A deep Q-network-based algorithm for multi-connectivity optimization in heterogeneous cellular-networks †

The use of multi-connectivity has become a useful tool to manage the traffic in heterogeneous cellular network deployments, since it allows a device to be simultaneously connected to multiple cells. The proper exploitation of this technique requires to adequately configure the traffic sent through each cell depending on the experienced conditions. This motivates this work, which tackles the problem of how to optimally split the traffic among the cells when the multi-connectivity feature is used. To this end, the paper proposes the use of a deep reinforcement learning solution based on a Deep Q-Network (DQN) in order to determine the amount of traffic of a device that needs to be delivered through each cell, making the decision as a function of the current traffic and radio conditions. The obtained results show a near-optimal performance of the DQN-based solution with an average difference of only 3.9% in terms of reward with respect to the optimum strategy. Moreover, the solution clearly outperforms a reference scheme based on Signal to Interference Noise Ratio (SINR) with differences of up to 50% in terms of reward and up to 166% in terms of throughput for certain situations. Overall, the presented results show the promising performance of the DQN-based approach that establishes a basis for further research in the topic of multi-connectivity and for the application of this type of techniques in other problems of the radio access networkThis paper is part of ARTIST project (ref. PID2020-115104RB-I00) funded by MCIN/AEI/10.13039/501100011033. The work is also funded by the Spanish Ministry of Science and Innovation under grant ref. PRE2018-084691.Peer ReviewedPostprint (published version

La tecnología LTE y su implementación en redes de seguridad pública

Tesis (Maestría en Ciencias en Ingeniería de Telecomunicaciones), Instituto Politécnico Nacional, SEPI, ESIME, Unidad Zacatenco, 2017, 1 archivo PDF, (111 páginas). tesis.ipn.m

Análisis de la tecnología RFID pasiva de 915 MHz para sus aplicaciones en productos comerciales.

Tesis (Licenciatura en Ingeniería en Comunicaciones y Eléctronica), Instituto Politécnico Nacional, ESIME, Unidad Zacatenco, 2015, 1 archivo PDF, (125 páginas). tesis.ipn.mx

Deep learning-based multi-connectivity optimization in cellular networks

Multi-connectivity emerges as a useful feature to handle the traffic in heterogeneous cellular scenarios and fulfill the demanding requirements in terms of data rate and reliability. It allows a device to be simultaneously connected to multiple cells belonging to different radio access network nodes from a single or multiple radio access technologies. This paper addresses the problem of optimally splitting the traffic among cells when multi-connectivity is used. For this purpose, it proposes the use of deep learning to determine the optimum amount of traffic of a device that needs to be sent through one or another cell depending on the current traffic and radio conditions. Obtained results reveal a promising capability of the proposed Deep Q Network solution to select quasi optimum traffic splits in the considered scenario.This paper is part of ARTIST project (ref. PID2020- 115104RB-I00) funded by MCIN/AEI/10.13039/ 501100011033. The work is also funded by the Spanish Ministry of Science and Innovation under grant ref. PRE2018-084691.Peer ReviewedPostprint (published version

A deep q network-based multi-connectivity algorithm for heterogeneous 4G/5G cellular systems

Multi-connectivity, which allows a user equipment to be simultaneously connected to multiple cells from different radio access network nodes that can be from a single or multiple radio access technologies, has emerged as a useful feature to handle the traffic in heterogeneous cellular scenarios and fulfill high data rate and reliability requirements. This paper proposes the use of deep reinforcement learning to optimally split the traffic among cells when multi-connectivity is considered in a heterogeneous 4G/5G networks scenario. Obtained results reveal a promising capability of the proposed Deep Q Network solution to select quasi optimum traffic splits depending on the current traffic and radio conditions in the considered scenario. Moreover, the paper analyses the robustness of the obtained policy in front of variations with respect to the conditions used during the training.This paper is part of ARTIST project (ref. PID2020-115104RB-I00) funded by MCIN/AEI/10.13039/ 501100011033. The work is also funded by the Spanish Ministry of Science and Innovation under grant ref. PRE2018-084691.Peer ReviewedPostprint (published version