throughput subject to the QoS requirement of each user. Then, the AP broadcasts the channel allocation results to the QoSSTAs through a QCRP frame (Section 3.2.3). The QCRP frame conveys the signature of the contention winner and transmission rates for future data transmission. When a subchannel is selected by multiple STAs, QCRQ frames from the STAs on the subchannel are collided at the AP. The QoSFi uses the OFDMbased Bloom filter technique in conjunction with prioritized signatures of variable length to resolve the channel contention for a subchannel, where the QCRP frames with longer signature lengths are selected with higher probability, thus achieving the prioritized channel access (Section 3.2).
3.2 Channel contention and estimation with QoSprovisioning
3.2.1 Signatures with QoSprovisioning
3.2.1.0 Signature
A signature is a binary bit sequence of 64 bits, which is uniquely assigned to an individual QoSFi STA by a QoSFi AP when the STA associates to the AP, where the AP manages a set of the assigned signatures S={s_{1},s_{2},…,s_{ N }}. The the number of bits l marked as ‘1’ in the signature is defined as the length of the signature l.
In QoSFi, the QoS provisioning mechanism is implemented by using variable length signatures, where the longer length a signature has, the higher a QoS priority it becomes. In particular, similar to 802.11e EDCA, QoSFi uses four signature lengths l_{1}>l_{2}>l_{3}>l_{4} to define four levels of the priority. Hence, every STA joining the network is assigned four unique signatures, each of which corresponds to one of the four access categories. The STA contends for wireless subchannels with QCRQ frames in which one of the four signatures is embedded based on the priority of the traffic from the upper layer.
3.2.1.0 Signature generation
The QoSFi AP uses hash functions^{a} to generate a signature of length l_{ i }, i∈{1,2,3,4}. The hash value from a hash function is used as a pointer that specifies one of the 64 bits (i.e., position of a bit) and the selected bit is marked as 1. A signature of length l can be generated through the superposition of l bit sequences of length 1, i.e., the result of binary OR, where the l bit sequences are generated by a certain set of hash functions^{b}. We use each STA’s unique identifier, e.g., MAC address, as a search key to choose a set of hash functions from the hash function pool to generate a signature for the STA.
3.2.1.0 Implementing signatures in OFDM systems
A signature is carried over one subchannel via a QCRQ frame; 1 bit over one subcarrier. We use binary amplitude modulation (BAM) to modulate a single bit on each subchannel. BAM uses onoff signaling that maps a binary ‘0’ to zero amplitude and a binary 1 to a random complex number on the unit circle (e^{jθ}) in a subcarrier. In other words, no signal is transmitted to modulate a binary 0 in a subcarrier and a fixed powered random complex signal is transmitted to modulate a binary 1 in a subcarrier. A receiver can easily detect a BAM symbol by measuring a signal power level on a subcarrier without demodulating an exact symbol.
3.2.2 Channel contention with QCRQ frame
3.2.2.0 Frequencydomain channel contention
In our previous work [7], we have introduced a frequencyaware channel access protocol, DFi. QoSFi follows the DFi’s OFDMbased channel model and extends DFi’s channel access protocol. QoSFi divides a WiFi band into several orthogonal subchannels and each of them is used as a channel access unit, as shown in Figure 1. We choose 64 contiguous subcarriers for a subchannel, forming total 8 subchannels (bandwidth is 2.5MHz.). Among 64 subcarriers, 60 subcarriers are used for data transmission and the rest 4 subcarriers are used as a pilot channel that tracks the subchannel quality while the data is being transferred (see [7] for the details).
3.2.2.0 Subchannel request
QoSFi STAs conduct channel contention in the frequency domain through RTS/CTSlike QCRQ/QCRP frames. Each QoSFi STA selects multiple candidate subchannels, in particular K subchannels, and contends for the selected subchannels by transmitting QCRQ frames over K subchannels simultaneously. Upon the receipt of CRQs sent from multiple STAs, the QoSFi AP estimates the uplink channel quality of the STAs and performs subchannel allocation to the STAs based on a predefiend channel allocation policy, for example, proportional fairness or throughput optimum.
