Semi-Data-Aided Channel Estimation for MIMO Systems via Reinforcement Learning

Kim, Tae‐Kyoung; Jeon, Yo-Seb; Li, Jun; Tavangaran, Nima; Poor, H. Vincent

doi:10.1109/twc.2022.3227312

Cited by 5 publications

(12 citation statements)

References 39 publications

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“…As a non-iterative approach, the reinforcement learning (RL)-aided channel estimator was introduced in [ 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. The basic concept of this approach is the sequential selection of detected data symbols to minimize the channel estimation errors.…”

Section: Introductionmentioning

confidence: 99%

“…In [ 32 ], a low-complexity algorithm was investigated by introducing sub-blocks and finite backup samples, and the computational complexity and latency were significantly reduced without performance loss. Recently, a general framework for RL-aided channel estimation was studied in [ 33 ] based on Monte Carlo tree search. However, the RL-aided channel estimators in [ 31 , 32 , 33 ] were originally considered in time-invariant channels; they perform insufficiently in time-varying channels.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a general framework for RL-aided channel estimation was studied in [ 33 ] based on Monte Carlo tree search. However, the RL-aided channel estimators in [ 31 , 32 , 33 ] were originally considered in time-invariant channels; they perform insufficiently in time-varying channels.…”

Section: Introductionmentioning

confidence: 99%

“…First, we define the optimization problem in time-varying channels to select the detected data symbols and minimize the estimation error between the estimated and current channels. This optimization problem is different from those in [ 31 , 32 , 33 ], where the selection of the detected data symbols is unchanged because the current channel remains unchanged with the time slot index. We propose an RL algorithm for the optimization problem that captures the time-varying nature of a channel.…”

Section: Introductionmentioning

confidence: 99%

“…Using this adjustment, we derive the optimal policy as a closed-form solution. Note that the proposed optimal policy differs from those in [ 31 , 32 , 33 ] because the influence of soft-decision symbols in the virtual state for future rewards gradually diminishes as the time slot index increases. We propose a further performance improvement scheme to refine the state elements.…”

Section: Introductionmentioning

confidence: 99%

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Reinforcement Learning-Aided Channel Estimator in Time-Varying MIMO Systems

Kim

Min

2023

Sensors

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This paper proposes a reinforcement learning-aided channel estimator for time-varying multi-input multi-output systems. The basic concept of the proposed channel estimator is the selection of the detected data symbol in the data-aided channel estimation. To achieve the selection successfully, we first formulate an optimization problem to minimize the data-aided channel estimation error. However, in time-varying channels, the optimal solution is difficult to derive because of its computational complexity and the time-varying nature of the channel. To address these difficulties, we consider a sequential selection for the detected symbols and a refinement for the selected symbols. A Markov decision process is formulated for sequential selection, and a reinforcement learning algorithm that efficiently computes the optimal policy is proposed with state element refinement. Simulation results demonstrate that the proposed channel estimator outperforms conventional channel estimators by efficiently capturing the variation of the channels.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reinforcement Learning-Aided Channel Estimator in Time-Varying MIMO Systems

Kim

Min

2023

Sensors

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A systematic literature review of flying ad hoc networks: State‐of‐the‐art, challenges, and perspectives

Pasandideh

Costa

Kunst

et al. 2023

Journal of Field Robotics

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Unmanned aerial vehicles (UAVs), also known as drones, communicate, collaborate, and form flying ad hoc networks (FANETs) to perform many different missions, ranging from delivery tasks to agriculture applications. Recently, FANETs have been integrated with different technologies, such as artificial intelligence (AI), virtual reality, and Internet of Things. Such new avenues for the use of UAVs directly impact the research on FANETs and cause some major challenges, such as security and physical layer issues, resource management, and UAV positioning issues that need to be addressed. Several researchers have been working for the last few years to propose AI and machine learning (ML)‐based solutions for different use cases in UAV‐based networks. They present the limitations of the existing research work and highlight some possible future works on FANETs. However, exhibiting the trends in the UAV research papers in a quantitative manner is still required to motivate researchers to rethink the research on FANETs. Therefore, this study covers more than 170 scientific publications extracted from five trusted academic databases published from 2013 to 2021 to provide a thorough overview of the main research and development statistics in the area of FANETs, the open challenges existing in this area and the ML‐based solutions to solve these challenges. In addition, the investigation of emerging technologies integrated with FANETs, as well as the simulation tools employed for evaluating FANETs' performance are discussed. Moreover, the future research directions in the area of FANETs are considered within a prospective vision discussion.

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Semi-Data-Aided Channel Estimation for MIMO Systems via Reinforcement Learning

Cited by 5 publications

References 39 publications

Reinforcement Learning-Aided Channel Estimator in Time-Varying MIMO Systems

Reinforcement Learning-Aided Channel Estimator in Time-Varying MIMO Systems

DRL at the Physical Layer

A systematic literature review of flying ad hoc networks: State‐of‐the‐art, challenges, and perspectives

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