Resource allocation in mmWave 5G IAB networks: A reinforcement learning approach based on column generation

Zhang, Bibo; Devoti, Francesco; Filippini, Ilario; Donno, Danilo De

doi:10.1016/j.comnet.2021.108248

Cited by 13 publications

(9 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This work uses an N-relay Gaussian Full-Duplex (FD) 1-2-1 network model which is different from our network model. [28] proposes a reinforcement learning approach based on column generation to address the resource allocation problem for mmWave IAB backhaul, as the method is claimed to be able to capture the environment dynamics such as moving obstacles in the network.…”

Section: Related Workmentioning

confidence: 99%

Towards the Maximum Traffic Demand and Throughput Supported by Relay-Assisted mmWave Backhaul Networks

Hu¹,

Liu²,

Yan³

et al. 2022

Preprint

View full text Add to dashboard Cite

This paper investigates the throughput performance issue of the relay-assisted mmWave backhaul network. The maximum traffic demand of small-cell base stations (BSs) and the maximum throughout at the macro-cell BS have been found in a tree-style backhaul network through linear programming under different network settings, which concern both the number of radio chains available on BSs and the interference relationship between logical links in the backhaul network. A novel interference model for the relay-assisted mmWave backhaul network in the dense urban environment is proposed, which demonstrates the limited interference footprint of mmWave directional communications. Moreover, a scheduling algorithm is developed to find the optimal scheduling for tree-style mmWave backhaul networks. Extensive numerical analysis and simulations are conducted to show and validate the network throughput performance and the scheduling algorithm.

show abstract

Section: Related Workmentioning

confidence: 99%

Towards the Maximum Traffic Demand and Throughput Supported by Relay-Assisted mmWave Backhaul Networks

Hu¹,

Liu²,

Yan³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This work uses an N-relay Gaussian Full-Duplex (FD) 1-2-1 network model which is different from our network model. [30] proposes a reinforcement learning approach based on column generation to address the resource allocation problem for mmWave IAB backhaul, as the method is claimed to be able to capture the environment dynamics such as moving obstacles in the network. [31] is a work investigating the multihop link scheduling problem with the objective of minimizing the end-to-end delay experienced by a typical packet.…”

Section: Related Workmentioning

confidence: 99%

An efficient distributed scheduling algorithm for relay-assisted mmWave backhaul networks

Hu¹,

Liu²,

Yan³

et al. 2022

Preprint

View full text Add to dashboard Cite

In this paper, a novel distributed scheduling algorithm is proposed, which aims to efficiently schedule both the uplink and downlink backhaul traffic in the relay-assisted mmWave backhaul network with a tree topology. The handshaking of control messages, calculation of local schedules, and the determination of final valid schedule are all discussed. Simulation results show that the performance of the distributed algorithm can reach very close to the maximum traffic demand of the backhaul network, and it can also adapt to the dynamic traffic with sharp traffic demand change of small-cell BSs quickly and accurately.

show abstract

“…Risk-sensitive RL is adopted in [21] to control transmitter beamwidth and power so as to maximize data rate. In [22], the authors propose a resource allocation framework based on advantage actor critic and column generation to maximize the throughput of mmWave IAB networks.…”

Section: Related Workmentioning

confidence: 99%

“…UEs can move in the scenario with arbitrary directions and speeds, and they may undergo link blockages caused by random mobile blockers. This makes access links shortlived and unstable, thus posing a big challenge to resource allocation problems, especially to those based on traditional optimization techniques [22]. Indeed, these techniques rely on static assumptions and usually require a non-negligible amount of time to provide an optimal solution for a single network snapshot.…”

Section: Case Iii: When the Los Is A Secant Of The Blocker's Cross-se...mentioning

confidence: 99%

See 1 more Smart Citation

Mobility-Aware Resource Allocation for mmWave IAB Networks: A Multi-Agent RL Approach

Zhang¹,

Filippini²

2022

Preprint

Self Cite

View full text Add to dashboard Cite

MmWaves have been envisioned as a promising direction to provide Gbps wireless access. However, they are susceptible to high path losses and blockages, which directional antennas can only partially mitigate. That makes mmWave networks coverage-limited, thus requiring dense deployments. Integrated access and backhaul (IAB) architectures have emerged as a cost-effective solution for network densification. Resource allocation in mmWave IAB networks must face big challenges to cope with heavy temporal dynamics, such as intermittent links caused by user mobility and blockages from moving obstacles. This makes it extremely difficult to find optimal and adaptive solutions. In this article, exploiting the distributed structure of the problem, we propose a Multi-Agent Reinforcement Learning (MARL) framework to optimize user throughput via flow routing and link scheduling in mmWave IAB networks characterized by user mobility and link outages generated by moving obstacles. The proposed approach implicitly captures the environment dynamics, coordinates the interference, and manages the buffer levels of IAB relay nodes. We design different MARL components, considering full-duplex and half-duplex IAB-nodes. In addition, we provide a communication and coordination scheme for RL agents in an online training framework, addressing the feasibility issues of practical systems. Numerical results show the effectiveness of the proposed approach.

show abstract

Resource allocation in mmWave 5G IAB networks: A reinforcement learning approach based on column generation

Cited by 13 publications

References 32 publications

Towards the Maximum Traffic Demand and Throughput Supported by Relay-Assisted mmWave Backhaul Networks

Towards the Maximum Traffic Demand and Throughput Supported by Relay-Assisted mmWave Backhaul Networks

An efficient distributed scheduling algorithm for relay-assisted mmWave backhaul networks

Mobility-Aware Resource Allocation for mmWave IAB Networks: A Multi-Agent RL Approach

Contact Info

Product

Resources

About