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Spectrum Sharing for Internet of Things: A Survey

Lin Zhang, Ying-Chang Liang, Ming Xiao

arXiv:1810.04408v1cs.ITcs.NI

TL;DR

Massive IoT connectivity in 5G and beyond faces scarce spectrum and distinct licensed- and unlicensed-spectrum deployment preferences. The paper surveys prevalent IoT technologies and their spectrum-sharing solutions, including shared spectrum, interference models, and interference management. It compares their advantages, disadvantages, challenges, and potential research directions.

  • Problem

    Massive IoT connections require advance spectrum planning, while IoT spectrum sharing differs from conventional sharing because IoT is dominated by uplink short-packet communication.

  • Method

    The paper surveys eMTC, NB-IoT, Bluetooth, Zigbee, LoRaWAN, SigFox, and ambient backscatter communication across licensed and unlicensed spectrum.

  • Results

    For each technology, the survey analyzes shared spectrum, interference models, interference management schemes, and associated advantages and disadvantages.

  • Takeaways & Limitations

    Licensed-spectrum deployment offers centralized coexistence and QoS guarantees, whereas unlicensed technologies face unpredictable interference and limited or absent QoS guarantees.

  • Takeaways & Limitations

    Ambient backscatter communication has a complicated reader-side interference model and short transmission distance.

Abstract

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The Internet of Things (IoT) is a promising paradigm to accommodate massive device connections in 5G and beyond. To pave the way for future IoT, the spectrum should be planed in advance. Spectrum sharing is a preferable solution for IoT due to the scarcity of available spectrum resource. In particular, mobile operators are inclined to exploit the existing standards and infrastructures of current cellular networks and deploy IoT within licensed cellular spectrum. Yet, proprietary companies prefer to deploy IoT within unlicensed spectrum to avoid any licence fee. In this paper, we provide a survey on prevalent IoT technologies deployed within licensed cellular spectrum and unlicensed spectrum. Notably, emphasis will be on the spectrum sharing solutions including the shared spectrum, interference model, and interference management. To this end, we discuss both advantages and disadvantages of different IoT technologies. Finally, we identify challenges for future IoT and suggest potential research directions.

I. INTRODUCTION

IoT is positioned as a major 5G-and-beyond connectivity paradigm whose massive device population requires spectrum planning and sharing. The survey examines licensed and unlicensed IoT technologies, emphasizing their spectrum-sharing mechanisms and trade-offs.

  • IoT connects end-devices wirelessly to the core Internet for centralized data processing and control across smart systems.
  • IoT spectrum sharing differs from conventional sharing because IoT is dominated by uplink short-packet communication from end-devices to access points.Conventional designs target downlink long-packet communication and are therefore inappropriate or inefficient for IoT short-packet scenarios.
  • The survey covers eMTC and NB-IoT in licensed cellular spectrum, Bluetooth and Zigbee in unlicensed spectrum, and ambient backscatter across either spectrum.
  • It emphasizes shared spectrum, interference models, interference management, advantages, disadvantages, future challenges, and research directions.

II. IOT TECHNOLOGIES DEPLOYED WITHIN LICENSED CELLULAR SPECTRUM

eMTC and NB-IoT are LTE-based licensed-spectrum IoT technologies designed to reduce device costs and energy use while supporting coverage and massive connections. They simplify LTE functionality for IoT traffic and requirements while retaining substantial LTE features.

  • eMTC and NB-IoT were standardized in 3GPP LTE Releases 13/14 and exploit cellular standards and existing infrastructure.
  • Both technologies target low hardware costs, low energy consumption, wide coverage, and massive device connections.
  • Both inherit LTE elements such as numerologies, channel coding, rate matching, interleaving, and related protocol functions.
  • Both use OFDMA downlink, SC-FDMA uplink, single-antenna half-duplex operation, and narrow bandwidth to reduce end-device complexity.

2) Different features between eMTC and NB-IoT:

eMTC and NB-IoT differ mainly in target spectrum, deployment modes, bandwidth, and uplink transmission options. These differences give NB-IoT more flexible spectrum coexistence and support for reduced-rate scheduling.

  • Deployment modes: NB-IoT supports stand-alone, in-band, and guard-band deployment modes, while eMTC can only operate within LTE spectrum.

1) Spectrum sharing solution of Bluetooth:

Bluetooth shares globally unlicensed 2.4 GHz spectrum using frequency hopping, while Zigbee combines direct-sequence spreading with carrier-sense collision avoidance. These mechanisms address interference through frequency diversity, signal spreading, and channel sensing.

