scholarly journals Antenna Combining for Interference Limited MIMO Cellular Networks

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4210
Author(s):  
Tae-Kyoung Kim
Keyword(s):  
Cellular Networks ◽  
Spectral Efficiency ◽  
Optimal Number ◽  
Base Stations ◽  
Multiple Antenna ◽  
Spherical Cap ◽  
Downlink Transmission ◽  
Partial Channel ◽  
Inter Cell Interference ◽  
Spatial Division Multiplexing

This paper considers a downlink cellular network where multi-antenna base stations (BSs) simultaneously serve their associated multi-antenna users. Each BS is distributed according to a homogeneous Poisson point process and uses zero-forcing beamforming for spatial division multiplexing with partial channel state information (CSI). During downlink transmission, each user combines the multiple antenna outputs and quantizes the CSI to feed back to its associated BS. Specifically, this paper focuses on antenna combining at the receiver. Conventional quantization-based combining (QBC) effectively reduces the quantization error; however, inter-cell interference in the cellular networks degrades the QBC gain. This degradation is analyzed using a spherical-cap approximation of vector quantization (SCVQ). From the SCVQ, the ergodic spectral efficiency and the optimal number of feedback bits are investigated, and it is shown that the QBC degrades the gain of the effective channel. To address this problem, an optimization solution is proposed that selects the antenna combining to maximize the spectral efficiency. The solution is also derived by considering the expected beamforming vectors of other cells. It is demonstrated by simulation that the proposed solution outperforms the conventional methods.

scholarly journals New framework for interference and energy analysis of soft frequency reuse in 5G networks

2020 ◽  
Vol 9 (5) ◽  
pp. 1941-1949
Author(s):  
Achonu Adejo ◽  
Osbert Asaka ◽  
Habeeb Bello- Salau ◽  
Caroline Alenoghena
Keyword(s):  
Cellular Networks ◽  
Spectral Efficiency ◽  
Network Performance ◽  
Base Station ◽  
Frequency Reuse ◽  
Base Stations ◽  
System Capacity ◽  
Management Technique ◽  
5G Networks ◽  
Soft Frequency Reuse

Cellular networks are expanding massively due to high data requirements from mobile devices. This has motivated base station densification as an essential requirement for the 5G network. The implication is obvious benefits in enhanced system capacity, but also increased challenges in terms of interference. One important interference management technique which has been widely adopted in cellular networks is frequency reuse. In this article, an analysis is presented based on network interference and energy expended by base stations in downlink communication when Soft frequency reuse (SFR) is deployed. A framework is presented that captures the bandwidth overlaps in SFR across base station assignments, computes the interference probabilities arising and derives new performance equations which are verified using simulations. Results show an improvement of over previous SFR implementations that do not consider the interference probabilities. Thus, a more in-depth and accurate modelling of SFR in 5G networks is achieved. Furthermore, the downlink power allocation is investigated as against other parameters like the center ratio and edge bandwidth. The result shows that signal-to-interference-noise ratio (SINR) and spectral efficiency give different performance under energy consideration. A framework is developed on how to tune a base station to achieve desired network performance in user SINR or cell spectral efficiency depending on the operator’s preference.

scholarly journals 5G Cellular Networks: Coverage Analysis in the Presence of Inter-Cell Interference and Intentional Jammers

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1538
Author(s):  
Muhammad Qasim ◽  
Muhammad Sajid Haroon ◽  
Muhammad Imran ◽  
Fazal Muhammad ◽  
Sunghwan Kim
Keyword(s):  
Cellular Networks ◽  
Orthogonal Frequency Division Multiplexing ◽  
Denial Of Service ◽  
Base Station ◽  
Base Stations ◽  
Network Coverage ◽  
Transmit Power ◽  
Coverage Analysis ◽  
Small Base ◽  
Inter Cell Interference

