Real-time Sampling and Estimation on Random Access Channels: Age of Information and Beyond

Next generation multiple access channels require to provision for unprecedented massive user access in a plethora of applications in cyber-physical systems. This work proposes decentralized policies for the real-time monitoring and estimation of autoregressive processes over random access channels.

Two classes of policies are investigated: (i) oblivious schemes in which sampling and transmission policies are independent of the processes that are monitored, and (ii) non-oblivious schemes in which transmitters causally observe their corresponding processes for decision making. In the class of oblivious policies, we show that minimizing the expected time-average estimation error is equivalent to minimizing the expected age of information. Consequently, we prove lower and upper bounds on the minimum achievable estimation error in this class. Next, we consider non-oblivious policies and design a threshold policy, called error-based thinning, in which each transmitter node becomes active if its instantaneous error has crossed a fixed threshold (which we optimize). Active nodes then transmit stochastically following a slotted ALOHA policy. A closed-form, approximately optimal, solution is found for the threshold as well as the resulting estimation error. It is shown that non-oblivious policies offer a multiplicative gain close to 3 compared to oblivious policies. Moreover, it is shown that oblivious policies that use age of information for decision making improve the state-of-the-art at least by the multiplicative factor 2. The performance of all discussed policies is compared using simulations. Numerical comparison shows that the performance of the proposed decentralized policy is very close to that of centralized greedy scheduling. Finally, we extend our framework to unreliable random access channels. Simulations