Frame Aggregation Research Articles

Deep Reinforcement Learning Based Multi-Link Frame Aggregation Length Optimization in Next Generation Wi-Fi Networks

Deep Reinforcement Learning Based Multi-Link Frame Aggregation Length Optimization in Next Generation Wi-Fi Networks

VoWiFi cell capacity improvement using A‐MPDU frame aggregation technique for VBR traffic

VoWiFi cell capacity improvement using A‐MPDU frame aggregation technique for VBR traffic

Improved VoWiFi cell capacity using A‐MPDU for frame aggregation in sixth‐generation WLAN standard

AbstractThe rapid progress in wireless communication technology and proliferation of multimedia applications, including voice over WiFi (VoWiFi), demand exploration of innovative approaches to enhance the network performance and quality of service. We propose a technique for enhancing the cell capacity of wireless local area network (WLAN) access point that provides VoWiFi service in the sixth‐generation WLAN standard. The proposed technique uses the aggregate media access control protocol data unit (A‐MPDU) for frame aggregation with constant bit rate (CBR) traffic in the WiFi 6 standard (i.e., IEEE 802.11ax). On the other hand, the retransmission of voice packets substantially deteriorates the VoWiFi cell capacity. We compare the results obtained from the use of WiFi 6 with currently existing WLAN standards, such as IEEE 802.11b/g/n/ac. This comparison focuses on distributed coordination function interframe spacing (DIFS) and arbitration interframe spacing (AIFS) using CBR traffic. Using our technique, we can increase the VoWiFi cell capacity for CBR traffic by 24.25% and 25.20% when using DIFS and AIFS, respectively, while considering the A‐MPDU frame aggregation technique.

Channel Bonding Effects of the IEEE802.11n Standard on the WLANs Performance

The evolution of wireless communication technologies has led to the widespread adoption of Wireless Local Area Networks (WLANs). IEEE 802.11n provides many enhancements in physical and MAC layers such as (channel bonding(CB), Multiple Input/Output (MIMO), frame aggregation and block acknowledgment) to achieve higher throughput and improved reliability. As a result, CB is a key technique for enhancing the performance of wireless network. It is frequently used in various fields, such as workplace networks and live streaming. This research paper focuses on analyzing the effect of channel bonding on the performance of the IEEE802.11n WLANs, especially in two terms (throughput and delay). By using the OPNET (Riverbed) Modular version 17.5, five various number of nodes (5, 10, 15, 18 and 30 nodes) uniform topologies are modeled and simulated to investigate the WLANs performance for various MIMO up to 4 SS. Simulation outcomes indicated that applying of CB enhanced both delay and velocity performance. Specifically, in the scenarios (30 nodes), for MIMO (4×4) spatial streams, the average throughput value for channel bonding is (157.28 Mbps) compared to (87.41 Mbps) for no-channel bonding, and the average delay value for channel bonding is (0.0476 Sec) compared to (0.06762 Sec) for no-channel bonding. The enhancement values in the performance of WLANs are 42% and 79.9% for latency and throughput (velocity), respectively.

Enhancement of VoWiFi cell capacity using A‐MPDU frame aggregation technique in WiFi 6 considering VBR traffic

SummaryThe rapid growth in wireless communication technology and the proliferation of multimedia applications, specifically Voice over Wireless Fidelity (VoWiFi), necessitate the exploration of innovative techniques for improving network performance and quality‐of‐service (QoS). This research paper presents an innovative approach to enhance VoWiFi cell capacity using aggregate medium access control protocol data unit (A‐MPDU) frame aggregation for variable bit rate (VBR) traffic. In this research article, we investigated the effect that the retransmission of voice packets has on the cell capacity of a wireless local area network (WLAN) standard that provides VoWiFi service while taking into account the A‐MPDU approach. When taking into consideration the constant bit rate (CBR) and VBR traffics, we compared the results obtained using WiFi 6 (i.e., IEEE 802.11ax) with older WLAN standards such as IEEE 802.11b/g/n/ac.

