Dynamic admission control and bandwidth reservation for IEEE 802.16e mobile WiMAX networks
- Chiapin Wang^{1}Email author,
- Wan-Jhen Yan^{1} and
- Hao-Kai Lo^{1}
https://doi.org/10.1186/1687-1499-2012-143
© Wang et al; licensee Springer. 2012
Received: 28 April 2011
Accepted: 14 April 2012
Published: 14 April 2012
Abstract
The article presents a dynamic connection admission control (CAC) and bandwidth reservation (BR) scheme for IEEE 802.16e Broadband Wireless Access networks to simultaneously improve the utilization efficiency of network resources and guarantee QoS for admitted connections. The proposed CAC algorithm dynamically determines the admission criteria according to network loads and adopts an adaptive QoS strategy to improve the utilization efficiency of network resources. After new or handoff connections enter the networks based on current admission criteria, the proposed adaptive BR scheme adjusts the amount of reserved bandwidth for handoffs according to the arrival distributions of new and handoff connections in order to increase the admission opportunities of new connections and provide handoff QoS as well. We conduct simulations to compare the performance of our proposed CAC algorithm and BR scheme with that of other approaches. The results illustrate that our approach can effectively improve the network efficiency in terms of granting more connections by as large as about 22% in comparison with other schemes, and can also guarantee adaptive QoS for admitted new and handoff connections.
Keywords
1. Introduction
Broadband wireless access networks have rapidly been growing in these years to support the increasing demands of wireless multimedia services, like streaming audio/video, Internet Protocol TV, and video conferencing. Mobile Worldwide Interoperability for Microwave Access (WiMAX), which has been standardized by IEEE 802.16e [1], is one of the most promising solutions to provide ubiquitous wireless access with high data rates, high mobility, and wide coverage. The IEEE 802.16e Media Access Control (MAC) layer provides differential Quality of service (QoS) for various classes of scheduling services, which are Unsolicited Grant Service (UGS), Extended Real-Time Polling Service (ertPS), Real-Time Polling Service (rtPS), Non-real-time Polling Service (nrtPS), and Best Effort (BE). Each scheduling class is associated with a set of QoS parameters for quantifying its bandwidth requirement, e.g., maximum/minimum data rates and maximum delays. The radio resources (i.e., time slots and frequency spectrums) for different scheduling services are centrally controlled by the base station (BS). To provide QoS for data transmissions in WiMAX networks, BS generally applies a Connection Admission Control (CAC) scheme which determines whether a new connection should be established according to the available network resources. Essentially, the effectiveness of CAC schemes can be critical to both the performances of QoS for admitted connections and the utilization efficiency of network resources. However, the IEEE 802.16e standards do not specify how to implement CAC mechanisms and remain that as open issues.
On the other hand, a bandwidth reservation (BR) mechanism is also important to the provisioning of QoS for some prioritized users like users in a handoff process. Handoff occurs when mobile station (MS) transfers its connection from the original serving BS with worse and worse link qualities to a neighboring BS with better qualities. In general, a handoff user will be prioritized over a new incoming user in order to provide better user-perceived satisfaction especially when it is with real-time applications which have specific QoS requirements, e.g., throughput demands and delay/jitter constraints. Since the reserved bandwidth cannot be taken by a new coming user, the design of BR mechanisms can significantly affect the performance of handoff QoS and also the utilization efficiency of network resources.
The CAC and BR problems have largely been investigated in previous study [2–20]. The authors of [2, 3] propose to adopt minimum bandwidth requirements as the admission criteria for all classes of scheduling services. The approach can provide more connections admitted into networks but may cause a relatively low QoS performance. The authors of [10] propose to divide the scheduling services into two groups: one group consists of UGS, ertPS, and rtPS which adopt maximum bandwidth requirements for the admission criteria, while another group consists of nrtPS and BE which adopt minimum bandwidth requirements. The approach may over favor the higher-class services and cause a starvation of lower-class services. Instead of using fixed criteria for an admission control as described above, the studies in [11, 12] propose to dynamically determine the admission criteria by using a game-theoretic approach. However, it does not take the network load into consideration and may introduce great computational complexities.
