Autonomous device-to-device communications for mission-critical internet-of-things
Lead Research Organisation:
Queen's University Belfast
Department Name: Sch of Electronics, Elec Eng & Comp Sci
Abstract
Internet of Things (IoT) market will reach $1.7 trillion by 2020, growing to 75 billion connected devices in 5 years. IoT technologies that allow literally billions of everyday objects to connect to each other over the internet have tremendous opportunities to enhance all of our lives. Within IoT, short-range device-to-device (D2D) communications promise to interconnect diverse devices with a significant increase in data traffics (a 1000-fold increase by 2020).
Orthogonal frequency division multiplexing (OFDM) has been a key technology in the majority of modern wireless communication systems. Due to its robustness to multipath fading by transforming a frequency selective fading channel into parallel flat fading channels, OFDM has been adopted in the majority of current and future wireless communications standards such as IEEE 802.15.4 (smart utility network), IEEE 802.11 WiFi, and 4G LTE-A.
The benefits of OFDM systems are not for free; they come at the cost of a loss of energy efficiency due to the distribution of finite power to multicarrier signals, an increased sensitivity to frequency offset and Doppler shift as well as transmission nonlinearity caused by the non-constant power ratio of OFDM signals. Such drawbacks challenge its direct application to D2D, especially in future healthcare or industrial communications that may integrate ultra-reliable connectivity with numerous IoT devices.
With demands for high performance sensors, future D2D OFDM is challenged to increase the data rate much faster than today D2D. A large spectrum of tens of gigahertz (GHz) band (e.g., 60 GHz band) is allocated for future D2D in the 5G and this band is more than 200 times larger than today WiFi systems. However, larger channels, higher transmit power spending. For example, in 60 GHz band, 200 % of transmit power is needed, under line-of-sight propagation. This overhead challenges the direct use of today OFDM to a power-limited D2D.
In addition, rapid developments in IoT power sensing/interpretation and intelligent algorithms have not been balanced by communications engineering principles. Today sensors comprise simple processors which communicate typically in open spaces to some central resource for further processing but in medical and unmanned factory applications, there is a need for more intelligent monitoring, multi-sensing cooperation processing by more multipath resilience monitoring devices which will be deployed dominantly in confined environments to communicate with sensors and central resource. This suggests the need for advanced, autonomous D2D platforms which offer high quality of performance at low power, but which can also be flexible and easily implemented.
Main challenges are keen for D2D to bring autonomy with its flexible operation, improve the performance and concurrently offer low-complexity transceivers. It is vital especially for fully autonomous critical IoT systems. This points to increase need for advanced D2D techniques, offering high rate and reliability at very low power, particularly, in a highly dense environment, but which can be easily implemented.
This project has four main objectives:
(i) Creation of simpler, autonomous D2D realisation structure.
(ii) Derivation of physical layer ultra-high reliability D2D in mission-critical IoTs.
(iii) Integrated multiple access and D2D features to advance the performance of mission-critical IoT communications, in ultra-dense, and non-conventional fading environments.
(iv) Realisation of more intelligent D2D using machine learning algorithms.
Our goal is to provide theoretical references and guidelines for a successful autonomous D2D implementation in mission-critical IoT applications. Producing significant advance over classical D2D, the proposal will challenge how D2D is developed under a strict requirement in terms of reliability and energy by leveraging special multicarrier index keying algorithms and reducing its transceiver complexity.
Orthogonal frequency division multiplexing (OFDM) has been a key technology in the majority of modern wireless communication systems. Due to its robustness to multipath fading by transforming a frequency selective fading channel into parallel flat fading channels, OFDM has been adopted in the majority of current and future wireless communications standards such as IEEE 802.15.4 (smart utility network), IEEE 802.11 WiFi, and 4G LTE-A.
The benefits of OFDM systems are not for free; they come at the cost of a loss of energy efficiency due to the distribution of finite power to multicarrier signals, an increased sensitivity to frequency offset and Doppler shift as well as transmission nonlinearity caused by the non-constant power ratio of OFDM signals. Such drawbacks challenge its direct application to D2D, especially in future healthcare or industrial communications that may integrate ultra-reliable connectivity with numerous IoT devices.
With demands for high performance sensors, future D2D OFDM is challenged to increase the data rate much faster than today D2D. A large spectrum of tens of gigahertz (GHz) band (e.g., 60 GHz band) is allocated for future D2D in the 5G and this band is more than 200 times larger than today WiFi systems. However, larger channels, higher transmit power spending. For example, in 60 GHz band, 200 % of transmit power is needed, under line-of-sight propagation. This overhead challenges the direct use of today OFDM to a power-limited D2D.
In addition, rapid developments in IoT power sensing/interpretation and intelligent algorithms have not been balanced by communications engineering principles. Today sensors comprise simple processors which communicate typically in open spaces to some central resource for further processing but in medical and unmanned factory applications, there is a need for more intelligent monitoring, multi-sensing cooperation processing by more multipath resilience monitoring devices which will be deployed dominantly in confined environments to communicate with sensors and central resource. This suggests the need for advanced, autonomous D2D platforms which offer high quality of performance at low power, but which can also be flexible and easily implemented.
Main challenges are keen for D2D to bring autonomy with its flexible operation, improve the performance and concurrently offer low-complexity transceivers. It is vital especially for fully autonomous critical IoT systems. This points to increase need for advanced D2D techniques, offering high rate and reliability at very low power, particularly, in a highly dense environment, but which can be easily implemented.