One may inquire how much the QCRQ/QCRPbased channel contention is efficient, when compared to the legacy binary exponential backoff (BEB)based one. Table 1 shows the timing parameters of QoSFi and 802.11n. Assuming a 1,500byte packet and the MCS of 54 Mbps, 210 μs is required to transmit a single packet. This is computed by using the smallest overhead that the legacy 802.11 produces. If more faster MCS schemes are used or smaller packets are transmitted, the gain of the QCRQ/QCRP based channel contention over the BEBbased one will be increased. Note that the longer cyclic prefix (CP) is required to compensate the propagation delay of stations for the QCRQ symbol.
3.2.2.0 QoS request in QCRQ frames
Each QoSFi STA has four unique signatures of length l_{ i }, i∈{1,2,3,4} and performs channel contention by conveying one of the signatures to the AP via QCRQ frames according to the QoS level of the current traffic. For instance, when the QoSFi STA has traffic of the highest priority, e.g., VoIP, it transmits a QCRQ frame with its longest signature of length l_{1} on the selected K subchannels. Since each QoSFi STA transmits QCRQ frames over K subchannels simultaneously, multiple QCRQs sent from different STAs may be overlapped on a subchannel.
To resolve such simultaneous channel contention, QoSFi uses the Bloom filter [24]. As depicted in Figure 2a, we consider a subchannel over which one or more signatures are transmitted as a Bloom filter consisting of 64 bits and identify a STA by checking whether the signature assigned to the STA is present in the filter or not. If only one signature is transmitted over the subchannel, then the signature can be identified easily. If two or more signatures collide, the AP uses the Bloom filter technique to identify which signature(s) are present in the collision. The identified signatures inform the AP of the possible transmitters and their priority levels of the traffic. The process of identifying signatures from a Bloom filter is called ‘QCRQ decoding’ (Figure 2b).
3.2.2.0 QCRQ decoding
Figure 2b depicts an example of the QCRQ decoding process on a subchannel for a scenario where a Bloom filter, an array of 64 bits, at the QoSFi AP is built from two signatures s_{1} and s_{2} sent from STA1 and STA2, respectively. To test if a signature s_{ i }∈S, 1≤i≤N, is in the Bloom filter, the AP checks whether all the filter bits corresponding to all the bits set to 1 in s_{ i } are also marked as 1. If not, then clearly s_{ i } is not a member of the filter and the AP considers that STA i has not requested for the subchannel. If all corresponding bits are set to 1, the AP assumes that s_{ i } is in the filter and STA i has requested for the subchannel, although it may be a false positive case, i.e., the signature s_{ i } that actually is not present in the filter. In the example, only two false positive cases (of length 2) are presented while other longer false positive signatures can be possible.
3.2.2.0 Ambiguity problem of Bloom filter
In QCRQ decoding, we should handle two types of ambiguity: the physical and logical errors.
3.2.2.0 Physical errors
On the selected subchannels, a STA will transmit a signal over the OFDM subcarriers representing its signature. A single OFDM subcarrier should represent only one corresponding bit of a Bloom filter. However, since the frequency separation between subcarriers is imperfect in practice, a signal may spill over adjacent subcarriers, i.e., a subcarrier suffers from socalled ‘spectral leakage’ [28]. Furthermore, subcarrierlevel signal detection is typically implemented by thresholdbased decision approaches [11, 12] which compare the received signal power level with a predetermined threshold. As a result, signals can be falsely detected (or falsely missed). We call these two types of physical errors ‘bitwise false positive (bitwiseFP)’ and ‘bitwise false negative (bitwiseFN)’, respectively. We will show that this physical error problem can be mitigated effectively through an adaptive threshold adjustment technique in Section 6. Our implementation with a software radio platform (explained in Section 6) is shown to achieve very low physical error rate.
3.2.2.0 Logical errors
There can be ‘false positive (FP)’ cases during the QCRQ decoding process when using a Bloom filter. That is, the QoSFi AP may falsely decode the signatures and indicate the STAs that are not actually requested. For example, suppose that two STAs STA_{1} and STA_{2}, whose signatures (of length 2) are ‘11000...00’ and ‘01100...00’, respectively, have requested the same subchannel, generating the Bloom filter of ‘1110...00’. In this case, due to the inherent ambiguity, the AP may falsely indicate a signature of ‘10100...00’ from a superposition of the two signatures ‘1110...00’, which is neither STA_{1}’s nor STA_{2}’s^{c}.
To address the ambiguity problem of the Bloom filter, we analyze the FP probability with QoSFi protocol and derive the protocol parameter K that minimizes the FP (Section 5), where QoSFi STAs request K subchannels at once.