Pendant drop motion and stability in vertical airflow

When exposed to an ascending flow, pendant drops oscillate at magnitudes determined by windspeed, drop diameter, and needle diameter. In this study, we investigate the retention stability and oscillations of pendant drops in a vertical wind tunnel. Oscillation is captured by a high-speed camera for a drop Reynolds number Re = 200–3000. Drops at Re ≲ 1000 oscillate up to 12 times the frequency of drops with high Re. Increasing windspeed enables larger volume drops to remain attached to the needles above Re = 500. We categorize drop dynamics into seven behavioral modes according to the plane of rotation and deformation of shape. Video frame aggregation permits the determination of a static, characteristic shape of our highly dynamic drops. Such a shape provides a hydraulic diameter and the evaluation of the volume swept by the oscillating drops with time. The maximum swept volume per unit drop volume occurs at Re = 600, corresponding to the peak in angular velocity.

Hybrid hierarchical aggregation with semi dynamic optical line terminal distribution algorithms optimization for ethernet passive optical networks

The goal of hybrid fiber-wireless (Fi-Wi) access networks is to ubiquitously integrate the accessible vast amounts of optical network bandwidth with wireless network mobility at minimal cost and simple network installation. Fi-Wi networks can employ both Radio over Fiber (RoF) and Radio and Fiber (R&F) technologies. The difference between them is that R&F uses two MAC controls: one for optical fiber and one for wireless networks. One can reduce interference within the wireless subnetwork while avoiding the negative effects of fiber optic propagation delays on the network. RoF, on the other hand, uses MAC control for both optical and wireless networks, resulting in network latency and congestion. This paper demonstrates (i) the use of semi-dynamic bandwidth allocation and (ii) the use of hierarchical frame aggregation for access in next-level integrated Ethernet passive optical network EPON/WLAN-based generations and presents the ongoing research to improve the Quality of Service in wireless and fiber access networks. The study includes an offered load up to 10 Gbps, reaching the up-to-date levels. The obtained results reveal that the data, voice, and video latency are reduced, respectively, by 35%, 25%, and 30%. At the same time, the corresponding throughput is increased, respectively, for data, voice, and video by 35%, 25%, and 30%.

TCP BBR-n: Increased throughput for wireless-AC networks

Google proposed a new TCP congestion control algorithm (CCA), Bottleneck Bandwidth and Round-trip propagation time (BBR) which has opened up new dimensions in congestion control. BBR tries to operate near Kleinrock’s operating point to avoid excessive queue formation at the bottleneck and to use the link bandwidth optimally. BBR creates a model of the network path by measuring the bottleneck bandwidth and minimum round-trip time (RTT) to maximize the delivery rate and minimize latency. BBR v2 is an updated version of BBR which addresses many shortcomings of the original BBR (BBR v1) such as interprotocol fairness, RTT fairness, and excessive retransmissions. However, BBR v2 has certain limitations in its operation in IEEE 802.11ac (Wi-Fi 5) networks. The default BBR v2 limits the throughput of Wi-Fi 5 and an increased latency has been observed. This is because the Wi-Fi 5 frame aggregation logic is underutilized and fewer frames are being sent to the Wi-Fi 5 interface. In this paper, we have proposed BBR-n (BBR new) which provides better throughput than the generic BBR v2 in the Wi-Fi 5 networks. Real-time experiments were performed over a physical testbed using Flent to confirm that BBR-n achieves over double throughput as compared to generic BBR v2 and reduced latency in networks as compared to pure loss-based variants such as Cubic and Reno.

Federated reinforcement learning based task offloading approach for MEC-assisted WBAN-enabled IoMT

The exponential proliferation of wearable medical apparatus and healthcare information within the framework of the Internet of Medical Things (IoMT) introduces supplementary complexities pertaining to the elevated Quality of Service (QoS) of intelligent healthcare in the forthcoming 6G era. Healthcare services and applications need ultra-reliable data transfer and processing with ultra-low latency and energy usage. Wireless Body Area Network (WBAN) and Mobile Edge Computing (MEC) technologies enabled IoMT to handle large amounts of data sensing, transmission, and processing while maintaining good QoS. Traditional frame aggregation (FA) systems in WBAN, on the other hand, create an excessive number of control frames during data transmission, resulting in significant latency and energy consumption, as well as a lack of flexibility. A Federated Reinforcement Learning (FRL) based TO Approach is recommended in this research. In the beginning, different types of service-related information were separated into queues with equal QoS needs. The duration of the FA was then automatically determined by the aggregation vertex based on energy consumption, latency, and throughput using FRL. Finally, based on the existing status, the amount of tasks offloaded was determined. The simulation results demonstrate that, as compared to the baseline schemes, the suggested FRLTO efficiently reduces energy consumption and latency while enhancing throughput and total WBAN utilization. Numerical results show that the proposed scheme improves the throughput by 37.06% and reduced the energy consumption by around 69.84% and time delay by about 6.23%, as compared to the state-of-the-art existing baseline schemes.

