Narrowband Internet of Things (NB-IoT): From Physical (PHY) and Media Access Control (MAC) Layers Perspectives
- A comprehensive survey of NB-IoT, from Release 13 to the ongoing Release 16 prospects.
- An all-inclusive overview of the state of the art of PHY and MAC layers by addressing the key improvement concerns in terms of challenges and the corresponding potential solutions.
- The possible NB-IoT deployment strategies for synchronous and asynchronous network structures in HetNet scenarios to foster the NB-IoT coexistence with legacy technologies as well as with the fifth generation (5G) networks.
- Discussion on the open research challenges to motivate future research directions.
2. Narrowband-IoT Standard and Releases
2.1. Release 13
2.1.1. Mode of Operation
2.1.2. Multi-Tone Transmission Support
2.1.3. Complexity and Cost Reduction Techniques
2.1.4. Power Reduction Method
2.1.5. Physical Channels and Signals
- Narrowband Physical Random Access Channel (NPRACH).
- Narrowband Physical Uplink Shared Channel (NPUSCH).
- Demodulation Reference Signal (DMRS).
- Narrowband Physical Downlink Shared Channel (NPDSCH).
- Narrowband Physical Downlink Control Channel (NPDCCH).
- Narrowband Reference Signal (NRS).
- Narrowband Primary Synchronization Signal (NPSS).
- Narrowband Secondary Synchronization Signal (NSSS).
- Narrowband Physical Broadcast Channel (NPBCH).
2.1.6. Coverage Enhancement Method
2.2. Release 14 Enhancements
2.2.1. Improved Positioning Technique
2.2.2. Multicast Services
2.2.3. New Power Class for Narrowband-IoT User Equipment
2.2.4. New Transport-Block-Size Support
2.2.5. Multicarrier Operation
2.2.6. User Equipment Mobility Enhancement
2.3. Release 15 Enhancements
2.3.1. Latency Reduction
2.3.2. Semi-Persistent Scheduling
2.3.3. Small Cell Support
2.3.4. Enhanced User Equipment Measurements
2.3.5. Time Division Duplex (TDD) Support
2.4. Release 16 Enhancement Prospects
2.4.1. Grant-Free Access
2.4.2. Simultaneous Multi-User Transmission
2.4.3. Enhanced Group Message Mechanism
2.4.4. Inter-RAT Idle-Mode Mobility
2.4.5. Network Management Tool Enhancement to Improve UE Differentiation
3. Narrowband-IoT: Protocol Stack
3.1. Physical Layer
3.1.1. Cell Acquisition and Synchronization
3.1.2. Random Access Procedure
3.1.3. Channel Estimation and Error Correction
3.1.4. Co-Channel Interference
3.2. Media Access Control Layer
3.2.1. Radio Resource Allocation
3.2.2. Link Adaptation
3.2.3. Coverage and Capacity
3.2.4. Power and Energy Management
3.3. Upper Layers
Control and User Plane Optimization
- Mandatory CP CIoT EPS;
- Optional UP CIoT EPS.
4. Narrowband-IoT Possible Deployment Strategies
- Synchronous NB-IoT deployment in all small cells;
- Asynchronous NB-IoT deployment in all small cells;
- Synchronous NB-IoT deployment in small cells and Macrocells;
- Asynchronous NB-IoT deployment in small cells and LTE in macrocells.
4.1. Synchronous NB-IoT Deployment in All Small Cells
4.2. Asynchronous NB-IoT Deployment in All Small Cells
4.3. Synchronous NB-IoT Deployment in Small Cells and Macro Cells
4.4. Asynchronous NB-IoT Deployment in Small Cells and LTE in Macrocells
5. Open Research Questions and Discussion
5.1. Battery Life
5.2. Radio Resource Management
5.2.1. Tones Allocation
5.2.2. Interference Mitigation
5.3. Mobility Management
5.5. Semi-Persistent vs. Dynamic Scheduling
5.6. Random Access
5.7. Timing Advance (TA)
5.8. Cell Search and Initial Synchronization
5.9. Unified NB-IoT Testing Tool
5.10. Backward Compatibility and Interoperability
Conflicts of Interest
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|Survey||The Third Generation Partnership Project||Layers||Deployment Strategies|
|[Ref]||Rel 13||Rel 14||Rel 15||Rel 16||Physical||Media Access Control|
|Feature||Article||Technique Used||Enhancement Criteria||Limitation|
|Cell Acquisition||||Maximum-Likelihood (ML) NPSS detector||Average latency reduction for timing synchronization||It is a computationally complex detection method|
|||Cell search and initial synchronization algorithm||Time and frequency synchronization by using NPSS and NSSS with two-stage time domain NPSS correlation||mobility and new NB-IoT transmit power are not considered which have a direct impact on inter-RAT camping and the detected SNR, respectively|
|||Non-orthogonal spectral efficient frequency division multiplexing (SEFDM) waveform and an overlapped sphere decoding (OSD) detector||Resource optimization by the use of less bandwidth with better data rates compared to OFDM signal waveform||The proposed method would lead to sampling rate mismatch, carrier frequency offset and also will need to raise the computation complexity to NB-IoT UE|
|||New synchronization signal structure with Zadoff-Chu conjugates||Minimization of timing errors due to low-complexity NB-IoT frequency offset||If the same model is used for uplink synchronization it might lead to estimation errors if mobility is involved in NB-IoT|
|||NPRACH detection and time-of-arrival estimation for NB-IoT system||Enhancement on cell acquisition and channel estimation accuracy||The algorithm might not work for multi-tone allocation. Also, frequency hopping may raise power consumption as well as device complexity|
