The ever-increasing needs of supporting real-time applications have spurred new studies on minimizing Age-of-Information (AoI), a novel metric characterizing the data freshness of the system. This work studies the single-queue information update system and strengthens the seminal results of Sun et al. on the following fronts: (i) When designing the optimal offline schemes with full knowledge of the delay distributions, a newfixed-point-basedmethod is proposed withquadratic convergence rate, an order-of-magnitude improvement over the state-of-the-art; (ii) When the distributional knowledge is unavailable (which is the norm in practice), two new low-complexity online algorithms are proposed, which provably attain the optimal average AoI penalty; and (iii) the online schemes also admit a modular architecture, which allows the designer toupgradecertain components to handle additional practical challenges. Two such upgrades are proposed for the situations: (iii.1) The AoI penalty function is also unknown and must be estimated on the fly, and (iii.2) the unknown delay distribution is Markovian instead of i.i.d. The performance of our schemes is either provably optimal or within 3% of the omniscient optimal offline solutions in all simulation scenarios.

The ubiquitous usage of communication networks in modern sensing and control applications has kindled new interests on the timing coordination between sensors and controllers, i.e., how to use the “waiting time” to improve the system performance. Contrary to the common belief that a zero-wait policy is optimal, Sunet al.showed that a controller can strictly improve the data freshness, the so-called Age-of-Information (AoI), by postponing transmission in order to lengthen the duration of staying in a good state. The optimal waiting policy for thesensorside was later characterized in the context of remote estimation. Instead of focusing on the sensor and controller sides separately, this work develops the jointly optimal sensor/controller waiting policy in a Wiener-process system. This work generalizes the above two important results in the sense that not only do we consider joint sensor/controller designs (as opposed to sensor-only or controller-only schemes), but we also assume random delay in both the forward and feedback directions (as opposed to random delay in only one direction). In addition to provable optimality, extensive simulation is used to verify the performance of the proposed scheme.

One main objective of ultra-low-latency communications is to minimize the data staleness at the receivers, recently characterized by a metric called Age-of-Information (AoI). While the question of when to send the next update packet has been the central subject of AoI minimization, each update packet also incurs the cost of transmission that needs to be jointly considered in a practical design. With the exponential growth of interconnected devices and the increasing risk of excessive resource consumption in mind, this work derives an optimal joint cost-and-AoI minimization solution for multiple coexisting source-destination (S-D) pairs. The results admit a new AoI-market-price-based interpretation and are applicable to the setting of (a) general heterogeneous AoI penalty functions and Markov delay distributions for each S-D pair, and (b) a general network cost function of aggregate throughput of all S-D pairs. Extensive simulation is used to demonstrate the superior performance of the proposed scheme.

The ubiquitous usage of communication networks in modern sensing and control applications has kindled new interests on the timing-based coordination between sensors and controllers, i.e., how to use the "waiting time" to improve the system performance. Contrary to the common belief that a zero-wait policy is always optimal, Sun et al. showed that a controller can strictly improve the data freshness, the so-called Age-of-Information (AoI), by postponing transmission in order to lengthen the duration of staying in a good state. The optimal waiting policy for the sensor side was later characterized in the context of remote estimation. Instead of focusing on the sensor and controller sides separately, this work develops the optimal joint sensor/controller waiting policy in a Wiener-process system. The results can be viewed as strict generalization of the above two important results in the sense that not only do we consider joint sensor/controller designs (as opposed to sensor-only or controller-only schemes), but we also assume random delay in both the forward and feedback directions (as opposed to random delay in only one direction). In addition to provable optimality, extensive simulation is used to verify the performance of the proposed scheme in various settings.

The ever-increasing needs of supporting real-time applications have spurred a considerable number of studies on minimizing Age-of-Information (AoI), a new metric characterizing the data freshness of the system. This work revisits and significantly strengthens the seminal results of Sun et al. on the following fronts: (i) The optimal waiting policy is generalized from the 1-way delay to the 2-way delay setting; (ii) A new way of computing the optimal policy with quadratic convergence rate, an order-of-magnitude improvement over the state-of-the-art bisection methods; and (iii) A new low-complexity adaptive online algorithm that provably converges to the optimal policy without knowing the exact delay distribution, a sharp departure from the existing AoI algorithms. Contribution (iii) is especially important in practice since the delay distribution can sometimes be hard to know in advance and may change over time. Simulation results in various settings are consistent with the theoretic findings.

The development of indoor small cell provides network operators with new opportunities to address surging mobile data traffic, coverage infill and capacity boost scenarios, and finally results in growing expectations relative to quality of experience (QoE). However, the deployment of indoor small cell base station also raises some resource allocation issues. This study aims to propose an max‐utility resource allocation mechanism that maximises all users’ utility for indoor small cell networks. The objective of the proposed max‐utility resource allocation algorithm is to optimise the total user's QoE while guaranteeing as many as possible users can enjoy their request service at an acceptable quality. This is achieved by considering user's QoE state and signal strength into the resource allocation algorithm. Multiservice scenario is also considered in the proposed max‐utility resource allocation algorithm. To compare the performance of the proposed max‐utility resource allocation mechanism with round‐robin, max‐throughput and max‐minMOS approach, a simulation analysis is implemented. The simulation result showed that the proposed strategy achieves the best average utility compared with other approaches. Result also showed that the proposed strategy is suitable for indoor small cell networks under various kinds of scenarios with different user behaviours and network topologies.