Random Access Schemes in Wireless Systems With Correlated User Activity

Traditional random access schemes are designed based on the aggregate process of user activation, which is created on the basis of independent activations of the users. However, in Machine-Type Communications (MTC), some users are likely to exhibit a high degree of correlation, e.g. because they observe the same physical phenomenon. This paves the way to devise access schemes that combine scheduling and random access, which is the topic of this work. The underlying idea is to schedule highly correlated users in such a way that their transmissions are less likely to result in a collision. To this end, we propose two greedy allocation algorithms. Both attempt to maximize the throughput using only pairwise correlations, but they rely on different assumptions about the higher-order dependencies. We show that both algorithms achieve higher throughput compared to the traditional random access schemes, suggesting that user correlation can be utilized effectively in access protocols for MTC.Comment: Submitted to SPAWC 201

Traffic Prediction Based Fast Uplink Grant for Massive IoT

This paper presents a novel framework for traffic prediction of IoT devices activated by binary Markovian events. First, we consider a massive set of IoT devices whose activation events are modeled by an On-Off Markov process with known transition probabilities. Next, we exploit the temporal correlation of the traffic events and apply the forward algorithm in the context of hidden Markov models (HMM) in order to predict the activation likelihood of each IoT device. Finally, we apply the fast uplink grant scheme in order to allocate resources to the IoT devices that have the maximal likelihood for transmission. In order to evaluate the performance of the proposed scheme, we define the regret metric as the number of missed resource allocation opportunities. The proposed fast uplink scheme based on traffic prediction outperforms both conventional random access and time division duplex in terms of regret and efficiency of system usage, while it maintains its superiority over random access in terms of average age of information for massive deployments.Comment: Accepted to IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) 202

Stochastic Resource Optimization of Random Access for Transmitters with Correlated Activation

For a range of scenarios arising in sensor networks, control and edge computing, communication is event-triggered; that is, in response to the environment of the communicating devices. A key feature of device activity in this setting is correlation, which is particularly relevant for sensing of physical phenomena such as earthquakes or flooding. Such correlation introduces a new challenge in the design of resource allocation and scheduling for random access that aim to maximize throughput or expected sum-rate, which do not admit a closed-form expression. In this paper, we develop stochastic resource optimization algorithms to design a random access scheme that provably converge with probability one to locally optimal solutions of the throughput and the sum-rate. A key feature of the stochastic optimization algorithm is that the number of parameters that need to be estimated grows at most linearly in the number of devices. We show via simulations that our algorithms can outperform existing approaches by up to 30% for a moderate number of available time slots in realistic networks