|||Receiver algorithm for NPRACH timing advance estimation and detection||Modeling the detection threshold to satisfy the NPRACH performance by lowering the probabilities of false alarm||The paper did not explain how receiver sensitivity can affect the NPRACH detection|
|||Mathematical modeling of NB-IoT performance||Throughput enhancement and NPRACH optimization by the use of repetition number, NPRACH preamble transmission per second and intersite distance||The work did not include some parameters such as the impact of mobility and how the achieved MCL for different coverage classes can impact the repetition assignment|
|||NPSS and NSSS frequency diversity reception||Time and frequency synchronization for cell search improvement||Alternative switching of NPSS and NSSS may require additional control commands which may lead to higher device complexity|
|Random Access||||Configurable signal propagation model||System performance analysis in terms of number of supported devices, BER performance, preamble retransmissions, etc.||The impact of preamble retransmission on the overall transmission latency is not considered|
|||Mathematical evaluation of RACH preamble transmission||Analysis of NB-IoT transmission delay by using periodicity, start time, number of repetitions, number of preamble attempts and random access response window||Their model used minimum, intermediate, and maximum values for simulation which is so deterministic. However, it could be better to use random distribution to characterize NB-IoT realistic channel variations|
|||Random Access with differentiated barring (RADB) algorithm||Minimization of random access collision||Not resource efficient method since it does not include the impact of scheduling in different tone configurations|
|||New frequency hopping pattern of NPRACH preamble||Time-of-arrival estimation by the use of all the hopping distances||It only used a small cell scenario, if applied in dense NB-IoT network, estimation by considering all hopping distances may lead to system overhead and possible interference|
|Channel estimation||||Frequency tracking algorithm||Frequency synchronization, as well as channel estimation for NB-IoT systems||More pilot signals, are used. This increases the overhead and hence can degrade the spectral efficiency|
|||Timing advance (TA) adjustment||Preamble sequence decoding by means of round trip estimation for coverage enhancements (on the sea)||It might not work for applications that do not involve a direct line of sight such as in dense urban environment|
|||MCS and coverage level optimization||Mobility effect on different coverage levels and how MCS affect paging performance||The channel model does not include other factors such as the effect of repetition, multipath, different Tx power for NB-IoT UEs as well as carrier frequency offset and inter-RAT operability|
|||New iterative algorithm for NB-IoT transmission scheme||NB-IoT error correction by using cryptographic redundancy and error correcting code||The channel estimation model to characterize NB-IoT transmission is not good, because some errors might be due to intersymbol interference and others due to intercarrier interference however the model does not explain|
|Interference mitigation||||Channel Equalization algorithm||Intersymbol Interference mitigation by the phase-shifted channel frequency responses (CFR) to conquer the sampling mismatch between NB-IoT and base station||The proposed model did not consider the NPSS and NSSS impact ON time and frequency synchronization|
|||Mathematical model for sample duration in LTE and NB-IoT system||Interference and close-form interference analysis due to sampling mismatch between NB-IoT and base station||The model is computational complex when implemented in NB-IoT systems|
|Resource allocation||||Resource blanking||Interference cancellation by resource blanking||The proposed technique may lead to performance degradation in terms of spectral efficiency, especially for NB-IoT massive deployment.|
|||Iterative algorithm by a cooperative approach||Radio resource management in terms of scheduling index, repetition number and interference||The proposed solution is sub-optimal hence it does not provide maximum achievable performance in terms of maximum rate and capacity|
|||Scheduling algorithm||Efficient resource allocation by reducing the NPDCCH periods||Mobility is not considered and reducing NPDCCH period could lower the channel estimation quality hence may degrade the performance by unrealistic channel estimation|
|||Resource allocation technique by extending the specific PRB for paging traffic offload||power consumption reduction for NB-IoT UE during paging loading and offloading||The use of specific PRB for paging offloading is not an efficient use of the existing resource blocks. Also, the model is not applicable in standalone mode.|
|||NB-IoT scheduling algorithm||Interference analysis for 15 kHz LTE coexistence with kHz guard-band NB-IoT||Emptying the LTE resource is not efficient resource use. Also, the model is not applicable for the standalone mode of deployment|
