Azimuth and elevation sectorization for the stadium environment

Maslennikov, Roman; Trushanin, Alexey; Testov, Oleg A.; Vechkanov, Maxim; Antipova, Anastasia; Thomas, Timothy A.; Ghosh, Amitava; Visotsky, Eugene

doi:10.1109/glocom.2013.6831694

Cited by 5 publications

(8 citation statements)

References 2 publications

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“…Moreover, such a placement mitigates human blockage losses [6], [8]. Regarding the orientation of the transmitting antennas, the main approach is to direct the main beam of the antenna to the center of its sector [11], [15].…”

Section: B Ap Placementmentioning

confidence: 99%

“…Most existing work focuses on specific antenna patterns. Useful comparisons between narrow-and broad-beamwidth antennas were made in [11]- [13], [15]. It was found that the directive (narrow-beamwidth) antennas offered better signalto-noise and interference levels at high-density AP deployments (124 and 344 APs), compared to broader-beamwidth antennas.…”

Section: Antenna Patternsmentioning

confidence: 99%

“…In the literature, all these aforementioned approaches have been employed for propagation modeling in stadiums. Measurement campaigns were undertaken in [2]- [10], RT simulations were used in [11]- [14], and propagation models were derived in [4], [8], [15]. Moreover, the geometry of the stadiums also varied, from small, open-track soccer stadiums [3] and closed basketball courts [9], to larger venues, such as multi-level stadiums, with capacities of tens of thousands of spectators [6], [7], [10].…”

Section: Introductionmentioning

confidence: 99%

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Generalizable Machine-Learning-Based Modeling of Radiowave Propagation in Stadiums

Seretis,

Jevremovic,

Jemmali

et al. 2023

IEEE Open J. Antennas Propag.

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Providing high throughput and quality of service in modern stadiums necessitates the placement of hundreds of access points (APs). Optimizing the locations of APs in such venues via measurements requires significant resources. Even simulation methods, such as ray-tracing, can be computationally costly. We provide a solution to this problem by building a propagation model based on machine learning (ML) that rapidly predicts received signal strengths in stadiums. We train the model with a small set of simulated data generated by a ray-tracer. We use input features, such as the electrical distance between the transmitter and the receiver and the antenna gain along the direct path between the two, to generalize to new transmitter locations, antenna patterns and stadium geometries. Geometry and pattern generalization have not been included in existing propagation models for stadiums. Finally, we present a novel sampling approach for the input features in a given stadium, ensuring the computational efficiency and accuracy of the ML model. The results demonstrate the accuracy of our propagation model for new transmitter locations, patterns and stadiums. The trained model is also considerably faster than a ray-tracer, making it an efficient tool for resource planning tasks, such as optimal placement of APs.

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Section: B Ap Placementmentioning

confidence: 99%

Section: Antenna Patternsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Generalizable Machine-Learning-Based Modeling of Radiowave Propagation in Stadiums

Seretis,

Jevremovic,

Jemmali

et al. 2023

IEEE Open J. Antennas Propag.

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“…This array balances the flexibility and gain of full-dimensional beamforming with the simplicity and efficiency of more traditional arrays. Operators have traditionally preferred sectorization and tilt control as a means of beamforming [41], [42], so we limit the investigation to one-dimensional geometry. We leave the evaluation of twodimensional arrays to future work.…”

Section: B Multicell System Configurationmentioning

confidence: 99%

Deep Reinforcement Learning-Based Scheduling for Multiband Massive MIMO

Lopes

Nahum²,

Dreifuerst

et al. 2022

IEEE Access

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Fifth-generation (5G) cellular communication systems have embraced massive multipleinput-multiple-output (MIMO) in the low-and mid-band frequencies. In a multiband system, the base station can serve different users in each band, while the user equipment can operate only in a single band simultaneously. This paper considers a massive MIMO system where channels are dynamically allocated in different frequency bands. We treat multiband massive MIMO as a scheduling and resource allocation problem and propose deep reinforcement learning (DRL) agents to perform user scheduling. The DRL agents use buffer and channel information to compose their observation space, and the agent's reward function maximizes the transmitted throughput and minimizes the packet loss rate. We compare the proposed DRL algorithms with traditional baselines, such as maximum throughput and proportional fairness. The results show that the DRL models outperformed baselines obtaining a 20% higher network sum rate and an 84% smaller packet loss rate. Moreover, we compare different DRL algorithms focusing on training time to assess the online implementation of the DRL agents, showing that the best agent needs about 50K training steps to converge.

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“…Taking into account the first challenge inherent to future wireless communication, in [2], the authors investigated several stationarity properties of a massive multiple input and multiple output (MIMO) channel in a stadium at 1.4 and 4.4 GHz. Also, in [3] the design of twodimensional (2D) arrays in a stadium was considered for increasing the capacity through large cell sectorization. Regarding the second challenge, measurements and research have been conducted in stadiums in order to analyze the coverage and capacity at 28 and 5 GHz, considering body loss as parameter that can severely affect system performance [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

mmWave Channel Measurements for 3-D Path Loss Analysis and Model Design in Stadiums

Diago-Mosquera

Aragón‐Zavala

Rodrigues-Lopez

et al. 2022

IEEE Wireless Commun. Lett.

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A stadium is a multiuser scenario where the wireless system should be able to support real-time service delivery when the stadium is at full capacity during an event. To address radio propagation design for this type of challenging scenario, theoretical and empirical method is needed. Extensive open-space static measurements and research have been conducted at 28 and 60 GHz frequencies in an empty seating area of a stadium with a capacity of 60000 seats. The findings of this work lay a good foundation towards a greater understanding of mmWave channel propagation in a stadium, where a sectorization approach is considered to characterize the path loss behavior in 3D and predict it at unmeasured locations through the Kriging-aided model proposed with only 1 dB RMSE.

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Azimuth and elevation sectorization for the stadium environment

Cited by 5 publications

References 2 publications

Generalizable Machine-Learning-Based Modeling of Radiowave Propagation in Stadiums

Generalizable Machine-Learning-Based Modeling of Radiowave Propagation in Stadiums

Deep Reinforcement Learning-Based Scheduling for Multiband Massive MIMO

mmWave Channel Measurements for 3-D Path Loss Analysis and Model Design in Stadiums

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