Hybrid-beamforming-based millimeter-wave cellular network optimization

Liu, Jia; Bentley, Elizabeth Serena

doi:10.23919/wiopt.2017.7959916

Cited by 14 publications

(26 citation statements)

References 22 publications

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“…Here, we assume that each BS adopts the hybrid beamforming architecture, which enjoys both analog and digital beamforming techniques [16]. For the analog beamforming, let g (t) (i,j) and g (r) (i,j) denote the transmitter and receiver beamforming gain at the transmitter i and the receiver j, respectively.…”

Section: System Modelmentioning

confidence: 99%

Path selection and rate allocation in self-backhauled mmWave networks

Liu

Bennis

et al. 2018

2018 IEEE Wireless Communications and Networking Conference (WCNC)

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We investigate the problem of multi-hop scheduling in self-backhauled millimeter wave (mmWave) networks 1 . Owing to the high path loss and blockage of mmWave links, multi-hop paths/routes between the macro base station and the intended users via full-duplex small cells need to be carefully selected. This paper addresses the fundamental question: "how to select the best paths and how to allocate rates over these paths subject to latency constraints?" To answer these questions, we propose a new system design, which factors in mmWave-specific channel variations and network dynamics. The problem is cast as a network utility maximization subject to a bounded delay constraint and network stability. The studied problem is decoupled into: (i) a path/route selection and (ii) rate allocation, whereby learning the best paths is done by means of a reinforcement learning algorithm, and the rate allocation is solved by applying the successive convex approximation method. Via numerical results, our approach ensures reliable communication with a guaranteed probability of 99.9999%, and reduces latency by 50.64% and 92.9% as compared to baselines.

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Section: System Modelmentioning

confidence: 99%

Path selection and rate allocation in self-backhauled mmWave networks

Liu

Bennis

et al. 2018

2018 IEEE Wireless Communications and Networking Conference (WCNC)

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“…Each SC adopts the hybrid beamforming architecture, which enjoys both analog and digital beamforming techniques [13]. Let g (t x) bk and g (r x) bk denote the analog transmitter and receiver beamforming gains at the SC b and UE k, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…We denote θ as a vector of the transmitter beamwidth of all SCs. We adopt the widely used antenna radiation pattern model [13] to determine the analog beamforming gain as…”

Section: Introductionmentioning

confidence: 99%

“…By applying a linear precoding scheme V bk (Ĥ bk ) [13], i.e, V bk (Ĥ bk ) =Ĥ bk for the conjugate precoding, the achievable rate 2 of UE k from SC b can be calculated as…”

Section: Introductionmentioning

confidence: 99%

“…We assume that each BS sends a single stream to its users via the main beams 3. As studied in[13], for η ≤ 1 3 , the problem of selecting beamwidth for the transmitter and receiver can be done by adjusting the transmitter beamwidth with a fixed receiver beamwidth.…”

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confidence: 99%

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

Bennis

Debbah

et al. 2018

IEEE Commun. Lett.

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Abstract-In this letter, we investigate the problem of providing gigabit wireless access with reliable communication in 5G millimeter-Wave (mmWave) massive multiple-input multipleoutput (MIMO) networks. In contrast to the classical network design based on average metrics, we propose a distributed risk-sensitive reinforcement learning-based framework to jointly optimize the beamwidth and transmit power, while taking into account the sensitivity of mmWave links due to blockage. Numerical results show that our proposed algorithm achieves more than 9 Gbps of user throughput with a guaranteed probability of 90%, whereas the baselines guarantee less than 7.5 Gbps. More importantly, there exists a rate-reliability-network density tradeoff, in which as the user density increases from 16 to 96 per km 2 , the fraction of users that achieve 4 Gbps are reduced by 11.61% and 39.11% in the proposed and the baseline models, respectively.

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Overview of evolutionary algorithms and neural networks for modern mobile communication

Kouhalvandi

Shayea

Özoğuz

et al. 2022

Trans Emerging Tel Tech

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The sixth generation (6G) of mobile networks must support the huge growth of mobile connections and provides various intelligent services in future mobile networks. For that, designing high-performance network architecture and communication systems (CSs) as well as finding coherent solutions for the determined problems is critical demand that needs to be achieved in 6G networks.Optimal solutions are mainly sought by using modifiable, smart, and perceptive algorithms aimed at optimizing more specific tasks. Therefore, advanced optimization methods are highly required to accommodate the requirements of CSs efficiently. That will be in various parts of the future networks such as advanced mobility management, multi communication links, efficient power consumption, ultra-lower latency, ultra-security, high speed, and reliable connectivity.Accordingly, highly accurate and smart functions must be modeled to optimize the required communication parameters in the network. This study provides a comprehensive overview of optimization methods that may need further investigations and developments to be applied in 6G networks at various parts of networks. A detailed theoretical description for each method is presented and discussed to elucidate future research directions for optimizing specific characteristics with multi-objective optimizations. Moreover, this article illustrates the conceptual and structural viewpoints of reported optimization methods. Also, the capability of various optimization methods that can offer industrial solutions in 6G is discussed. The potential applications of each method are also analyzed. Finally, this article presented the research issues and future directions of optimization technology and research gaps that need to be addressed before the standardization of 6G networks. INTRODUCTIONRecently, innovative solutions of communication systems (CSs) have been developed impressively, as data traffic is increasing exponentially by more than 50% per year. 1 Several factors will contribute for increasing data demand massively, for example, as predicted by CISCO, the video will make up 82% of the all IP traffic by 2022. 2,3 This massive increase in data traffic will cause several new issues that will facing the implementation of future mobile networks. That will include a dramatic increase for wider spectrum need, 4 the necessity for big system capacity, 5 further mobility issues, 6,7 and higher energy consumption, 8 in which cannot be handled efficiently by the current cellular networks.

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Hybrid-beamforming-based millimeter-wave cellular network optimization

Cited by 14 publications

References 22 publications

Path selection and rate allocation in self-backhauled mmWave networks

Path selection and rate allocation in self-backhauled mmWave networks

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

Overview of evolutionary algorithms and neural networks for modern mobile communication

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