ECG signal reconstruction based on facial videos via combined explicit and implicit supervision

Accurate and continuous electrocardiogram (ECG) signal measurements are of paramount importance for the prevention of cardiovascular disease. Existing physiological measurement methods require the placement of sensors on the human body, which proves uncomfortable and inconvenient during long-term monitoring. Recent research has found that heart rates can be extracted from noncontact RGB facial videos by measuring subtle color changes in the skin region. However, designing an accurate feature representation structure for physiological measurements remain a challenge. This study presents a novel end-to-end ECG signal synthesis network using short facial video sequences. We combined explicit and implicit supervision structures, which allows the network to perceive physiological-related features from unlabeled feature sets and adopts a channel and frame aggregation attention mechanism to boost the network acquisition of periodic spatiotemporally correlated features. Finally, the generated features were fed into a physiological estimator consisting of a serially connected “Generative Adversarial” module for ECG signal reconstruction. Experimental results on publicly available datasets and a new dataset collected by us demonstrate that the proposed network outperforms state-of-the-art methods in ECG signal measurements. This is a powerful tool for use in telemedicine and health monitoring.

A DQN-Based Frame Aggregation and Task Offloading Approach for Edge-Enabled IoMT

The rapid expansion of wearable medical devices and health data of Internet of Medical Things (IoMT) poses new challenges to the high Quality of Service (QoS) of intelligent health care in the foreseeable 6G era. Healthcare applications and services require ultra reliable, ultra low delay and energy consumption data communication and computing. Wireless Body Area Network (WBAN) and Mobile Edge Computing (MEC) technologies empowered IoMT to deal with huge data sensing, processing and transmission in high QoS. However, traditional frame aggregation schemes in WBAN generate too much control frames during data transmission, which leads to high delay and energy consumption and is not flexible enough. In this paper, a Deep Q-learning Network (DQN) based Frame Aggregation and Task Offloading Approach (DQN-FATOA) is proposed. Firstly, different service data were divided into queues with similar QoS requirements. Then, the length of the frame aggregation was selected dynamically by the aggregation node according to the delay, energy consumption, and throughput by DQN. Finally, the number of tasks offloaded was selected due to the current state. Compared with the traditional scheme, the simulation results show that the proposed DQN-FATOA has effectively reduced delay and energy consumption, and improved the throughput and overall utilization of WBAN.

Two-level frame aggregation with enhanced A-MPDU for signal-to-noise ratio efficiency in IEEE 802.11n WLANs

<p>One of the most important frame aggregation features for enhancing the speed of IEEE 802.11n wireless local area networks (WLANs) through sharing headers and timing overheads is an aggregate MAC protocol data unit (A-MPDU). However, because aggregation overhead affects A-MPDU frame size, the A-MPDU performance falls short of user expectations. The variable signal-to-noise ratio (SNR) is significantly decreased as a result of the influence of lost sub-frame on the volume of sub-frames that may be aggregated (the level of aggregation). In order to solve this issue, this study suggests an improved A-MPDU with reduced header overheads as well as a efficient two-level aggregation technique based on enhanced A-MPDU. To test the suggested plan, a simulation experiment was run on NS-3. The results show that the suggested two-level aggregation approach works better than the existing methods by achieving higher throughput, SNR, and a efficient medium access control (MAC) layer.</p>

Performance Analysis and Energy Efficiency of MU- (OFDMA & MIMO) Based Hybrid MAC Protocol of IEEE 802.11ax WLANs

