Eng Hwee Ong

IEEE 802.11ac: Enhancements for very high throughput WLANs

The IEEE 802.11ac is an emerging very high throughput (VHT) WLAN standard that could achieve PHY data rates of close to 7 Gbps for the 5 GHz band. In this paper, we introduce the key mandatory and optional PHY features, as well as the MAC enhancements of 802.11ac over the existing 802.11n standard in the evolution towards higher data rates. Through numerical analysis and simulations, we compare the MAC performance between 802.11ac and 802.11n over three different frame aggregation mechanisms, viz., aggregate MAC service data unit (A-MSDU), aggregate MAC protocol data unit (A-MPDU), and hybrid A-MSDU/A-MPDU aggregation. Our results indicate that 802.11ac with a configuration of 80MHz and single (two) spatial stream(s) outperforms 802.11n with a configuration of 40 MHz and two spatial streams in terms of maximum throughput by 28% (84%). In addition, we demonstrate that hybrid A-MSDU/A-MPDU aggregation yields the best performance for both 802.11n and 802.11ac devices, and its improvement is a function of the maximum A-MSDU size.

On optimal network selection in a dynamic multi-RAT environment

A significant challenge to enable multimedia service delivery in a dynamic multiple radio access technologies (multi-RAT) environment is the coordination of vertical handovers between different RATs. Cost function approach has been widely adopted to make vertical handover decision by ranking candidate networks. Although this mechanism can reflect accurately the change of network states and user requirements, it results in frequent, and often, unnecessary handovers which have detrimental impact on QoS, signaling load and system capacity. In this letter, we introduce a novel measurement-based network selection technique that provides a pragmatic way to acquire QoS information. In addition, it augments the handover decision of existing cost function approach, through handover initiation, to provide an optimal network selection outcome. OPNET simulations verify that the proposed technique can reduce unnecessary handovers considerably, improve overall QoS and system capacity.

Dynamic Access Network Selection with QoS Parameters Estimation: A Step Closer to ABC

Always best connected (ABC) services allows multi- mode mobile terminals to stay connected to the best available networks, at anytime according to user preferences. One of the key aspects in realizing such ABC service is mainly attributed to an effective and dynamic access network selection process. However, most of the previous works consider the access network selection process as a static optimization problem which fails to address the dynamic QoS conditions intrinsic in wireless networks. One of the main challenges remains as no efficient way in obtaining dynamic QoS parameters such as packet delay, packet loss and jitter. In this paper, we proposed a novel dynamic access network selection algorithm capable of adapting to prevailing network conditions. Our algorithm is a dual stage estimation process where network selection performed using sequential Bayesian estimation relies on dynamic QoS parameters estimated through bootstrap approximation. Simulations demonstrate the effectiveness of our proposed algorithm which outperforms static optimization approach in a highly efficient manner.

Radio Resource Management of Composite Wireless Networks: Predictive and Reactive Approaches

Recently, the IEEE 1900.4 standard specified a policy-based radio resource management (RRM) framework in which the decision making process is distributed between network-terminal entities. The standard facilitates the optimization of radio resource usage to improve the overall composite capacity and quality of service (QoS) of heterogeneous wireless access networks within a composite wireless network (CWN). Hence, the study of different RRM techniques to maintain either a load- or QoS-balanced system through dynamic load distribution across a CWN is pivotal. In this paper, we present and evaluate three primary RRM techniques from different aspects, spanning across predictive versus reactive to model-based versus measurement-based approaches. The first technique is a measurement-based predictive approach, known as predictive load balancing (PLB), commonly employed in the network-distributed RRM framework. The second technique is a model-based predictive approach, known as predictive QoS balancing (PQB), typically implemented in the network-centralized RRM framework. The third technique is a measurement-based reactive approach, known as reactive QoS balancing (RQB), anchored in the IEEE 1900.4 network-terminal distributed RRM framework. Comprehensive performance analysis between these three techniques shows that the IEEE 1900.4-based RQB algorithm yields the best improvement in QoS fairness and aggregate end-user throughput while preserving an attractive baseline QoS property.

