Performance Evaluation of 5G Millimeter-Wave Cellular Access Networks Using a Capacity-Based Network Deployment Tool

Matalatala, Michel; Deruyck, Margot; Tanghe, Emmeric; Martens, Luc; Joseph, Wout

doi:10.1155/2017/3406074

Cited by 26 publications

(15 citation statements)

References 24 publications

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“…The ever-growing data rate demand as well as the shortage of current frequency resources are the main challenges for the upcoming fifth generation (5G) of mobile communications [1][2][3][4]. The congestion of the current frequency band (below 6 GHz) and the narrowness of the wireless bandwidth are key problems for fifth generation wireless networks.…”

Section: Introductionmentioning

confidence: 99%

Indoor Corridor Wideband Radio Propagation Measurements and Channel Models for 5G Millimeter Wave Wireless Communications at 19 GHz, 28 GHz, and 38 GHz Bands

Al-Samman

Rahman

Azmi

2018

Wireless Communications and Mobile Computing

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This paper presents millimeter wave (mmWave) measurements in an indoor environment. The high demands for the future applications in the 5G system require more capacity. In the microwave band below 6 GHz, most of the available bands are occupied; hence, the microwave band above 6 GHz and mmWave band can be used for the 5G system to cover the bandwidth required for all 5G applications. In this paper, the propagation characteristics at three different bands above 6 GHz (19, 28, and 38 GHz) are investigated in an indoor corridor environment for line of sight (LOS) and non-LOS (NLOS) scenarios. Five different path loss models are studied for this environment, namely, close-in (CI) free space path loss, floating-intercept (FI), frequency attenuation (FA) path loss, alpha-beta-gamma (ABG), and close-in free space reference distance with frequency weighting (CIF) models. Important statistical properties, such as power delay profile (PDP), root mean square (RMS) delay spread, and azimuth angle spread, are obtained and compared for different bands. The results for the path loss model found that the path loss exponent (PLE) and line slope values for all models are less than the free space path loss exponent of 2. The RMS delay spread for all bands is low for the LOS scenario, and only the directed path is contributed in some spatial locations. For the NLOS scenario, the angle of arrival (AOA) is extensively investigated, and the results indicated that the channel propagation for 5G using high directional antenna should be used in the beamforming technique to receive the signal and collect all multipath components from different angles in a particular mobile location.

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Section: Introductionmentioning

confidence: 99%

Indoor Corridor Wideband Radio Propagation Measurements and Channel Models for 5G Millimeter Wave Wireless Communications at 19 GHz, 28 GHz, and 38 GHz Bands

Al-Samman

Rahman

Azmi

2018

Wireless Communications and Mobile Computing

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“…We propose a simulation-based tool, developed in java, that we denote Capacity-based network deployment tool, which optimizes the initial set of base stations deployed by the operators in the area of interest based on the BS-user association algorithm with respect to the following optimization constraints: minimize the power consumption of the obtained network, respond to the instantaneous bit rates requested by the users and provide a coverage to at least 95% of the users. In [8], the same tool has been used but with beamforming capability only. Here, in addition to beamforming, we implement the massive MIMO requirements: the new bit rate distribution to comply with the user's 5G applications, the sum spectral efficiency described in (2), the 3GPP LTE Rel.…”

Section: Methodsmentioning

confidence: 99%

“…In this study, we propose numerical simulations for realistic designs of massive MIMO-based 5G cellular networks that provide higher throughput to the users, with low power consumption thanks to on optimal deployment of base stations within suburban areas in Kinshasa, the Democratic Republic of Congo and Ghent, Belgium. We use the capacity-based network deployment tool developed in [8] to build energyefficient 5G cellular networks with an optimal number of the base stations needed to guarantee the requested quality of service (QoS). The results of the simulations are analyzed and compared with the LTE networks.…”

Section: Introductionmentioning

confidence: 99%

Multi-cell Massive MIMO Network Optimization towards power consumption in Suburban Scenarios

Matalatala

Deruyck

Tanghe

et al. 2018

2018 Global Information Infrastructure and Networking Symposium (GIIS)

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Future wireless communication networks are challenged to meet the demand for reliable high throughput while improving both the spectral and energy efficiency, by using appropriate technology like massive MIMO. In this paper, we propose a simulation-based method to design low power multi-cell multiuser massive MIMO network by optimizing the positions of the base stations. Two realistic outdoor suburban areas have been considered in Ghent, Belgium (Europe) and Kinshasa, the Democratic Republic of Congo (Africa), in which the power consumption, the energy efficiency, the network capacity and the multiplexing gain are investigated and compared with LTE networks. The results of the simulations demonstrated that massive MIMO networks provide better performance in the crowded scenario where user's mobility is relatively low. A massive MIMO BS consumes 5-8 times less power than the LTE networks, with a pilot reuse pattern of 3 that helps obtaining a good tradeoff between the higher bit rate requested and the low power requirements in cellular environment.

