Deep Learning-based Coordinated Beamforming for Massive MIMO-Enabled Heterogeneous Networks

Zhang, Yinghui; Zhang, Biao; Wang, Huayu; Zhang, Tiankui; Qian, Yi

doi:10.1109/globecom46510.2021.9685628

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…We can first prove the validity of the probability relation (9) for n = 2. For n = 2, we can reformulate (10) as…”

Section: Outage Probabilitymentioning

confidence: 99%

A novel approach to QoS‐aware resource allocation in NOMA cellular HetNets using multi‐layer optimization

Mirzaei

2022

Concurrency and Computation

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The dense deployment of small cells is a key feature of the next-generation cellular networks aimed at providing the necessary capacity increase. But in such networks the problem of green networking will be of great importance, because the uncontrolled installation of too many cells may increase operational costs and emit more carbon dioxide. Nowadays, unmanned aerial vehicles (UAVs) offer an efficient approach to improve the quality, data rate, and meet the demanding performance requirements of applications in ultra-dense cellular networks. This article proposes a dynamic optimization model which minimizes the overall energy consumption of UAV cellular networks in addition to guarantying the essential coverage and capacity. The proposed model optimizes user association and power utility to meet users' QoS requirements with the highest level of energy efficiency (EE). For this purpose, the surplus energy pattern of UAVs is first mathematically formulated and then applied to calculate its ruin probability. In fact, the ruin probability denotes the vulnerability of a UAV when it runs out of energy. The ruin probability is used in the next step to efficiently connect every user to the UAVs. Also, power allocation is implemented for the applications to achieve the optimal value of the accessible network's data rate through the water-filling method. This model also performs coded routing in the multi-hop backhauls to efficiently use the existing infrastructure of the cooperative networks for coding dual-hop transmissions. The simulation results exhibit considerable network throughput and EE increase by 42% and 30%, respectively, for the proposed ruin-based energy-efficient approach in comparison with the conventional signal-to-interference/noise ratio-based schemes.

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“…We can first prove the validity of the probability relation (9) for n = 2. For n = 2, we can reformulate (10) as…”

Section: Outage Probabilitymentioning

confidence: 99%

A novel approach to QoS‐aware resource allocation in NOMA cellular HetNets using multi‐layer optimization

Mirzaei

2022

Concurrency and Computation

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Deep Reinforcement Learning-Based Coordinated Beamforming for mmWave Massive MIMO Vehicular Networks

Tarafder

Choi

2023

Sensors

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As a critical enabler for beyond fifth-generation (B5G) technology, millimeter wave (mmWave) beamforming for mmWave has been studied for many years. Multi-input multi-output (MIMO) system, which is the baseline for beamforming operation, rely heavily on multiple antennas to stream data in mmWave wireless communication systems. High-speed mmWave applications face challenges such as blockage and latency overhead. In addition, the efficiency of the mobile systems is severely impacted by the high training overhead required to discover the best beamforming vectors in large antenna array mmWave systems. In order to mitigate the stated challenges, in this paper, we propose a novel deep reinforcement learning (DRL) based coordinated beamforming scheme where multiple base stations serve one mobile station (MS) jointly. The constructed solution then uses a proposed DRL model and predicts the suboptimal beamforming vectors at the base stations (BSs) out of possible beamforming codebook candidates. This solution enables a complete system that facilitates highly mobile mmWave applications with dependable coverage, minimal training overhead, and low latency. Numerical results demonstrate that our proposed algorithm remarkably increases the achievable sum rate capacity for the highly mobile mmWave massive MIMO scenario while ensuring low training and latency overhead.

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Deep Learning-based Coordinated Beamforming for Massive MIMO-Enabled Heterogeneous Networks

Cited by 2 publications

References 17 publications

A novel approach to QoS‐aware resource allocation in NOMA cellular HetNets using multi‐layer optimization

A novel approach to QoS‐aware resource allocation in NOMA cellular HetNets using multi‐layer optimization

Deep Reinforcement Learning-Based Coordinated Beamforming for mmWave Massive MIMO Vehicular Networks

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