With regard to the BR schemes, a fixed guard channel scheme [13] is proposed to reserve a certain amount of bandwidth for upcoming handoff connections to assure seamless handoff processes. When the total bandwidth utilization of existing users reaches the threshold, no more new connections can be admitted into the network. Nevertheless, when a fixed amount of bandwidth can never be used for new connections, a certain portion of network resources may be wasted. The study [10] proposes to dynamically adjust the quantity of reserved bandwidth based on the arrival and departure behavior of handoff connections to make the resource utilization more efficient. However, if handoff connections occur infrequently, the quantity of reserved bandwidth for handoffs is almost fixed and this approach would be similar to the fixed guard channel scheme and cause a waste of network resources as well.
Both CAC schemes and BR mechanisms are important research issues in wireless networks due to scarce radio resources, dynamic channel qualities, and diverse user demands. However, to the best of authors' knowledge, most efforts tackle one of the two problems individually while little work considers the joint design of the two mechanisms. We are thus motivated to present a joint design of CAC and BR mechanisms which aim at simultaneously improving the utilization efficiency of network resources and guaranteeing QoS for admitted new connections and handoff connections. The proposed CAC scheme dynamically determines the admission criteria according to network loads and adopts an adaptive QoS strategy to increase the amount of admitted connections for the network efficiency. The key idea of our CAC scheme is based on the fact that most scheduling services are with adaptive QoS requirements, e.g., maximum and minimum rates. Therefore, the admission criteria can be determined according to the amount of available wireless resources for increasing the number of admitted connections with adaptive QoS. For example, if the network capacity is adequate or sufficient, bandwidth requirements for higher QoS might be adopted as the admission criteria. Alternatively, if the network load is quite heavy, the admission criteria may be degraded to meet lower QoS requirements. After the admission criteria are determined, the proposed BR scheme dynamically adjusts the amount of reserved bandwidth for handoffs according to the arrival distributions of new/handoff connections to increase the connection admission opportunities and also guarantee the bandwidth requirements for handoff QoS. The basic idea of our adaptive BR scheme is a rational inference that generally the occurrences of new incoming connections may be much more frequent than that of handoff connections [21–24]. This observation originates from common BS deployment that the overlap areas of a given BS between its neighboring stations are parts of its coverage area. Since handoffs arise only when users cross through the overlap areas, it is a general situation to observe more new connections occurring than handoff connections. Thus, the optimal BR should take into account the arrival behavior of not only handoff connections, but also new connections in order to avoid a waste of network resource as possible.
We conduct simulations of 802.16e transmission scenarios to evaluate and compare the performances of the proposed CAC algorithm and BR scheme with that of other approaches. Simulations results illustrate that our approach can effectively improve the network efficiency in terms of increasing the number of granted connections by as large as about 22% in comparison with other schemes, and also can guarantee adaptive QoS for admitted new and handoff connections. The remainder of this article is organized as follow. In Section 2, we briefly illustrate the QoS architecture and resource allocation mechanism of IEEE 802.16e networks. Section 3 presents the proposed CAC algorithm and BR scheme. In Section 4, we construct simulation scenarios to demonstrate the effectiveness of our approach. Section 5 draws our conclusions.
2. IEEE 802.16e QoS architecture and resource allocation mechanism
2.1 IEEE 802.16e QoS architecture
- (1)
UGS: UGS is designed to support real-time service flows with fixed-size packets generated at periodic intervals (i.e., constant bit rate--CBR), such as T1 services and voice-over-Internet-Protocol (VoIP) applications without silence suppression. This service can grant a fixed amount of bandwidth for CBR real-time applications without any requests.
- (2)
rtPS: rtPS is designed to support real-time service flows with variable-size packets generated at periodic intervals (i.e., variable bit rate--VBR), such as Motion Pictures Experts Group (MPEG) video. Based on a polling mechanism to request bandwidth periodically, this service can guarantee QoS such as the minimum data rate and maximum latency for VBR real-time applications.
