An Initial Access Optimization Algorithm for millimeter Wave 5G NR Networks

Perera, A. Indika; Manosha, K. B. Shashika; Rajatheva, Nandana

doi:10.1109/vtc2020-spring48590.2020.9128625

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…The availability of cell context information at the BS was shown to reduce the search delay of analog beamforming in [9]. A heuristic weight-based beam sweeping algorithm to improve the cell search in mmWave 5G networks is given in [10]. In [11], an initial access scheme is developed with the aim to minimize the initial access delay by optimizing the activated beams and regions at each slot.…”

Section: Introductionmentioning

confidence: 99%

Initial Access Optimization for RIS-assisted Millimeter Wave Wireless Networks

Bou¹,

Assaad²,

Chahed³

2023

Preprint

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Reconfigurable Intelligent Surfaces (RIS) are considered a key enabler to achieve the vision of Smart Radio Environments, where the propagation environment can be programmed and controlled to enhance the efficiency of wireless systems. These surfaces correspond to planar sheets comprising a large number of small and low-cost reflecting elements whose parameters are adaptively selected with a programmable controller. Hence, by optimizing these coefficients, the information signals can be directed in a customized fashion. On the other hand, the initial access procedure used in 5G is beam sweeping, where the base station sequentially changes the active beam direction in order to scan all users in the cell. This conventional protocol results in an initial access latency. The aim of this paper is to minimize this delay by optimizing the activated beams in each timeslot, while leveraging the presence of the RIS in the network. The problem is formulated as a hard optimization problem. We propose an efficient solution based on jointly alternating optimization and Semi Definite Relaxation (SDR) techniques. Numerical results are provided to assess the superiority of our scheme as compared to conventional beam sweeping.

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Section: Introductionmentioning

confidence: 99%

Initial Access Optimization for RIS-assisted Millimeter Wave Wireless Networks

Bou¹,

Assaad²,

Chahed³

2023

Preprint

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“…Given the importance of this procedure, optimization of BM performance has been extensively pursued in the literature. In [3], a dynamic weight-based algorithm is proposed for initial access (IA) synchronization signal block (SSB) allocation, using static user distribution, to optimize the number of SSBs per sweeping direction. The authors in [4] introduce different beam switching algorithms to improve robustness of BM operations for intra-cell mobility scenarios.…”

Section: Introductionmentioning

confidence: 99%

Improving Beam Management Signalling for 5G NR Systems using Hybrid Beamforming

Fernandes

Rom²,

Harrebek³

et al. 2022

2022 IEEE Wireless Communications and Networking Conference (WCNC)

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5th Generation (5G) millimeter wave (mmWave) communications are enabled through directive and narrow beams that mitigate these frequencies' challenging propagation conditions. In the future, 5G-Advanced and 6G will go even higher in the frequency spectrum, to allow for progressively larger bandwidths. The need for a larger number of narrower beams will put a strain in the current analog beamforming (BF) based beam management (BM) framework. This paper proposes an alternative signalling method for BM to parallelize the beam sweeping procedure using a hybrid analog-digital (HAD) BF architecture to enable mmWave signal multiplexing with a manageable overhead. The proposed solution is shown to significantly enhance beam alignment performance while reducing signalling overhead and latency.

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“…Authors in [7] discuss and compared IA approaches suggested for 5G networks such as iterative, and exhaustive search. The authors of [8] propose an algorithm that dynamically scales the resources allocated to beam sweeping directions to optimize the beam management of the IA procedure in 5G mmWave networks. Recently, machine learning (ML) based approaches have been explored as alternatives to replace conventionally non-ML bases systems in communication systems [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

Deep Contextual Bandits for Fast Initial Access in mmWave Based User-Centric Ultra-Dense Networks

Ismath

Manosha

Ali

et al. 2021

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)

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Millimeter wave (mmWave) based multiple-input multiple-output (MIMO) capable user-centric (UC) ultra-dense (UD) networks are suggested to facilitate high throughput requirements of future networks. Due to the high blockage susceptibility of mmWave, the connections may drop frequently. Hence efficient and fast beam management in initial access (IA) is essential. Current cellular systems use beam sweeping based IA mechanisms. UC UD concept requires all of its access points (APs) to perform IA. This leads to a shortage of orthogonal radio resources. Nonorthogonal resource allocation causes interference which leads to a higher misdetection probability. In this paper, we propose a novel deep contextual bandit (DCB) based approach to perform fast and efficient IA in mmWave based UC UD networks. The DCB model uses one reference signal from the user to predict the IA beam. The reduced use of reference signals improves beam discovery delay and relaxes the requirement for radio resources. Ray-tracing and stochastic channel modelbased simulations show that the suggested system outperforms its beam sweeping counterpart in terms of probability of beam misdetection and beam discovery delay in mmWave based UC UD networks.

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An Initial Access Optimization Algorithm for millimeter Wave 5G NR Networks

Cited by 7 publications

References 5 publications

Initial Access Optimization for RIS-assisted Millimeter Wave Wireless Networks

Initial Access Optimization for RIS-assisted Millimeter Wave Wireless Networks

Improving Beam Management Signalling for 5G NR Systems using Hybrid Beamforming

Deep Contextual Bandits for Fast Initial Access in mmWave Based User-Centric Ultra-Dense Networks

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