Near Interference-Free Space-Time User Scheduling for mmWave Cellular Network

Sha, Ziyuan; Chen, Siyu; Hanzo, Lajos

doi:10.1109/twc.2022.3148782

Cited by 11 publications

(9 citation statements)

References 36 publications

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“…1) Multi-user scenarios: The dual-band fusion approach can be straightforwardly applied to the multi-user scenarios by separately predicting the optimal mmWave beam of each user. Based on the predicted optimal beams, hybrid beamforming is usually adopted at each BS to achieve a good tradeoff between the system capability and the hardware costs [44], [45], while flexible beam scheduling can be applied to avoid the interference among different mmWave BSs [46], [47].…”

Section: H Extensions To More Scenariosmentioning

confidence: 99%

Deep Learning Assisted mmWave Beam Prediction for Heterogeneous Networks: A Dual-Band Fusion Approach

Zou

et al. 2023

IEEE Trans. Commun.

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In this paper, motivated by the inter-base station (BS) channel dependence due to the shared wireless environment, we propose to fuse sub-6 GHz channel information and mmWave low-overhead measurement to predict the optimal mmWave beam in heterogeneous networks (HetNets) and reduce the overhead of both mmWave BS selection and beam training. Moreover, deep learning is adopted to extract the complex dependence between sub-6 GHz and mmWave channels for achieving high prediction accuracy. Specifically, we propose to leverage a few user equipment (UE)-specific high-quality mmWave wide beams predicted by the sub-6 GHz channel state information (CSI) as the mmWave low-overhead measurement. In order to adapt to different confidences of the mmWave wide beam prediction for diverse UE, the sum-probability criterion is proposed to flexibly adjust the number of measured wide beams. Besides, to fully fuse the diversified features extracted from the sub-6 GHz CSI and mmWave wide beams, the attention mechanism is further exploited to adaptively weight the features for improving the prediction accuracy. Simulation results show that our proposed scheme achieves higher beamforming gain while imposing smaller mmWave measurement overhead over the conventional deep learning based schemes.

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Section: H Extensions To More Scenariosmentioning

confidence: 99%

Deep Learning Assisted mmWave Beam Prediction for Heterogeneous Networks: A Dual-Band Fusion Approach

Zou

et al. 2023

IEEE Trans. Commun.

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“…As an example, they also show the optimal solution of the maximum throughput fair scheduling (MTFS). Recently, the authors of [27] investigate two aspects of the near interference-free (NIF) space-time user scheduling in a multi-cell mmWave network with multi-RF-chain base stations. Firstly, they study the NIF user scheduling problem to minimize the unfulfilled user requirements; secondly, they study the joint NIF user scheduling and power allocation problem to minimize the total transmit power under the constraint of rate requirements.…”

Section: Related Workmentioning

confidence: 99%

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

Hu¹,

Liu²,

Yan³

et al. 2022

Preprint

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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.

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“…We consider two versions of the problem presented in Section IV-A. The first version, denoted as VSM-PF, optimizes proportional fairness and uses exactly the formulation described in (15). The second version, denoted as VSM-TH, maximises the throughput, and is obtained by omitting the log function in the objective of (15).…”

Section: A Experimental Setupmentioning

confidence: 99%

“…The first version, denoted as VSM-PF, optimizes proportional fairness and uses exactly the formulation described in (15). The second version, denoted as VSM-TH, maximises the throughput, and is obtained by omitting the log function in the objective of (15). To solve the problems, we run the optimization every T = 500 ms, with weights w c = U c to achieve per-user fairness, α k = 1, and p = 20, i.e., we aim at the maximization of the average throughput fairness over a window of 10s.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…In particular, [14] derives the best temporal pattern duration, given a set of chosen patterns to maximize the total user throughput. Near interference free transmissions are also possible with selective resource muting combined with highly directional antennas at base stations and mobile devices using mmWave technologies, although they have a high complexity in practice [15]. Other highly efficient approaches, at least in terms of boosting throughput, are based on using CoMP techniques [16], which are however highly demanding in terms of distributed coordination.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Muting with Short-range Relay Analysis

Vitale¹,

Sciancalepore

Mancuso

2022

2022 20th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

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Resource muting in the time-frequency domain is a tool for controlling co-channel interference in cellular networks. However, coordinated resource of multiple cells implies nontrivial complexity to be effective. Moreover, resource muting does not guarantee fair resource allocation to users. However, we analytically show that stochastic resource muting jointly with opportunistic short-range relay operated by neighbor mobile devices permits to abate the complexity of muting coordination and boosts throughput fairness among users. We therefore formulate a convex proportional fairness optimization problem and leverage our analysis to design VSM-PF, a practical optimization scheme that significantly outperforms legacy systems.

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Near Interference-Free Space-Time User Scheduling for mmWave Cellular Network

Cited by 11 publications

References 36 publications

Deep Learning Assisted mmWave Beam Prediction for Heterogeneous Networks: A Dual-Band Fusion Approach

Deep Learning Assisted mmWave Beam Prediction for Heterogeneous Networks: A Dual-Band Fusion Approach

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

Stochastic Muting with Short-range Relay Analysis

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