Joint Adaptive OFDM and Reinforcement Learning Design for Autonomous Vehicles: Leveraging Age of Updates

Millimeter wave (mmWave)-based orthogonal frequency-division multiplexing (OFDM) stands out as a suitable alternative for high-resolution sensing and high-speed data transmission. To meet communication and sensing requirements, many works propose a static configuration where the wave's hyperparameters such as the number of symbols in a frame and the number of frames in a communication slot are already predefined. However, two facts oblige us to redefine the problem, (1) the environment is often dynamic and uncertain, and (2) mmWave is severely impacted by wireless environments. A striking example where this challenge is very prominent is autonomous vehicle (AV). Such a system leverages integrated sensing and communication (ISAC) using mmWave to manage data transmission and the dynamism of the environment. In this work, we consider an autonomous vehicle network where an AV utilizes its queue state information (QSI) and channel state information (CSI) in conjunction with reinforcement learning techniques to manage communication and sensing. This enables the AV to achieve two primary objectives: establishing a stable communication link with other AVs and accurately estimating the velocities of surrounding objects with high resolution. The communication performance is therefore evaluated based on the queue state, the effective data rate, and the discarded packets rate. In contrast, the effectiveness of the sensing is assessed using the velocity resolution. In addition, we exploit adaptive OFDM techniques for dynamic modulation, and we suggest a reward function that leverages the age of updates to handle the communication buffer and improve sensing. The system is validated using advantage actor-critic (A2C) and proximal policy optimization (PPO). Furthermore, we compare our solution with the existing design and demonstrate its superior performance by computer simulations.

Optimal Joint Radar and Communication User Association in Cell-Free mMIMO Systems

The cell-free massive multiple-input multiple-output (CF-mMIMO) systems are crucial for 6G development due to their high spectral efficiency and uniform user-experienced data rates. A key aspect of CF-mMIMO is user association (UA) and optimal cluster formation. Traditional methods focusing solely on communication-related metrics fall short in this context, as sensing is becoming integral to 6G. This study delves into a framework for joint radar and communication (JRC) in CF-mMIMO systems and investigates JRC-based UA techniques. We propose a novel method to optimize UA, enhancing both communication spectral efficiency and sensing accuracy. Existing literature has not explored this dual requirement integration for UA. Our proposed two-step scheme optimizes UA clusters for both communication and sensing. The first step involves selecting access points (APs) based on channel quality, followed by a second step that further refines the selection by choosing APs from the initial group that are also optimal for sensing. We utilize the signal-clutter plus noise ratio to exclude APs with clutter in front of the user equipment (UE) and the AP view angle, ensuring that radar echoes are received only from the specific UE, not the surrounding clutter. Theoretical analysis and simulations demonstrate that the same APs optimized for communication are not necessarily optimal for sensing, highlighting the need for schemes that incorporate sensing requirements in UA. The results show the effectiveness of the proposed method, showing its potential to improve CF-mMIMO system performance in JRC scenarios.