Raja Karmakar

Dynamic link adaptation for High Throughput wireless access networks

IEEE 802.11n and IEEE 802.11ac amendments of IEEE 802.11 standard are introduced to achieve high throughput in wireless local area networks (WLANs) with modifications in both Physical layer (PHY) and Medium Access Control (MAC) sublayer. Wireless channels are time-varying systems which are shared by multiple heterogeneous wireless stations. Every wireless station should be capable to tune dynamically its transmission rate by tuning the link properties, otherwise high transmission failures may lead to impose low system performance. In this paper, we propose a closed-loop, cross-layer and statistic-based link adaptation algorithm called High Throughput Wireless Link Adaptation (HiWiLA) for high throughput wireless networks (HT-WLANs) where the metric of link adaptation is computed by “Received Signal Strength Indicator” (RSSI) of channel and observed MAC throughput considering channel bonding, short guard interval (SGI), frame aggregation and different Modulation and Coding Scheme (MCS) levels. We analyze the performance of the proposed scheme through simulation and a practical testbed, and show that HiWiLA achieves on average 10% - 70% better throughput than the existing state of the art schemes.

Channel Access Fairness in IEEE 802.11ac: A Retrospective Analysis and Protocol Enhancement

High throughput wireless access networks based on IEEE 802.11ac support a number of protocol enhancements at the physical and medium access control sublayer for supporting data rates in the order of Gigabits per second. These include multiple antenna technologies, wider bandwidth via channel bonding, reducing access overhead via short guard intervals, higher order modulation and coding rates, frame aggregation and block acknowledgements. As these features have their internal trade-offs based on channel conditions, the protocol generally employs a rate/link adaptation technique (sometime called dynamic bandwidth channel access) at the data link layer, that dynamically selects the channel access parameters based on the environment condition. However, this paper shows that such heterogeneity in selecting channel access parameters among neighboring wireless access points results in severe unfairness. In this paper, we address such unfairness in channel access, and develop an intelligent decentralized link parameter selection procedure that significantly improves the protocol performance in terms of fairness and overall network throughput. The proposed scheme, FairHT-MAC, has been implemented in a 26-node (6 access points and 20 client stations) indoor testbed, and the performance is analyzed and compared with other state-of-the-art link adaptation methods, like SampleLite and Minstrel-HT. We observe that FairHT-MAC significantly boosts up access fairness and overall network throughput, while keeps the access overhead (in terms of channel access delay) and average power consumption almost same that of Minstrel-HT and SampleLite.

Dynamic Link Adaptation in IEEE 802.11ac: A Distributed Learning Based Approach

High throughput wireless access networks based on IEEE 802.11ac show a significant challenge in dynamically selecting the link configuration parameters based on channel conditions due to large pool of design set, like number of spatial streams, channel bonding, guard intervals, frame aggregation and different modulation and coding schemes. In this paper, we develop a learning based approach for link adaptation motivated by the multi-armed bandit based distributed learning algorithm. The proposed link adaptation algorithm, BanditLink, explores different possible configuration options based on observing their impact over the network performance at various channel conditions. We analyze the performance of BanditLink from simulation results, and observe that it performs significantly better compared to other competing mechanisms proposed in the literature.

Performance modeling and analysis of high throughput wireless media access with QoS in noisy channel for different traffic conditions

The high throughput wireless extensions based on IEEE 802.11 or wireless-fidelity (Wi-Fi) support varieties of physical and media access control (MAC) sublayer features to boost up the physical data rate in wireless media. These include multiple input multiple out (MIMO) spatial multiplexing and spatial diversity, channel bonding, short guard intervals, advanced modulation and coding schemes (MCS), frame aggregations and block acknowledgements; for different Wi-Fi extensions like IEEE 802.11n, IEEE 802.11ac and IEEE 802.11ad. The existing studies show that although such physical extensions improve data rates, they have internal trade-offs in channel error and sustainability that directly impact the MAC layer frame aggregation and block acknowledgement performance. In this paper, we model the impact of the channel errors over MAC layer channel access with frame aggregation and block acknowledgement, considering the standard IEEE 802.11 service class differentiation for quality of service (QoS). The evolution of aggregated frame transmission has been modeled using a three dimension Markov chain diagram, considering channel error from physical layer and different traffic conditions. The model is validated through simulation results. The mathematical model is further explored to observe and analyze the impact of channel error over the aggregated frame based MAC scheduling with different QoS performance parameters, like channel throughput, frame loss probability and channel access delay. We observe that frame aggregation sometimes shows negative impact on channel access performance that demands the need for designing an adaptive aggregation strategy.

SmartLA: Reinforcement learning-based link adaptation for high throughput wireless access networks

