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Wireless voice over internet protocol (VoIP) is an important emerging service in telecommunication due to its potential for replacing cell phone communication wherever a wireless local area network (WLAN) is installed. Recent studies, however, suggest that the number of voice calls that can be supported in the widely deployed IEEE 802.11 WLAN is li...
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... a scenario where multiple voice calls are initiated simultaneously in an infrastructure WLAN network, as shown in Fig. 1 In this network voice traffic to and from any mobile node must flow through the common AP acting as a base station. Since every station including the AP has the same chance to access the wireless channel, the probability of the AP winning a channel access is decreasing with an increasing number of wireless nodes that maintain a voice ...
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... λ n be the packet arrival rate of a wireless node in the network shown in Fig. 1. The arrival rate at the AP is a superposition of all the individual rate of voice traffic from N −1 wireless nodes and is given by λ a = (N −1)λ n . Denote the packet service rate of the AP and a wireless node by µ a and µ n , respectively. Assuming packets arrive at a node according to a Poisson process, an M/G/1/K queueing model can ...
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... Results obtained in this section below are based on this increased service rate using the EDCA mechanism. In addition to the analytical and simulations results, mea- surements obtained from a WLAN testbed are also presented in this section. The testbed is an infrastructure WLAN and consists of one AP and multiple wireless nodes as depicted in Fig. 1. In this testbed, a desktop PC is used as an AP and the wireless nodes are a combination of desktop PCs, Netbooks as well as embedded devices. Each system is equipped with an Atheros 802.11 wireless card using a version of the MADWIFI [18] wireless driver. The MADWIFI wireless drivers are used because of their support of the 802.11e ...
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... codecs using 10 ms sampling rate, respectively. Observe that for a range of TXOP values, there is a minimum buffer size (K min = 30) at which the maximum voice capacity is achieved. For buffers with smaller size than K min , however, the number of voice calls which can be accommodated is reduced due to excessive packet loss at the AP, as shown in Fig. 10. It can also be seen in Fig. 10 that with increasing the buffer beyond K min the packet loss at the AP is reduced slightly, however, the overall capacity does not ...
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... respectively. Observe that for a range of TXOP values, there is a minimum buffer size (K min = 30) at which the maximum voice capacity is achieved. For buffers with smaller size than K min , however, the number of voice calls which can be accommodated is reduced due to excessive packet loss at the AP, as shown in Fig. 10. It can also be seen in Fig. 10 that with increasing the buffer beyond K min the packet loss at the AP is reduced slightly, however, the overall capacity does not ...
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... addition, we show in Fig. 11, the average end-to-end delay of a voice call on the downlink for different buffer sizes K and default TXOP value (η = 1). Observe that with increasing buffer size the delay on the downlink increases. This increasing delay with larger buffer size is not unexpected, however, for a buffer size of K min , observe that the average ...
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... AP is the bottleneck in the WLAN, the number of additional calls in this scenario (i.e. η = 2) can be approximated as C1 2 /2. This is because each additional call adds an equal number of packets on the uplink and the downlink voice traffic. As a result, the total number of calls using η = 2 is C 1 + C1 2 /2 . The above approach is illustrated in Fig. 14 where C 1 is assumed to be four calls. In this scenario, increasing η from 1 to 2 reduces the number of packets sent by the AP by 50% which then enables us to add a new call as depicted in Fig. 14. Based on similar arguments the voice capacity with increas- ing TXOP value can be approximated as ...
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... the uplink and the downlink voice traffic. As a result, the total number of calls using η = 2 is C 1 + C1 2 /2 . The above approach is illustrated in Fig. 14 where C 1 is assumed to be four calls. In this scenario, increasing η from 1 to 2 reduces the number of packets sent by the AP by 50% which then enables us to add a new call as depicted in Fig. 14. Based on similar arguments the voice capacity with increas- ing TXOP value can be approximated as ...
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... This argument, however, ignores that some transmissions (i.e. those originated from the AP) are now involved multiple packets and therefore capture the channel much longer. On the other hand, further increasing η beyond the above value carries no benefit because of the bottleneck shift as discussed in the previous section. Thus the results in Figs. 15 and 16 show that the approximation is reasonably accurate in terms of the voice capacity for the range of the TXOP values that are of interest. The approximation formula provides a simple alternative to the complete analytical model that can be used to estimate the voice capacity attainable in WLAN. Note that the analytical results have been ...
