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Random access (RA) schemes are a topic of high interest in machine-type communication (MTC). In RA protocols, backoff techniques such as exponential backoff (EB) are used to stabilize the system to avoid low throughput and excessive delays.…

In this paper, we explore various multi-agent reinforcement learning (MARL) techniques to design grant-free random access (RA) schemes for low-complexity, low-power battery operated devices in massive machine-type communication (mMTC)…

Massive Machine-Type Communication (mMTC) is expected to be strongly supported by future 5G wireless networks. Its deployment, however, is seriously challenged by the high risk of random access (RA) collision. A popular concept is to reduce…

In mMTC mode, with thousands of devices trying to access network resources sporadically, the problem of random access (RA) and collisions between devices that select the same resources becomes crucial. A promising approach to solve such an…

The massive machine-type communications (mMTC) service will be part of new services planned to integrate the fifth generation of wireless communication (B5G). In mMTC, thousands of devices sporadically access available resource blocks on…

Finding feasible, collision-free paths for multiagent systems can be challenging, particularly in non-communicating scenarios where each agent's intent (e.g. goal) is unobservable to the others. In particular, finding time efficient paths…

Reinforcement learning is nowadays a popular framework for solving different decision making problems in automated driving. However, there are still some remaining crucial challenges that need to be addressed for providing more reliable…

The thesis is dedicated to studying methods to improve the efficiency of random access schemes and to facilitate their deployment in machine-type communications (MTC). First, a joint user activity identification and channel estimation…

Random access schemes in modern wireless communications are generally based on the framed-ALOHA (f-ALOHA), which can be optimized by flexibly organizing devices' transmission and re-transmission. However, this optimization is generally…

In this work, we study adaptive data-guided traffic planning and control using Reinforcement Learning (RL). We shift from the plain use of classic methods towards state-of-the-art in deep RL community. We embed several recent techniques in…

As the number of devices getting connected to the vehicular network grows exponentially, addressing the numerous challenges of effectively allocating spectrum in dynamic vehicular environment becomes increasingly difficult. Traditional…

Coordinating traffic signals along multimodal corridors is challenging because many multi-agent deep reinforcement learning (DRL) approaches remain vehicle-centric and struggle with high-dimensional discrete action spaces. We propose…

The massiveness of devices in crowded Machine-to-Machine (M2M) communications brings new challenges to existing random-access (RA) schemes, such as heavy signaling overhead and severe access collisions. In order to reduce the signaling…

Routing in multi-hop wireless networks is a complex problem, especially in heterogeneous networks where multiple wireless communication technologies coexist. Reinforcement learning (RL) methods, such as Q-learning, have been introduced for…

In this paper, we propose a compressive random access (CRA) scheme using multiple resource blocks (RBs) to support massive connections for machine type communications (MTC). The proposed CRA scheme is scalable. As a result, if the number of…

As travel demand increases and urban traffic condition becomes more complicated, applying multi-agent deep reinforcement learning (MARL) to traffic signal control becomes one of the hot topics. The rise of Reinforcement Learning (RL) has…

The high risk of random access collisions leads to huge challenge for the deployment massive Machine-Type Communications (mMTC), which cannot be sufficiently overcome by current solutions in LTE/LTE-A networks such as the extended access…

A cross-layer optimization approach is adopted for the design of symmetric random access wireless systems. Instead of the traditional collision model, a more realistic physical layer model is considered. Based on this model, an Incremental…

A sudden roadblock on highways due to many reasons such as road maintenance, accidents, and car repair is a common situation we encounter almost daily. Autonomous Vehicles (AVs) equipped with sensors that can acquire vehicle dynamics such…

Grant-free access (GFA) has been envisioned to play an active role in massive Machine Type Communication (mMTC) under 5G and Beyond mobile systems, which targets at achieving significant reduction of signaling overhead and access latency in…