- Research Article
- Open Access
Frame-Aggregated Link Adaptation Protocol for Next Generation Wireless Local Area Networks
© Kai-Ten Feng et al. 2010
- Received: 4 August 2009
- Accepted: 10 May 2010
- Published: 8 June 2010
The performance of wireless networks is affected by channel conditions. Link Adaptation techniques have been proposed to improve the degraded network performance by adjusting the design parameters, for example, the modulation and coding schemes, in order to adapt to the dynamically changing channel conditions. Furthermore, due to the advancement of the IEEE 802.11n standard, the network goodput can be enhanced with the exploitation of its frame aggregation schemes. However, none of the existing link adaption algorithms are designed to consider the feasible number of aggregated frames that should be utilized for channel-changing environments. In this paper, a frame-aggregated link adaptation (FALA) protocol is proposed to dynamically adjust system parameters in order to improve the network goodput under varying channel conditions. For the purpose of maximizing network goodput, both the optimal frame payload size and the modulation and coding schemes are jointly obtained according to the signal-to-noise ratio under specific channel conditions. The performance evaluation is conducted and compared to the existing link adaption protocols via simulations. The simulation results show that the proposed FALA protocol can effectively increase the goodput performance compared to other baseline schemes, especially under dynamically-changing environments.
- Medium Access Control
- Transmission Attempt
- Payload Size
- Frame Aggregation
- Packet Aggregation
A wireless network is a type of computer networks that utilizes wireless communication technologies to maintain connectivity and exchange messages between stations over wireless media, such as infrared, laser, ultrasound, and radio waves. Due to the wireless nature, wireless networks possess many advantages against its wired counterpart, for example, capable of device mobility, simple installation, and ease of deployment. Depending on the coverage, wireless networks can in general be divided into five different categories, including wireless regional area networks (WRANs), wireless wide area networks (WWANs), wireless metropolitan area networks (WMANs), wireless local area networks (WLANs), and wireless personal area networks (WPANs). The IEEE standards association establishes five standard series of IEEE , , , , and for the corresponding networks. Among these wireless standard series, the IEEE standard is considered the well-adopted suite for WLANs due to its remarkable success in both design and deployment.
In recent years, the IEEE standard has been used both for indoor and mobile communications. The applications for WLANs include wireless home gateways, hotspots for commercial usages, and ad hoc networking for intervehicular communications. Various amendments are contained in the IEEE standard suite, mainly including IEEE a/b/g [1–3], IEEE e  for quality-of-service (QoS) support. With the increasing demands to support multimedia applications, the new amendment IEEE n [5, 6] has been proposed for achieving higher goodput performance. The IEEE task group N (TGn) enhances the PHY layer data rate up to Mbps by adopting advanced communication techniques, such as orthogonal frequency-division multiplexing (OFDM) and multiinput multioutput (MIMO) technologies . It is noted that MIMO technique utilizes spatial diversity to improve both the range and spatial multiplexing for achieving higher data rate. However, it has been investigated in  that simply improves the PHY data rate will not be suffice for enhancing the system goodput from the medium access control (MAC) perspective. Accordingly, the IEEE TGn further exploits frame aggregation and block acknowledgment techniques  to moderate the drawbacks that are originated from the MAC/PHY overheads.
There is research work proposed in [10–19] that focus on packet aggregation schemes for WLANs. Two-level aggregation techniques, that is, the aggregate MAC service data unit (A-MSDU) and the aggregate MAC protocol data unit (A-MPDU), are exploited in the current IEEE n draft. Performance comparisons between IEEE , e, and n protocols have been presented in . The benefits of adopting two-level packet aggregation have been shown in [11, 12] for the enhancement of network goodput; while experimental studies on packet aggregation were conducted in . Feasible fragmentation and retransmission of packets has been studied in [15, 16] for goodput enhancement with the consideration of contending stations . It has been suggested in  to adopt packing, concatenation, and multiple frame transmission in order to reduce the MAC/PHY overheads. For goodput enhancement of VoIP traffic, Lu et al.  recommended the MAC queue aggregation (MQA) scheme; while Lee et al.  exploits intercall aggregation for multihop networks. Nevertheless, most of the existing schemes do not consider the effectiveness of packet aggregation techniques under time-varying channel conditions.
On the other hand, in order to improve the network performance within dynamically changing environments, link adaptation techniques are proposed by adjusting major design parameters according to the channel conditions, for example, based on the signal-to-noise ratio (SNR) values. The automatic rate fallback (ARF) algorithm as developed in  regulates the packet transmission rate based on the available feedback information from the acknowledgment (ACK) frames. Due to the severe delay problems encountered by the ARF scheme under highly varying channel conditions, cross link adaptation (CLA) algorithms [21–23] are proposed to alleviate the degraded network goodput. A mapping table between the SNR value and the modulation and coding scheme (MCS) is pre-established by the CLA algorithms, where an optimal MCS scheme is obtained in order to maximize the saturated network goodput. However, none of the existing link adaptation algorithms is specifically designed under the scenarios with frame aggregation. It will be beneficial to provide an efficient link adaptation scheme such as to enhance the system goodput for the IEEE n networks.
