Managing Vertical Handovers in Millimeter Wave Heterogeneous Networks

Zang, Shizhe; Bao, Wei; Yeoh, Phee Lep; Vucetic, Branka; Li, Yonghui

doi:10.1109/tcomm.2018.2877326

Cited by 49 publications

(23 citation statements)

References 28 publications

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“…Other works have addressed handover decision-making problems in mmWave networks by using user mobility information or pedestrian mobility information [30]- [32]. User mobility information facilitates the prediction of future data rates in mmWave links with blockage effects that occur when users are entering areas blocked by static obstacles [30], [31]. However, the proactive prediction of the data rate degradations caused by moving obstacles is not addressed.…”

Section: A Handover Decision Problemsmentioning

confidence: 99%

“…It should be noted that in many existing studies [14], [15], [19], [30], [31], the handover decision process was formulated as an MDP, although it was assumed that the interval between the decision epochs was several seconds long, which is longer than a realistic service disruption time of several tens or hundreds of milliseconds [40]. Hence, the service disruption occurs within an interval between the successive decision epochs.…”

Section: B States Actions Rewards and State Transition Rulesmentioning

confidence: 99%

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Handover Management for mmWave Networks With Proactive Performance Prediction Using Camera Images and Deep Reinforcement Learning

Koda

Nakashima

Yamamoto

et al. 2020

IEEE Trans. Cogn. Commun. Netw.

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For millimeter-wave networks, this paper presents a paradigm shift for leveraging time-consecutive camera images in handover decision problems. While making handover decisions, it is important to predict future long-term performance-e.g., the cumulative sum of time-varying data rates-proactively to avoid making myopic decisions. However, this study experimentally notices that a time-variation in the received powers is not necessarily informative for proactively predicting the rapid degradation of data rates caused by moving obstacles. To overcome this challenge, this study proposes a proactive framework wherein handover timings are optimized while obstacle-caused data rate degradations are predicted before the degradations occur. The key idea is to expand a state space to involve timeconsecutive camera images, which comprises informative features for predicting such data rate degradations. To overcome the difficulty in handling the large dimensionality of the expanded state space, we use a deep reinforcement learning for deciding the handover timings. The evaluations performed based on the experimentally obtained camera images and received powers demonstrate that the expanded state space facilitates (i) the prediction of obstacle-caused data rate degradations from 500 ms before the degradations occur and (ii) superior performance to a handover framework without the state space expansion.

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Section: A Handover Decision Problemsmentioning

confidence: 99%

Section: B States Actions Rewards and State Transition Rulesmentioning

confidence: 99%

Handover Management for mmWave Networks With Proactive Performance Prediction Using Camera Images and Deep Reinforcement Learning

Koda

Nakashima

Yamamoto

et al. 2020

IEEE Trans. Cogn. Commun. Netw.

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“…As redes 5G são ultradensas e heterogêneas, fato que, por si só, gera handovers frequentes, indesejados, desnecessários (efeito ping-pong) e falhos, além de aumentar o tempo do processo de handover [Bilen et al 2017]. Diferentes estratégias de handover são apresentadas em outros trabalhos [Polese et al 2017, Mouawad et al 2018, Rizvi and Akram 2018, Gharsallah et al 2018, Peng et al 2019, Zang et al 2019. Um mecanismo de handover suave (SoftH), que utiliza o conceito de SDNé capaz de realizar decisões de handovers de uma maneira mais assertiva [Oliveira et al 2019].…”

Section: Referênciaunclassified

“…As aplicações que foram consideradas nas avaliações foram geradas através do modelo de tráfego "envio em massa" (Bulk-Send) e são classificadas de acordo com o seu QCI ou 5QI. As simulações foram executadas em um ambiente computacional de alto desempenho, fornecido pelo cluster F37 do CEFET-MG. Os cenários foram avaliados com intervalo de confiança de 95% para dez ensaios de simulação com duração de 100 segundos, como apresentado na Tabela 4, com base na literatura [Zang et al 2019] O cenário I, apresentado na Figura 3(a), consiste em umaárea urbana com nove quarteirões, cada uma com 250 x 433 m e 2 pistas em cada lado com 3,5 m cada, nas quais os veículos se movem em sentido horário para tornar possível as conversões em cada quadrante. Velocidades distintas foram adotadas (15, 30 e 60 km/h) para tornar possível a classificação das células e regras de handover por faixas de baixa velocidade e alta velocidade pela solução.…”

