Adaptive Video Streaming for Massive MIMO Networks via Approximate MDP and Reinforcement Learning

Lan, Qiao; Lv, Bojie; Wang, Rui; Huang, Kaibin; Gong, Yi

doi:10.1109/twc.2020.2995944

Cited by 13 publications

(5 citation statements)

References 27 publications

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“…The decision-making process of wireless service providers, incorporated in the network, is based on MDP. Data transmission policy, based on MDP, not only helps in achieving low-complexity in data transmission scheduling but also high energy efficiency and low packet loss [129], [130]. The Radio head clustering and server matching problem is attempted to solve with MDP, which in turn reduces the system delay.…”

Section: Algorithms Referencesmentioning

confidence: 99%

“…References Research topic Research analysis/findings Reinforcement learning [128] Resource scheduling scheme Analysis of average utility per user [129] Data transmission scheduling scheme Analysis of packet loss rate and network goodput [130] Video data streaming scheduling scheme Analysis of per cell utility and convergency of learning process [131] Computational offloading optimization in MEC network…”

Section: Algorithmsmentioning

confidence: 99%

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A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

et al. 2022

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The evolution of the wireless network systems over decades has been providing new services to the users with the help of innovative network and device technologies. In recent times, the 5G network systems are about to be deployed which creates the opportunity to realize massive connectivity with high throughput, low latency, high energy efficiency and security. It also focuses on providing massive Internet of Things (IoT) network connectivity as well as services for good health, large-scale agricultural and industrial production, intelligent traffic control and electricity generation, transmission and distribution systems. However, the ever-increasing number of user devices is directing the researchers towards beyond 5G systems to allocate these user devices with higher bandwidth. Researches on the 6G wireless network systems have already begun to provide higher bandwidth availability for densely connected larger network devices with QoS surety. Researchers are leveraging artificial intelligence (AI)/machine learning (ML) for enhancing future IoT network operations and services. This paper attempts to discuss AI/ML algorithms that can help in developing energy efficient, secured and effective IoT network operations and services. In particular, our article concentrates on the major issues and factors that influence the design of the communication systems for future IoT with the integration of AI/ML. It also highlights application domains, including smart healthcare, smart agriculture, smart transportation, smart grid and smart industry that can operate efficiently and securely. Finally, this paper ends with the discussion on future research scopes with these algorithms in addressing the open issues of the future IoT network systems.

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Section: Algorithms Referencesmentioning

confidence: 99%

Section: Algorithmsmentioning

confidence: 99%

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

et al. 2022

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“…First, the instantaneous SER correlates strongly to the instantaneous SNR. Then from (15), as M grows large, it can be inferred that γ converges to a constant independent of the small-scale fading coefficients. That is, γ changes at the same rate of large-scale fading factor.…”

Section: Adaptive Modulation For Ed-based Non-coherent Massive Simo Systemsmentioning

confidence: 99%

“…In [15], an adaptive streaming for massive MIMO networks by employing approximate Markov decision process and reinforcement learning, where the dynamics of user arrivals and departures are considered. Apart from coherent systems, we emphasize that the adaptive modulation is equally important in non-coherent counterparts.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Modulation for ED-Based Non-Coherent Massive SIMO Systems

Chang¹,

2021

IEEE Access

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Link adaptation in coherent wireless communications has sparked great research enthusiasm. However, adaptive techniques are equally important for non-coherent counterparts although relatively less attention has been dedicated to. This letter studies the adaptive modulation for the energy detection (ED)based non-coherent systems. Specifically, we consider a massive single-input multiple-output (SIMO) network, where a single-antenna user communicates with a base station (BS) with tens/hundreds of antennas. In particular, an optimization problem is formulated in order to find the signal-to-noise ratio (SNR) switching thresholds that maximize the average spectral efficiency (ASE). After deriving the SNR distribution and symbol error rate (SER), this problem is solved by applying the Lagrangian method. With switching thresholds, rate and power adaptation are made based on the instantaneous SNR. Numerical results indicate for the proposed scheme, there exists a trade-off between the target SER and ASE. That is, the ASE falls if one wishes to reduce the target SER. Under this circumstance, mounting more antennas can avoid the loss in ASE.INDEX TERMS Non-coherent communications, massive SIMO systems, energy detection, SNR switching thresholds, average spectral efficiency, nonnegative PAM.

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“…Meanwhile, the wireless channel is fast fading, due to high-mobility. Accordingly, there are following three important assumptions behind this scenario: 1) Due to the buildings and trees alongside the road and many vehicles on the road, it is a rich-scatter environment for communications [20]- [23]; 2) All vehicles are equipped with multiple antennas, because massive MIMO is an effective way to meet high data rate requirement of 6G [24]- [26]; and 3) Due to the high-mobility of vehicles, the wireless channel is fast fading, which leads to the delayed feedback from the receivers to the transmitters [27]- [29]. In the following, it will be seen that such 6G-enabled V2V may fall into the MIMO interference channel with confidential messages and local output feedback.…”

Section: A the Considered Scenario Of 6g-enabled V2vmentioning

confidence: 99%

On Secure Degrees of Freedom of the MIMO Interference Channel with Local Output Feedback

Zhang

Wang

et al. 2021

Preprint

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This paper studies the problem of sum-secure degrees of freedom (SDoF) of the (M, M, N, N ) multiple-input multiple-output (MIMO) interference channel with local output feedback, so as to build an information-theoretic foundation and provide practical transmission schemes for 6G-enabled vehiclesto-vehicles (V2V). For this problem, we propose two novel transmission schemes, i.e., the interference decoding scheme and the interference alignment scheme, and thus establish a sum-SDoF lower bound. In particular, to optimize the phase duration, we analyze the security and decoding constraints and formulate a linear-fractional optimization problem. Furthermore, we show that the derived sum-SDoF lower bound is the sum-SDoF for M ≤ N/2, N = M , and 2N ≤ M antenna configurations, and reveal that for a fixed N , the optimal M to maximize the sum-SDoF is not less than 2N . Through simulations, we examine the secure sum-rate performance of proposed transmission schemes, and reveal that using local output feedback can lead to a higher secure sum-rate than that by using delayed channel state information at the transmitter.

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Adaptive Video Streaming for Massive MIMO Networks via Approximate MDP and Reinforcement Learning

Cited by 13 publications

References 27 publications

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

Adaptive Modulation for ED-Based Non-Coherent Massive SIMO Systems

On Secure Degrees of Freedom of the MIMO Interference Channel with Local Output Feedback

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