  • Bluetooth spectrum sharing: Bluetooth operates in the unlicensed 2.4 GHz ISM band and divides 2.4–2.4835 GHz into 79 1-MHz channels.
  • Bluetooth spectrum sharing: Bluetooth uses FHSS, transmitting successive packets on channels selected according to a predetermined hopping order.
  • Bluetooth interference management: Bluetooth faces frequency-static and frequency-dynamic interference, with dynamic management illustrated through orthogonal hopsets per time slot.
  • Zigbee spectrum sharing: Zigbee uses DSSS to spread narrowband signals across wideband channels, making them resilient to narrowband interference.
  • Zigbee spectrum sharing: Zigbee also uses CSMA/CA, transmitting only after sensing that the target channel is idle.

B. LoRaWAN and SigFox technologies

LoRaWAN and SigFox target low-cost, low-power IoT deployment in sub-GHz ISM spectrum, using different approaches to coverage, energy use, and data transmission. Their designs trade reduced hardware and energy requirements against constrained data rates and regulated access patterns.

  • Basic features: LoRaWAN classifies end devices into Classes A, B, and C according to downlink latency requirements to reduce energy consumption.Class A devices open receive windows only after uplink transmission, whereas Class B devices open them periodically.
  • Comparison: The section presents a comparison of LoRaWAN and SigFox in Table II.The supplied table passage identifies the comparison but does not provide its individual entries.
  • Basic features: LoRaWAN retransmits collided data up to eight times and adapts among six data rates from 0.3 kbps to 37.5 kbps for coverage flexibility.Lower data rates correspond to wider coverage, while higher rates provide narrower coverage.
  • Basic features: SigFox uses 100 Hz ultra-narrow bandwidth to reduce noise, transmit power, hardware requirements, and extend coverage, but its data rate is as low as 100 bps.Its cognitive functionality is another recognizable feature, while each end device may transmit a signal three times to improve decoding probability.

2) Spectrum sharing solutions of LoRaWAN and SigFox:

LoRaWAN and SigFox share regulated sub-GHz ISM spectrum using duty-cycle limits, while ambient backscatter communication can operate in either licensed or unlicensed spectrum. Ambient backscatter uses harvested ambient signals and reflected-wave modulation to support low-cost, low-energy transmission.

  • LoRaWAN and SigFox: LoRaWAN and SigFox each use a duty cycle no larger than 1% to comply with spectrum regulation and limit interference on shared sub-GHz ISM channels.LoRaWAN uses regional sub-GHz bands, while SigFox uses 868 MHz in Europe and 902 MHz in the US.
  • Ambient backscatter communication: Ambient backscatter communication has energy acquisition and data transmission phases, in which a tag harvests ambient signals and modulates data onto them.The tag activates its internal circuit using harvested energy before transmitting data through ambient wireless signals.
  • Ambient backscatter communication: Ambient backscatter scenarios include an AP serving as an RF source, an AP serving as a reader, and a client serving as a reader.These scenarios differ in which network node supplies the ambient signal and receives the tag’s reflected data.
  • Ambient backscatter communication: Tags modulate reflected-wave amplitude or phase by adjusting antenna impedance, allowing readers to map the reflected signal to transmitted data.The modulation relies on differences in electromagnetic-wave reflection at media boundaries.
  • Ambient backscatter communication: Ambient backscatter avoids complicated RF components at the tag and can share either licensed or unlicensed spectrum.A typical tag mainly contains a digital logic integrated circuit and an antenna, reducing hardware cost and energy consumption.

B. Spectrum sharing solutions

Ambient backscatter spectrum-sharing solutions depend on how the ambient transmitter, tag, reader, and client are arranged. In the first scenario, the reader must detect tag data while the AP’s downlink may interfere with the reflected signal.

  • Scenario I: Scenario I contains an AP, client, reader, and tag; the AP transmits to the client and simultaneously supplies the ambient RF signal used by the tag.The tag exploits the AP’s wireless signal and delivers its data to a separate reader.
  • Scenario I: In Scenario I, the reader receives the AP downlink and tag-reflected signal simultaneously, so the downlink can interfere with tag-data detection.Existing approaches differ according to whether the downlink signal is cancelled before detecting the reflected data.
  • Scenario I: One approach combines on-off keying with differential coding, enabling the reader to extract tag data from received-power variations without channel-state information.Another approach exploits the repeated cyclic prefix of an OFDM symbol to cancel OFDM interference.

