This paper investigates a multiuser scheduling problem within an uplink multiple-input multi-output (MIMO) status update network, consisting of a multi-antenna base station (BS) and multiple single-antenna devices. The presence of multiple antennas at the BS introduces spatial degrees-of-freedom, enabling concurrent transmission of status updates from multiple devices in each time slot. Our objective is to optimize network-wide information freshness, quantified by the age of information (AoI) metric, by determining how the BS can best schedule device transmissions, while taking into account the random arrival of status updates at the device side. To address this decision-making problem, we model it as a partially observable Markov decision process (POMDP) and establish that the evolution of belief states for different devices is independent. We also prove that feasible belief states can be described by finite-dimensional vectors. Building on these observations, we develop a dynamic scheduling (DS) policy that minimizes a configurable drift in each time slot to solve the POMDP, and then derive an upper bound of its AoI performance, which is used to optimize the parameter configuration. To gain more design insights, we investigate a symmetric network, and put forth a fixed scheduling (FS) policy that minimizes the drift with an optimized fixed number scheduling, thereby yielding lower computational complexity. An action space reduction strategy is applied to further reduce the computational complexity of both DS and FS policies. Our numerical results validate our analyses and indicate that the DS policy with the reduced action space performs almost identically to the original DS policy, and both outperform the baseline policies.

The latest WiFi standard, IEEE 802.11ax (WiFi 6), introduces a novel uplink random access mechanism called uplink orthogonal frequency division multiple access-based random access (UORA). While existing work has evaluated the performance of UORA using conventional performance metrics, such as throughput and delay, its information freshness performance has not been thoroughly investigated in the literature. This is of practical significance as WiFi 6 and beyond are expected to support real-time applications. This paper presents the first attempt to fill this gap by investigating the information freshness, quantified by the Age of Information (AoI) metric, in UORA networks. We establish an analytical framework comprising two discrete-time Markov chains (DTMCs) to characterize the transmission states of stations (STAs) in UORA networks. Building on the formulated DTMCs, we derive an analytical expression for the long-term average AoI (AAoI), facilitating the optimization of UORA parameters for enhanced AoI performance through exhaustive search. To gain deeper design insights and improve the effectiveness of UORA parameter optimization, we derive a closed-form expression for the AAoI and its approximated lower bound for a simplified scenario characterized by a fixed backoff contention window and generate-at-will status updates. By analyzing the approximated lower bound of the AAoI, we propose efficient UORA parameter optimization algorithms that can be realized with only a few comparisons of different possible values of the parameters to be optimized. Simulation results validate our analysis and demonstrate that the AAoI achieved through our proposed parameter optimization algorithm closely approximates the optimal AoI performance obtained via exhaustive search, outperforming the round-robin and max-AoI policies in large and low-traffic networks.

This paper develops scheduling policies to optimize the information freshness, quantified by the age of information (AoI) metric, in an uplink multi-user SIMO status update system. The multi-user scheduling problem is formulated as a Markov decision process (MDP) to derive the optimal policy that minimizes the average AoI across devices. However, the optimal policy suffers from high complexity due to dimensionality in large networks. To address this, a low-complexity max-weight (MW) policy is developed for large-scale networks using the Lyapunov optimization framework. The MW policy dynamically determines the subset of devices to schedule in each time slot by maximizing the expected AoI drop of the subsequent time slot. Simulations are conducted to compare the performance of the optimal policy, the MW policy, and the baseline fixed scheduling (FS) policy that always schedules a fixed number of devices with the highest AoI. The results show that the MW policy achieves close-to-optimal performance. Moreover, for a given network setup, there exists an FS policy with a particular number of scheduled devices that can approach the MW policy. This observation inspired the development of another low-complexity scheduling policy, termed optimized FS (OFS). This policy further optimizes the number of devices scheduled under the FS policy based on specific network configurations. Closed-form expressions for the average peak AoI and the approximated average AoI of the FS policy with a given number of scheduled devices are derived to determine the optimal number of scheduled devices for the OFS policy under different network setups. Simulation results validate the theoretical analysis and show that the OFS policy achieves performance comparable to the MW policy while circumventing the need for per-slot optimization.