3.2.2.0 Prioritized channel access
The QoSFi AP uses the Bloom filter technique to identify the contending STAs for each subchannel as well as their QoS information. Recall that in QoSFi, the QoS provisioning mechanism is implemented by using variable length signatures, where the longer length a signature has, the higher a QoS priority it becomes. It is also worth mentioning that the Bloom filter decodes the longer signatures with the lower false positive rates when variable elements are mixed in the filter. Hence, the QoSFi AP identifies QCRQ frames with higher priorities (i.e., longer signatures) more accurately. This property is very important for service differentiation in QoSFi; it is straightforward that the false positive in the decoding process for a higher priority signature is more harmful than that of a lower priority signature since the higher priority STAs are allocated channels more frequently. This resembles the EDCA of 802.11e in the aspects that, by using various arbitration interframe spaces (AIFSes), high priority traffic has higher channel access and lower collision probabilities. However, the QoSsupport in QoSFi is more stringent than EDCA because various channel allocation policies can be applied for high priority traffic by the QoSFi AP.
Consequently, when a STA has the most delaysensitive, i.e., the highest priority level, traffic to transmit, it participates the channel contention with the longest signature among his own signatures. The STA who contends for the channel with a longer signature has a higher channel access probability than the STA with a shorter one.
3.2.3 Channel assignment with QCRP Frame
To inform STAs of subchannel allocation results, a QoSFi AP broadcasts a QCRP frame. The QCRP frame consists of two consecutive OFDM symbols: one for conveying the signature of a contention winner, and the other for data rate information for future data transmission, for each subchannel.
3.2.4 Channel quality estimation
During the QCRQ decoding process, the AP can recognize that some bits marked as 1 in the filter are set by only one STA’s signature, not overlapped by two or more another signatures. We refer to these bits as unique bits. After the QCRQ decoding, we exploit the unique bits to estimate the uplink channel quality of STAs. We employ a simple channel quality estimation method using the signal strength of the OFDM symbol. We assume that all STAs use the same transmission power and the total transmission energy spreads evenly over each of l bits marked as 1 when sending a QCRQ symbol. Then, the AP uses the average energy level of the unique bits that belong to a signature as the channel quality of the STA that transmitted that signature (Figure 2).We evaluate the performance of our channel estimation method in Figure 3. Despite its simplicity, the results show that, in most cases (≥ 90%), the estimation error of our method is less than or equal to 1 dB. Unlike channel state information (CSI) used in MIMO systems, this lightweight method, i.e., using the signal strength as channel quality, requires no complicated channel information exchange between an AP and STAs and thus is simply applicable to our system.
4 Channel allocation with QoS provisioning
There are two design issues while using the QoSFi protocol. The first revolves around the behavior of the QoSFi STAs that how each STA selects several subchannels for channel request. The second issue is how the QoSAP allocates subchannels to the STAs so that it maximizes the overall throughput subject to the QoS requirements of each STA.
In this section, we present (i) the (decentralized) userside algorithm which explores/exploits frequency diversity and (ii) the APside frequencyaware channel allocation algorithm that performs the channel assignment based on the channel quality estimates and the QoS requirement of each STA. We consider a scenario where N STAs are conducting channel contention for C subchannels.
4.1 Userside distributed channel explore/exploit algorithm
In QoSFi, each STA employs a simple distributed learning algorithm to adaptively select K subchannels that are likely to have good channel quality, thus achieving throughput improvement. To this end, each STA maintains a state vector, [x(1),x(2),…,x(C)], where let x(i) denote the preference factor for subchannel i with \sum _{i}^{C}x\left(i\right)=1. All x(i), i∈{1,2,3,…,C}, are initialized to 1/C.
Upon every receipt of QCRQs sent from multiple STAs, the QoSFi AP estimates the uplink channel quality of the STAs and informs STAs of channel allocation results for each subchannel by broadcasting QCRP frames over C channels. Then, the STAs learn the channel allocation results from the QCRP frames, including whether they are granted subchannels or not. Note that if a STA is granted a requested subchannel, it implies that the channel has a good condition for the STA and vice versa. Based on the allocation result for each channel, each STA adjusts x(·) in an additive increase/multiplicative decrease (AIMD^{d}) manner. When the STA is granted subchannel i, the STA increases the value of x(i) by α. For nonselected subchannel i, the STA decreases the value of x(i) multiplicatively by 1/β. The updated state vector, [x(1),x(2),…,x(C)], is then normalized so that their sum is to be 1.
