System-Level Dynamics of Highly Directional Distributed Networks

While highly directional communications may offer considerable improvements in the link data rate and over-the-air latency of high-end wearable devices, the system-level capacity trade-offs call for separate studies with respect to the employed multiple access procedures and the network dynamics in general. This letter proposes a framework for estimating the system-level area throughput in dynamic distributed networks of highly-directional paired devices. We provide numerical expressions for the steady-state distribution of the number of actively communicating pairs and the probability of successful session initialization as well as derive the corresponding closed-form approximation for dense deployments.Comment: Accepted to IEEE Wireless Communications Letters on April 5, 2021. Copyright may be transferred without further notice after which this version may become non-availabl

Grant-Free Power Allocation for Ultra-Dense Internet of Things Environments: A Mean-Field Perspective

Grant free access, in which each Internet of Things (IoT) device delivers its packets through a randomly selected resource without spending time on handshaking procedures, is a promising solution for supporting the massive connectivity required for IoT systems. In this paper, we explore grant free access with multi packet reception capabilities, with an emphasis on ultra low end IoT applications with small data sizes, sporadic activity, and energy usage constraints. We propose a power allocation scheme that integrates the IoT device's traffic and energy budget by using a stochastic geometry framework and meanfield game theory to model and analyze mutual interference among active IoT devices.We also derive a Markov chain model to capture and track the IoT device's queue length and derive the successful transmission probability at steady state. Simulation results illustrate the optimal power allocation strategy and show the effectiveness of the proposed approach.Comment: Submitted to Journal of Network and Computer Application

Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?

In this paper, we aim to improve the Quality-of-Service (QoS) of Ultra-Reliability and Low-Latency Communications (URLLC) in interference-limited wireless networks. To obtain time diversity within the channel coherence time, we first put forward a random repetition scheme that randomizes the interference power. Then, we optimize the number of reserved slots and the number of repetitions for each packet to minimize the QoS violation probability, defined as the percentage of users that cannot achieve URLLC. We build a cascaded Random Edge Graph Neural Network (REGNN) to represent the repetition scheme and develop a model-free unsupervised learning method to train it. We analyze the QoS violation probability using stochastic geometry in a symmetric scenario and apply a model-based Exhaustive Search (ES) method to find the optimal solution. Simulation results show that in the symmetric scenario, the QoS violation probabilities achieved by the model-free learning method and the model-based ES method are nearly the same. In more general scenarios, the cascaded REGNN generalizes very well in wireless networks with different scales, network topologies, cell densities, and frequency reuse factors. It outperforms the model-based ES method in the presence of the model mismatch.Comment: Submitted to IEEE journal for possible publicatio