In this paper, a frame-aggregated link adaptation (FALA) protocol is proposed to maximize the goodput performance for the IEEE n networks based on cross-layer information. The conventional rate-adaptive schemes simply consider the choice of the PHY-layer modulation and coding schemes (MCS) in the goodput modeling. Therefore, in order to further enhance the network goodput performance, the proposed FALA algorithm additionally adopts the MAC-layer frame payload size as another degree of freedom to theoretically model the system goodput. Moreover, the A-MPDU/A-MSDU frame aggregation scheme adopted in the IEEE n MAC protocol is also taken into account under the saturated goodput performance. According to the results obtained from the goodput analysis, a table containing both the optimal MCS scheme and optimal MPDU payload size will be pre-established in order to facilitate the implementation of the proposed FALA algorithm. After acquiring the SNR value from the communication channel, an appropriate combination of both the MCS scheme and the frame payload size will be selected in order to maximize the network goodput. Simulations are also implemented to evaluate the effectiveness of the proposed FALA algorithm under the existence of channel variations. Compared with other baseline schemes, higher MCS can be utilized by the proposed FALA protocol under the same signal-to-noise condition, which can be observed that the FALA scheme outperforms other existing link adaptation algorithms with improved network goodput.
The remainder of this paper is organized as follows. Section 2 describes existing link adaptation algorithms. The proposed FALA protocol associated with the goodput analysis is presented in Section 3. Section 4 provides the performance evaluation of the proposed FALA scheme; while the conclusions are drawn in Section 5.
The mechanism of link adaptation denotes the concept of establishing the mapping between the modulation, coding, or other protocol parameters toward the channel conditions. Two well-adopted link adaptation algorithms, that is, the ARF and the CLA schemes, are briefly summarized as follows. Both schemes will be evaluated and compared via simulations in Section 4.
2.1. Automatic Rate Fallback (ARF) Algorithm
The ARF scheme in  determines the required packet transmission rate based on the success of transmission attempts. Two counters are utilized to trace the consecutively received correct and missed ACK frames, respectively, which are adopted to reflect the corresponding channel conditions. If the successive ACK frames that are correctly received have reached the number of ten, the packet transmission rate for next transmission attempt will be upgraded to a higher-level rate. On the other hand, as the number of consecutively missed ACK frames reaches two, the packet transmission rate will fallback to a lower-level rate. The advantage of adopting the ARF algorithm is its simple computation which only involves the design of several counters and timers within the MAC layer protocol. However, without the consideration of PHY layer information (e.g., the channel SNR values), the adaptation scheme within the ARF protocol is in general insensitive to the channel variations. As the degree of channel variation is raised, considerable delayed performance will be incurred by exploiting the ARF algorithm.
2.2. Cross-Layer Link Adaptation (CLA) Algorithm
In order to alleviate the problem as described in the ARF scheme, the CLA algorithm  associated with its derivative schemes [22, 23] are proposed by incorporating PHY layer information for the MAC protocol design. The saturated goodput analysis of the IEEE distributed coordination function (DCF) is utilized for the determination of transmission rate within the CLA algorithm. For achieving the maximal goodput performance, a mapping table is established to obtain an optimal MCS scheme based on a given channel SNR value. It is noted that this mapping table is constructed offline, and will be served as a realtime lookup table for each device to obtain a feasible MCS scheme under specific channel condition. Owing to the online mapping from the SNR value to the corresponding optimal MCS scheme, the goodput performance by adopting the CLA scheme can be greatly improved, especially under severe channel variation.
By using the PHY layer information, it is intuitive that the CLA scheme should result in enhanced goodput performance compared to the ARF algorithm under channel variations. Considering the protocol design for IEEE n standard, it can be beneficial to incorporate the frame aggregation within the link adaptation scheme in order to maximize the network goodput. Section 3.1 discusses the observations that are acquired from the goodput characteristics of IEEE n protocol. The saturated goodput analysis with the consideration of frame aggregation is described in Section 3.2; while the implementation of proposed FALA protocol is explained in Section 3.3.
3.1. Goodput Observation Based on IEEE 802.11n Protocol
On the other hand, a single FCS that exists within the frame structure of an MPDU will be utilized to conduct error correction for the entire A-MSDU. As the number of aggregated MSDUs is increased, there is no guarantee that the goodput performance will be enhanced owing to the existence of channel noises. In other words, the entire A-MSDU will be dropped while an uncorrectable error happens, which will decrease the transmission efficiency if the number of aggregated MSDUs has surpassed a certain limit. As can be seen from Figure 2(b), the goodput performance will be drastically decreased as the BER value is augmented. Based on the observations as above, it will be beneficial to obtain a feasible length of the MPDU payload (i.e., the parameter as in Figure 1) such that the maximal goodput can be achieved under different SNR values. As will be shown in the next subsection, the optimal parameters, including both the MPDU payload size and the MCS scheme, will be acquired for achieving the maximal goodput under different channel conditions.
3.2. Goodput Analysis with Frame Aggregation
Modulation and coding schemes of the IEEE n standard.