Performance Analysis of IEEE 802.11ac DCF with Hidden Nodes

Recently, the IEEE 802.11 standard based Wireless Local Area Networks (WLAN) have become more popular and are widely deployed. It is anticipated that WLAN will play an important rule in the future wireless communication systems in order to provide several gigabits data rate. IEEE 802.11ac is one of the ongoing WLAN standard aiming to support very high throughput (VHT) with data rate of up to 6 Gbps below the 6 GHz band. In the development of IEEE 802.11ac standard, several new physical layer (PHY) and medium access control layer (MAC) features are taken into consideration, such as employing wider bandwidth in PHY and incrementing the limits of frame aggregation in MAC. However, due to the newly introduced features, some traditional techniques used in previous standards could face some problems. This paper presents a performance analysis of 802.11ac Distributed Coordination Function (DCF) in presence of hidden nodes in overlapping BSS (OBSS) environment. The effectiveness of DCF in IEEE 802.11ac WLAN when using different primary channels and different frequency bandwidth has also been discussed. Our results indicate that the traditional RTS/CTS handshake mechanism faces shortcomings and needs to be modified in order to support the newly defined 802.11ac amendment.

QoS provisioning for VoIP over wireless local area networks

With proliferation of the IEEE 802.11 WLAN and the emerging 802.11n standard, the WLAN is poised as a promising ubiquitous networking technology to support VoIP services, which is gaining popularity due to their high cost efficiency. However, the 802.11 WLAN is not designed to support delay sensitive traffic such as VoIP. This problem is magnified during a handover as user roam the WLAN network resulting in excessive handover latency and consequently packet loss. In addition, a 802.11 WLAN handover process is predominantly based on the physical layer detection without QoS considerations. This often causes overloading of access points and consequently all its associated connections would suffer from high delay, resulting in unacceptable QoS for the VoIP services. The former can be resolved by reducing handover latency to achieve seamless handover and the latter can be mitigated by employing link layer detection in the 802.11 WLAN handover process and having an appropriate admission control scheme. In this paper, we proposed an integrated load balancing scheme incorporating (i) QoS-based fast handover to support seamless handover by eliminating both detection and scanning phases from the 802.11 WLAN handover process; and (ii) soft admission control to protect QoS of existing voice connections when resources are low. This synergy guarantees service QoS during and after handover respectively. Simulations showed that our proposed integrated load balancing scheme is capable of providing seamless handover and QoS enhancements in terms of increased throughput, reduced packet loss and bounded delay when considering heterogeneous voice traffic of different packetization intervals. Particularly, our proposed scheme effectuate QoS balance of both delay and throughput which jointly optimize overall system utilization.

Cooperative radio resource management framework for future IP-based multiple radio access technologies environment

Heterogeneity and convergence are two distinctive connotations of future wireless networks emanated from International Telecommunications Union (ITU)s vision of Optimally Connected, Anywhere, Anytime. Multiple access networks, multiple terminals and multiple services are expected to converge in a manner where heterogeneity can be exploited to realize this ultimate goal. This raises the importance of radio resource management (RRM) for a multiple radio access technologies (multi-RAT) environment, where coalitions of heterogeneous access networks are each connected to a common Internet Protocol (IP)-based core network. In this article, we develop a cooperative RRM framework for future IP-based multi-RAT environment to coordinate better utilization of radio resources in an opportunistic yet altruistic manner. We motivate the importance of cooperation which can exploit heterogeneity as an enabler to improve system capacity and quality of service (QoS) of users. We exemplify the proof of concept based on a heterogeneous multiple access points (multi-AP) wireless local area network (WLAN) and argue that our technology agnostic approach is readily applicable to future IP-based multi-RAT environment. We demonstrate that our cooperative RRM framework benefits from the unified actions of joint optimization and results in a QoS-balanced system by enabling different functional entities to form synergies and multiple access networks to interact. We further show that a QoS-balanced system has salient traits of providing statistical QoS guarantee to support demanding multimedia applications while maximizing overall system capacity. Consequently, we advocate the notion of QoS balancing as criterion to quantify the state of balance in future IP-based multi-RAT environment.