Algorithm 1 shows the pseudocode of the userside channel exploration/exploitation algorithm. A STA selects total K subchannels where subchannel i is selected with the probability proportional to the weight x_{ i }. Obviously, the optimal value of K depends on the number of active STAs (N) in a network. An AP estimates the number of active STAs in the network [29] and periodically broadcasts an appropriate K value. We adjust K such that the false positive probability is not large (e.g., ≤10%) based on the analysis shown in Section 5.
4.2 APside algorithm
Every time the QoSFi AP receives the requests from its associated STAs, the AP measures the channel quality of each STA and uses it to allocate the subchannels in such a way that maximizes the overall throughput subject to the QoS requirements of each STA. In particular, the APside channel allocation algorithm comprises two combined phases.
The first phase is the QoS provisioning phase that for a given combination of subchannel allocation, the QoS AP exams whether the allocation satisfies the QoS requirement of each STA’s request or not. The AP utilizes an admission control algorithm for this test.
We assume that STA i,i∈{1,2,…,N} using subchannel c,c∈{1,2,…,C} in data transmission phase t,t\in \mathbb{N} has a data rate r_{i,c}(t) and a specified delay bound D_{max} (predefined with the QoS types). The delay experienced by the packet of STA i transferred by subchannel c in data transmission phase t is D_{i,c}(t) and is required to be less than or equal to the delay bound D_{max} for the QoS requirement (Equation 2).In Equation 2, the delay needs to be estimated for each packet. Since multiple packets sent from multiple stations are transferred simultaneously, each data transmission phase lasts until the end of the longest packet transmission among multiple packets transferred in all the subchannels, as shown in Figure 1. Therefore, each packet in the same transmission phase will experience equal delay, which definitely depends on the future channel allocation decision.
Among a set of candidate allocation that satisfies the QoS requirement, the second phase selects the allocation that maximizes the sum rate transferred over all the subchannels, in the following data transmission phase. To relax the complexity of the problem, we assume that the total transmission power of station i is equally divided among the subchannels assigned to the station.
Let r_{i,c}(t) be the data rate of STA i using subchannel c in data transmission phase t. Then, the strategy of maximizing the sum rate subject to the QoS requirement of each STA is written as
The transmit rate of station i using subchannel c can be calculated according to Shannon’s capacity process. Then, the sum rate can be further rewritten as a function of estimated SNRs, i.e., SNR_{i,c}.
The challenges of this problem lie in its complexity. Obviously, our problem is an integer program (IP) and is proven to be strongly NPhard [30], even with the assumption of the fixed power allocation. In addition, though the IP problem can be approximately solved with the wellknown LP relaxation technique [31] or the equivalent dual problem by using Lagrangian method [32], these methods still suffer from several difficulties to be applied in practice. For example, a subgradient algorithm that finds the solution of the dual problem converges too slowly to be useful. Thus, it cannot be applied to our QoSFi protocol because every channel contention is required to produce a quick channel allocation result (SIFS duration).
We then propose a more efficient suboptimal solution with much lower complexity. It is shown that the channel allocation of QoSFi becomes close to the optimal performance.
Algorithm 2 shows the pseudocode of the APside channel allocation algorithm. Our APside algorithm is briefly summarized as follows: (i) it first determines the initial channel allocation without considering the QoS requirement of each station, (ii) computes the delay that STAs will experience in the followings data transmission phase based on the initial channel allocation, and (iii) according to the computed delay, cuts off the station that cannot meet the QoS requirement.
5 Analysis of false positive probability
In this section, we analyze the false positive probability in QoSFi.
5.1 Network model
We consider a scenario where N STAs are conducting channel contention for C subchannels by requesting K subchannels (K≤C) repeatedly. QoSFi uses a separate Bloom filter for each subchannel, and thus, C Bloom filters are generated every time the QoSFi STAs contend for C subchannels, where a Bloom filter consists of m binary bits (i.e., subcarriers). As explained in Section 3.2, the bits marked as 1 in each signature is uniformly distributed^{e}; thus, the probability that a certain bit is marked as 1 by a hash function is evenly fair over the bit sequence in a signature.