Future wireless networks are characterized by densely populated stations (STAs) with limited battery capacity. These networks demand high aggregate throughput, increased energy efficiency, and high Random Access (RA) capability, i.e., the system's ability to allow new STAs to join the network quickly. WiFi-6 (also known as IEEE 802.11 ax) is the new generation WLAN standard proposed to meet these diverse system requirements. In this paper, we analyze the effect of Multi-User Multi-Input Multi-Output (MU-MIMO) & MU-Orthogonal Frequency Division Multiple Access (MU-OFDMA)-based Hybrid Access (HA) mechanism of WiFi-6 systems. We propose a fixed Point model for analyzing this HA mechanism. By solving the system model, we investigate the impact on critical system metrics such as throughput, energy efficiency, Random Access capability, and delay due to a variation in (i) the number of resource units (RUs), (ii) the channel sounding interval, and (iii) the Target Wake-Up time interval. We have studied the effect of the non-saturated traffic model driven by the Poisson and Pareto arrival processes. Further, we have modeled the buffer status-based dynamic scheduling of RUs for the MU-OFDMA-based HA mechanism. We have also analyzed the novel Multi-Traffic Identifier-based frame aggregation feature. Further, we have analyzed the hand-off mechanism for a multi-BSS (Basic Service Set) scenario using the Stochastic geometry-based mathematical tool. We have used Matlab-based simulations for our study and found that the simulation results closely match the analytical results. Our study sheds light on the trade-off that exists between different performance metrics of the 802.11ax HA mechanism.

Improved Feature Extraction and Similarity Algorithm for Video Object Detection

Video object detection is an important research direction of computer vision. The task of video object detection is to detect and classify moving objects in a sequence of images. Based on the static image object detector, most of the existing video object detection methods use the unique temporal correlation of video to solve the problem of missed detection and false detection caused by moving object occlusion and blur. Another video object detection model guided by an optical flow network is widely used. Feature aggregation of adjacent frames is performed by estimating the optical flow field. However, there are many redundant computations for feature aggregation of adjacent frames. To begin with, this paper improved Faster RCNN by Feature Pyramid and Dynamic Region Aware Convolution. Then the S-SELSA module is proposed from the perspective of semantic and feature similarity. Feature similarity is obtained by a modified SSIM algorithm. The module can aggregate the features of frames globally to avoid redundancy. Finally, the experimental results on the ImageNet VID and DET datasets show that the mAP of the method proposed in this paper is 83.55%, which is higher than the existing methods.

Video Sampled Frame Category Aggregation and Consistent Representation for Cross-Modal Retrieval

Many current video and text cross-modal retrieval research works focus on narrowing the semantic gap between video and text, but ignore the semantic difference between different sampled frames in the same video and the correlation of feature distribution of objects contained in different sampled frames in the same video, as a result, the features of the sampled frames in the final learned video cannot well represent the semantic features of the whole video. To overcome the shortcomings of existing studies, we first use a pre-trained video frame classification-aggregation network to make the object categories contained in different sampled frames in the same video be more close to the important object categories contained in the whole video, so as to promote the feature distribution of different sampled frames in the same video to be consistent, and increase the relevance of object features in different frames. Then we propose a video internal frame aggregation loss module to solve the problem of inconsistent feature distribution between different frame features encoded by video encoder in the same video and the aggregation feature of the sampled frame, thus enhancing the ability of video sampled frame aggregation feature representation. Experiments conducted on three common datasets MSVD, MSR-VTT and DiDeMo demonstrate the validity of the proposed approach.

Frame aggregation aware cluster based association management for heterogeneous users

Frame aggregation aware cluster based association management for heterogeneous users

A two dimensional Markov chain model for aggregation-enabled 802.11 networks

Frame aggregation technique opts for optimising channel usage efficiency for 802.11-based networks by amortising the transmission overhead over several aggregated packets. Despite its potential benefit, the gain achieved by this technique is still far from the expected levels. The underlying causes are attributed to certain deficiencies in the specification as well as the implementation of the conventional frame aggregation scheme. Throughout this paper, we focus on MAC protocol data unit aggregation (A-MPDU) technique. We provide a simple, yet, highly accurate mathematical model for the conventional A-MPDU technique that reflects the effect of the block acknowledgement (Block Ack) window limit on the maximum aggregation size. The effectiveness of our model is validated by ns-3 simulator. An analytical-based study is further conducted to compare the performance of the greedy A-MPDU aggregation scheme and that of the conservative scheme supported by most of wireless fidelity (Wi-Fi) card drivers.

