Ultra-Reliable Communication in 5G mmWave Networks: A Risk-Sensitive Approach

Vu, Trung Kien; Bennis, Mehdi; Debbah, Mérouane; Latva-aho, Matti; Hong, Choong Seon

doi:10.1109/lcomm.2018.2802902

Cited by 35 publications

(24 citation statements)

References 18 publications

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“…In Fig. 3-bottom showing u t (s) given in (20), we observe that the probability of visiting a risk-state when the fixed policy is followed is much higher than that obtained when the minimum-risk policy π * is performed. For example, at the time step t = 5, u t (s) is equal to 0.42 when the policy π f is performed whereas this value reduces to 0.02 when the policy π * is followed.…”

Section: A Minimum-risk Vs Maximum-value Policymentioning

confidence: 85%

“…Most risk-sensitive approaches consist in analyzing higher order statistics than the average metric such as the variance of the reward [5], [6], [13], [16]. For instance, a risk-sensitive reinforcement learning is studied in [20] in millimeter-wave communications to optimize both the bandwidth and transmit power. The authors consider a utility (data rate) that incorporates both the average and the variance to capture the tail distribution of the rate, useful for the reliability requirement of URLLC traffic.…”

Section: A Related Workmentioning

confidence: 99%

“…Initialization: for each s 2: u * T (s) ← 0, u * T (s) ← 0, u * ξ,T (s) ← 0 3: Endfor 4: t ← T − 1 For each s 7: update u * t (s), u * t (s), and u * ξ,t (s) according to(19),(20), and (21), respectively 8: EndFor 9: t ← t − 1 10: Reinforcement Learning Model.…”

mentioning

confidence: 99%

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Risk-Sensitive Reinforcement Learning for URLLC Traffic in Wireless Networks

Khalifa

Assaad

Debbah

2019

2019 IEEE Wireless Communications and Networking Conference (WCNC)

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In this paper, we study the problem of dynamic channel allocation for URLLC traffic in a multi-user multichannel wireless network where urgent packets have to be successfully transmitted in a timely manner. We formulate the problem as a finite-horizon Markov Decision Process with a stochastic constraint related to the QoS requirement, defined as the packet loss rate for each user. We propose a novel weighted formulation that takes into account both the total expected reward (number of successfully transmitted packets) and the risk which we define as the QoS requirement violation. First, we use the value iteration algorithm to find the optimal policy, which assumes a perfect knowledge of all the model parameters, namely the channel statistics. We then propose a Q-learning algorithm where the controller learns the optimal policy without having knowledge of neither the CSI nor the channel statistics. We illustrate the performance of our algorithms with numerical studies.

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Section: A Minimum-risk Vs Maximum-value Policymentioning

confidence: 85%

Section: A Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Risk-Sensitive Reinforcement Learning for URLLC Traffic in Wireless Networks

Khalifa

Assaad

Debbah

2019

2019 IEEE Wireless Communications and Networking Conference (WCNC)

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“…In the next subsection a series of reliability enablers will be discussed to reduce the delay incurred to counteract the intermittent blockages and temporal disruptions of the mmWave channel. Largely, these techniques are in line with the idea of overbooking radio resources as a protection against channel vulnerability [30] or to consider risk-sensitive approaches [31].…”

Section: Low Latency Enabler 1 High Capacity Mmwave Linksmentioning

confidence: 99%

Wireless Edge Computing With Latency and Reliability Guarantees

et al. 2019

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Edge computing is an emerging concept based on distributing computing, storage, and control services closer to end network nodes. Edge computing lies at the heart of the fifth generation (5G) wireless systems and beyond. While current state-of-the-art networks communicate, compute, and process data in a centralized manner (at the cloud), for latency and compute-centric applications, both radio access and computational resources must be brought closer to the edge, harnessing the availability of computing and storage-enabled small cell base stations in proximity to the end devices. Furthermore, the network infrastructure must enable a distributed edge decision-making service that learns to adapt to the network dynamics with minimal latency and optimize network deployment and operation accordingly. This article will provide a fresh look to the concept of edge computing by first discussing the applications that the network edge must provide, with a special emphasis on the ensuing challenges in enabling ultra-reliable and lowlatency edge computing services for mission-critical applications such as virtual reality (VR), vehicle-to-everything (V2X), edge artificial intelligence (AI), and so forth. Furthermore, several case studies where the edge is key are explored followed by insights and prospect for future work.

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“…The positioning method utilizes measurement of time of arrival and angle of departure, and the position can be tracked by an Extended Kalman Filter. [28] overcame the challenge of sensitivity of mmWave links by utilizing multiple antennas diversity and higher bandwidth. A risk-sensitive reinforcement learning framework was formulated where each cell optimizes its transmission while considering signal fluctuations.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Power Control of Train–Ground mmWave Communication for High-Speed Trains

Wang

Niu

et al. 2019

IEEE Trans. Veh. Technol.

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High speed train system has proven to be a very flexible and attractive system that can be developed under various circumstances and in different contexts and cultures. As a result, high speed trains are widely deployed around the world. Providing more reliable and higher data rate communication services for high speed trains has become one of the most urgent challenges. With vast amounts of spectrum available, the millimeter wave (mmWave) system is able to provide transmission rates of several gigabits per second for high speed trains. At the same time, mmWave communication also suffers from high attenuation, thus higher energy efficiency should be considered. This paper proposes an energy efficient power control scheme of train-ground mmWave communication for high speed trains. Considering a beam switching method for efficient beam alignment, we first establish position prediction model, the realistic direction antenna model and receive power model. And then we allocate the transmission power rationally through the power minimization algorithm while ensuring the total amount of transmission data. Based on this, this paper also develops a hybrid optimization scheme and finds the limit of total energy consumption when the number of segments goes to infinity. Through simulation with various system parameters and taking velocity estimation error into account, we demonstrate the superior performance of our schemes.

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Ultra-Reliable Communication in 5G mmWave Networks: A Risk-Sensitive Approach

Cited by 35 publications

References 18 publications

Risk-Sensitive Reinforcement Learning for URLLC Traffic in Wireless Networks

Risk-Sensitive Reinforcement Learning for URLLC Traffic in Wireless Networks

Wireless Edge Computing With Latency and Reliability Guarantees

Energy-Efficient Power Control of Train–Ground mmWave Communication for High-Speed Trains

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