Modified Leakage Based User Selection for MU-MIMO Systems

Coronato

Ullah

et al. 2022

Sensors

Self Cite

Multiple Input Multiple Output (MIMO) systems have been gaining significant attention from the research community due to their potential to improve data rates. However, a suitable scheduling mechanism is required to efficiently distribute available spectrum resources and enhance system capacity. This paper investigates the user selection problem in Multi-User MIMO (MU-MIMO) environment using the multi-agent Reinforcement learning (RL) methodology. Adopting multiple antennas’ spatial degrees of freedom, devices can serve to transmit simultaneously in every time slot. We aim to develop an optimal scheduling policy by optimally selecting a group of users to be scheduled for transmission, given the channel condition and resource blocks at the beginning of each time slot. We first formulate the MU-MIMO scheduling problem as a single-state Markov Decision Process (MDP). We achieve the optimal policy by solving the formulated MDP problem using RL. We use aggregated sum-rate of the group of users selected for transmission, and a 20% higher sum-rate performance over the conventional methods is reported.

Section: Related Workmentioning

confidence: 99%

“…The former information is available to each user while the latter is received through BS.

where

and

indicate values of SINR, SLNR and Gram Schmidt Orthogonalization, respectively, for

user form a state at time t , may be calculated as given in Equation ( 4 ) according to [ 19 ], Equation ( 5 ) according to [ 30 ] and Equation ( 8 ), respectively.

where G is the subgroup of selected users, while

and

are channel and precoding vector of

user, respectively.…”

Section: System Modelmentioning

confidence: 99%

Optimal User Scheduling in Multi Antenna System Using Multi Agent Reinforcement Learning

Coronato

Ullah

et al. 2022

Sensors

Self Cite

“…Radio Resource management [ 256 ] and user scheduling [ 257 , 258 ] in MU-MIMO and Ma-MIMO is very crucial for achieving good performance and often solve using techniques from optimization theory. The heterogeneity and increased complexity of MIMO systems like Ma-MIMO demands a paradigm shift from conventional resource management mechanisms.…”

Section: Rl and DL Application In Mimomentioning

confidence: 99%

Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems

Pietro

Coronato

2021

Sensors

Self Cite

The current wireless communication infrastructure has to face exponential development in mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems and their variants (i.e., Multi-User MIMO and Massive MIMO) are the most promising 5G wireless communication systems technology due to their high system throughput and data rate. However, the most significant challenges in MIMO communication are substantial problems in exploiting the multiple-antenna and computational complexity. The recent success of RL and DL introduces novel and powerful tools that mitigate issues in MIMO communication systems. This article focuses on RL and DL techniques for MIMO systems by presenting a comprehensive review on the integration between the two areas. We first briefly provide the necessary background to RL, DL, and MIMO. Second, potential RL and DL applications for different MIMO issues, such as detection, classification, and compression; channel estimation; positioning, sensing, and localization; CSI acquisition and feedback, security, and robustness; mmWave communication and resource allocation, are presented.

“…Efficient user scheduling in multi-user Multiple Input Multiple Output (MIMO) and massive MIMO systems are very important for better utilization of available resources at base station [388], [389], [390] and [391]. The RL policy gradient algorithms studied in [392], are used for user scheduling in [393] and for resource management of computer systems in [394] respectively and shows superior performance.…”

Section: H Scheduling Management and Configuration Of Resourcesmentioning

confidence: 99%

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Rizvi

Coronato³

2020

IEEE Access

Self Cite

146

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has become one of the most important and useful technology. It is a learning method where a software agent interacts with an unknown environment, selects actions, and progressively discovers the environment dynamics. RL has been effectively applied in many important areas of real life. This article intends to provide an in-depth introduction of the Markov Decision Process, RL and its algorithms. Moreover, we present a literature review of the application of RL to a variety of fields, including robotics and autonomous control, communication and networking, natural language processing, games and self-organized system, scheduling management and configuration of resources, and computer vision.