Machine Learning based Frame Size Optimization in WLAN Downlink MU-MIMO Channel with the Least Cost of Delay

The key enhancement in the medium access control (MAC) layer is frame aggregation introduced by the IEEE 802.11n/ac standard to accommodate the growing traffic demand in the WLAN by allowing multiple packets aggregated per transmission. Frame aggregation efficiently reduces control overhead in the MAC layer, such as the MAC header and thus it helps to enhance transmission efficiency and throughput performance of WLAN. However, heterogeneous traffic demand among streams in the WLAN downlink MU-MIMO channel creates a challenge to efficiently utilize the benefits of frame aggregation. Transmission efficiency is also compromised during frame size setting determination because when a frame size is larger, the impact of the overhead frame can be lower, but they are also more vulnerable to transmission errors. Thus, this trade-off between maximizing frame size and minimizing overhead frames should be addressed by employing an adaptive frame aggregation technique to derive the optimal frame size that would maximize the throughput in WLAN downlink MU-MIMO channel. Moreover, when frame aggregation approach is employed, more frames must wait before transmission in a buffer which causes a delay in the performance of WLAN. Thus, analysing the trade-off between maximizing throughput and minimizing delay is a critical issue that should also be addressed to enhance the performance of WLAN. However, the majority of the existing adaptive aggregation algorithms in the WLAN downlink MU-MIMO channel are focused to maximize the throughput or minimize the delay. The main contribution of this paper is to propose a machine learning-based frame size optimization algorithm by extending our earlier approach in considering the cost of delay to maximize the system throughput of WLAN. The effectiveness of the proposed scheme is evaluated over the FIFO Baseline Approach and earlier conventional approaches under the effects of various traffic patterns, channel conditions, and the number of STAs.

Improvement of Solid Photoluminescence of Tridentate Schiff Base Pt(II) Foldamer by Hybridizing Poly (4‐Vinylpyridine)

AbstractA series of bis(tridentate Schiff base platinum) complex (Pt2L2) linked with 2‐methylenepropane‐1,3‐diyl were synthesized, and their photophysical properties were examined to analyze different solid states. X‐ray crystallographic analysis indicated that Pt2L2 containing 4‐vinylpyridine (Pt2L24VP) as a ligand had a helically‐folded structure. It caused upfield chemical shifts and a redshift in UV‐Vis absorption spectra compared to the analogous mononuclear complex. Pt2L2 containing dimethylsulfoxide (Pt2L2DMSO) had no such helically‐folded structure due to the steric repulsion of DMSO. Pt2L24VP did not exhibit solid photoluminescence owing to the crystal packing. The mixture of poly(4‐vinylpyridine) (P4VP) and Pt2L2DMSO (Pt2L2DMSO−P4VP) showed a similar redshift in UV‐Vis absorption and excitation spectra to Pt2L24VP, implying the helically‐folded structure of Pt2L2 with P4VP coordination. The Pt2L2DMSO−P4VP exhibited solid photoluminescence with a longer phosphorescence lifetime (2.4 μs) than the Pt2L24VP crystals (0.5 μs), indicating regulated aggregation of the Pt2L2 frames in the P4VP matrix.

Aggregating Without Bloating: Hard Times for TCP on Wi-Fi

Since the definition of the bufferbloat phenomenon, several Linux kernel modules have been introduced in its TCP/IP stack, and there is a lack of experimental studies on their effects when coupled with WLAN technologies, in particular, IEEE 802.11n and IEEE 802.11ac. One essential algorithm introduced is named TCP Small Queues (TSQ) and has the role of limiting the number of packets that a TCP socket can enqueue in the stack, waiting for the physical layer to send the packets before enqueueing extra data. A second significant TCP algorithm is named TCP Pacing (TP) and regulates the pace used by the socket to enqueue packets in the stack, regulating the formation of bursts of data. These mechanisms affect the frame aggregation logic on WLAN networks and compromise the throughput-latency tradeoff of all the TCP variants. This paper presents an experimental evaluation of these techniques investigating the wireless network performance of several TCP congestion control variants under the presence of different TSQ and TP policies, modeling also their interaction.