In the transmission control protocol (TCP), the receiver is required to send acknowledgment (ACK) segments to inform the sender about the successful reception of the data segments. In wireless networks such as Wi-Fi, TCP ACKs introduce additional contention in the shared wireless channel that adversely affects the end-to-end TCP performance. But thanks to the cumulative nature of TCP ACKs, the receiver can send fewer ACKs. A number of studies have improved TCP’s performance on Wi-Fi by using various ACK reduction schemes. However, none have taken advantage of the important property of Wi-Fi’s frame aggregation scheme, which carries multiple data segments and thus causes bulk arrivals at the receiver. This property can be efficiently exploited to dramatically reduce the number of TCP ACKs. We propose an aggregation-aware ACK delaying (TCP-AAD) scheme where the receiver TCP sends only one ACK segment for all data segments received in a single Wi-Fi aggregate frame. This approach significantly reduces the number of ACKs, leading to less contention on the shared wireless medium and increased end-to-end throughput. The proposed and existing schemes were implemented in the ns-3 simulator, and their performance was evaluated for various numbers of stations, as well as different distances between stations and the access point.

The major drawback of Wi-Fi fingerprinting-based localization systems is the resource-heavy process of collecting fingerprint data. However, nowadays, generative models are drawing attention due to their ability to efficiently generate synthetic Wi-Fi fingerprints based on some real fingerprints collected from the target environment. Existing synthetic fingerprint generation approaches are based on generative adversarial networks or variational autoencoders. In this work, we propose a new scheme to generate synthetic Wi-Fi fingerprints using a conditional denoising diffusion probabilistic model (cDDPM). Performance evaluations with two open datasets show that the proposed method can perform well, particularly in scenarios where the number of real fingerprints is extremely limited.

Aggregation-enabled wireless local area networks (WLANs) have an automatic repeat-request (ARQ) mechanism called a block acknowledgement window (BAW), which has a significant impact on frame aggregation size, throughput, and delay under noisy channel conditions. While accurate estimations of network performance are essential for traffic engineering and guaranteed quality of service, the existing mathematical modeling approaches do not include BAW operations, and thus, are not applicable to aggregation-enabled WLANs with BAW mechanism. In this paper, we propose a new and accurate Markov chain model to estimate the average aggregation size resulting from BAW operations under noisy channel conditions. The obtained average aggregation size is used in the proposed enhanced performance model of aggregation-enabled WLANs. Our numerical and simulation analysis shows that, for 50 nodes transmitting saturated uplink traffic on a channel with a packet error rate (PER) of 0.25, ignoring the BAW operations when modeling the current protocol results in throughput and access delay errors as high as 55 and 77 percent, respectively; but the proposed enhanced model, which captures the BAW's impact, produces less than 2.5 percent errors in throughput and access delay estimations.

Currently available aggregation-enabled wireless local area network (WLAN) implementations do not apply aggregate MAC protocol data unit (A-MPDU) aggregation to real-time voice over Internet protocol (VoIP) traffic because of its strict end-to-end delay requirements. When the number of VoIP sessions in WLAN increases, both mobile station (MS) and access point (AP) face heavy voice packet losses leading to unacceptable quality of service (QoS). Therefore, VoIP capacity of aggregation-enabled WLANs like IEEE 802.11n and 802.11ac is very limited under currently available implementations. In this paper, we propose a QoS-aware adaptive A-MPDU aggregation scheduler for enhanced VoIP capacity over aggregation-enabled WLANs, which adaptively applies A-MPDU aggregation to voice traffic at both MS and AP nodes, considering dynamic contention in WLAN, delay in backbone network, and end-to-end QoS requirements of real-time voice traffic. Performance evaluations of the proposed scheme showed that it can achieve as much as 5.3 times bigger capacity compared to the existing implementations, while delivering all voice packets to destination with less than 150ms end-to-end delay and less than 50ms average jitter.

Currently available IEEE 802.11n implementations do not apply aggregate MAC protocol data unit (A-MPDU) aggregation to voice traffic because of its strict end-to-end delay requirements. When multiple nodes contend to send voice and other lower-priority traffic, 802.11n network faces serious performance degradation problems for two major reasons: i) due to the medium access control (MAC) and physical (PHY) layer overheads of individual short voice packet transmissions, the available network throughput for lower-priority traffic significantly decreases, and ii) increased contention results in high voice packet loss rate (PLR). This paper proposes a QoS-aware adaptive A-MPDU aggregation scheduler for voice traffic in 802.11n/ac WLANs that adaptively applies A-MPDU aggregation to voice traffic based on QoS requirements and periodically obtained average medium access delay and end-to-end delay statistics. Performance evaluations on 20 nodes sending uplink 64Kbps voice and saturated best-effort traffic at 150Mbps PHY rate showed that the proposed scheme achieved 160% throughput improvement compared to default implementations in driver. During the simulations with 50 nodes transmitting at 600Mbps PHY rate, the proposed scheme provided up to 275Mbps throughput against 0.13Mbps by default scheme; the proposed scheme delivered all voice packets (PLR 1/4 0 percent) and over 99.9 percent of them had less than 150ms end-to-end delay.