This paper focuses on optimizing the long-term average age of information (AoI) in device-to-device (D2D) networks through age-aware link scheduling. The problem is naturally formulated as a Markov decision process (MDP). However, finding the optimal policy for the formulated MDP in its original form is challenging due to the intertwined AoI dynamics of all D2D links. To address this, we employ the Lyapunov optimization framework to develop a dynamic age-aware scheduling policy. Specifically, we explore two scenarios: known statistical channel state information (CSI) and known instantaneous CSI. For the statistical CSI case, we propose a message passing neural network (MPNN)-based policy for real-time scheduling. The MPNN is trained in an unsupervised manner with a loss function designed to minimize per-slot Lyapunov drift. For the instantaneous CSI case, we introduce a Gurobi-based policy, using the solver to minimize per-slot Lyapunov drift for scheduling decisions. To further reduce the computational complexity, we also propose a greedy heuristic policy that approximates drift minimization. Extensive simulation results show that our proposed age-aware scheduling policies have superior performance compared to the baselines, and can be applied to large-scale D2D networks.

Enhancing information freshness in wireless networks has gained significant attention in recent years. To optimize or analyze information freshness, which is often characterized by the age of information (AoI) metric, extensive theoretical studies have been conducted on various wireless networks. Early research has demonstrated the significance of last-come-first-served (LCFS) packet scheduling and controlled status sampling (i.e., packet generation) in improving information freshness. These mechanisms have been widely adopted in subsequent studies. However, the effective implementation of these mechanisms in commercial off-the-shelf (COTS) wireless devices has not been thoroughly investigated, which could limit the practical application of information freshness-oriented protocols in real-world systems. Our work aims to address the gap by exploring the effective implementation of the information freshness-oriented mechanisms mentioned above in COTS WiFi devices that use the Linux operating systems. Our attempts reveal that the physical layer queue of WiFi devices operates on a first-come-first-served (FCFS) basis, and the packet generation process cannot be precisely controlled by default. To overcome these challenges, we develop Fresh-Fi, an information freshness-oriented protocol stack that involves careful customizations to the Linux networking protocol stack. Fresh-Fi mainly incorporates a mac80211 subsystem-based LCFS queue and a real-time kernel-based cross-layer tunnel between the mac80211 subsystem and the application layer for triggered packet generation. Our experiments show that implementing Fresh-Fi can significantly improve AoI performance. Specifically, we observed that Fresh-Fi improved AoI performance by over 13 times when compared to a baseline design that relies on an LCFS queue implemented in the application layer in standard Linux.

This paper investigates a multiuser scheduling problem within an uplink multiple-input multi-output (MIMO) status update network, consisting of a multi-antenna access point (AP) and multiple single-antenna devices. The presence of multiple an-tennas at the AP introduces spatial degrees-of-freedom, enabling concurrent transmission of status updates from multiple devices in each time slot. Our objective is to optimize network-wide information freshness, as measured by the age of information (AoI) metric, by determining how the AP can best schedule device transmissions, while taking into account the random arrival of status updates at the device side. It is worth noting that the AP has partial observations of the system time of the latest updates at each device when making scheduling decisions. To address this decision-making problem, we model it as a partially observable Markov decision process (POMDP) and establish that the evolution of belief states for different devices is independent. We also manage to characterize feasible belief states using three-dimensional vectors. Building on this foundation, we develop a dynamic scheduling (DS) policy to solve the POMDP and implement an action space reduction. Our numerical results indicate that the DS policy with the reduced action space performs almost identically to the original DS policy, and both outperform the baseline policy that schedules a fixed number of devices.

