Scheduling Algorithms for Minimizing Age of Information in Wireless Broadcast Networks with Random Arrivals

Abstract

Age of information is a new network performance metric that captures the freshness of information at end-users. This paper studies the age of information from a scheduling perspective. To that end, we consider a wireless broadcast network where a base-station (BS) is updating many users on random information arrivals under a transmission capacity constraint. For the offline case when the arrival statistics are known to the BS, we develop a structural MDP scheduling algorithm and an index scheduling algorithm, leveraging Markov decision process (MDP) techniques and the Whittle's methodology for restless bandits. By exploring optimal structural results, we not only reduce the computational complexity of the MDP-based algorithm, but also simplify deriving a closed form of the Whittle index. Moreover, for the online case, we develop an MDP-based online scheduling algorithm and an index-based online scheduling algorithm. Both the structural MDP scheduling algorithm and the MDP-based online scheduling algorithm asymptotically minimize the average age, while the index scheduling algorithm minimizes the average age when the information arrival rates for all users are the same. Finally, the algorithms are validated via extensive numerical studies.Index Terms-Age of information, scheduling algorithms, Markov decision processes. ✦ • Y.-P. Hsu is with

A Whittle Index Approach to Minimizing Functions of Age of Information

“…In words, the strong-switch-type property implies that if a policy decides to activate arm i for a state vector x, then for a state vector x with a higher age for the i th source and lower ages for all the other sources, it still decides to activate source i. Note that our definition of strong-switch-type policies is a stronger version of the switch-type policies introduced in [13].…”

“…Scheduling problems with weighted linear combinations of age have also been considered with throughput constraints in [9] and with general interference constraints in [10]. AoIbased scheduling with stochastic arrivals was considered in [13], where a Whittle Index policy was shown to have good performance.…”

“…However, their approach does not extend to the setting with unreliable channels. In this work, we consider unreliable channels and also build upon results from [12] and [13] to derive stronger structural properties for optimal policies. These properties hint at why the performance of the heuristic Whittle index policy may be close to optimal.…”

Regulating Competition in Age of Information Under Network Externalities

“…Proposition IV.4 (Large δ Regime). Under incomplete information, if δ ≥δ th 1 = max{δ th 1 (c H ), δ th 1 (c L )}, all the N platforms will follow the approximate cooperation profile (λ 1 (δ),λ 2 (δ), · · · , λ N (δ)) = (λ 1 ,λ 2 , · · · ,λ N ) in (20)-(21) all the time, without triggering punishment ((λ * 1 (c H ), λ * 1 (c L )), λ * 2 , · · · , λ * N ) in (11)- (13). When all the platforms have the same average costs, i.e., p H c H + (1 − p H )c L = c 2 = · · · = c N , we can analytically prove the following proposition.…”

“…Platform 1's one-shot cost when c 1 = c H under incomplete information is π 1 (λ * 1 (c H ), N j=2 λ * j ), where λ * 1 (c H ) and λ * j are given in (11)- (13), and its one-shot cost when c 1 = c H under complete information is π 1 (λ 1 (c H ), N j=2λ j (c H )) using (31). To prove platform 1 obtains larger one-shot cost under incomplete information when c 1 = c H , it's equivalent to prove π 1 (λ *…”