Browsing by Subject "Age of Information"

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Open Access
Age of information-oriented comparative evaluation of channel access mechanisms in multi-rate wireless lans
(2023-08) Erdem, Umut Utku
Delay-sensitive applications have recently garnered significant attention because of the increasing demand for real-time data and time-critical information. In delay-sensitive systems, the timeliness of the delivered information is crucial to guarantee a reliable operation. A performance metric called Age of Information (AoI) is introduced in the literature to measure the freshness of information. In this study, various channel access methods are comparatively evaluated for stations transmitting age-sensitive status update packets over a multi-rate IEEE 802.11 WLAN. For wireless networks carrying conventional data traffic, the legacy channel access mechanism imposed by the Distributed Coordination Function (DCF) allows sources to access the channel equally. This mechanism results in a throughput-fair bandwidth allocation which is also known as a performance anomaly in the literature. Airtime-fair channel access methods have been proposed in the literature for multirate wireless LANs to mitigate this anomaly. Recently, there has been a surge of interest in status update systems with the emergence of performance criteria called age of information. Age-based performance metrics (AoI, peak AoI) are more effective to satisfy the requirements of the carried age-sensitive traffic as opposed to using conventional performance metrics (throughput, delay, or loss). In this study, we propose a novel channel access mechanism for age-sensitive traffic which is devised to lessen the mean Peak AoI (PAoI) averaged over all the sources in the network, which is termed as the system PAoI. The proposed channel access mechanism effectively reduces the system PAoI compared to LCA and PFCA. Although system PAoI performance improvement depends on the system configuration, i.e. packet size, the multi-rate mixture of the network etc., system PAoI can be reduced up to 12.04% and 27.44% compared to the legacy channel access and airtime-fair channel access, respectively, for the considered system configurations in this study. Although the proposed channel access mechanism outperforms legacy and airtime-fair channel access mechanisms in terms of system PAoI, it may lead to a reduction in the overall throughput of the system compared to airtime-fair channel access.
Open Access
Aging wireless bandits: regret analysis and order-optimal learning algorithm
(IEEE, 2021-11-13) Atay, Eray Unsal; Kadota, Igor; Modiano, Eytan
We consider a single-hop wireless network with sources transmitting time-sensitive information to the destination over multiple unreliable channels. Packets from each source are generated according to a stochastic process with known statistics and the state of each wireless channel (ON/OFF) varies according to a stochastic process with unknown statistics. The reliability of the wireless channels is to be learned through observation. At every time-slot, the learning algorithm selects a single pair (source, channel) and the selected source attempts to transmit its packet via the selected channel. The probability of a successful transmission to the destination depends on the reliability of the selected channel. The goal of the learning algorithm is to minimize the Age-of-Information (AoI) in the network over T time-slots. To analyze its performance, we introduce the notion of AoI-regret, which is the difference between the expected cumulative AoI of the learning algorithm under consideration and the expected cumulative AoI of a genie algorithm that knows the reliability of the channels a priori. The AoI-regret captures the penalty incurred by having to learn the statistics of the channels over the T time-slots. The results are two-fold: first, we consider learning algorithms that employ well-known solutions to the stochastic multi-armed bandit problem (such as ϵ-Greedy, Upper Confidence Bound, and Thompson Sampling) and show that their AoI-regret scales as Θ(log T); second, we develop a novel learning algorithm and show that it has O(1) regret. To the best of our knowledge, this is the first learning algorithm with bounded AoI-regret.