Let r denote the average number of requests from the STAs for a subchannel. Then, r elements (signatures) will be inserted into a Bloom filter. For example, assuming the uniformly distributed channel requests, i.e., each STA selects all subchannels with the same probability, r is given by \frac{N\times K}{C}. Recall that for QoS support with four levels of prioritized channel acccess, each STA is assigned four (unique) signatures of length l_{1}>⋯>l_{4}, where a longer signature implies a higher priority. We assume that the ratio of QoS priorities is ρ_{1}:ρ_{2}:ρ_{3}:ρ_{4}.
5.2 Single request case (r=1)
We first consider the case that the number of requests for a subchannel is one. Let P_{ b } denote the probability that a certain bit is marked as 1. Then, P_{ b } is given by
The false positive probability is influenced by the spectral sidelobes problem [28] in OFDMbased systems as well as the inherent false positive property (i.e., logical false positive) of Bloom filters. The spectral sidelobes problem [28] refers to that a subcarrier may accidentally be set to 1 because of the leakage of power from nearby subcarriers. Therefore, the spectral leakage is considered in deriving the false positive probability. Let P_{leak} be the probability of the spectral leakage, and we assume that the only two adjacent subcarriers cause power leakage. Then, the probability that a certain tagged bit is set to 1 due to the spectral leakage from two adjacent bits is given by 2P_{leak}−2P_{ b }P_{leak}, where we subtract the probability 2P_{ b }P_{leak} of the event that both the two adjacent bits are set to 1 and they simultaneously affect the tagged bit due to the spectral leakage. Thus, the probability that a certain bit is set to 1, denoted by {P}_{\text{positive}}^{1}, is as follows:
Then, the probability that the bit is set to 0 is given by 1{P}_{\text{positive}}^{1}.
5.3 Multiple requests case (r>1)
Next, we extend to the case of multiple requests onto a subchannel. If there are r requests to a subchannel, the probability that a certain bit is not set to 1 is by any of the hash functions is given as {P}_{\text{negative}}^{r}={(1{P}_{\text{positive}}^{1})}^{r}. Similarly, the probability that the bit is set to 1 is {P}_{\text{positive}}^{r}=1{(1{P}_{\text{positive}}^{1})}^{r}. Then, we can derive the false positive probability that the AP incorrectly considers that a certain STA has requested for a given subchannel although the STA actually has not requested for the subchannel. The probability for a signature of length l is obtained as:
since it is the case that all of the l signature bits assigned to the STA are set to 1.
5.4 Discussions
False positive may cause the waste of wireless channels, since it can make the QoSAP to wrongly allocate corresponding subchannels to the STA that has not requested the subchannels and thus the falsely allocated channels will remain unused until the next channel allocation. Note that the false positive does not always lead to the false subchannel allocations, since the falsely recognized request might not be accepted by the AP, particularly in the case of the false positive for low priority requests. However, the false positive for signatures with higher priority will likely suffer from the false channel allocation for the request  resulting in the channel underutilization  because the AP basically grants more channel access opportunity to the STAs that have high priority packets.
Therefore, it is important to carefully determine the system parameters for QoS provisioning, i.e., lengths of signatures (l_{ i }). We consider the following to choose proper values of l_{ i }.

Although a longer signature has a lower false positive probability, it may suffer from the decrease in the channel quality estimation accuracy. This is because the total transmission energy spreads evenly over each of l bits marked as 1 when transmitting a QCRQ symbol. To address this tradeoff, we set the maximum length of a signature to 16, i.e., the QCRQ symbol energy is spread at most to the quarter of a subchannel, so that the channel estimation error is maintained below 1dB.

The difference in length among the four QoS types of signatures, i.e., l_{ i }−l_{i+1},i∈{1,2,3}, affects the false positive probability as well. If we set the difference in signature length between two adjacent QoS levels to be big, it makes the traffic with a higher priority have a very low false positive probability at the cost of the false probability of the lower priority traffic. This may be seen as the debate of QoS differentiation. For a given target QoS differentiation degree, the proper values of signature lengths can be derived. In this work, we apply the exponential decay in setting the signature, that is, we set the lengths for four levels of signatures, l_{1}=16,l_{2}=8,l_{3}=6, and l_{4}=4. Consequently, the highest priority traffic has a very low false positive rate.