2) Spectrum sharing solution in Scenario II:

Ambient backscatter spectrum sharing faces distinct interference-management constraints across deployment scenarios. Full-duplex AP reception can make reflected tag signals correlated with self-interference, while other arrangements require joint detection or advanced interference handling.

  • Scenario II: In Scenario II, the AP transmits to the client and receives the tag’s reflected signal simultaneously, creating self-interference that constrains the tag-to-reader rate.The reflected signal is correlated with the AP’s downlink self-interference, unlike the independent required signal assumed in conventional full-duplex cancellation.
  • Scenario II: Classical full-duplex self-interference cancellation cannot be directly applied in Scenario II because it may cancel both the interference and the desired reflected signal.An optimized link-layer design is used to prevent cancellation of the required tag component.
  • Scenario III: In Scenario III, the client receives the AP downlink and tag-reflected signal simultaneously, requiring joint detection of both signals.An optimal maximum-likelihood detector is proposed, but its complexity grows exponentially with downlink modulation size; a lower-complexity successive-interference-cancellation detector is also developed.
  • Technology comparison: Cellular-spectrum deployment can reuse existing resources for rapid IoT coverage and centralized coexistence, supporting interference management and QoS guarantees for cellular and IoT links.These advantages accompany the higher cost of sharing cellular spectrum for IoT applications.
  • Technology comparison: Unlicensed-spectrum technologies face unpredictable and complicated interference, limiting their QoS guarantees despite using smart cancellation or mitigation approaches.Existing proprietary technologies may provide limited QoS guarantees or none.
  • Technology comparison: Ambient backscatter can use licensed or unlicensed spectrum and reduce tag hardware and energy costs, but it requires advanced reader interference management and offers meter-scale transmission distances.Its reflected-signal mechanism produces a complicated reader interference model and short transmission range.

VI. OTHER POTENTIAL SPECTRUM SHARING TECHNOLOGIES FOR FUTURE IOT

Future IoT spectrum sharing may use cognitive radio, device-to-device communication, NOMA, and LTE-U to address heterogeneous connectivity and traffic requirements.

  • Potential technologies include cognitive radio, device-to-device communication, NOMA, and LTE-U.These approaches support opportunistic access, direct device links, simultaneous scheduling, or licensed-spectrum traffic offloading.
  • Cognitive radio lets secondary users access spectrum opportunistically while primary users retain priority.
  • Device-to-device communication connects neighboring devices directly rather than through a base station.
  • NOMA schedules multiple devices on the same channel simultaneously and can improve performance for different application types and data rates.
  • Future IoT must accommodate diverse latency, reliability, and throughput requirements that existing technologies may not address well.

A. Use case analysis

A representative use case involves latency-sensitive IoT devices served by one operator under limited spectrum and dynamic traffic. The paper discusses inter-operator spectrum sharing and edge processing as possible responses, while noting broader heterogeneity challenges.

  • A. Use case analysis: Latency-sensitive IoT devices may require stringent uplink collection and downlink response timing under limited, dynamically varying spectrum.
  • A. Use case analysis: Inter-operator spectrum sharing can let devices covered by an underutilized neighboring operator use that operator’s spectrum.
  • A. Use case analysis: Compared with intra-operator sharing, inter-operator sharing provides more flexible utilization of limited spectrum resources.
  • A. Use case analysis: Edge data-processing nodes can reduce latency by responding directly to some data and preprocessing raw data before forwarding useful information.
  • A. Use case analysis: Future IoT requires heterogeneous network topologies, traffic models, and interference models that current technologies cannot fully settle.

1) More use cases:

The paper identifies short-packet traffic and insufficient spectrum as central challenges for future IoT spectrum sharing. It surveys technologies and proposes broader theoretical analysis, additional spectrum, and corresponding sharing designs.

  • 1) More use cases:: IoT traffic is dominated by short-packet communications, making direct reuse of conventional long-packet spectrum-sharing designs inappropriate and inefficient.
  • 1) More use cases:: The paper calls for more theoretical analysis and spectrum-sharing designs tailored to short-packet communications.
  • 1) More use cases:: Insufficient available spectrum limits IoT spectrum sharing as IoT traffic demand increases.
  • 1) More use cases:: Millimeter wave can provide high-rate short-range communication for some IoT scenarios, but its distinct interference model makes sharing challenging.
  • 1) More use cases:: The survey analyzes seven IoT technologies by their basic principles, shared spectrum, interference model, interference management, advantages, and disadvantages.
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