Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks
In this paper, we focus on the radio resource planning in the uplink of licensed Orthogonal Frequency Division Multiple Access (OFDMA) based Internet of Things (IoT) networks. The average behavior of the network is considered by assuming that active sensors and collectors are distributed according t...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-12-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/24/7173 |
| _version_ | 1797544695885201408 |
|---|---|
| author | Yi Yu Lina Mroueh Philippe Martins Guillaume Vivier Michel Terré |
| author_facet | Yi Yu Lina Mroueh Philippe Martins Guillaume Vivier Michel Terré |
| author_sort | Yi Yu |
| collection | DOAJ |
| description | In this paper, we focus on the radio resource planning in the uplink of licensed Orthogonal Frequency Division Multiple Access (OFDMA) based Internet of Things (IoT) networks. The average behavior of the network is considered by assuming that active sensors and collectors are distributed according to independent random Poisson Point Process (PPP) marked by channel randomness. Our objective is to statistically determine the optimal total number of Radio Resources (RRs) required for a typical cell. On one hand, the allocated bandwidth should be sufficiently large to support the traffic of the devices and to guarantee a low access delay. On the other hand, the over-dimensioning is costly from an operator point of view and induces spectrum wastage. For this sake, we propose statistical tools derived from stochastic geometry to evaluate, adjust and adapt the allocated bandwidth according to the network parameters, namely the required Quality of Service (QoS) in terms of rate and access delay, the density of the active sensors, the collector intensities, the antenna configurations and the transmission modes. The optimal total number of RRs required for a typical cell is then calculated by jointly considering the constraints of low access delay, limited power per RR, target data rate and network outage probability. Different types of networks are considered including Single Input Single Output (SISO) systems, Single Input Multiple Output (SIMO) systems using antenna selection or Maximum Ratio Combiner (MRC), and Multiuser Multiple Input Multiple Output (MU-MIMO) systems using Zero-Forcing decoder. |
| first_indexed | 2024-03-10T14:04:11Z |
| format | Article |
| id | doaj.art-b08996b16b894ee6b57c394a9d14424b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:04:11Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b08996b16b894ee6b57c394a9d14424b2023-11-21T00:50:52ZengMDPI AGSensors1424-82202020-12-012024717310.3390/s20247173Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT NetworksYi Yu0Lina Mroueh1Philippe Martins2Guillaume Vivier3Michel Terré4Institut Supérieur d’Electronique de Paris, 92130 Issy Les Moulineaux, FranceInstitut Supérieur d’Electronique de Paris, 92130 Issy Les Moulineaux, FranceTelecom Paris, 91120 Palaiseau, FranceSequans Communications, 92700 Colombes, FranceConservatoire National des Arts et des Métiers, 75003 Paris, FranceIn this paper, we focus on the radio resource planning in the uplink of licensed Orthogonal Frequency Division Multiple Access (OFDMA) based Internet of Things (IoT) networks. The average behavior of the network is considered by assuming that active sensors and collectors are distributed according to independent random Poisson Point Process (PPP) marked by channel randomness. Our objective is to statistically determine the optimal total number of Radio Resources (RRs) required for a typical cell. On one hand, the allocated bandwidth should be sufficiently large to support the traffic of the devices and to guarantee a low access delay. On the other hand, the over-dimensioning is costly from an operator point of view and induces spectrum wastage. For this sake, we propose statistical tools derived from stochastic geometry to evaluate, adjust and adapt the allocated bandwidth according to the network parameters, namely the required Quality of Service (QoS) in terms of rate and access delay, the density of the active sensors, the collector intensities, the antenna configurations and the transmission modes. The optimal total number of RRs required for a typical cell is then calculated by jointly considering the constraints of low access delay, limited power per RR, target data rate and network outage probability. Different types of networks are considered including Single Input Single Output (SISO) systems, Single Input Multiple Output (SIMO) systems using antenna selection or Maximum Ratio Combiner (MRC), and Multiuser Multiple Input Multiple Output (MU-MIMO) systems using Zero-Forcing decoder.https://www.mdpi.com/1424-8220/20/24/7173LPWANlicensed OFDMA-based IoTresource planningstochastic geometry |
| spellingShingle | Yi Yu Lina Mroueh Philippe Martins Guillaume Vivier Michel Terré Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks Sensors LPWAN licensed OFDMA-based IoT resource planning stochastic geometry |
| title | Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks |
| title_full | Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks |
| title_fullStr | Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks |
| title_full_unstemmed | Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks |
| title_short | Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks |
| title_sort | radio resource dimensioning for low delay access in licensed ofdma iot networks |
| topic | LPWAN licensed OFDMA-based IoT resource planning stochastic geometry |
| url | https://www.mdpi.com/1424-8220/20/24/7173 |
| work_keys_str_mv | AT yiyu radioresourcedimensioningforlowdelayaccessinlicensedofdmaiotnetworks AT linamroueh radioresourcedimensioningforlowdelayaccessinlicensedofdmaiotnetworks AT philippemartins radioresourcedimensioningforlowdelayaccessinlicensedofdmaiotnetworks AT guillaumevivier radioresourcedimensioningforlowdelayaccessinlicensedofdmaiotnetworks AT michelterre radioresourcedimensioningforlowdelayaccessinlicensedofdmaiotnetworks |