Figures 4 and 5 show the false positive probabilities of QoSFi for two different scenarios as a function of the number of requests for a subchannel per a channel contention, with P_{leak}=0.1 and m=64. Figure 4 represents the case that traffic is evenly distributed throughout the levels of priority (ρ_{1}:ρ_{2}:ρ_{3}:ρ_{4}=0.25:0.25:0.25:0.25), while Figure 5 represents the case that the network is dominated by high priority traffic (ρ_{1}:ρ_{2}:ρ_{3}:ρ_{4}=0.4:0.2:0.2:0.2). As anticipated, the false positive probability of QoSFi increases as the number of requests for a subchannel increases. Especially, when the number of requests becomes large, QoSFi suffers from high false positives, which is a conventional issue caused when using a Bloom filter^{f}.
We can also observe that the false positive probability of each QoS priority level request is affected by the signature lengths as well as the ratio of QoS priorities given by offered load. We empirically have observed that the set of parameters, i.e., l_{1}=16,l_{2}=8,l_{3}=6,l_{4}=4, yields an acceptable false positive rate throughout various traffic load conditions; thus this parameter set is used for our simulations.
6 Performance evaluation
6.1 Implementation
6.1.1 QoSFi prototype and experiment setup
We prototyped the QoSFi OFDMbased PHY/MAC on a small testbed of four USRP nodes [33] each with GNU software define radio (SDR) [34]. We used the XCVR2450 daughterboard that uses transmissions near the 2.4/5 GHz frequency band. We employ BAM that modulates each bit by onoff signaling for use of each QCRQ and QCRP frame.
We adaptively configure the signal power level comparison threshold to minimize the false positive rate of the subcarrierlevel signal detection as follows. We first assign a bit pattern for each STA, then the combined signal (from all the STAs) must be the bitwise OR of all the bit patterns assigned. Given a threshold value, this combined signal can be converted into a binary bit sequence. Comparing this converted bit sequence with the (computed) bitwise OR of all the bit patterns assigned, the AP can determine whether the threshold value is the appropriate set. Adjusting threshold values, the AP searches the threshold value that yields the smallest error probabilities. In practice, this adaptation is triggered when a new STA joins the network or wrong QCRQ decoding results are frequently produced.The experiments were conducted at a indoor laboratory (Figure 6a) to show the feasibility of the QoSFi PHY/MAC protocol real wireless networks. The QoSFi protocol implementation in the USRP node is shown in Figure 6b. We choose four positions randomly, and let one node serve as a QoSFi AP and the other three nodes as QoSFi STAs associated with the AP. A rich set of the TX powers provided by the USRP/GNURadio is used, resulting up to 10 dB difference between the min and max received signal strengths at a given topology.
6.1.2 Experimental results
Figure 7 depicts the experimental results. In our experiments, multiple QCRQ symbols are combined at the receiver, since three STAs simultaneously transmit the QCRQ symbol. The SNR of this combined QCRQ symbol, measured at the receiver, is plotted along the xaxis. Let P_{ i },i∈{STA_{1},STA_{2},STA_{3}} be the signal strength of the individual QCRQ symbol transmitted from STA_{ i }. We call the case that satisfies the condition max{STA_{ i }−STA_{ j }}<5d B, i≠j ‘similar case’ and otherwise ‘different case’. The experimental results show the bitwise false positive and negative rates for various degrees of the received signal strength. Overall, both false positive and negative rates are close to zero when the proper threshold is set.
Typically, for ‘similar case’, the threshold value found from our adaptive threshold setup is 3 ∼5 dB lower than the SNR of the combined QCRQ symbol, and for ‘different case’, the value is 5 ∼8 dB lower than the SNR of the combined QCRQ symbol. When the combined signal itself exhibits very low SNRs, careful adaption is required, which results in the threshold value close to 0 dB lower than the SNR of the combined QCRQ symbol (hence, cannot be quantified at dB scale). Even so, in the whole range of our experiment setups, the QoSFi’s subcarrierlevel signaling performs reliably.Next, we show the accuracy of our channel estimation method. As shown in Figure 3, for most of the cases (≥ 90%), the estimation error is less than or equal to 1 dB. These two results show that the QoSFi’s channel contention and estimation mechanisms are practically feasible in typical indoor environments.
6.2 Tracedriven simulation
6.2.1 Simulation setup
The results in the QoSFi USRP prototype show that the Bloom filterbased channel contention and estimation method is feasible in the real wireless network. However, with USRP experimentations, it is difficult to show how well QoSFi exploits the frequency diversity and provides QoS. In USRP, the signal processing is mainly conducted at the software; thus, it is very limited to process a narrowband signal. In contrast, the WiFi generally uses a wideband (20 MHz or more) signal. In addition, the supported data rate is not as high as that in hardware radios at the current development stage of USRP. Therefore, we resort to tracedriven MATLAB simulations to assess the performance of QoSFi.