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... of the voice capacity for the range of the TXOP values that are of interest. The approximation formula provides a simple alternative to the complete analytical model that can be used to estimate the voice capacity attainable in WLAN. Note that the analytical results have been validated earlier in Sec. IV and thus simulation results are omitted in Figs. 15 The approximation formula in (25) also allows us to gain further insights into the voice capacity in WLAN and to study its relation to MAC parameters such as the TXOP parameter. In particular, from (24) and (25) it can be seen that the voice capacity is depending on the TXOP value and the initial voice capacity obtained using default ...
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Citations
... VoIP is a mechanism for transmitting time-sensitive voice over the packet-switched network [3]. VoIP has turned out to be a serious competitor to the traditional public switched telephone network (PSTN) [4]. However, providing precise QoS considered as an issue for real-time multimedia applications such as VoIP, video over IP and online games. ...
... However, this work requires a numerical value for the packet loss. the ratio of dropped voice packet i to total voice packets multiplied by 100%, as demonstrated by (4). ...
This research developed a novel algorithm to evaluate Voice over Internet Protocol (VoIP) metrics of different IEEE 802.11 technologies in order to identify the optimum network architecture among Basic Service Set (BSS), Extended Service Set (ESS), and the Independent Basic Service Set (IBSS). The proposed algorithm will yield the rank order of different IEEE 802.11 technologies. By selecting the optimum network architecture and technology, the best overall network performance that provides a good voice quality is guaranteed. Furthermore, it meets the acceptance threshold values for the VoIP quality metrics. This algorithm was applied to various room sizes ranging from 2x3m to 10x14m and the number of nodes ranged from one to forty. The spatial distributions considered were circular, uniform, and random. The Quality of Service (QoS) metrics used were delay, jitter, throughput and packet loss.
... Cai et al. [6] proposed new analytical model to investigate performance of bidirectional voice traffic over WLAN. Stoeckigt and Vu [7] proposed a scheme that uses transmission opportunity (TXOP) with different durations to increase the voice capacity of 802.11e WLAN. ...
... WLANs. Moreover, those work can be divided into two major categories: methods for voice call admission control [18], [19], [20], [21], [22] and voice quality/capacity enhancement in VoWLAN [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33]. ...
... The performance of voice over WLAN (VoWLAN) has been extensively studied in the literature. Due to the enormous overhead associated with small voice frame transmissions, the quality and capacity of voice sessions were challenging problems in legacy WLANs; thus most available works in the literature addressed the issues of call admission control [18], [19], [20], [21], [22] and the quality/capacity enhancement of VoWLANs [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33]. ...
Currently, most mobile portable Internet devices such as smartphones, tablet
PCs, and laptops are using wireless local area networks (WLANs) for broadband mobile Internet access on different multimedia applications such as voice over Internet protocol (VoIP), high definition video streaming, file downloading, and many more.
The existing IEEE 802.11n implementations, such as an open-source ath9k
driver, do not apply A-MPDU aggregation to voice traffic due to its strict end-toend delay requirements. When multiple nodes contend to send voice and lower priority traffic, network faces serious performance degradation problems for two reasons: i) the available network throughput for lower priority traffic becomes extremely limited due to the big MAC and PHY layer protocol overhead associated with each individual short voice packet transmission, and ii) increased contention results in intolerably high voice packet loss rate (PLR). In order to mitigate those problems, we proposed QoS-aware adaptive A-MPDU aggregation scheduler of VoIP traffic. The proposed scheduler adaptively applies A-MPDU aggregation to voice traffic based on QoS requirements and periodically obtained access delay and end-to-end delay statistics. The proposed scheduler was implemented in the ath9k driver and NS-3 simulator. The performance evaluations under dynamic network delay and wireless channel conditions showed that the proposed scheme increases the available network throughput for lower priority traffics for several times, deliver all of the voice packets to their destination (PLR = 0%) and more than 99.9% of those packets are delivered with less than 150ms end-to-end delay. Network performance modeling with high accuracy is one of the key factors for network planning and traffic engineering. However, existing performance models of aggregation-enabled WLANs, such as 802.11n/ac, do not consider the crucial part of aggregation mechanism called BlockACK window (BAW) sliding. BAW determines the next aggregation size (number of packets in A-MPDU) depending on the position of first retry packet in A-MPDU. Thus under usual/erroneous channel conditions, the aggregation size always fluctuates. Since existing works in literature do not consider the BAW’s impact on aggregation size, those works assume that A-MPDU size always equals the maximum allowed aggregation size consequently leading inaccurate estimation of important network performance metrics such as network throughput and access delay. We proposed a new and simple bi-dimensional Markov chain model to estimate the average aggregation size for the given packet error rate (PER). Obtained aggregation size is employed in an enhanced model of throughput and access delay of aggregationenabled WLANs. The performance evaluations showed that for PER=0.5, the existing modeling approaches produce as high as 48.76% and 97.5% errors for throughput and access delays, respectively; the proposed enhanced model, on the other hand, produces less than 7% throughput and access delay estimation errors.