|Link adaptation||||NB-IoT basic scheduler algorithm||Optimal resource usage by considering an average device delay and processing time||The scheduler did not consider semi-persistent scheduling, especially for inter-RAT networks|
|||Offset index selection and UE specific and common search spaces for NB-IoT dense networks||Cell capacity enhancement by means of optimal scheduling||Did not consider the number of sessions that each device has to transmit with respect to different requirements and use cases|
|||Link adaptation algorithm by using the mathematical expression of Shannon theorem||Coverage enhancement by characterizing SNR, repetition number and NB-IoT supported bandwidth||The work did not consider the impact of channel state information on UE link adaptation|
|||Two-dimensional NB-IoT dynamic link adaptation algorithm||Optimization of repetition number by dynamically adjusting MCS to ensure better BLER and BER performance||the model does not encompass the effect of speed and the deployment of the optional HARQ process to ensure better channel modeling|
|Coverage and capacity||||NB-IoT coverage comparisons in different scenarios for 15 kHz and kHz spacing||The channel estimation impairments, carrier offset as well as mobility with respect to different configurations are not considered for the claimed 170 dB of achieved MCL of NB-IoT|
|||Preconfigured access scheme and the joint spatial and code domain scheme||capacity and spectral efficiency improvement||It can only be applicable in small cell configurations when NB-IoT is deployed in large scale, preconfiguring access for different require|
|||Control plane small data transmission scheme||Effective data transmission enhancement by transmitting small packets in RRC connection set up||This scheme may results in NB-IoT signaling overhead due to Radio Resource Control (RRC) connection setup process encompassed with small data|
|||UE coverage and capacity simulation measurement based on real operators network parameters||NB-IoT enhanced coverage measurements by the use of real network configuration parameters||Optimal repetition number for NB-IoT devices is not considered, with additional penetration loss, it does not explain the additional repetition requirement to enhance the coverage while guaranteeing the required performance|
|||Low Earth Orbit (LEO) satellite to extend NB-IoT coverage||NB-IoT Coverage extension beyond LTE achieved link budget||The work did not consider the impact of repetition number on extended coverage as well as time and frequency synchronization that can lead to sampling rate mismatch as well as carrier frequency offset for low-end NB-IoT modules|
|Power management||||Practical power measurement||Power consumption analysis for NB-IoT by varying payloads and repetition numbers, I-eDRx and PSM||Using two devices is not representative massive NB-IoT devices in the because different chips have different power consumption depending on the enabled features such as inter-RAT support that can affect the overall device consumption|
|||Prediction-based energy-saving algorithm||Reduction of power consumption by reducing the scheduling request procedure||The solution is not optimal because it reduces scheduling request without considering the device requirement with respect to channel parameters|
|||Semi-Markov chain for energy evaluation||Energy consumption and delay requirement evaluation for NB-IoT systems by considering the four states, namely power saving mode, idle mode, RACH procedure, and transmission mode||The model does not include the energy consumption during transition between the four mentioned modes and it does not include the impact of repetition on the device power consumption|
|Physical Layer||MAC Layer||Standard|
|Radio resource management||Timing advance adjustment||Support for small cell|
|Frequency and time synchronization||Dynamic scheduling and semi-persistent scheduling||TDD support|
|Random access||Latency||Antenna diversity|
|Channel estimation||Power management||Mobility and handover support|
|Error correction||Network throughput||More efficient group messages|
|Link adaptation||Control packet overhead||Multicarrier operation|
|Interference mitigation||Control plane small data transmission||Network management tool for UE differentiation|
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Mwakwata, C.B.; Malik, H.; Mahtab Alam, M.; Le Moullec, Y.; Parand, S.; Mumtaz, S. Narrowband Internet of Things (NB-IoT): From Physical (PHY) and Media Access Control (MAC) Layers Perspectives. Sensors 2019, 19, 2613. https://doi.org/10.3390/s19112613
Mwakwata CB, Malik H, Mahtab Alam M, Le Moullec Y, Parand S, Mumtaz S. Narrowband Internet of Things (NB-IoT): From Physical (PHY) and Media Access Control (MAC) Layers Perspectives. Sensors. 2019; 19(11):2613. https://doi.org/10.3390/s19112613Chicago/Turabian Style
Mwakwata, Collins Burton, Hassan Malik, Muhammad Mahtab Alam, Yannick Le Moullec, Sven Parand, and Shahid Mumtaz. 2019. "Narrowband Internet of Things (NB-IoT): From Physical (PHY) and Media Access Control (MAC) Layers Perspectives" Sensors 19, no. 11: 2613. https://doi.org/10.3390/s19112613