High throughput wireless standards based on IEEE 802.11n and IEEE 802.11ac have been developed and released within the last few years as new amendments over the commercially popular IEEE 802.11. IEEE 802.11n and IEEE 802.11ac support a large pool of parameter set such as increased number of spatial streams via multiple input multiple output (MIMO) communications, channel bonding, guard intervals, different modulation and coding schemes, several levels of frame aggregation, block acknowledgement etc. As a consequence, they boost up physical data rate in the order of Gigabits per second. However, all these enhancements have their internal trade-offs with the channel quality, as explored in the existing literature. For example, higher channel bonding levels result in poor performance under high bit error rate. In a free wireless environment, multiple heterogeneous stations share the wireless channel which is again a time-varying system. Consequently, none of these link level parameters provide an optimal performance for all channel quality instances. Therefore, to practically meet the theoretical high throughput, each wireless device should adapt its physical data transmission rate dynamically by an appropriate tuning of different link parameters. Otherwise, high transmission failure may arise. In this paper, we design an adaptive automated on-line learning mechanism, called “Smart Link Adaptation” (SmartLA), for dynamic selection of link parameters, motivated by “State-Action-Reward-State-Action” (SARSA) model, a variant of reinforcement learning. SmartLA can make a wireless station quite intelligent to cope up with various network conditions by exploiting the best suited data rate observed so far for various channel conditions from the past experience as well as by exploring different possible set of parameters. We analyze the performance of SmartLA in both from simulation analysis and over a 26 nodes IEEE 802.11ac testbed (6 access points and 20 client devices). We observe that the proposed link adaptation mechanism performs significantly better compared to other competing mechanisms mentioned in the literature.

Supporting Throughput Fairness in IEEE 802.11ac Dynamic Bandwidth Channel Access: A Hybrid Approach

Wi-Fi enabled hand-held devices have quickly occupied the consumer market as a result of the remarkable customer acceptance of IEEE 802.11 standard. In this regard, the demand of high throughput introduces high throughput standards such as IEEE 802.11ac. It supports Dynamic Bandwidth Channel Access (DBCA), where a wireless station selects channel bandwidth dynamically based on the availability of the secondary channels. But the widely-used contention based medium access mechanism provides an opportunistic access of secondary channels and affects the performance of DBCA. Consequently, unfairness in channel access is increased in DBCA, which further reduces average throughput of stations. In this paper, we develop a hybrid adaptive resource reservation mechanism, Hybrid Adaptive DBCA (HA-DBCA), for supporting fair channel access in DBCA. In HA-DBCA, a polling based online learning mechanism is designed to avoid starvation of primary channel users. Through IEEE 802.11ac testbed implementation, we show that HA-DBCA improves throughput fairness in DBCA significantly along with other performance parameters.

Linkcon: Adaptive Link Configuration over SDN Controlled Wireless Access Networks

High throughput wireless access networks such as IEEE 802.11ac show a significant challenge in choosing link configuration parameters dynamically based on channel condition. It is due to a large pool of design set like channel bonding, number of spatial streams, guard intervals, different modulation and coding schemes, frame aggregation etc. Selection of such parameters is far challenging in mobile environment where signal strength fluctuates frequently. In this paper, we design a software-defined networking (SDN) framework for link adaptation in mobile environment, that engages an adaptive learning-based methodology, ϵ--greedy policy. The proposed link adaptation mechanism, Linkcon, explores several possible configuration options on the basis of their impact on network performance in various channel conditions. We analyze the performance of Linkcon from simulation results. We observe that this approach provides a significant better performance compared to other competing schemes proposed in the literature.

Impact of IEEE 802.11n/ac PHY/MAC High Throughput Enhancements on Transport and Application Protocols—A Survey

Since the inception of IEEE 802.11 wireless local area networks (WLANs) in 1997, wireless networking technologies have tremendously grown in the last few decades. The fundamental IEEE 802.11 physical (PHY) and medium access control (MAC) protocols have continuously been enriched with new technologies to provide the last mile wireless broadband connectivity to end users. Consequently, several new amendments of the basic IEEE 802.11 gradually came up in the forms of IEEE 802.11a, IEEE 802.11b, and IEEE 802.11g. More recently, IEEE 802.11n, IEEE 802.11ac, and IEEE 802.11ad are introduced with enhanced PHY and MAC layers that boost up physical data rates to the order of Gigabit per second. So, these amendments are generally known as high throughput WLANs (HT-WLANs). In HT-WLANs, PHY layer is enhanced with multiple-input multiple-output antenna technologies, channel bonding, short guard intervals, enhanced modulation and coding schemes. The MAC sublayer overhead is reduced by introducing frame aggregation and block acknowledgement technologies. However, several existing studies reveal that, many a time, the aforesaid PHY and MAC enhancements yield negative impact on various upper layer protocols, that is end-to-end transport and application layer protocols. As a consequence, a large number of researchers have focused on improving the coordination among PHY/MAC and upper layer protocols. In this survey, we discuss impact of HT-WLAN PHY and MAC layer enhancements on various transport and application layer protocols. This paper also summarizes several research works that use aforesaid enhancements effectively to boost up data rate of end-to-end protocols. We also point out limitations of the existing researches and list down different open challenges that can be meaningfully explored for the development of the next generation HT-WLAN technologies.

CrowdAP: Crowdsourcing driven AP coordination for improving energy efficiency in wireless access networks

Internet access via wireless hotspots is an ever increasing demand with the inception of smart cities, where most of the users connect the Internet with their WiFi enabled devices. A set of wireless devices forms a basic service set (BSS) connected to an access point (AP). However, large number of APs are deployed in the form of extended service set (ESS) to balance the traffic load and to provide seamless data connectivity. Recent studies show that in a public WiFi hotspot, a mobile device remains in the overlapping region of multiple APs. Due to geographically sparse distributions of mobile devices, an AP may need to keep its interfaces on to serve only a few devices which otherwise can be shifted to another active AP. In this paper, we develop CrowdAP, an energy balancing AP coordination mechanism; where the minimum number of APs are computed such that the underlying mobile devices can be served without any degradation in performance, while the rest of the APs can go to the sleep state to save power. We analyze the performance of CrowdAP through simulation as well as from testbed, and show that it is able to save significant energy in the network.