This paper investigates user scheduling in a slotted downlink multi-user multiple-input multi-output (MU-MIMO) system, where a base station (BS) equipped with multiple antennas transmits status updates to multiple single-antenna devices. We assume perfect channel station information (CSI) is available at the BS to make scheduling decisions. The goal is to optimize the information freshness of the network, quantified by the network-wide average age of information (AoI), subject to the peak transmission power constraint in each time slot. To tackle this problem, we employ the Lyapunov optimization method to convert the constrained long-term optimization problem into a per-slot optimization problem. We propose two low-complexity algorithms to solve the per-slot optimization problem, namely the AoI-aware scheduling (AS) algorithm and the joint age- and channel-aware scheduling (JACS) algorithm. These algorithms aim to determine the optimal device or devices for delivering the latest status updates to them in each time slot. We evaluate the performance of the proposed algorithms through numerical simulations in both symmetric and asymmetric networks and compare them with two baseline algorithms: semi-orthogonal user group (SUG) and exhaustive search (ES) algorithms. Regardless of the network setup, our proposed algorithms consistently outperform SUG, and demonstrate performance comparable to that of the exhaustive search algorithm. Notably, when the peak transmission power is high enough, the proposed algorithms perform almost as well as the ES algorithm. Thanks to the joint consideration of CSI and AoI, JACS tends to have better performance than AS at the cost of slightly higher computational complexity.

This paper analyzes and optimizes the average Age of Information (AAoI) of Frame Slotted ALOHA with Reservation and Data slots (FSA-RD) in a multi-access network, where multiple users transmit their randomly generated status updates to a common access point in a framed manner. Each frame consists of one reservation slot and several data slots. The reservation slot is further split into several mini-slots. In each reservation slot, users that want to transmit a status update will randomly send short reservation packets in one of the mini-slots to contend for data slots of the current frame. The reservation is successful only if one reservation packet is sent in a mini-slot. The data slots are then allocated to those users that succeed in the reservation slot. In the considered FSA-RD scheme, one user with a status update for transmission, termed active user, may need to perform multiple reservation attempts before successfully delivering it. As such, the number of active user(s) in different frames are dependent and thus the probability of making a successful reservation varies from frame to frame, making the AAoI analysis non-trivial. We manage to derive an analytical expression of AAoI for FSA-RD by characterizing the evolution of the number of active user(s) in each frame as a discrete-time Markov chain. We then consider the FSA-RD scheme with one reservation attempt per status update, termed FSA-RD-One. Thanks to the independent frame behaviors of FSA-RD-One, we attain a closed-form expression for its AAoI, which is further used to find the near-optimal reservation probability. Our analysis reveals the impact of key protocol parameters, such as frame size and reservation probability, on the AAoI. Simulation results validate our analysis and show that the optimized FSA-RD outperforms the optimized slotted ALOHA.

This paper investigates the Age of Information (AoI) performance of Frame Slotted ALOHA with Reservation and Data slots (FSA-RD). We consider a symmetric multi-access network where each user transmits its randomly generated status updates to an access point in a framed manner. Each frame consists of one reservation slot and several data slots. The reservation slot is made up of some mini-slots. In each reservation slot, users, with a status update packet to transmit, randomly send short reservation packets in one of the mini-slots to contend for data slots of the frame. The data slots are assigned to those users that succeed in reservation slot. To provide insights in optimizing the information freshness of FSA-RD, we manage to derive a closed-form expression of the average AoI under FSA-RD by applying a recursive method. Numerical results validate the analytical expression and demonstrate the influence of the frame size and reservation probability on the average AoI. We finally perform a comparison between the AoI performance of FSA-RD with optimized frame size and reservation probability, and that of slotted ALOHA with optimized transmission probability. The comparison results show that FSA-RD can effectively reduce the AoI performance of multi-access networks, especially when the status arrival rate of the network becomes large.