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“…In our proposed model, the cell range is calculated using the propagation model, and the link budget determines the maximum allowed path loss model (MAPL), which calculates the cell radius R. We aim to determine the coverage and capacity, and some parameters need to be identified and computed such us penetration loss, propagation model, fading margin, shadowing, and the MAPL in the mmWave environment. An example of a link budget for mmWave at the spectrum of 28 GHz used in this study is given in Table 2, and the input parameters are selected from [36][37][38]. The transmission radius of the UMi is smaller than those of UMa, due to their deployment cost.…”

Section: Next Gen Core Networkmentioning

confidence: 99%

5G Base Station Deployment Perspectives in Millimeter Wave Frequencies Using Meta-Heuristic Algorithms

et al. 2019

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It can be predicted that the infrastructure of the existing wireless networks will not fill the requirement of the fifth generation (5G) wireless network due to the high data rates and a large number of expected traffic. Thus, a novel deployment method is crucial to satisfy 5G features. Meta-heuristic is expected to be a promising method for the complex deployment optimization problem of the 5G network. This work presents an implementation of a meta-heuristic algorithm based on swarm intelligence, to minimize the number of base stations (BSs) and optimize their placements in millimeter wave (mmWave) frequencies (e.g., 28 GHz and 38 GHz) in the context of the 5G network while satisfying user data rates requirement. Then, an iterative method is applied to remove redundant BSs. We formulate an optimization problem that takes into account multiple 5G network deployment scenarios. Further, a comparative study is conducted with the well-known simulated annealing (SA) using Monte Carlo simulations to assess the performance of the developed model. In our simulation results, we divide the region of interest into two subareas with different user distributions for different network scenarios while considering the intercell interference. The results demonstrate that the proposed approach has better network coverage with low percentage users in outage. In addition, the developed approach has less computational times to reach the desired target network quality of service (QoS). base station (BS) and user for urban micro-cells (UMi) street canyon and urban macro-cells (UMa) scenarios. For BS antenna height of 10 m and 25 m for UMi and Uma, respectively, the distance is 10 m and 35 m for UMi and Uma, respectively [4]. Thus, these very short distances allow better frequency reuse within a dense network coverage area. The usage of high frequencies is expected to be one of the key 5G technology enabling very high data rates and significant increases in capacity. The spectrum at 28 GHz and 38 GHz are still unexploited and have negligible atmospheric gases attenuation as compared to others high-frequency ranges according to International Telecommunication Union (ITU) L-series recommendations. 5G new radio deployments will require ultra-dense network topologies with the usage of high frequencies, which required many new cells, resulting in additional potential deployment challenges. Consequently, where and when to deploy cells while satisfying user data rates requirement will be challenging. To help assess this deployment challenge, the new approach of network planning is needed to meet the demand of 5G networks and beyond. Network planning is vital in order to deploy 5G networks efficiently, it is considered to be a promising solution to satisfy the user data rates requirement in 5G network [5], it depends on various parameters such as geographical area, cells configuration parameters, estimated number of users, estimated number of cells, path loss and propagation models, and frequency reuse patterns [6]. In the proposed model, UMa a...

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Performance Evaluation of 5G Millimeter-Wave Cellular Access Networks Using a Capacity-Based Network Deployment Tool

Cited by 26 publications

References 24 publications

Indoor Corridor Wideband Radio Propagation Measurements and Channel Models for 5G Millimeter Wave Wireless Communications at 19 GHz, 28 GHz, and 38 GHz Bands

Indoor Corridor Wideband Radio Propagation Measurements and Channel Models for 5G Millimeter Wave Wireless Communications at 19 GHz, 28 GHz, and 38 GHz Bands

Multi-cell Massive MIMO Network Optimization towards power consumption in Suburban Scenarios

5G Base Station Deployment Perspectives in Millimeter Wave Frequencies Using Meta-Heuristic Algorithms

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