- (3)
ertPS: The characteristic of this service class is between UGS and rtPS. On detecting that the allocated bandwidth is either insufficient or excessive, ertPS can send a request to change the amount of allocated bandwidth like rtPS does. Otherwise, if the bandwidth demand remains unchanged, ertPS behaves as UGS. ertPS is designed to support VBR real-time data services such as VoIP applications with silence suppression.
- (4)
nrtPS: This service class is to support non-real-time VBR services which require minimum-data-rate guarantees but can be tolerant to delay, such as File-Transfer-Protocol (FTP) applications.
- (5)
BE: The BE service is designed for best-effort applications which have no explicit QoS requirements, e.g., web services or e-mail.
IEEE802.16e QoS classes
QoS classes | Applications | QoS parameters |
---|---|---|
UGS | T1 services, VOIP without silence suppression | Max Rate |
Min Rate | ||
Jitter | ||
rtPS | Video Streaming | Max Rate |
Min Rate | ||
Max Latency | ||
ertPS | VOIP with silence suppression | Max Rate |
Min Rate | ||
Max Latency | ||
Jitter | ||
nrtPS | FTP | Max Rate |
Min Rate | ||
BE | Web browsing, e-mail | Max Rate |
2.2. Bandwidth allocation mechanism
The IEEE 802.16e physical layer (PHY) adopts an Orthogonal Frequency Division Multiple Access (OFDMA) slot as the minimum possible resource. The IEEE 802.16e PHY supports Frequency Division Duplex (FDD) and Time Division Duplex (TDD) for bandwidth allocation mechanisms. In FDD mode, the uplink (UL) and downlink (DL) channels are located on split frequencies, with which a fixed duration frame is used for both UL and DL transmissions. In TDD mode, the UL and DL transmissions are arranged at different time periods using the same frequency. In this article, we focus on the TDD mode for the IEEE 802.16e bandwidth allocation mechanism.
2.3. Packet scheduling mechanism
As shown in Section 2.1, the IEEE 802.16e standard defines five scheduling classes. However, it does not specify the scheduling mechanism for the five classes and the design is left for researchers [25]. The design of a scheduling mechanism must take into account the specific QoS constraints of different applications, e.g. the maximum allowable delay and minimum data rate [3]. A feasible solution is to decide on a service class first according to the characteristics of each class and next choose an appropriate user in the selected class [26]. In the second phase, the packet scheduling of different users among a given class may consider some performance metrics such as throughput and fairness, while the maximum rate scheduling (greedy algorithm) and Proportional Fairness (PF) scheduling can be applied, respectively. The maximum rate scheduling is effective to advance the overall system throughput as it allocates resources to users with relatively good channel qualities among them [27]. On the other hand, the PF scheduling can improve the fairness of channel utilization among users as it distributes resources among them with consideration of their previous records of utilization [28–30].
Throughput and fairness, however, are conflicting performance metrics [31]. To maximize system throughput, more resources should be allocated to the users in good channel conditions. This may cause most radio resources monopolized by a small number of users, leading to unfairness. On the contrary, if resources are allocated in a fair manner, resources may be allocated to the users with weak channel conditions. This can result in the degradation of system throughput. To escape from the "throughput-fairness" dilemma, we can consider "utility" for packet scheduling. Utilities are a performance metric which can fully represent the degree of user satisfaction for a given application [32, 33]. Thus, it is a more appropriate metric for packet scheduling since even users with the same service class actually are with various demands for network resources due to their specific characteristics. This fact implies that resources should be allocated to users according to the application performance metric of "satisfaction" rather than network performance metrics such as "throughput" or "fairness" [34].
In this article, we focus on the problems of CAC and BR for mobile WiMAX networks and do not discuss the design of packet scheduling. In the following section, we will propose a joint design of CAC and BR mechanisms as a solution of the addressed problems.