On Dynamic Load Distribution Algorithms for Multi-AP WLAN under Diverse Conditions

Future wireless networks will enjoy ubiquitous connectivity by taking advantage of the IP core convergence which is seen as the lingua franca of heterogeneous access networks ecosystem. It is expected that the prevalence of WLAN and the advent of IEEE 802.11n standard will continue to offer compelling opportunities and therefore be considered as one of the de-facto wireless access network. However, it is known that wireless network conditions in general are diverse owing to both traffic and wireless channel variations. This raises the importance of exploiting diversity across a multiple access points (multi-AP) WLAN, which requires an advanced network control mechanism to effectuate uniform load distribution, so that QoS of users and composite capacity can be improved. Although various load distribution algorithms for WLAN have been investigated in literature, there is a lack of performance comparison between different algorithms. In this paper, we present a comparison of three dynamic load distribution algorithms, viz. predictive load balancing (PLB), predictive QoS balancing (PQB) and reactive QoS balancing (RQB) for infrastructure-based WLAN with DCF access mechanism based on OPNET simulations.

An Integrated Load Balancing Scheme for Future Wireless Networks

With the emerging IEEE 802.11n standard, the WLAN is poised as a promising ubiquitous networking technology to support multimedia applications where providing QoS becomes imperative. However, the 802.11 WLAN is not designed to support delay sensitive traffic. This problem is magnified during a handover and typically results in excessive handover latency and packet loss. In addition, a 802.11 WLAN handover process is predominantly based on the physical layer detection without QoS considerations. This often causes overloading of access points and consequently all its associated connections would suffer from high delay. The former can be resolved by reducing handover latency to achieve seamless handover and the latter can be mitigated by employing link layer detection in the 802.11 WLAN handover process and having an appropriate admission control scheme. Although the IEEE 802.11e standard supports prioritized QoS, it cannot guarantee strict QoS required by real-time services under heavy load. In this paper, we proposed an integrated load balancing scheme incorporating (i) QoS-based fast handover to support seamless handover by eliminating both detection and scanning phases from the 802.11 WLAN handover process; and (ii) soft admission control to protect QoS of existing connections when resources are low. This synergy allows us to perform QoS-related handover opportunistically and guarantees service QoS during and after handover respectively. Simulations showed that our proposed integrated load balancing scheme is capable of providing seamless handover and QoS provisioning for real-time VoIP services in terms of bounded delay and packet loss when considering multimedia traffic.

Performance analysis of fast initial link setup for IEEE 802.11ai WLANs

The IEEE 802.11ai is an upcoming fast initial link setup (FILS) amendment that could enable a STA to achieve secure link setup in less than 100 ms. A successful link setup process will then allow the STA to send IP traffic with a valid IP address through the AP. In this paper, we first present a performance analysis on how well the legacy 802.11 can support FILS. We then demonstrate that the rate at which the medium saturates is mainly dependent on the offered load which has strong dependencies on the active scan rates (i.e., amount of transmitted probe request frames) and number of responding APs (i.e., amount of transmitted probe response frames) among others. Moreover, the average active scanning duration per channel could be maintained below 5 ms provided the medium is not saturated. Our results also indicate that a combination of the enhanced distributed channel access (EDCA) procedure and the proposed active scanning enhancements provides at least 20% and up to 250% improvements in supporting the functional requirements of FILS as compared to the 802.11 EDCA and legacy distributed coordination function (DCF), respectively.