Resource Management for 5G NR Integrated Access and Backhaul: A Semi-Centralized Approach

Pagin, Matteo; Zugno, Tommaso; Polese, Michele; Zorzi, Michele

doi:10.1109/twc.2021.3098967

Cited by 44 publications

(18 citation statements)

References 33 publications

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“…Although fully centralized scheduling is not 3GPP compliant for IAB, nodes can exchange information and, eventually, it can be assembled in a central node that reports general recommendations for resource allocation and scheduling to the other network nodes. This is the key idea of [58]. In [58], a central controller receives general information about network nodes, e.g., CSI and buffer status, and provides centralized recommendations for resource partitioning to the distributed schedulers.…”

Section: ) Resource Allocation For Access And/or Backhaulmentioning

confidence: 99%

Paving the Way Toward Mobile IAB: Problems, Solutions and Challenges

Monteiro

Lima

Moreira

et al. 2022

IEEE Open J. Commun. Soc.

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Deploying access and backhaul as wireless links, a.k.a. integrated access and backhaul (IAB), is envisioned as a viable approach to enable flexible and dense networks. Even further, mobile IAB (mIAB) is a candidate solution to enhance the connectivity of multiple user equipment (UE) moving together. In this context, different of other works from the literature, the present work overviews the basis for the deployment of mIAB by presenting: 1) the current status of IAB standardization in the fifth generation (5G) new radio (NR); 2) a new taxonomy for state-of-the-art works regarding fixed IAB and mIAB; 3) an extensive performance analysis of mIAB based on simulation results; and 4) open challenges and potential future prospects of mIAB. Specifically, the proposed taxonomy classifies IAB works according to different perspectives and categorizes mIAB works according to the type of mobile node. For each type of mobile node, the main studied topics are presented. Regarding the performance evaluation, we consider an urban macro scenario where mIAB nodes are deployed in buses in order to improve the passengers' connection. The results show that, compared to other network architectures, the deployment of mIAB nodes remarkably improves the passengers' throughput and latency in both downlink and uplink.

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Section: ) Resource Allocation For Access And/or Backhaulmentioning

confidence: 99%

Paving the Way Toward Mobile IAB: Problems, Solutions and Challenges

Monteiro

Lima

Moreira

et al. 2022

IEEE Open J. Commun. Soc.

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show abstract

“…The idea of improving the performance of RAN architecture in 5G and beyond has been considered in the literature, where power domain non-orthogonal multiple access (PD-NOMA) is one of the best candidates for for resource allocation policies. Furthermore, several works have tried to model the structure and efficiency of the network the structure and efficiency of the network by centralizing functionality in a baseband unit (BBU) pool [4], [5]. In [6], a distributed structure was proposed to improve the latency and proficiency of a network.…”

Section: A Resource Allocation For Wireless Communicationmentioning

confidence: 99%

“…The authors in [11] considered a semi-centralized framework for the resource allocation problem by using matching theory and a successive convex approximation (SCA) approach. Similarly, the authors in [4] proposed a semi-centralized resource allocation scheme to maximize the weighted matching (MWM) problem for integrated access and backhaul (IAB) networks. The authors in [12] compared both centralized and distributed algorithms regarding the BBU hotel location problem in C-RAN where their proposed solution is based on a distributed heuristic algorithm.…”

Section: A Resource Allocation For Wireless Communicationmentioning

confidence: 99%

“…Solution method [2] Offloading at the MEC via an soft actor-critic algorithm (AI solution) [4] Maximum weighted matching for semi-centralized resource management (conventional solution) [6] A multi-agent deep reinforcement learning (MADRL) method for networks with heterogeneous services (AI solution) [5] A DRL algorithm for multi-cell and multi-user (AI solution) [7] DRL for multi-carrier-single-cell NOMA system (AI solution) [8] Iterative resource allocation algorithm based on DRL (AI solution) [9] DRL in a decentralized structure based on cellular V2X communications (AI solution) [10] Matroid constrained sub-modular maximization problem and heuristic algorithms in a centralized-distributed network (conventional solution) [11] Matching theory and SCA in semi-centralized network (conventional solution) [12] Comparison performance of distributed and centralized frameworks via distributed heuristic algorithm (conventional solution) Our paper AI method based on smart solution centralized/distributed (AI solution and soft actor-critic based method)…”

Section: Refmentioning

confidence: 99%

See 1 more Smart Citation

Toward a Smart Resource Allocation Policy via Artificial Intelligence in 6G Networks: Centralized or Decentralized?

Nouruzi¹,

Rezaei²,

Khalili³

et al. 2022

Preprint

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In this paper, we design a new smart softwaredefined radio access network (RAN) architecture with important properties like flexibility and traffic awareness for sixth generation (6G) wireless networks. In particular, we consider a hierarchical resource allocation framework for the proposed smart soft-RAN model, where the software-defined network (SDN) controller is the first and foremost layer of the framework. This unit dynamically monitors the network to select a network operation type on the basis of distributed or centralized resource allocation architectures to perform decision-making intelligently. In this paper, our aim is to make the network more scalable and more flexible in terms of achievable data rate, overhead, and complexity indicators. To this end, we introduce a new metric, throughput overhead complexity (TOC), for the proposed machine learning-based algorithm, which makes a trade-off between these performance indicators. In particular, the decision making based on TOC is solved via deep reinforcement learning (DRL), which determines an appropriate resource allocation policy. Furthermore, for the selected algorithm, we employ the soft actor-critic method, which is more accurate, scalable, and robust than other learning methods. Simulation results demonstrate that the proposed smart network achieves better performance in terms of TOC compared to fixed centralized or distributed resource management schemes that lack dynamism. Moreover, our proposed algorithm outperforms conventional learning methods employed in other state-of-the-art network designs.

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“…The performance of an IAB network with respect to the coverage extension has been extensively investigated in the literature [5]. Resource partitioning is addressed in [6] while topology formation problem is solved in [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Integrated Access and Backhaul in Millimeter-Wave Cellular: Benefits and Challenges

Sadovaya¹,

Moltchanov²,

Mao³

et al. 2022

Preprint

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The recently proposed NR-ready integrated access and backhaul (IAB) architecture promises to bring a costefficient deployment solution for both coverage extension and capacity boosting in future 5G/5G+ systems. While its impact on the coverage extension was thoughtfully addressed in the literature, the effect of advanced functionalities such as multi-hop, multi-connectivity, and multi-beam operations on the throughput remains unclear. We review and characterize the system-level impact of these capabilities on the performance of self-backhauled IAB systems operating in half-duplex mode and utilizing millimeter-wave (mmWave) technology across both access and backhaul. Our results indicate that the throughput gain of multi-hopping and multi-beaming is significant even without multi-connectivity operation. Another important learning is that in all-mmWave systems with link blockage, multi-connectivity with link switching allows achieving self-load balancing. Finally, we outline future research directions.

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Resource Management for 5G NR Integrated Access and Backhaul: A Semi-Centralized Approach

Cited by 44 publications

References 33 publications

Paving the Way Toward Mobile IAB: Problems, Solutions and Challenges

Paving the Way Toward Mobile IAB: Problems, Solutions and Challenges

Toward a Smart Resource Allocation Policy via Artificial Intelligence in 6G Networks: Centralized or Decentralized?

Integrated Access and Backhaul in Millimeter-Wave Cellular: Benefits and Challenges

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