To conduct high fidelity emulation of realworld settings, we have used the 802.11n data traces provided by the authors of [6]. The traces are obtained from commodity Intel WiFi Link 5300 NIC and its modified driver [35]. The traces contain persubcarrier (30 subcarriers for 20 MHz) RSSI readings for both the 24 mobile and 30 static diverse links. With the 54 diverse links, we set up 50 nodes in our simulations. We assume that time is slotted, and each node receives QoS traffic with the ratio of ρ_{ i } (depending on simulation scenarios) from the upper layer queues. The offered load follows the Poisson distribution, with the arrival rate of λ. The bestperforming system parameters are empirically found to assess the best performance of QoSFi. As a result, we used α=0.1, β=2, and K=3 in the simulations.
6.3 Compared schemes
We compare the performance of QoSFi with the following schemes. Note that we modified all the previous schemes to use a subchannel as an access basis for fair comparison.

EDCA, the timedomain QoS support mechanism in WLANs. The EDCA protocol provides a distributed QoS provisioning function. Compared to other centralized algorithms, it cannot guarantee the performance of each QoS traffic.

K&H/RR [22], the stateoftheart scheme designed for cellular networks. Unlike EDCA, this provides the statistical QoS guarantee (formally defined with effective capacity) using the admission control, hence is classified as a centralized resource allocation strategy. K&H/RR combines two wellknown channel allocation strategies, i.e., K&H and RR with the predefined ratio.

The maximum throughput unit, the maximum capacity under the given offered load. This is given as the theoretical upper bound in terms of throughput and hence achieves the maximum Shannon’s capacity of wireless channels. However, it is QoSoblivious.
6.4 Simulation scenarios
To assess the performance of QoSFi in various environments, we conduct the simulation in the following scenarios.

Scenario 1: Evenly distributed traffic throughout the QoS types (ρ_{1}:ρ_{2}:ρ_{3}:ρ_{4}=1:1:1:1) and moderate offered load (λ = 0.2).

Scenario 2: Background traffic is twice as large as other types of traffic, (ρ_{1}:ρ_{2}:ρ_{3}:ρ_{4}=1:1:1:2) and moderate offered load (λ = 0.2).

Scenario 3: Evenly distributed traffic throughout the QoS types (ρ_{1}:ρ_{2}:ρ_{3}:ρ_{4}=1:1:1:1) and high offered load (λ = 0.5).

Scenario 4: Background traffic is twice as large as other types of traffic, (ρ_{1}:ρ_{2}:ρ_{3}:ρ_{4}=1:1:1:2) and high offered load (λ = 0.5).
6.4.1 Simulation results
6.4.1.0 System throughput
Figure 8 shows the overall aggregate throughput of QoSFi compared to the other schemes. We observe that QoSFi outperforms other QoSaware schemes in terms of aggregate throughput in all the scenarios, where the throughput gains over EDCA and K&H/RR are up to 1.39x and 1.29x, respectively. In addition, QoSFi achieves 93.5% of the maximum Shannon’s wireless capacity (represented by Maximum throughput), which shows that QoSFi harnesses frequency diversity while still providing QoS. The reasons are (i) QoSFi jointly achieves the frequency diversity gains and QoSawareness, since both are conducted in the frequency domain. The other QoSaware schemes such as EDCA are frequencyoblivious and cannot exploit the frequency diversity gains, and (ii) K&H/RR considers both frequencydiversity and QoSawareness like QoSFi, however, it does not efficiently amortize MAC overhead, hence the maximum achievable throughput is upper bounded by the MAC inefficiency.
