Handoff Design in User-Centric Cell-Free Massive MIMO Networks Using DRL

In the user-centric cell-free massive MIMO (UC-mMIMO) network scheme, user mobility necessitates updating the set of serving access points to maintain the user-centric clustering. Such updates are typically performed through handoff (HO) operations; however, frequent HOs lead to overheads associated with the allocation and release of resources. This paper presents a deep reinforcement learning (DRL)-based solution to predict and manage these connections for mobile users. Our solution employs the Soft Actor-Critic algorithm, with continuous action space representation, to train a deep neural network to serve as the HO policy. We present a novel proposition for a reward function that integrates a HO penalty in order to balance the attainable rate and the associated overhead related to HOs. We develop two variants of our system; the first one uses mobility direction-assisted (DA) observations that are based on the user movement pattern, while the second one uses history-assisted (HA) observations that are based on the history of the large-scale fading (LSF). Simulation results show that our DRL-based continuous action space approach is more scalable than discrete space counterpart, and that our derived HO policy automatically learns to gather HOs in specific time slots to minimize the overhead of initiating HOs. Our solution can also operate in real time with a response time less than 0.4 ms.

Channel Estimation with Asynchronous Reception for User-Centric Cell-Free MIMO Systems

The user-centric, cell-free wireless network is a promising next-generation communication system, but signal synchronization issues arise due to distributed access points and lack of cellular structure. We propose a novel method to recover synchronous pilot reception by introducing new pilot sequences and a matched filter window, enabling orthogonality even with asynchronous reception. Our approach mimics synchronous transmission by extending training sequences. Analysis shows asynchronous reception's impact on channel estimation, and our method significantly improves performance with a small increase of training time overhead. Results demonstrate a 7.26 dB reduction in normalized mean square error and 40% increase in data rate, achieving performance levels comparable to the synchronous case.