This paper considers a wireless network with a base station (BS) conducting timely status updates to multiple clients via adaptive non-orthogonal multiple access (NOMA)/orthogonal multiple access (OMA). Specifically, the BS is able to adaptively switch between NOMA and OMA for the downlink transmission to optimize the information freshness of the network, characterized by the Age of Information (AoI) metric. For the simple two-client case, we formulate a Markov Decision Process (MDP) problem and develop the optimal policy for the BS to decide whether to use NOMA or OMA for each downlink transmission based on the instantaneous AoI of both clients. The optimal policy is shown to have a switching-type property with obvious decision switching boundaries. A suboptimal policy with lower computation complexity is also devised, which is shown to achieve near-optimal performance via numerical simulations. For the more general multi-client scenario, the optimal solution is the computationally intractable due to the large state and action spaces. As such, we devote to provide a feasible suboptimal policy with low computation complexity. Specifically, inspired by the proposed suboptimal policy of the two-client scenario, we formulate a nonlinear optimization problem to determine the optimal power allocated to each client by maximizing the expected AoI drop of the network in each time slot (i.e., minimizing the expected network-wide AoI of the next slot). The problem is shown to be non-convex, we manage to solve it by approximating it as a convex optimization problem. Simulation results validate the tightness of the adopted approximation. Specifically, the performance of the adaptive NOMA/OMA scheme by solving the convex optimization is shown to be close to that of the max-weight policy solved by exhaustive search. Besides, the adaptive NOMA/OMA scheme achieves significant performance improvement compared to the OMA scheme, especially when the number of clients in the network is large and the transmission SNR is high.

This article considers a cooperative Internet-of-Things (IoT) system with a source aiming to transmit randomly generated status updates to a designated destination as timely as possible under the help of a relay. We adopt a recently proposed concept, the Age of Information (AoI), to characterize the timeliness of the status updates. In the considered system, delivering the status updates via the one-hop direct link will have a shorter transmission time at the cost of incurring a higher error probability, while the delivery of status updates through the two-hop relay link could be more reliable at the cost of suffering longer transmission time. Thus, it is important to design the relaying protocol of the considered system for optimizing the information freshness. Considering the limited capabilities of IoT devices, we propose two low-complexity Age-oriented Relaying (AoR) protocols, i.e., the source-prioritized AoR (SP-AoR) protocol and the relay-prioritized AoR (RP-AoR) protocol, to reduce the AoI of the considered system. Specifically, in the SP-AoR protocol, the relay opportunistically replaces the source to retransmit the successfully received status updates that have not been correctly delivered to the destination, but the retransmission at the relay can be preempted by the arrival of a new status update at the source. Differently, in the RP-AoR protocol, once the relay replaces the source to retransmit the status updates that have not been successfully received by the destination, the retransmission at the relay will not be preempted by new status update arrivals at the source. By carefully analyzing the evolution of the instantaneous AoI, we derive closed-form expressions of the average AoI for both the proposed AoR protocols. We further optimize the generation probability of the status updates at the source in both protocols. Simulation results validate our theoretical analysis and demonstrate that the two proposed protocols outperform each other under various system parameters. Moreover, the protocol with better performance can achieve near-optimal performance compared with the optimal scheduling policy attained by applying the Markov decision process (MDP) tool.

In this paper, we investigate the age minimization problem for a two-hop relay system, under a resource constraint on the average number of forwarding operations at the relay. We first design an optimal policy by modelling the considered scheduling problem as a constrained Markov decision process (CMDP) problem. Based on the observed multi-threshold structure of the optimal policy, we then devise a low-complexity double threshold relaying (DTR) policy with only two thresholds, one for relay's AoI and the other one for the age gain between destination and relay. We derive approximate closed-form expressions of the average AoI at the destination, and the average number of forwarding operations at the relay for the DTR policy, by modelling the tangled evolution of age at relay and destination as a Markov chain (MC). Numerical results validate all the theoretical analysis, and show that the low-complexity DTR policy can achieve near optimal performance compared with the optimal CMDP-based policy. Moreover, the relay should always consider the threshold for its local age to maintain a low age at the destination. When the resource constraint is relatively tight, it further needs to consider the threshold on the age gain to ensure that only those packets that can decrease destination's age dramatically will be forwarded.