3. Proposed CAC algorithm and BR scheme
3.1. Dynamic CAC algorithm
We design a dynamic CAC algorithm which adjusts the admission criteria depending on the network loads and uses an adaptive QoS provisioning strategy in order to increase the efficiency of bandwidth utilization. The key idea of our dynamic CAC scheme is based on the fact that most scheduling services are with adaptive QoS requirements, e.g., maximum and minimum rates. Therefore, the admission criteria can be adjusted with the amount of available wireless resources to increase the number of admitted connections with adaptive QoS. For example, if the network capacity is adequate or sufficient, higher QoS requirements could be adopted as the admission criteria. Alternatively, if the network load is rather heavy, the admission criteria may level down for lower QoS.
where $n{l}_{\mathsf{\text{th}}}^{n}$ refers to the n th threshold of network load (1 ≤ n ≤ N - 2). In particular, $n{l}_{\mathsf{\text{th}}}^{0}$ and $n{l}_{\mathsf{\text{th}}}^{N-1}$ refer to the minimum and maximum thresholds of network loads, $n{l}_{\mathsf{\text{th}}}^{\mathsf{\text{min}}}$ and $n{l}_{\mathsf{\text{th}}}^{\mathsf{\text{max}}}$, respectively. Here, ${b}_{i}^{k}$ (1 ≤ k ≤ N - 1) refers to the k th feasible admission criteria as the network load nl is within the range of [$n{l}_{\mathsf{\text{th}}}^{k-1}$, $n{l}_{\mathsf{\text{th}}}^{k}$). In particular, ${b}_{i}^{0}$ corresponds to b_{i, max}as nl < $n{l}_{\mathsf{\text{th}}}^{0}$, and ${b}_{i}^{N}$ corresponds to b_{i, min}as nl ≥ $n{l}_{\mathsf{\text{th}}}^{N-1}$, respectively. The value of N + 1 refers to the number of quantization levels. When N is 1 in particular, the quantized step function has two levels and is equal to the hard-decision function as shown in Figure 3a. In contrast, if N is infinite, the quantized step function has countless levels and is equal to the linear adaptation approach as shown in Figure 3b. Normally, the value of N can be determined considering a reasonable number of adaptive criteria b_{ i } between b_{i, min}and b_{i, max}, (e.g., N = 5-15 in general). When the value of N is determined, the unknown values of $n{l}_{\mathsf{\text{th}}}^{n}$ (1 ≤ n ≤ N - 2) and ${b}_{i}^{k}$ (1 ≤ k ≤ N - 1) can therefore be obtained by using a uniform quantizer which equally partitions the region within (${b}_{i}^{0}$, ${b}_{i}^{N}$) and ($n{l}_{\mathsf{\text{th}}}^{0}$, $n{l}_{\mathsf{\text{th}}}^{N-1}$). In this article, we consider the linear adaptation approach as shown in Figure 3b for our CAC algorithm and use it for performance evaluations in the following section.
where b_{i, new}is the admission criterion of new connection i provided in Equation (2). The first term in the rhs of Equation (5) aims for increasing the admitting opportunities for new connections when it only determines whether the resources allocated to new connections exceed the threshold. The last term in the rhs aims for ensuring a minimum BR for handoff (i.e. B - th_{max}). In Section 3.3 we will have a more detailed illustration for the criteria considered in Equation (5).