6.4.1.0 Average throughput per QoS priority level
Figure 8 also shows the QoS differentiation capability of QoSFi, against the other QoSoblivious and QoSaware schemes. Additional to the overall system throughput, we plot the average throughput for each QoS type for each scheme. The results imply the followings:

Although our QoS provision mechanism comprises the decentralized feature (the EDCAlike distributed QoS channel contention) and centralized feature (the APside admission control), the degree of QoSdifferentiation of QoSFi is most substantial, when compared with the other QoSaware schemes. Moreover, the degree of QoS differentiaion can be flexibly further tuned by varying the parameter settings.By comparing the scenario of moderate offered load and the scenario of high offered load (i.e., Figure 8a,b versus Figure 8c,b), the degree of QoSdifferentiation of QoSFi is larger in high offered load conditions. This is because the MAC layer queue holds the previously untransmitted (by channel contention) packets, until the new higher priority packets arrive at the queue; higher priority packets preempts the lower priority ones, depriving the lower priority packets of the opportunity to participate channel contention. This is more frequent in the high offered load scenarios, resulting in higher degree of QoSdifferentiation in the QoSFi scheme.

QoS differentiation is also observed in other QoS aware schemes. However, due to the QoSFi’s frequency diversity awareness plus the MAC efficiency, QoSFi yields much higher system throughput. Contrary to QoSaware schemes, the maximum throughput is QoSoblivious and hence provides no QoS prioritization under all the scenarios.

QoSFi achieves comparable performance with our previous scheme, DFi [7] in terms of aggregate throughput. However, we observe that QoSFi additionally provides the QoSfunctionality under all the scenarios.
6.4.1.0 Average MAC delay
Figure 9 shows the average MAC delay, defined the time duration from the event that a packet is arrived at the MAC layer to the event that the packet is received to the receiver, for all the schemes. Again, the simulations are conducted in the aforementioned four scenarios. The results imply the followings:

As anticipated, overall, average MAC delay for the QoSFi scheme is the shortest among all the compared schemes. We observe that the QoSawareness is achieved by all the schemes, except for the maximum throughput scheme which is QoSoblivious.

For QoSdifferentiation, higher priority packets have higher probabilities to be selected than the lower priority packets and experience lower MAC delay. Further, the MAC delay is also a function of TXbitrate and thus QoSFi outperforms MACinefficient QoS schemes such as EDCA and K&H/RR.
7 Conclusions
We presented QoSFi, a novel QoS provisioning WiFi PHY/MAC protocol based on the OFDM technique. The proposed protocol efficiently exploits frequency diversity to satisfy the diverse QoS requirements of heterogeneous realtime mobile users, with minimum channel estimation and contention coordination cost. We showed the feasibility of QoSFi by implementing it on a USRP/GNUradio testbed. We also showed that the QoSFi PHY/MAC protocol can efficiently exploit frequency diversity while meeting the diverse QoS requirements, with real trace driven MATLAB simulations.
Endnotes
^{a} Note that there are various ways to generate necessary signatures.
^{b} We assume that each hash function chooses a different bit for the sake of the convenience in the mathematical analysis.
^{c} For the sake of simplicity, we note the signature of length 2; however, there are signatures of other lengths that can be falsely decoded in the variable length Bloom filter.
^{d} Here, we hypothesize that the multiplicative decrease (MD) feature of the AIMD algorithm enables QoSFi to react to the intensive channel variation, e.g., fast channel fluctuation and/or growth of contention level in particular channels. It is well known that the AIMD algorithm is a feedback control widely used for performing resource allocation operating in a distributed manner (whose convergence to the optimal operational point has been proved in [36]). However, we do not provide the mathematical or empirical grounds for this hypothesis as it is out of the scope of this paper. For interested readers, see [37] that deals with this issue.
^{e} Signatures assigned to the associated STAs generated a set of hash functions, where each hash function selects each binary bit with equal probability.
^{f} In our previous work [7], we have presented two approaches to address this problem. Please see [7] for details of how we have mitigated false positives. However, our main focus in this paper is the QoS provisioning mechanism.
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Acknowledgements
This work was supported in part by the IT R&D program of MSIP/KEIT. [10041861, Development of Wired/Wireless iAVB System Technology for Concurrent Transmission of HD Media and Control Data], and the National Research Foundation of Korea(NRF) Grant funded by the Korean Government(MSIP)(No. 2012R1A1A1014755, 2013R1A1A1006823).
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Lee, S., Choi, J., Yoo, J. et al. Providing qualityofservice for frequencyaware WiFi using OFDMbased variablelength Bloom filters. J Wireless Com Network 2014, 152 (2014). https://doi.org/10.1186/168714992014152
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DOI: https://doi.org/10.1186/168714992014152
Keywords
 Diversity
 WiFi
 Variablelength Bloom filter
 QoS provisioning
 PHY/MAC protocols