This paper considers a wireless network with a base station (BS) conducting timely transmission to two clients in a slotted manner via hybrid non-orthogonal multiple access (NOMA)/orthogonal multiple access (OMA). Specifically, the BS is able to adaptively switch between NOMA and OMA for the downlink transmission to minimize the information freshness, characterized by Age of Information (AoI), of the network. If the BS chooses OMA, it can only serve one client within a time slot and should decide which client to serve; if the BS chooses NOMA, it can serve both clients simultaneously and should decide the power allocated to each client. To minimize the weighted sum of expected AoI of the network, we formulate a Markov Decision Process (MDP) problem and develop an optimal policy for the BS to decide whether to use NOMA or OMA for each downlink transmission based on the instantaneous AoI of both clients. We prove the existence of optimal stationary and deterministic policy, and perform action elimination to reduce the action space for lower computation complexity. The optimal policy is shown to have a switching-type property with obvious decision switching boundaries. A suboptimal policy with lower computation complexity is also devised, which can achieve near-optimal performance according to our simulation results. The performance of different policies under different system settings is compared and analyzed in numerical results to provide useful insights for practical system designs.

This article considers a cognitive radio-based IoT monitoring system, consisting of an IoT device that aims to update its measurement to a destination using cognitive radio technique. Specifically, the IoT device as a secondary user (SIoT), seeks and exploits the spectrum opportunities of the licensed band vacated by its primary user (PU) to deliver status updates without causing visible effects to the licensed operation. In this context, the SIoT should carefully make use of the licensed band and schedule when to transmit to maintain the timeliness of the status update. The timeliness of the status update characterizes how the destination knows the latest information of the SIoT. We adopt a recent metric, Age of Information (AoI), to characterize the timeliness of the status update of the SIoT. We aim to minimize the long-term average AoI of the SIoT while satisfying the collision constraint imposed by the PU by formulating a constrained Markov decision process (CMDP) problem. We first prove the existence of optimal stationary policy of the CMDP problem. The optimal stationary policy (termed age-optimal policy) is shown to be a randomized simple policy that randomizes between two deterministic policies with a fixed probability. We prove that the two deterministic policies have a threshold structure and further derive the closed-form expression of average AoI and collision probability for the deterministic threshold-structured policy by conducting Markov Chain analysis. The analytical expression offers an efficient way to calculate the threshold and randomization probability to form the age-optimal policy. For comparison, we also consider the throughput maximization policy (termed throughput-optimal policy) and analyze the average AoI performance under the throughput-optimal policy in the considered system. Numerical simulations show the superiority of the derived age-optimal policy over the throughput-optimal policy. We also unveil the impacts of various system parameters on the corresponding optimal policy and the resultant average AoI.

This paper considers an Industrial Internet of Thing (IIoT) system with a source monitoring a dynamic process with randomly generated status updates. The status updates are sent to an designated destination in a real-time manner over an unreliable link. The source is subject to a practical constraint of limited average transmission power. Thus, the system should carefully schedule when to transmit a fresh status update or retransmit the stale one. To characterize the performance of timely status update, we adopt a recent concept, Age of Information (AoI), as the performance metric. We aim to minimize the long-term average AoI under the limited average transmission power at the source, by formulating a constrained Markov Decision Process (CMDP) problem. To address the formulated CMDP, we recast it into an unconstrained Markov Decision Process (MDP) through Lagrangian relaxation. We prove the existence of optimal stationary policy of the original CMDP, which is a randomized mixture of two deterministic stationary policies of the unconstrained MDP. We also explore the characteristics of the problem to reduce the action space of each state to significantly reduce the computation complexity. We further prove the threshold structure of the optimal deterministic policy for the unconstrained MDP. Simulation results show the proposed optimal policy achieves lower average AoI compared with random policy, especially when the system suffers from stricter resource constraint. Besides, the influence of status generation probability and transmission failure rate on optimal policy and the resultant average AoI as well as the impact of average transmission power on the minimal average AoI are unveiled.