3.2. Estimation of system capacity
The mobile WiMAX PHY data rates (Source: WiMAX Forum [35])
Parameter | Downlink | Uplink | Downlink | Uplink | |
---|---|---|---|---|---|
System bandwidth | 5 MHz | 10 MHz | |||
FFT size | 512 | 1024 | |||
Null sub-carriers | 92 | 104 | 184 | 184 | |
Pilot sub-carriers | 60 | 136 | 120 | 280 | |
Data sub-carriers | 360 | 272 | 720 | 560 | |
Symbol period | 102.9 μs | ||||
Frame duration | 5 ms | ||||
OFDM symbols/frame | 48 | ||||
Data OFDM symbols | 44 | ||||
Mod | Coding rate | 5 MHz channel | 10 MHz channel | ||
Downlink rate (Mbps) | Uplink rate (Mbps) | Downlink rate (Mbps) | Uplink rate (Mbps) | ||
QPSK | 1/2 CTC, 6x | 0.53 | 0.38 | 1.06 | 0.78 |
1/2 CTC, 4x | 0.79 | 0.57 | 1.58 | 1.18 | |
1/2 CTC, 2x | 1.58 | 1.14 | 3.17 | 2.35 | |
1/2 CTC, 1x | 3.17 | 2.28 | 6.34 | 4.70 | |
3/4 CTC | 4.75 | 3.43 | 9.50 | 7.06 | |
16 QAM | 1/2 CTC | 6.34 | 4.57 | 12.67 | 9.41 |
3/4 CTC | 9.50 | 6.85 | 19.01 | 14.11 | |
64 QAM | 1/2 CTC | 9.50 | 6.85 | 19.01 | 14.11 |
2/3 CTC | 12.67 | 9.14 | 25.34 | 18.82 | |
3/4 CTC | 14.26 | 10.28 | 28.51 | 21.17 | |
5/6 CTC | 15.84 | 11.42 | 31.68 | 23.52 |
Here, our CAC scheme estimates the current system capacity B by consideration of the proportion of used MCSs [36, 37]. Take an example for the capacity estimation as follows. Consider a 10-MHz channel spectrum with a 2 × 2 MIMO mechanism in the downlink transmission. Assume the proportion of used MCSs is QPSK 3/4 = 25%, 16-QAM 1/2 = 25%, and 64-QAM 5/6 = 50%. Thus, the estimated system bandwidth B in this case will be (9.5*2)*25% + (12.67*2)*25% + (31.68*2)*50% = 42.77 Mbps. In general, B in the downlink can range from 2.12 to 63.36 Mbps widely with AMC in different channel situations. Based on the number of supported users with respect to each MCS and the proportion of adopted MCSs, our CAC scheme can estimate the current system capacity and network load and update the information periodically every frame period in the BS site.
3.3. Adaptive BR scheme
We propose an adaptive BR scheme which dynamically adjusts the amount of reserved bandwidth for handoffs according to the arrival distributions of both handoff and new connections. The objective of our scheme is to simultaneously increase the admission opportunities for new coming users and guarantee QoS for handoff users. The basic idea of our adaptive BR scheme is a rational inference that generally the occurrences of new incoming connections may be much more frequent than that of handoff connections [22–24]. Thus, the optimal BR should take into account the arrival behavior of not only handoff connections, but also new connections in order to avoid a waste of network resource as possible.
To more clearly illustrate the characteristics of our proposed BR scheme, we conduct a simplified transmission scenario to provide a preliminary performance comparison between our scheme and the fixed threshold (FT) and dynamic threshold (DT) schemes [10]. With the FT scheme, the threshold of reserved bandwidth, th_{fixed}, is fixed; the new connection would be accepted only if (b_{i, new}+ b_{ n } + b_{ h } ) ≤ th_{fixed}. With the DT scheme, the threshold of reserved bandwidth, th_{dyn}, will be adjusted within the range [th_{min}, th_{max}] depending on the arrival and departure of handoff connections as follows: when a handoff connection is accepted, the threshold th_{dyn} will be increased with the amount of resources allocated to the handoff user; when an existing handoff connection terminates, th_{dyn} will be decreased with the amount of released resources. The new connection will be accepted only when (b_{i, new}+ b_{ n } + b_{ h } ) ≤ th_{dyn}. With regard to handoff connections, all the three algorithms have the same admission strategy that a handoff connection will be accepted as long as the amount of bandwidth available can meet its requirement, i.e. (b_{i, ho}+ b_{ n } + b_{ h } ) ≤ B.
We exploit the following simplified scenario to compare the performances of the three BR schemes. Assume that the total amount of network resources B is 100 units. Consider that the network is empty in the beginning. Assume that 80 new connections, 5 handoff connections, and 5 new connections arrive sequentially. Assume that each of the arrival connections requests a unit of resources. For the FT scheme, th_{fixed} is set as 80 units. In the DT scheme, th_{min} and th_{max} are 80 and 90 units, respectively, and th_{dyn} is set as 80 units initially. In our scheme, th_{min} and th_{max} are 0 and 90 units, respectively, and consequently the initial value of th_{ad} is 45 units.
