Classical medium access control (MAC) protocols are interpretable, yet their task-agnostic control signaling messages (CMs) are ill-suited for emerging mission-critical applications. By contrast, neural network (NN) based protocol models (NPMs) learn to generate task-specific CMs, but their rationale and impact lack interpretability. To fill this void, in this article we propose, for the first time, a semantic protocol model (SPM) constructed by transforming an NPM into an interpretable symbolic graph written in the probabilistic logic programming language (ProbLog). This transformation is viable by extracting and merging common CMs and their connections, while treating the NPM as a CM generator. By extensive simulations, we corroborate that the SPM tightly approximates its original NPM while occupying only 0.02% memory. By leveraging its interpretability and memory-efficiency, we demonstrate several SPM-enabled applications such as SPM reconfiguration for collision-avoidance, as well as comparing different SPMs via semantic entropy calculation and storing multiple SPMs to cope with non-stationary environments.

With the envisioned massive connectivity era, one of the challenges for 5G/Beyond 5G (B5G) wireless systems will be handling the unprecedented spectrum crunch. A potential solution has emerged in the form of spectrum sharing, which deviates from a monopolistic spectrum usage system. This paper investigates the medium access control (MAC) as a means of increasing the viability of the spectrum sharing technique. We first quantify the opportunity of spectrum access in a probabilistic manner, a method referred to as opportunistic (OP) map. Based on the OP framework, we propose a random MAC algorithm in which the access of a node is randomly determined with its own OP value. As a possible application of our OP-map based random MAC, we propose a flexible half-duplex (HD)/full-duplex (FD) communication where each pair decides the duplexing mode according to the OP values of the two pair nodes. This approach fits well with the spectrum sharing system since it enables a flexible operation for the spectrum access according to the spectrum usage level. From the numerical analysis, we validate the feasibility and verify the performance enhancements by implementing a field-programmable gate array (FPGA) based real-time prototype. We further carry out extensive 3D ray-tracing based system-level simulations on investigating the network-level performance of the proposed system. Measurements and numerical results confirm that the proposed architecture can achieve higher system throughput than conventional LTE-TDD (time division duplex) systems.

This paper is presented at 28th International Joint Conference on Artificial Intelligence (IJCAI-19) 1st Wksp. Federated Machine Learning for User Privacy and Data Confidentiality (FML'19), Macau, August 2019.

Traditional distributed deep reinforcement learning (RL) commonly relies on exchanging the experience replay memory (RM) of each agent. Since the RM contains all state observations and action policy history, it may incur huge communication overhead while violating the privacy of each agent. Alternatively, this article presents a communication-efficient and privacy-preserving distributed RL framework, coined federated reinforcement distillation (FRD). In FRD, each agent exchanges its proxy experience replay memory (ProxRM), in which policies are locally averaged with respect to proxy states clustering actual states. To provide FRD design insights, we present ablation studies on the impact of ProxRM structures, neural network architectures, and communication intervals. Furthermore, we propose an improved version of FRD, coined mixup augmented FRD (MixFRD), in which ProxRM is interpolated using the mixup data augmentation algorithm. Simulations validate the effectiveness of MixFRD in reducing the variance of mission completion time and communication cost, compared to the benchmark schemes, vanilla FRD, federated reinforcement learning (FRL), and policy distillation (PD).