Section: Metodologia De Avaliaçãounclassified

Uma Solução de Decisão de Handover inter-V-Cell para Veı́culos Conectados em Redes 5G Ultradensas

Storck

Lousada²,

Silva

et al. 2020

Anais Do Simpósio Brasileiro De Computação Ubíqua E Pervasiva (SBCUP 2020)

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A rede de celular de quinta geração (5G) requer novas soluções para novos requisitos de rede, como o suporte à mobilidade para altas velocidades. Este trabalho apresenta uma solução de decisão de handover para veı́culos conectados em redes 5G ultradensas. Ele tem como objetivo principal tratar a alta mobilidade veicular e proporcionar ganhos de desempenho em handover para veı́culos, uma vez que a densificação da rede celular causa, apesar do ganho de capacidade esperado, dificuldades na seleção de células, maior número de handovers falhos e desnecessários (efeito ping-pong), maiores atrasos e consumo de energia, e altas perdas de pacotes. A solução tem como base a formação de células virtuais (V-Cells). A seleção de melhores células é feita com o auxı́lio de métricas de redes complexas para compor uma célula virtual, assim como critérios como potência de sinal, distância e velocidade. A solução se apoia em um controlador de rede definida por software (SDN) para tomada de decisão, que possui informação centralizada da rede. Simulações foram conduzidas no simulador de rede, ns-3. Os resultados obtidos mostram que a solução é eficaz no tratamento de handover para veı́culos.

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“…In [15]- [19], the temporal variation of AoA/AoD over the considered period of time is assumed to follow a Markov process, and the AoA's and AoD's deviations between two consecutive channel realizations are modeled as small Gaussian random variables, based on which various Kalman filter-based beam tracking algorithms have been developed. It is also worth mentioning that the authors in [20]- [23] have proposed to employ the mobile users' location and trajectory information to reduce the beam training overhead. However, these strategies are limited to vehicular networks and not universal.…”

Section: Introductionmentioning

confidence: 99%

Beam Allocation for Millimeter-Wave MIMO Tracking Systems

Zhang

Chen

et al. 2020

IEEE Trans. Veh. Technol.

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In this paper, we propose a new beam allocation strategy aiming to maximize the average successful tracking probability (ASTP) of time-varying millimeter-wave MIMO systems. In contrast to most existing works that employ one transmitting-receiving (Tx-Rx) beam pair once only in each training period, we investigate a more general framework, where the Tx-Rx beam pairs are allowed to be used repeatedly to improve the received signal powers in specific directions. In the case of orthogonal Tx-Rx beam pairs, a power-based estimator is employed to track the time-varying AoA and AoD of the channel, and the resulting training beam pair sequence design problem is formulated as an integer nonlinear programming (I-NLP) problem. By dividing the feasible region into a set of subregions, the formulated I-NLP is decomposed into a series of concave sub I-NLPs, which can be solved by recursively invoking a nonlinear branch-and-bound algorithm. To reduce the computational cost, we relax the integer constraints of each sub I-NLP and obtain a low-complexity solution via solving the Karush-Kuhn-Tucker conditions of their relaxed problems. For the case when the Tx-Rx beam pairs are overlapped in the angular space, we estimate the updated AoA and AoD via an orthogonal matching pursuit (OMP) algorithm. Moreover, since no explicit expression for the ASTP exists for the OMP-based estimator, we derive a closed-form lower bound of the ASTP, based on which a favorable beam pair allocation strategy can be obtained. Numerical results demonstrate the superiority of the proposed beam allocation strategy over existing benchmarks. Index TermsMillimeter wave, time-varying channel, beam training, beam tracking, training beam sequence design.

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Managing Vertical Handovers in Millimeter Wave Heterogeneous Networks

Cited by 49 publications

References 28 publications

Handover Management for mmWave Networks With Proactive Performance Prediction Using Camera Images and Deep Reinforcement Learning

Handover Management for mmWave Networks With Proactive Performance Prediction Using Camera Images and Deep Reinforcement Learning

Uma Solução de Decisão de Handover inter-V-Cell para Veı́culos Conectados em Redes 5G Ultradensas

Beam Allocation for Millimeter-Wave MIMO Tracking Systems

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