As the Internet-of-Things (IoT) emerges, connecting immense numbers of sensors and devices, the continual growth in wireless communications increasingly manifests itself in terms of a larger and denser population of nodes with intermittent traffic patterns. A crucial issue that arises in these conditions is how to set the activation rates as a function of the network density and traffic intensity. Depending on the scaling of the activation rates, dense node populations may either result in excessive activations and potential collisions, or long delays that may increase with the number of nodes, even at low load. Motivated by the above issues, we examine optimal activation rate scalings in ultra-dense networks with intermittent traffic sources. We establish stability conditions, and provide closed-form expressions which indicate that the mean delay is roughly inversely proportional to the nominal activation rate. We also discuss a multi-scale mean-field limit, and use the associated fixed point to determine the buffer content and delay distributions. The results provide insight in the scalings that minimize the delay while preventing excessive activation attempts. Extensive simulation experiments demonstrate that the mean-field asymptotics yield highly accurate approximations, even when the number of nodes is moderate.

We consider the problem of active sequential hypothesis testing where a Bayesian decision maker must infer the true hypothesis from a set of hypotheses. The decision maker may choose for a set of actions, where the outcome of an action is corrupted by independent noise. In this paper we consider a special case where the decision maker has limited knowledge about the distribution of observations for each action, in that only a binary value is observed. Our objective is to infer the true hypothesis with low error, while minimizing the number of action sampled. Our main results include the derivation of a lower bound on sample size for our system under limited knowledge and the design of an active learning policy that matches this lower bound and outperforms similar known algorithms.

With the rapid advance of the Internet of Everything, both the number of devices and the range of applications that rely on wireless connectivity show huge growth. Driven by these pervasive trends, wireless networks grow in size and complexity, supporting immense numbers of nodes and data volumes, with highly diverse traffic profiles and performance requirements. While well-established methods are available for evaluating the throughput of persistent sessions with saturated buffers, these provide no insight in the delay performance of flows with intermittent packet arrivals. The occurrence of empty buffers in the latter scenario results in a complex interaction between activity states and packet queues, which severely complicates the performance analysis. Motivated by these challenges, we develop a mean-field approach to analyze buffer contents and packet delays in wireless networks in a many-sources regime. The mean-field behavior simplifies the analysis of a large-scale network with packet arrivals and buffer dynamics to a low-dimensional fixed-point calculation for a network with saturated buffers. In particular, the analysis yields explicit expressions for the buffer content and packet delay distribution in terms of the fixed-point solution. Extensive simulation experiments demonstrate that these expressions provide highly accurate approximations, even for a fairly moderate number of sources.

We address the problem of developing a well-performing and implementable scheduler of users with wireless connection to the base station. The main feature of such real-life systems is that the quality conditions of the user channels are time-varying, which turn into the time-varying transmission rate due to different modulation and coding schemes. We assume that this phenomenon follows a Markovian law and most of the discussion is dedicated to the case of three quality conditions of each user, for which we characterize an optimal index policy and show that threshold policies (of giving higher priority to users with higher transmission rate) are not necessarily optimal. For the general case of arbitrary number of quality conditions we design a scheduler and propose its two practical approximations, and illustrate the performance of the proposed index-based schedulers and existing alternatives in a variety of simulation scenarios.