Frame aggregation

About: Frame aggregation is a research topic. Over the lifetime, 487 publications have been published within this topic receiving 14295 citations.

PHY-Supported Frame Aggregation for Wireless Local Area Networks

Abstract: An aggregate medium access control (MAC) service data unit (A-MSDU) contains multiple subframes with a single sequence number. Hence, it has a major drawback in environments with high error rates because if any subframes are corrupted, then the entire A-MSDU will be lost. In addition, performance of the A-MSDU depends strongly on the choice of parameters, such as frame size, modulation level, coding rate, and spatial mode. In this paper, a novel link-adaptation mechanism, dubbed physical (PHY)-supported frame aggregation (PSFA), is proposed over IEEE 802.11 networks, and its performance is analyzed. The proposed PSFA technique is based on a cross-layer interaction that enables joint optimization of various parameters between the PHY and MAC layers. This paper derives a new packet error rate (PER) expression for convolutionally coded multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems as an example. Then, this PER expression is used to efficiently estimate the link quality, given the channel conditions and system parameters, and most importantly, it is able to facilitate a parametric study of the cross-layer interaction. In the proposed PSFA algorithm, we present a rule for selecting the parameters so that MAC throughput is maximized. It is shown analytically and verified using Monte Carlo simulations that this choice of parameters can improve throughput performance significantly and also ensure quality of service (QoS) requirements compared to existing conventional algorithms.

Adaptive ML-Based Frame Length Optimisation in Enterprise SD-WLANs

Abstract: Software-Defined Networking (SDN) is gaining a lot of traction in wireless systems with several practical implementations and numerous proposals being made. Despite instigating a shift from monolithic network architectures towards more modulated operations, automated network management requires the ability to extract, utilise and improve knowledge over time. Beyond simply scrutinizing data, Machine Learning (ML) is evolving from a simple tool applied in networking to an active component in what is known as Knowledge-Defined Networking (KDN). This work discusses the inclusion of ML techniques in the specific case of Software-Defined Wireless Local Area Networks (SD-WLANs), paying particular attention to the frame length optimization problem. With this in mind, we propose an adaptive ML-based approach for frame size selection on a per-user basis by taking into account both specific channel conditions and global performance indicators. By relying on standard frame aggregation mechanisms, the model can be seamlessly embedded into any Enterprise SD-WLAN by obtaining the data needed from the control plane, and then returning the output back to this in order to efficiently adapt the frame size to the needs of each user. Our approach has been gauged by analysing a multitude of scenarios, with the results showing an average improvement of 18.36% in goodput over standard aggregation mechanisms.

Frame-aggregated link adaptation algorithm for IEEE 802.11n networks

Abstract: Channel condition is considered an important issue that affects the performance in wireless networks. Link Adaptation techniques have been proposed to improve the degraded network performance by adjusting the design parameters, e.g. the modulation and coding schemes, in order to adopt 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 algorithm is designed to consider the feasible aggregated frame length that should be adapted according to the changing environments. In this paper, A frame-aggregated link adaptation (FALA) algorithm is proposed to dynamically adjust system parameters in order to improve the network goodput under varying channel conditions. For the purpose of maximizing the network goodput, both the optimal frame payload size and the modulation and coding schemes are jointly acquired according to the signal-to-noise ratio under specific channel condition. Numerical results illustrate that the proposed FALA protocol can effectively increase the goodput performance comparing with other existing link adaptation schemes, especially under dynamically changing environments.

Implementation of video data transmission protocol for a narrow sewer pipe screening system using drifting wireless cameras

Abstract: Saving the cost for inspecting old sewer pipe is a key issue for keeping cities sustainable. To this end, we have been working on developing a low-cost sewer screening method by drifting small capsules with a camera and a wireless LAN function. We have developed a protocol for transmitting video data recorded by multiple camera-capsules drifting in the same pipe and have been investigating the performance of wireless LAN communication in narrow underground pipes. In this paper, we report the improvement of the video data transfer protocol considering the frame aggregation function of IEEE 802.11n Wireless LAN to improve the reliability, also report the measured performance of IEEE 802.11n in a narrow reinforced concrete sewer pipe.