Although the DT scheme and our proposed scheme both apply an adaptive manner for their BR strategies, there are two essential differences which lead to their dissimilar performances. One difference is between their criteria for accepting new connections. For the DT scheme, the new connection will be accepted if the amount of its bandwidth requirement and the bandwidth allocated to existing connections would not exceed the threshold, i.e. (b_{i, new}+ b_{ n } + b_{ h } ) ≤ th_{dyn}. In our scheme, one criterion for accepting a new connection as shown in the first term of the rhs in Equation (5) only examines whether the amount of the requirement and the bandwidth allocated to existing "new connections" does not exceed the threshold, i.e. (b_{i, new}+ b_{ n } ) ≤ th_{ad}. Thus, with our scheme, the existing handoff connections will not lessen the resource for an incoming new connection. That is, when the DT scheme and our proposed scheme are with the same amount of reserved bandwidth (th_{dyn} = th_{ad}), our scheme can increase a resource space of b_{ h } for accepting more new connections. Meanwhile, our BR scheme can guarantee QoS for handoff connections because of another condition for accepting a new connection as shown in the last term of the rhs in Equation (5). That is, the total amount of required bandwidth for a new connection and allocated bandwidth for existing connections would not exceed the amount of guarded bandwidth for handoff, th_{max}, i.e. (b_{i, new}+ b_{ n } + b_{ h } ) ≤ th_{max}. Thus, at last the B - th_{max} bandwidth amount can be reserved for handoff users.
Another difference is between their adjustment strategies for the threshold of BR, th_{dyn} and th_{ad}. With the DT scheme, th_{dyn} is increased or decreased when a handoff connection is established or terminated respectively, and will remain constant whether a new connection concludes or runs its course. When the occurrence of handoff connections is relatively infrequent as the scenario shown above, the DT scheme will be similar to the FT scheme and can cause a waste of network resources too. In our scheme, th_{ad} is increased or decreased when a new or handoff connection is admitted and runs its course, respectively, and will remain constant whether a new or handoff connection concludes. Note that a lower threshold of BR advances the acceptance rate of handoff users whereas a higher threshold increases the admission opportunities for new connections. In the sense, our scheme has potential to grant more new connections into networks especially when the occurrences of new connections are much more than that of handoff connections. As aforementioned, our scheme would not sacrifice handoff QoS for favoring new connections when no less than the bandwidth amount of B - th_{max} will be reserved for handoff users. Thus, the proposed adaptive BR scheme can simultaneously improve the network efficiency by granting more new connections and also guarantee handoff QoS.
- (1)
When a new connection or handoff connection arrives, it will inform the BS of its highest and lowest bandwidth requirements (i.e., b _{i, max}and b _{i, min}). The proposed dynamic CAC scheme will adjust the admission criterion using Equation (2) according to the currently estimated system capacity and network load.
- (2)
When the admission criterion is determined, the proposed adaptive BR scheme will accept or reject this handoff or new connection depending on the criterion in Equation (4) or Equation (5), respectively.
- (3)
If a handoff connection is established, th_{ad} will be decreased with the amount of allocated resources as Equation (7) shows; if a new connection is granted, th_{ad} will be increased with the amount of allocated resources as Equation (8) shows.
- (1)
The estimate of system capacity: The system capacity can be evaluated at the BS with the specific PHY characteristics like channel spectrum, the amount of data sub-carriers, supported MCSs, used MIMO mechanisms, etc. The estimation of system capacity can be obtained in the initial phase of a network built-up.
- (2)
The estimate of network loads: In general, the network loads can be evaluated at the BS with the information of currently adopted MCSs and the number of supported users with respect to each MCS. This part may need the exchanges of some context information between BS and SSs periodically, e.g., currently channel condition and used modulation.
- (3)
The determination of admission criteria for incoming connections: When a connection arrives and requests for an admission, it will inform the BS of its specific QoS requirements, e.g., maximum and minimum data rates. Based on the estimated system capacity and network loads and QoS parameters, the BS will compute the admission criteria for the incoming connection with its specific QoS parameters.
