DQN-Based Adaptive Modulation Scheme Over Wireless Communication Channels

Lee, Donggu; Sun, Young Ghyu; Kim, Soo Hyun; Sim, Isaac; Hwang, Yu Min; Shin, Yoan; Kim, Dong In; Kim, Jin Young

doi:10.1109/lcomm.2020.2978390

Cited by 27 publications

(17 citation statements)

References 13 publications

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“…Reinforcement learning algorithm, which is one of the machine learning algorithms, is based on interactions between environment and a learning model [13], which is called an agent. Reinforcement learning algorithms have been effectively employed in various decision-making areas of communication systems [14]- [20]. In [14], a fuzzy Q-learning-based MCS selection and MIMO configuration scheme for high-speed access evolution systems is proposed.…”

Section: A Background and Related Workmentioning

confidence: 99%

“…Here, it has been reported that the proposed DQN-based adaptive modulation scheme outperforms conventional outdated channel state information measurement schemes. Moreover, in [20], a DQN algorithm-based adaptive modulation scheme with a trial efficient strategy is proposed. The proposed scheme divides the SNR range into 4 different regions with different bits per symbol rate, which achieves a reduction in computational complexity compared to the scheme in [21].…”

Section: A Background and Related Workmentioning

confidence: 99%

“…By employing a distinctive memory structure of the NEC algorithm, it is able to make optimal decisions among the action candidates. Further, it has been reported that the adaptive modulation scheme in [20] can be viewed as a gradient-based approach. From this sense, a variable step-size algorithm [29] can be applied for our scheme to minimize the number of trials of the agent and the convergence time of the algorithm.…”

Section: B Main Contributionsmentioning

confidence: 99%

“…The agent evaluates its action by a reward, which is given by the environments and the primary goal of the agent is to maximize the cumulative reward. In this paper, the reinforcement learning agent has the following components: ■𝑺 = {𝑠 1 , 𝑠 2 , 𝑠 3 } represents the state values, which are the rate region boundaries of the SNR range [20]. In this paper the 4 different modulation schemes are allocated with the 3 state values.…”

Section: B Reinforcement Learning Modelmentioning

confidence: 99%

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Neural Episodic Control-Based Adaptive Modulation and Coding Scheme for Inter-Satellite Communication Link

et al. 2021

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Inter-satellite links (ISLs) play an important role in the global navigation satellite system (GNSS), which is known as one of the key technologies for the next generation of navigation satellite systems. Deep reinforcement learning algorithms have achieved significant improvement over various wireless communications systems. However, it has been reported that deep Q network (DQN) algorithm requires an enormous number of trials. To resolve this problem, in this paper we propose an adaptive modulation and coding scheme based on a neural episodic control (NEC) algorithm, which is one of deep reinforcement learning algorithms. The proposed scheme adjusts the modulation and coding scheme region boundaries with a differentiable neural dictionary of the NEC agent, which enables the effective integration of the previous experience. In addition, we propose a step-size varying algorithm to encourage the NEC agent to efficiently approach the suboptimal state. We confirm that the proposed scheme can reduce the number of trials to 1/8, compared to the DQN-based adaptive modulation scheme. To further evaluate the proposed scheme, we employ an online learning loss evaluation algorithm that calculates the loss in time-step based on interaction records of the reinforcement learning agent and the derived modulation and coding scheme region boundaries.INDEX TERMS Adaptive modulation and coding, deep learning, inter-satellite communications, neural episodic control, reinforcement learning.

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Section: A Background and Related Workmentioning

confidence: 99%

Section: A Background and Related Workmentioning

confidence: 99%

Section: B Main Contributionsmentioning

confidence: 99%

Section: B Reinforcement Learning Modelmentioning

confidence: 99%

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Neural Episodic Control-Based Adaptive Modulation and Coding Scheme for Inter-Satellite Communication Link

et al. 2021

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“…Recently, different kinds of adaptive modulation and coding (ACM) schemes have been proposed to achieve higher spectral efficiency as well as improving network reliability for optical networks [3], [4]. In addition, adaptive schemes in contribution of various machine learning algorithms have been of interest which aims to compute decision-making tasks automatically [5]- [7]. In many works considering adaptations mentioned above, the transmitter should access the instantaneous channel state information (CSI).…”

Section: -Introductionmentioning

confidence: 99%

A Low Complexity Adaptive Scheme for Massive MIMO Systems with MRC Receiver

Radbord

2021

Preprint

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This paper evaluates a low-complexity Multiuser Adaptive Modulation (MAM) scheme for an uplink massive multiple input and multiple output (MIMO) systems with maximum-ratio combining linear receivers, which requires only slow-varying large-scale shadowing information for the users. For a channel that includes both small-scale and large-scale fading, the probability density function of signal-to-interference-plus-noise ratio is analytically calculated for the first time and verified by simulation. Also, closed-form expressions are derived for the average spectral efficiency and bit error outage. Since, the MRC is desirable in low transmit power, the previous expression is approximated at low power values. Moreover, the paper investigates different users’ arrangements around the base station to achieve the maximum possible bit rate. For MAM with joint power control, optimal power adaptation strategy switching thresholds are also acquired. Compared to the research literature, the proposed adaptive scheme by the MRC receiver, gives similar performance as a proper adaptive method at low power transmission.

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