... The performance of voice over WLAN (VoWLAN) has been extensively studied in the literatures. Due to the enormous overhead associated with small voice frame transmissions, the quality and capacity of voice sessions were challenging problems in legacy WLANs; thus most available works in the literature addressed the issues of call admission control [15], [16], [17], [18], [19] and the quality/capacity enhancement of VoWLANs [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30]. ...
... They proposed an analytical model for bi-directional unsaturated traffic that can be used in network planning and call admission control. Stoeckigt and Vu [27] utilized transmission opportunity (TXOP) parameter with different durations to increase the voice capacity in a WLAN. Lee et al. [28] found that in the presence of deep-fading and hidden terminals, existing state-of-the-art rate adaptation algorithms lead to increased voice packet losses, and they proposed mechanisms to adapt the transmission rate and retry scheduling to improve voice quality in such cases. Salvador et al. [29] proposed the VoIPiggy scheme, which aims to increase MAC efficiency by piggybacking voice frames onto acknowledgement (ACK) frames. ...
Currently available IEEE 802.11n implementations do not apply aggregate MAC protocol data unit (A-MPDU) aggregation to voice traffic because of its strict end-to-end delay requirements. When multiple nodes contend to send voice and other lower-priority traffic, 802.11n network faces serious performance degradation problems for two major reasons: i) due to the medium access control (MAC) and physical (PHY) layer overheads of individual short voice packet transmissions, the available network throughput for lower-priority traffic significantly decreases, and ii) increased contention results in high voice packet loss rate (PLR). This paper proposes a QoS-aware adaptive A-MPDU aggregation scheduler for voice traffic in 802.11n/ac WLANs that periodically obtains average access delay and maximum end-to-end delay, and adaptively applies AMPDU aggregation to voice traffic, considering QoS requirements. Performance evaluations on 20 nodes sending uplink 64Kbps voice and saturated best-effort traffic showed that the proposed scheme achieved 160% throughput improvement compared to default implementations in driver at 150Mbps PHY rate. During the simulations with 50 nodes transmitting at 600Mbps PHY rate, the proposed scheme provided up to 275Mbps throughput against 0.13Mbps by default scheme; the proposed scheme delivered all voice packets (PLR=0%) and over 99.9% of them had less than 150ms end-to-end delay.
... A natural idea is giving AP a channel access priority [9][10][11] to strike a balance between the downlinks and the uplinks. Studies also suggest that network collision reduction, either by adjusting the contention window size [12][13][14][15] or tuning the transmission opportunity coordination [16][17][18][19][20], can enhance the capacity of the VoIP traffic. ...
... To select a proper VoIP codec for our experiments, we list five common ones as illustrated in Table 3. We finally pick G.711 encoder in the experiment as it is one of the most often used codecs in prior work [16,23,24]. ...
... We record the average packet delay by measuring the difference between the packet transmission and reception time and estimate the packet loss rate by dividing the number of received packets by the total number sent. Based on the average packet delay and the loss rate, we determine the -score defined in (16). We make sure that thescore is no less than 60, which is above the low quality of the VoIP service as shown in Table 5. ...
This paper considers the performance problem of VoIP over 802.11e WLANs
caused by the unfairness between uplink and downlink as well as the inefficient
EDCA. A novel medium access control scheme named BEDCA (Balanced
EDCA) is presented, which provides service differentiation between the
access point (AP) and the mobile stations (STAs) to enhance VoIP capacity.
