@article{articlec,

title = {The road to 6G: a comprehensive survey of deep learning applications in cell-free massive MIMO communications systems},

author = {Lazaros Alexios Iliadis and Zaharias Zaharis and Sotirios Sotiroudis and Panagiotis Sarigiannidis and George Karagiannidis and Sotirios Goudos},

url = {https://www.researchgate.net/publication/362590698_The_road_to_6G_a_comprehensive_survey_of_deep_learning_applications_in_cell-free_massive_MIMO_communications_systems},

doi = {10.1186/s13638-022-02153-z},

year  = {2022},

date = {2022-08-01},

journal = {EURASIP Journal on Wireless Communications and Networking},

volume = {2022},

abstract = {The fifth generation (5G) of telecommunications networks is currently commercially deployed. One of their core enabling technologies is cellular Massive Multiple-Input-Multiple-Output (M-MIMO) systems. However, future wireless networks are expected to serve a very large number of devices and the current MIMO networks are not scalable, highlighting the need for novel solutions. At this moment, Cell-free Massive MIMO (CF M-MIMO) technology seems to be the most promising idea in this direction. Despite their appealing characteristics, CF M-MIMO systems face their own challenges, such as power allocation and channel estimation. Deep Learning (DL) has been successfully employed to a wide range of problems in many different research areas, including wireless communications. In this paper, a review of the state-of-the-art DL methods applied to CF M-MIMO communications systems is provided. In addition, the basic characteristics of Cell-free networks are introduced, along with the presentation of the most commonly used DL models. Finally, future research directions are highlighted.},

keywords = {6G, Cell-free massive MIMO, Deep Learning, User-centric cell-free massive MIMO},

pubstate = {published},

tppubtype = {article}

}

@article{9820768,

title = {Joint Wireless Resource and Computation Offloading Optimization for Energy Efficient Internet of Vehicles},

author = {Dimitrios Pliatsios and Panagiotis Sarigiannidis and Thomas D Lagkas and Vasileios Argyriou and Alexandros-Apostolos A Boulogeorgos and Peristera Baziana},

url = {https://www.researchgate.net/publication/361864374_Joint_Wireless_Resource_and_Computation_Offloading_Optimization_for_Energy_Efficient_Internet_of_Vehicles},

doi = {10.1109/TGCN.2022.3189413},

issn = {2473-2400},

year  = {2022},

date = {2022-07-08},

journal = {IEEE Transactions on Green Communications and Networking},

volume = {6},

number = {3},

pages = {1468-1480},

abstract = {The Internet of Vehicles (IoV) is an emerging paradigm, which is expected to be an integral component of beyond-fifth-generation and sixth-generation mobile networks. However, the processing requirements and strict delay constraints of IoV applications pose a challenge to vehicle processing units. To this end, multi-access edge computing (MEC) can leverage the availability of computing resources at the edge of the network to meet the intensive computation demands. Nevertheless, the optimal allocation of computing resources is challenging due to the various parameters, such as the number of vehicles, the available resources, and the particular requirements of each task. In this work, we consider a network consisting of multiple vehicles connected to MEC-enabled roadside units (RSUs) and propose an approach that minimizes the total energy consumption of the system by jointly optimizing the task offloading decision, the allocation of power and bandwidth, and the assignment of tasks to MEC-enabled RSUs. Due to the original problem complexity, we decouple it into subproblems and we leverage the block coordinate descent method to iteratively optimize them. Finally, the numerical results demonstrate that the proposed solution can effectively minimize total energy consumption for various numbers of vehicles and MEC nodes while maintaining a low outage probability.},

keywords = {6G, B5G, block coordinate descent, Computation offloading},

pubstate = {published},

tppubtype = {article}

}

@article{Spyridis2021b,

title = {Towards 6g iot: Tracing mobile sensor nodes with deep learning clustering in uav networks},

author = { Y. Spyridis and T. Lagkas and P. Sarigiannidis and V. Argyriou and A. Sarigiannidis and G. Eleftherakis and J. Zhang},

url = {https://www.researchgate.net/publication/352197709_Towards_6G_IoT_Tracing_Mobile_Sensor_Nodes_with_Deep_Learning_Clustering_in_UAV_Networks},

doi = {10.3390/s21113936},

year  = {2021},

date = {2021-01-01},

journal = {Sensors},

volume = {21},

number = {11},

abstract = {Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target’s radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.},

keywords = {6G, Deep Learning, Graph convolutional network, IoT, RSSI, Sensor tracking, unmanned aerial vehicles},

pubstate = {published},

tppubtype = {article}

}