Intentional jammers (IJs) can be used by attackers for the launching of distributed denial-of-service attacks in 5G cellular networks. These adversaries are assumed to have adequate information about the network specifications, such as duration, transmit power and positions. With these assumptions, the IJs gain the ability to disrupt the legitimate communication of the network. Heterogeneous cellular networks (HetNets) can be considered a vital enabler for 5G cellular networks. Small base stations (SBSs) are deployed inside macro base station (MBS) to improve spectral efficiency and capacity. Due to orthogonal frequency division multiplexing assumption, HetNets’ performance is mainly limited by inter-cell interference (ICI). Additionally, there exist IJs-interference (IJs-I), which significantly degrades the network coverage depending on the IJs’ transmit power levels and their proximity with the target. The proposed work explores the uplink (UL) coverage performance of HetNets in the presence of both IJs-I and ICI. Moreover, to reduce the effects of ICI and IJs-I, reverse frequency allocation (RFA) is employed which is a proactive interference abating scheme. In RFA, different sub-bands of the available spectrum are used by MBS and SBS in alternate regions. The proposed setup is evaluated both analytically as well as with the help of simulation. The results demonstrate considerable UL coverage performance improvement by effectively mitigating IJs-I and ICI.

Results on Energy- and Spectral-Efficiency Tradeoff in Cellular Networks With Full-Duplex Enabled Base Stations

2017 ◽  
Vol 16 (3) ◽  
pp. 1494-1507 ◽  
Author(s):  
Dingzhu Wen ◽  
Guanding Yu ◽  
Rongpeng Li ◽  
Yan Chen ◽  
Geoffrey Ye Li
Keyword(s):  
Cellular Networks ◽  
Spectral Efficiency ◽  
Base Stations ◽  
Full Duplex

scholarly journals Full-Duplex Massive MIMO Cellular Networks with Low Resolution ADC/DAC

2021 ◽  
Author(s):  
Elyes Balti
Keyword(s):  
Cellular Networks ◽  
Spectral Efficiency ◽  
Scaling Laws ◽  
Multiple Input Multiple Output ◽  
Hexagonal Lattice ◽  
Analytical Framework ◽  
Base Stations ◽  
Full Duplex ◽  
Low Resolution ◽  
Special Cases

In this paper, we provide an analytical framework for full-duplex (FD) massive multiple-input multiple-output (MIMO) cellular networks with low resolution analog-to-digital and digital-to-analog converters (ADCs and DACs). Matched filters are employed at the FD base stations (BSs) at the transmit and receive sides. For both reverse and forward links, we derive the expressions of the signal-to-quantization-plus-interference-and-noise ratio (SQINR) for general and special cases. We further evaluate the outage probability and spectral efficiency for reverse and forward links, and quantify the effects of the quantization error, loopback self-interference and inter-user interference for cells arranged in a hexagonal lattice and Poisson Point Process (PPP) tessellations. Finally, we derive analytical expressions for spectral efficiency for asymptotic cases as well as for power scaling laws.

scholarly journals Spectral and Energy Efficiencies in mmWave Cellular Networks for Optimal Utilization

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Abdulbaset M. Hamed ◽  
Raveendra K. Rao
Keyword(s):  
Energy Efficiency ◽  
Cellular Networks ◽  
Spectral Efficiency ◽  
Cellular Network ◽  
Channel Model ◽  
Signal To Noise Ratio ◽  
Base Stations ◽  
Spectrum Efficiency ◽  
Line Of Sight ◽  
Model Parameters