In BEDCA, the expression of AP’s contention window is obtained which is
a relative constant value independent of the participating STAs. The minimum
contention window of the STAs is traffic-aware based on the proposed
algorithm. The performance improvement of BEDCA is verified through intensive
simulations and the results show the capacity improvement of 82.1%
compared to EDCA.
... In [1][9] [10] FA is used to improve channel efficiency and data throughput in IEEE 802.11n networks. In regards to the wireless VoIP capacity, the authors in [11] [12] derive a detailed analytical model to evaluate the performance gain in terms of VoIP capacity that can be achieved using configurable TXOP parameter in IEEE 802.11e standard. Similarly, in [13] the authors evaluate the performance of VoIP traffic in terms of throughput, delay and the number of simultaneous sessions that can be established with various configurations of EDCA via analytical modelling and simulations. ...
Wireless Fidelity (WiFi) networks have gained unprecedented growth in recent years and have played an important role in the popularity of wireless VoIP. IEEE 802.11n is the most widely used WiFi standard today. Transmission Opportunity (TXOP) and Frame Aggregation (FA) are two important Medium Access Control (MAC) Layer enhancements provided by IEEE 802.11n standard. In this work our focus is on the determination of optimal values of TXOP and FA that maximize VoIP capacity. We first determine the optimal value of FA that maximizes VoIP capacity. Our simulation results show that optimal value of FA that maximizes VoIP capacity is 14. At 10 ms packetization interval this value of FA provides a gain of 26% in VoIP capacity as compared to the VoIP capacity with no FA. Secondly, we find that the optimal value of TXOP that maximizes VoIP capacity is 13. At 10ms packetization interval this value of TXOP gives a gain of 32% in VoIP capacity as compared to VoIP capacity with default value of TXOP. We then determine the VoIP capacity when optimal values of TXOP and FA are simultaneously used. Our study reveals that simultaneous use of optimal values of both FA and TXOP further increase VoIP capacity. A gain of 44% in VoIP capacity is achieved when optimal values of TXOP and FA are used simultaneously as compared to the VoIP capacity with default values of both FA and TXOP. We further determine VoIP capacity over User Datagram Protocol (UDP) and TCP Friendly Rate Control (TFRC) protocol in the presence of Transmission Control Protocol (TCP) traffic. We note that in the presence of TCP traffic, TFRC with optimal values of TXOP and FA provides an average gain of 37% as compared to TFRC with default values of FA and TXOP.
... IP multimedia broadcast) or try to change the buffer size during a call between two talk bursts (enhanced VoIP software [12]). Another approach is the optimization of the underlying transport networks [13], but this approach is not suitable in the assumed scenario in this work. Thus, the buffer size need to be optimized, because a longer buffer results in longer delays and response times and so a more negative quality of experience within a critical communication call [14]. ...
As the basis of Next Generation Public Safety Communication (PSC) systems for critical group communication, 4G (LTE) technologies have been chosen from the different stakeholder. Existing 2G and 3G equivalent technologies are and will be used to allow for a sufficient network coverage. Due to the long investment cycles within the public sector, for a long time small band 2G Professional Mobile Radio (PMR) networks will be the backbone of mission critical voice communication. To enable enhanced data exchange 4G networks will provide this capability as an add-on. It is a well known fact that every transmission technology has its own footprint of delay and delay-spread (jitter). This lead to challenges within heterogeneous system environments, because different behavior introduces unfairness into the system, negligent users in legacy networks. The proposed solution in this paper is based on the identification of the unique footprint of the used networks to increase the fairness.
In this paper, the authors present an Active Delay Management (ADeM) PSC System that allows for critical group communication in next generation public safety communication network based on xG technologies. Therefore, the performance limitations of an IP overlay for heterogeneous communication networks have been studied and analyzed for different mobile radio technologies, based on mobile field trials performed in commercial 2G, 3G and 4G networks. Build upon this study, the gathered information is used to classify the transmission networks to enable the proposed ADeM system to actively consider network dependent delays in its Active Queue Management (AQM) decisions. Thereby, enhanced fairness for the different information streams as well as an improved performance in critical communication scenarios can be achieved.
... Recently Stoeckigt and Vu [12] determined voice capacity improvement using the transmission opportunity (TX_OP) of IEEE 802.11e and also calculated an optimal buffer size for maximum number of voice calls. However, their capacity analysis was based on packet loss only and ignored end-to-end delay totally; thereby, the estimation does not ensure voice quality. ...