- (4)
The adaptation of BR for handoff connections: If a connection is admitted in the network, the BS will therefore adapt the BR threshold depending on the type of connection, i.e., new or handoff.
4. Performance evaluations and results
In this section, we conduct simulations of 802.16e transmission scenarios to demonstrate the effectiveness of the proposed CAC algorithm and BR scheme. The simulator is constructed in C and followed the IEEE 802.16e standard closely [35, 38]. The channel spectrum is 10 MHz. The MAC frame duration is 5 ms, which consists of 1024 OFDM subcarriers (840 data and pilot subcarriers). One MAC frame includes 48 OFDM symbols, while the first symbol is used for a preamble. The ratio of the symbols of the uplink subframe to those of the downlink subframe is 18:29. In the uplink, three symbols are used for control signaling, and there are 44 OFDM symbols used for data transmissions in the uplink and downlink in total. The simulation set-up considers a 2 × 2 MIMO mechanism and the AMC schemes. We used the following distribution for MCS levels: QPSK 1/12 = 3.71%, QPSK 1/8 = 12.01%, QPSK 1/4 = 29.10%, QPSK 1/2 = 29.67%, QPSK 3/4 = 9.23%, 16-QAM 1/2 = 12.51%, 64-QAM 1/2 = 0.75%, and 64-QAM 3/4 = 3.02% [39, 40]. The OFDMA PHY parameters and their values are listed in Table 2.
We provide different simulation scenarios to examine the proposed CAC algorithm and BR scheme individually or jointly to clearly show the performance effects with the two schemes. For each of the scenarios, we assume the connection arrivals and departures are with the Poisson process with a mean arrival rate λ and a mean departure rate equal to one-tenth of the arrival rate. We assume that the BS is aware of the amount of connections with regard to each kind of MCS and the bandwidth requirement of each connection. The BS can therefore exploit this knowledge to estimate the current system capacity and also network loads. Generally, the system capacity and network loads can therefore be estimated for our scheme with the information of currently adopted MCSs and the number of supported users with respect to each MCS. The total simulation period is 1000 s while the results are provided with the average values over 20 times of simulations.
4.1. The connection blocking rates and achieved throughput with different CAC schemes
The maximum and minimum rates associated with different scheduling services
Scheduling services | Maximum rate (kbps) | Minimum rate (kbps) |
---|---|---|
UGS | 100 | 100 |
rtPS | 100 | 25 |
ertPS | 75 | 25 |
nrtPS | 60 | 20 |
BE | 20 | 0 |
We compare the performance of our CAC algorithm with that of the static maximum (Static-max), static minimum (Static-min), and bandwidth adaptation (Adapt) scheme considered in [11, 12]. The Static-max and Static-min schemes adopt the highest and lowest QoS requirements, respectively, as the admission criteria. The Adapt scheme considers the highest QoS criteria for a new coming user. If the resources available are insufficient to meet the bandwidth requirement of a new connection, the bandwidth allocated to existing users will be decreased to satisfy the QoS requirement of the new connection. In our scheme, we consider a linear adaptation approach as shown in Figure 3b which adjusts the admission criteria according to the variation of network loads. We use the following ratios of ($n{l}_{\mathsf{\text{th}}}^{\mathsf{\text{min}}}$/B, $n{l}_{\mathsf{\text{th}}}^{\mathsf{\text{max}}}$/B): (2/5, 4/5), (2/6, 4/6), and (1/5, 3/5), for rtPS, ertPS, and nrtPS, respectively, as we consider in Section 3.1. Then, the minimum and maximum thresholds of network loads ($n{l}_{\mathsf{\text{th}}}^{\mathsf{\text{min}}}$, $n{l}_{\mathsf{\text{th}}}^{\mathsf{\text{max}}}$) in Equation (2) for rtPS, ertPS, and nrtPS can be derived, respectively, depending on the currently estimated system capacity B. The performances are indexed as the connection blocking rate and the achieved QoS in terms of per-flow throughput, i.e., the average data rates supported per established connection.