Code rate ( )
Data rate (Mbps)
According to the RTS/CTS scheme as described in , the time durations for successful and failure transmissions (as in (15) and (16)) are considered equal as , where represents the propagation delay. It is noted that the meaning for these timing parameters are denoted by their corresponding subscripts. The time interval for the occurrence of collision as in (17) is obtained as . As a result, the saturated goodput as in (13) based on specific values of the MPDU payload size and the MCS scheme can be acquired.
3.3. Implementation of FALA Algorithm
where for , and . Any SNR value that falls within the range of will be approximated by the corresponding center value . Associated with the discretized set of SNR values, the saturated goodput value as derived in (13) can be obtained. The major limitation of the offline computation is on the granularity of SNR value. If the granularity is too large, the system goodput computed by the approximated center value will deviate from the exact value. In order to acquire better approximation, the granularity should be kept small.
Consequently, the offline FALA table can be constructed as for all . After the establishment of FALA table, the online adaptation phase can be initiated. As shown in Figure 4, the SNR estimator at the receiving end is utilized to estimate the SNR value from the wireless channel. The SNR value will consequently be fed into the FALA table for the selection of optimal parameter set in order to achieve the maximal goodput performance under the given SNR value. The parameter set will be provided to both the MAC and PHY layers of the conventional IEEE n protocol for the selection of feasible MPDU payload size and MCS scheme. It is also noted that the selection of MPDU payload size corresponds to the determination of the number of aggregated MSDUs within an A-MSDU. As a result, enhanced goodput performance can be achieved with adaptive selection of the system parameters and .
With the realization of pre-established FALA table, the pseudo code of FALA algorithm is shown in Algorithm 1. It can be seen that the conventional transmitting and receiving mechanisms of the IEEE MAC protocol remain unchanged. Additional efforts are conducted in system runtime to keep trace of the channel conditions in order to determine the optimal MCS scheme and the optimal MPDU payload size for the next transmission attempt. As was described, with the construction of offline table , there is no additional calculation required for the proposed FALA algorithm to conduct realtime implementation.
Algorithm 1: Proposed Frame-Aggregated Link Adaptation (FALA) Algorithm.
Pre-establishment of FALA table ;
: the MPDU payload size in the current transmission attempt of an A-MPDU;
: the initial MCS scheme in the current transmission attempt;
: the retry limit;
the queue of data packet i onempty
count_fail = 0;
, the count of transmission attempts;
: the channel condition in the current transmission attempt;
obtain and based on the FALA table and ;
(the first frames at the head of data queue are transmitted as an A-MPDU);
if an A-MPDU is received then
for all MPDUs
(check all MPDUs in the A-MPDU, and remove
successfully transmitted frames in the data queue);
an MPDU in the A-MPDU is received without error then
count_fail = count_fail + 1;
(this indicates that the entire MPDUs are received with error);
count_fail = 0;
(the frames in the data queue are dropped);
count_fail = 0;
System parameters for performance evaluation.
Block Ack packet
Propagation Delay ( )
Slot_Time ( )
Minimal Contention Window Size ( )
Maximal Contention Window Size ( )
4.1. Construction of FALA Table
4.2. Performance Comparison under Fixed Channel Conditions
4.3. Performance Comparison under Variable Channel Conditions
In this subsection, the performance comparison between the FALA, the ARF, and the CLA algorithms are conducted under time-varying channels. In order to compare and verify the adaptability to the channel variations, the discrete Markov chain model [21, 31] is suggested. The Markov chain model specified in  for the SNR variation is constructed by the trace collection of the packet SNR measurement. The trace collection can be viewed as the training input for this model. Based on the model testing, the eight-state model shows its accuracy to measure the channel variations represented by the trace collection. However, due to the lack of the training source of the packet SNR measurement, the measurement-based model in  can not be established in our protocol evaluation.
In this paper, a frame-aggregated link adaptation (FALA) protocol is proposed to maximize the network goodput performance from the cross-layer perspective. Instead of simply utilizing the PHY-layer modulation and coding schemes (MCS), the proposed FALA protocol further considers the effects from the MAC-layer optimal payload size based on frame aggregation. With the additional consideration of adjustable payload size, the network goodput can be effectively improved under different signal-to-noise ratios (SNRs). Numerical results show that with the increase of SNR values and the optimal selection of payload size, the proposed FALA algorithm can change to higher MCS schemes faster than the baseline algorithms, leading to higher goodput performance. In the simulation-based performance evaluation, it shows and validates that the proposed FALA algorithm outperforms the existing link adaptation schemes in the network goodput performance, especially under the environments with time-varying channels. Protocol realization on a hardware platform and the validation of field experiments will be included in our future work.
This paper was in part funded by the Aiming for the Top University and Elite Research Center Development Plan, NSC 96-2221-E-009-016, NSC 98-2221-E-009-065, the MediaTek research center at National Chiao Tung University, the Universal Scientific Industrial (USI) Co., and the Telecommunication Laboratories at Chunghwa Telecom Co. Ltd, Taiwan.
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