To ensure customer satisfaction and greater market acceptance, voice over Wi-Fi networks must ensure voice quality under various network parameters, configurations and traffic conditions, and other practical effects, e.g., channel noise, and capturing effects. An accurate voice capacity estimation model considering these factors can greatly assist network designers. In the current work, we propose an analytical model to estimate voice over Internet Protocol (VoIP) capacity over Wi-Fi networks addressing these issues. We employ widely used ITU-T E-model to assess voice quality and VoIP call capacity is presented in the form of an optimization problem with voice quality requirement as a constraint. In particular, we analyze delay and loss in channel access and queue, and their impacts on voice quality. The proposed capacity model is first developed for a single hop wireless local area network (WLAN) and then extended for multihop scenarios. To model real network scenario closely, we also consider channel noise and capture effect, and analyze the impacts of transmission range, interference range, and WLAN radius. In absence of any existing call capacity model that considers all the above factors concomitantly, our proposed model will be extremely useful to network designers and voice capacity planners.
... This mechanism is alternative to DCF and it is called point coordination function (PCF) [9], [10], and [17]. Third one formulates the problem as minimization of the downlink delay with DCF mechanism [11] - [16], [18], and [19]. However, authors in third scheme built their hypothesis based on IEEE 802.11e mechanism. ...
... Nonetheless, proposed approach has not discussed the capacity improvement through minimizing downlink delay. In [18] authors carried out intensive analysis for optimizing TXOP in IEEE 802.11e toward improving VoWLAN capacity. However, investigating its efficiency with using generalized voice codec (e.g. ...
Integration of delivering VoIP services over WLAN access networks (VoWLAN) increased the demand to support multiple of active simultaneous calls. Thus improving VoWLAN capacity has been addressed. The researchers are continuously contributing in improving VoWLAN capacity, (i.e. increasing the number of VoIP simultaneous calls), for various WLAN architectures. Although a lot of modifications have been developed for IEEE 802.11 standards toward improving its performance with real-time applications, all issued versions of this standard have a unique characteristic for infrastructure-based WLAN. That is MAC controls the access mechanisms for shared WLAN channels via distributed coordination function (DCF) entity. This paper aims to improve VoWLAN capacity by means of backoff time minimization for WLAN base station (i.e. Access Point – AP). AP MAC processing time (i.e. backoff overhead time) has significant effect on the downlink delay that all stations’ packets suffer from. Weighting factor α is adopted for AP backoff time to minimize this downlink delay. This weighting factor points to the number of active VoIP clients during certain period. The new AP back-off time is the ratio of a uniformly distributed backoff random number to such weighting factor α. VoIP calls generated using adaptive multi-rate voice codec AMR (12 Kbps). OPNET simulator has been used to validate proposed mechanism. Obtained results show notable improvement for VoWLAN capacity (i.e. 25% increment in simultaneous VoIP calls)
... The widespread use of multimedia applications requires new features such as high bandwidth and small average delay in wireless LANs [1]. Unfortunately, the IEEE 802.11 medium access control (MAC) protocol cannot support quality of service (QoS) requirements [2][3][4]. In order to support multimedia applications with tight QoS requirements, the IEEE 802.11e has been standardized [5]. ...
... In order to support multimedia applications with tight QoS requirements, the IEEE 802.11e has been standardized [5]. It introduces a contention-based new channel access mechanism called enhanced distributed channel access (EDCA) [4][5][6]. The EDCA supports QoS by introducing four access categories (ACs). ...
The IEEE 802.11e EDCA (Enhanced Distributed Channel Access) is able to provide QoS (Quality of Service) by adjusting the transmission opportunities (TXOPs), which control the period to access the medium. The EDCA has a fairness problem among competing stations, which support multimedia applications with different delay bounds. In this paper, we propose a simple and effective scheme for alleviating the fairness problem. The proposed scheme dynamically allocates the TXOP value based on the delay bounds of the data packets in a queue and the traffic load of network. Performance of the proposed scheme is investigated by simulation. Our results show that compared to conventional scheme, the proposed scheme significantly improves network performance, and achieves a high degree of fairness among stations with different multimedia applications.