Millimeter wave (mmWave) spectrum has been proposed for use in commercial cellular networks to relieve the already severely congested microwave spectrum. Thus, the design of an efficient mmWave cellular network has gained considerable importance and has to take into account regulations imposed by government agencies with regard to global warming and sustainable development. In this paper, a dense mmWave hexagonal cellular network with each cell consisting of a number of smaller cells with their own Base Stations (BSs) is presented as a solution to meet the increasing demand for a variety of high data rate services and growing number of users of cellular networks. Since spectrum and power are critical resources in the design of such a network, a framework is presented that addresses efficient utilization of these resources in mmWave cellular networks in the 28 and 73 GHz bands. These bands are already an integral part of well-known standards such as IEEE 802.15.3c, IEEE 802.11ad, and IEEE 802.16.1. In the analysis, a well-known accurate mmWave channel model for Line of Sight (LOS) and Non-Line of Sight (NLOS) links is used. The cellular network is analyzed in terms of spectral efficiency, bit/s, energy efficiency, bit/J, area spectral efficiency, bit/s/m2, area energy efficiency, bit/J/m2, and network latency, s/bit. These efficiency metrics are illustrated, using Monte Carlo simulation, as a function of Signal-to-Noise Ratio (SNR), channel model parameters, user distance from BS, and BS transmission power. The efficiency metrics for optimum deployment of cellular networks in 28 and 73 GHz bands are identified. Results show that 73 GHz band achieves better spectrum efficiency and the 28 GHz band is superior in terms of energy efficiency. It is observed that while the latter band is expedient for indoor networks, the former band is appropriate for outdoor networks.

scholarly journals Machine Learning for the Dynamic Positioning of UAVs for Extended Connectivity

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4618
Author(s):  
Francisco Oliveira ◽  
Miguel Luís ◽  
Susana Sargento
Keyword(s):  
Machine Learning ◽  
Cellular Networks ◽  
Real Data ◽  
Emerging Technology ◽  
Machine Learning Algorithms ◽  
Base Stations ◽  
Aerial Vehicle ◽  
Positioning Algorithm ◽  
The Military ◽  
Better Than

Unmanned Aerial Vehicle (UAV) networks are an emerging technology, useful not only for the military, but also for public and civil purposes. Their versatility provides advantages in situations where an existing network cannot support all requirements of its users, either because of an exceptionally big number of users, or because of the failure of one or more ground base stations. Networks of UAVs can reinforce these cellular networks where needed, redirecting the traffic to available ground stations. Using machine learning algorithms to predict overloaded traffic areas, we propose a UAV positioning algorithm responsible for determining suitable positions for the UAVs, with the objective of a more balanced redistribution of traffic, to avoid saturated base stations and decrease the number of users without a connection. The tests performed with real data of user connections through base stations show that, in less restrictive network conditions, the algorithm to dynamically place the UAVs performs significantly better than in more restrictive conditions, reducing significantly the number of users without a connection. We also conclude that the accuracy of the prediction is a very important factor, not only in the reduction of users without a connection, but also on the number of UAVs deployed.

scholarly journals Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhuofan Liao ◽  
Jingsheng Peng ◽  
Bing Xiong ◽  
Jiawei Huang
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Cellular Networks ◽  
Physical Distance ◽  
Edge Computing ◽  
Base Stations ◽  
Mobile Edge Computing ◽  
Total Energy Consumption ◽  
Average Delay ◽  
Server Selection

AbstractWith the combination of Mobile Edge Computing (MEC) and the next generation cellular networks, computation requests from end devices can be offloaded promptly and accurately by edge servers equipped on Base Stations (BSs). However, due to the densified heterogeneous deployment of BSs, the end device may be covered by more than one BS, which brings new challenges for offloading decision, that is whether and where to offload computing tasks for low latency and energy cost. This paper formulates a multi-user-to-multi-servers (MUMS) edge computing problem in ultra-dense cellular networks. The MUMS problem is divided and conquered by two phases, which are server selection and offloading decision. For the server selection phases, mobile users are grouped to one BS considering both physical distance and workload. After the grouping, the original problem is divided into parallel multi-user-to-one-server offloading decision subproblems. To get fast and near-optimal solutions for these subproblems, a distributed offloading strategy based on a binary-coded genetic algorithm is designed to get an adaptive offloading decision. Convergence analysis of the genetic algorithm is given and extensive simulations show that the proposed strategy significantly reduces the average latency and energy consumption of mobile devices. Compared with the state-of-the-art offloading researches, our strategy reduces the average delay by 56% and total energy consumption by 14% in the ultra-dense cellular networks.

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