Cell-free massive MIMO (CF-mMIMO), which performs centralized MIMO processing for densely distributed access points (APs), has been proposed for future mobile networks. In CF-mMIMO, user equipments (UEs) are served by a limited number of APs to reduce the computational load for signal processing while meeting throughput requirements. AP clustering, which is selecting APs for each UE, in dynamic environments that UEs move in is the main challenge. To find optimal AP clustering, one approach is to use deep reinforcement learning (DRL). However, the number of possible actions in DRL increases with the number of APs and Ues. The issue of scalability due to the increasing size of the neural network (NN) in a large-scale environment remains a problem. To address this problem, we propose user-centric AP clustering with distributed-DRL which has actors for each UE, and an action design which specifies the difference in AP cluster size from the previous time step. In the proposed method, since the number of possible actions does not depend on the number of APs and UEs, the size of NN can be reduced. The numerical simulation shows the proposed method achieves AP clustering that reduces the computational load while meeting the throughput requirements, with high scalability in terms of the number of UEs and APs.
Spatial queries in IoT-enabled Wireless Sensor Networks enable the interrogation of specific areas using various geometric shapes such as rectangles or circles. Based on how spatial queries have been designed to be processed, the numerous approaches proposed to tackle this issue can be categorized into parallel and serial query- ing. On one hand, serial approaches have been proven to be more efficient in this context, but on the other hand, they are unable to handle deployments with communication voids. In particular, they fail to visit all nodes located inside the targeted windows, and thus fail to guarantee query completeness/accuracy. To fill this gap, which to the extent of our knowledge has not been addressed by any of the state-of-the-art approaches, we present in this paper, a novel serial approach that has been designed to handle all net- work topologies, including sparse, dense, large, hole-free, and more specifically topologies with communication voids. The obtained simulation results demonstrate the efficiency of the proposed ap- proach in terms of time and energy, and show that it can ensure the accuracy of window queries under various topology conditions and changes.
Millimeter-Wave (mmWave) communication is susceptible to blockages, which can significantly reduce the signal strength at the receiver. Mitigating the negative impacts of blockages is a key requirement to ensure reliable and high throughput mmWave communication links. Previous research on blockage mitigation has introduced several model and protocol based blockage mitigation solutions that focus on one technique at a time, such as handoff to a different base station or beam adaptation to the same base station. In this paper, we address the overarching problem: what blockage mitigation method should be employed? and what is the optimal sub-selection within that method? To address the problem, we developed a Gated Recurrent Unit (GRU) model that is trained using periodically exchanged messages in mmWave systems. We gathered extensive amount of simulation data from a commercially available mmWave simulator, show that the proposed method does not incur any additional communication overhead, and that it achieves outstanding results in selecting the optimal blockage mitigation method with an accuracy higher than 93%. We also show that the proposed method significantly increases the amount of transferred data compared to several other blockage mitigation policies.
The wide spread of Artificial Intelligence-based services in recent years has encouraged research into new Machine Learning paradigms. Federated Learning (FL) represents a new distributed approach capable of achieving higher privacy and security guarantees than other methodologies since it allows multiple users to collaboratively train a global model without sharing their local training data. In this paper, an analysis of the characteristics of Federated Learning is therefore carried out, with a particular focus on security aspects. In detail, currently known vulnerabilities and their respective countermeasures are investigated, focusing on aggregation algorithms that provide robustness against Byzantine failures. Following this direction, Median-Krum is proposed as a new aggregation algorithm whose validity is observed on a set of simulations that recreate realistic scenarios, in the absence and presence of Byzantine adversaries. It combines the Distance-based Krum approach with the Statistical strategy of median based aggregation algorithm. Achieved results demonstrate the functionality of the proposed solutions in terms of accuracy and convergence rounds in comparison with FedAvg, Krum, Multi-Krum and FedMedian FL approaches under a correct and incorrect estimation of the attackers number.
The intelligent reflective surface (IRS) is a new technology that can reduce the number of base stations and improve signal quality. Whereas in the literature, IRS is often considered in one-hop scenarios, we propose to address two-hop IRS in a vehicular context. On the one hand, two-hop IRS should improve connectivity by enabling blind devices in blind areas to be connected to the network, and on the other, connections may be more volatile due to a higher number of intermediate devices. In this work, their impact on network performance is investigated through different performance criteria. Our approach is validated using real vehicular traces in the city of Roma, Italy.