4.2. The new connection blocking rate and handoff dropping rate with different BR schemes
In this section, we conduct simulation scenarios which consider new connections and handoff connections to examine the performance of the proposed BR scheme without CAC. The compared BR schemes for performance evaluations are the FT and DT schemes [10] described in Section 3.2. The simulation set-up in this scenario considers five classes of scheduling services, UGS, rtPS, ertPS, nrtPS, and BE, each of which is associated with specific maximum rates and minimum rates as shown in Table 3. To simply and fairly compare the performances of different BR schemes, our approach here considers the same fixed admission criteria as that used by the FT and DT schemes: the maximum rate is adopted as the admission criteria for UGS, rtPS, and ertPS, and the minimum rate is adopted for nrtPS and BE [10]. Assume that the occurrences of the five scheduling services are with uniform probabilities, with which the arrival rates of new connections and handoff connections are λ_{ n } and λ_{ h } , respectively. We refer to [10] and adopt the following threshold setting. For the FT scheme, th_{fixed}/B is equal to 80%. In the DT scheme, th_{min}/B and th_{max}/B are set as 80 and 90%, respectively; th_{dyn}/B is set as 80% initially. In our scheme, th_{min}/B and th_{max}/B are equal to 0 and 90%, respectively, and consequently the initial value of th_{ad}/B is 45%. We examine the performance of different BR schemes with different ratios of handoff arrival rates to new-connection arrival rates which can be 1:1 or 1:20 in particular. The performance metrics are indexed as the new connection blocking rates and handoff dropping rates.
4.3. The new connection blocking rate and hadoff dropping rate with different CAC and BR schemes
In this section, we conduct simulation scenarios to examine the joint performance of our dynamic CAC algorithm and adaptive BR scheme. The simulation set-up for this scenario is similar to that described in Section 4.2, except that our scheme further adopts the proposed CAC algorithm to dynamically adjust the admission criteria for the five scheduling classes. We use the following ratios of ($n{l}_{\mathsf{\text{th}}}^{\mathsf{\text{min}}}$/B, $n{l}_{\mathsf{\text{th}}}^{\mathsf{\text{max}}}$/B): (3/6, 5/6), (2/5, 4/5), (2/6, 4/6), (1/5, 3/5) and (1/6, 3/6), for UGS, rtPS, ertPS, nrtPS, and BE, respectively, as we consider in Section 3.1. Then, the minimum and maximum thresholds of network loads ($n{l}_{\mathsf{\text{th}}}^{\mathsf{\text{min}}}$, $n{l}_{\mathsf{\text{th}}}^{\mathsf{\text{max}}}$) in Equation (2) for UGS, rtPS, ertPS, nrtPS, and BE can be derived, respectively, depending on the currently estimated system capacity B. The FT and DT schemes use the same admission strategy aforementioned that the scheduling classes of UGS, rtPS, and ertPS adopt the maximum rate for the admission criteria while the nrtPS and BE classes use the minimum rate for that.
5. Conclusion
The mechanisms of CAC and BR are important research issues in wireless networks due to scarce radio resources, dynamic channel qualities, and diverse user demands. The article presents a dynamic CAC algorithm and an adaptive BR scheme for IEEE 802.16e mobile WiMAX networks to simultaneously improve the utilization efficiency of network resources and guarantee QoS for admitted new connections and handoff connections. Our CAC algorithm dynamically determines the admission criteria according to network loads to provide adaptive QoS for admitted users and improve the utilization efficiency of networks. When new connections and handoff connections enter the network based on the adaptive admission criteria, the proposed BR scheme further dynamically adjusts the amount of reserved bandwidth for handoffs according to the arrival distributions of new/handoff connections to increase the connection admission opportunities and also guarantee the bandwidth requirements for handoff QoS. Simulations results demonstrate that our approach can simultaneously improve the efficiency of resource utilization by granting more connections in the network and provide adaptive QoS for admitted new and handoff connections.
Declarations
Acknowledgements
This work was supported in part by Taiwan National Science Council under Grant 99-2221-E-003-005 and 100-2221-E-003-020.
Authors’ Affiliations
References
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