Link scheduling for mmWave WMN backhaul

Kilpi, Jorma; Seppänen, Kari; Suihko, Tapio; Paananen, Jori; Chen, David T.; Wainio, Pekka

doi:10.1109/icc.2017.7996621

Cited by 9 publications

(23 citation statements)

References 9 publications

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“…Resource management in mmWave access networks has been largely investigated in recent literature, taking into account the new challenges brought in by directional transmissions compared with the conventional omnidirectionality assumption of works on sub-6GHz networks. Several papers have investigated the optimization of bandwidth allocation [6,18,27], power allocation [6,23,30,15,16,33,36], beamwidth assignment [30], frame / slots design [28,15,34], transmission delay [8,24,12]. Among all these works, it draws remarkable attention that a large part of them dedicates to the traffic routing and transmission scheduling problem.…”

Section: Related Workmentioning

confidence: 99%

“…Authors in [25] investi-gate on the performance of different distributed greedy hopby-hop path selection to the core network. The work in [12] proposes a routing scheme using multiple overlapping spanning trees and schedules transmissions to minimize the endto-end delay along a subset of paths computed from the routing scheme. Authors in [31] study path selection and rate allocation to maximize the network data rate by leveraging Lyapunov stochastic optimization.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Zhang¹,

Devoti²,

Filippini³

et al. 2021

Computer Networks

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Zhang¹,

Devoti²,

Filippini³

et al. 2021

Computer Networks

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“…Existing research works on mmWave backhaul scheduling favor centralized solutions for various optimization goals, including throughput [7], [10], [11], delay [21], energy consumption [12], makespan [8], [22], wireless bandwidth [23] and flows with satisfied QoS requirements [9]. To achieve these optimization goals, most of the works perform link scheduling to maximize spatial reuse while some control routing [7], [8], [10], [22], [23], transmission power [11], [12], [23] and bandwidth allocation [23].…”

Section: Related Workmentioning

confidence: 99%

Optimal and Approximation Algorithms for Joint Routing and Scheduling in Millimeter-Wave Cellular Networks

Yuan,

Lin,

Widmer

et al. 2020

Preprint

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Millimeter-wave (mmWave) communication is a promising technology to cope with the exponential increase in 5G data traffic. Such networks typically require a very dense deployment of base stations. A subset of those, so-called macro base stations, feature high-bandwidth connection to the core network, while relay base stations are connected wirelessly. To reduce cost and increase flexibility, wireless backhauling is needed to connect both macro to relay as well as relay to relay base stations. The characteristics of mmWave communication mandates new paradigms for routing and scheduling. The paper investigates scheduling algorithms under different interference models. To showcase the scheduling methods, we study the maximum throughput fair scheduling problem. Yet the proposed algorithms can be easily extended to other problems. For a fullduplex network under the no interference model, we propose an efficient polynomial-time scheduling method, the scheduleoriented optimization. Further, we prove that the problem is NPhard if we assume pairwise link interference model or halfduplex radios. Fractional weighted coloring based approximation algorithms are proposed for these NP-hard cases. Moreover, the approximation algorithm parallel data stream scheduling is proposed for the case of half-duplex network under the no interference model. It has better approximation ratio than the fractional weighted coloring based algorithms and even attains the optimal solution for the special case of uniform orthogonal backhaul networks.

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“…Solving the above (single-slot or multi-slot) link scheduling problem in the context of 5G mm-wave backhaul networks has been a subject of interest in the recent literature. See, e.g., [4]- [7] and [8]. In particular, the works in [4] and [5] use contention graphs to solve the link scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

“…The link scheduling problem is, thus, limited only by the number of available RF chains. The work in [7] considers a simplified interference model in which the set of interfering links (with every link in the network) is known in advance. Again, this simplified model abstracts away the additive nature of interference.…”

Section: Introductionmentioning

confidence: 99%

On Millimeter Wave 5G Backhaul Link Scheduling

Saad

Abdallah

2019

IEEE Access

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Heterogeneous networks that consist of densely deployed base stations of different (macro and small-cell) tiers are envisioned to be among the key technologies for providing the high data rates required in 5G systems. Moreover, interconnecting small-cell base stations via millimeter-wave (mm-wave) backhaul links is a flexible and cost-effective alternative to fiber optic backhauling. Given a set of mm-wave backhaul links, this paper addresses the problem of scheduling all links in the minimum number of time slots such that the subset of links scheduled to each time slot can be activated simultaneously. In particular, a set of links can be activated simultaneously if all link signal-to-interference-plus-noise-ratio (SINR) targets are satisfied, and the number of RF chains of every base station is not exceeded. We give a succinct optimization-based formulation of the problem, capturing both SINR constraints and number of RF chains limitations. Using reduction from the set-cover problem, we devise a provably good polynomial-time algorithm for the problem. Our numerical results further indicate the significant superiority of the presented approach. INDEX TERMS Backhaul network, greedy algorithms, link scheduling, millimeter wave.

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Link scheduling for mmWave WMN backhaul

Cited by 9 publications

References 9 publications

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

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

Optimal and Approximation Algorithms for Joint Routing and Scheduling in Millimeter-Wave Cellular Networks

On Millimeter Wave 5G Backhaul Link Scheduling

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