This project has four main objectives:
(i) Creation of simpler, autonomous D2D realisation structure.
(ii) Derivation of physical layer ultra-high reliability D2D in mission-critical IoTs.
(iii) Integrated multiple access and D2D features to advance the performance of mission-critical IoT communications, in ultra-dense, and non-conventional fading environments.
(iv) Realisation of more intelligent D2D using machine learning algorithms.
Our goal is to provide theoretical references and guidelines for a successful autonomous D2D implementation in mission-critical IoT applications. Producing significant advance over classical D2D, the proposal will challenge how D2D is developed under a strict requirement in terms of reliability and energy by leveraging special multicarrier index keying algorithms and reducing its transceiver complexity.
Organisations
Publications
Luong T
(2018)
Spread OFDM-IM With Precoding Matrix and Low-Complexity Detection Designs
in IEEE Transactions on Vehicular Technology
Luong T
(2018)
Precoding for Spread OFDM IM
Luong T
(2019)
Deep Learning-Based Detector for OFDM-IM
in IEEE Wireless Communications Letters
Luong T
(2018)
The BER Analysis of MRC-Aided Greedy Detection for OFDM-IM in Presence of Uncertain CSI
in IEEE Wireless Communications Letters
Luong T
(2018)
Repeated MCIK-OFDM With Enhanced Transmit Diversity Under CSI Uncertainty
in IEEE Transactions on Wireless Communications
Van Luong T
(2017)
A Tight Bound on BER of MCIK-OFDM With Greedy Detection and Imperfect CSI
in IEEE Communications Letters
Van Luong T
(2018)
Enhanced Spread Spectrum OFDM-IM with Rotated Zadoff-Chu Sequences
Van Luong T
(2018)
Impact of CSI Uncertainty on MCIK-OFDM: Tight Closed-Form Symbol Error Probability Analysis
in IEEE Transactions on Vehicular Technology
Van Luong T
(2020)
Deep Energy Autoencoder for Noncoherent Multicarrier MU-SIMO Systems
in IEEE Transactions on Wireless Communications
Van Luong T.
(2017)
Symbol error outage performance analysis of MCIK-OFDM over complex TWDP fading
in European Wireless 2017 - 23rd European Wireless Conference
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509541/1 | 01/10/2016 | 30/09/2021 | |||
| 1786429 | Studentship | EP/N509541/1 | 01/10/2016 | 31/12/2019 | Thien Luong |
| Description | OFDM with index modulation (OFDM-IM) has recently emerged as a promising multicarrier scheme to replace conventional OFDM schemes in future communication systems due to its superiority in terms of both reliability and energy efficiency. Accordingly, this project is to develop/design and analyze novel IM-based multicarrier modulation schemes, aiming at ultra-high reliability and ultra-low power and complexity, which are particularly important in a range of mission-critical IoT applications. The most significant achievements of the project are: (1) a novel framework of analyzing symbol error probability and bit error rate of a generalized OFDM-IM system is proposed, which provides new insights into impacts of IM parameters, detection types and channel estimation errors on the error performance. This is particularly helpful in designing OFDM-IM systems to balance a trade-off between performance and complexity or power consumption under certain channel conditions. Besides, this work is extended to multi-antenna systems to further improve performance of OFDM-IM. (2) Thanks to insights into performance of OFDM-IM systems in (1), we developed a simple repetition code scheme for OFDM-IM that provides better performance than existing IM-based schemes, along with the performance analysis over practically imperfect channel conditions. (3) To further enhance performance of OFDM-IM, we proposed spread OFDM-IM that employs spreading matrices such as Walsh-Hadamard and Zadoff-Chu to considerably increase the transmit diversity gain, leading to a significant performance improvement over current schemes. For practical implementations, a number of low-complexity near-optimal detectors are designed, which make spread OFDM-IM desirable to ultra-reliable IoT communications. (4) To break the limits of IM-based schemes with respect to reliability and complexity, we applied deep learning to multicarrier systems, where an end-to-end multicarrier autoencoder (MC-AE) system is proposed, which employs deep neural network at both transmitter and receiver. Using dataset generated by simulations, MC-AE is trained to minimize the error rate to accordingly attain transmitter and receiver optimized for any channel environments of interest. As a result, MC-AE not only achieves much better error performance, but also lower detection complexity than existing IM-based schemes. Moreover, we extended this work to multi-user scenarios with both uplink and downlink transmissions, where multi-user multicarrier autoencoder systems (MU-MC-AE) are proposed. It is shown that MU-MC-AE outperforms state-of-the-art schemes such as MC-CDMA, IM-MC-CDMA and SCMA at even lower complexity. In addition, a novel noncoherent energy autoencoder for multicarrier systems (termed as NC-EA) is proposed, which can efficiently decode data without any channel estimation schemes at either the transmitter or the receiver. This scheme is then extended to multiple access scenarios (termed as NC-EAMA), in order to accommodate any numbers of users, sub-carriers, antennas and data streams, as well as any transmission directions. The proposed learning-based schemes achieve higher reliability and flexibility at even lower complexity compared to the baseline schemes, and thus are attractive to various mission-critical IoT applications, which require reliable, low-latency and low-complexity communications. In fact, the project contributions are not just limited to aforementioned achievements. So far, we developed a number of other advanced multicarrier schemes such as deep learning-based detector for OFDM-IM, joint spread spectrum and index modulation for OFDM, and opportunistic scheduling scheme for OFDM-IM. Such outcomes of the project encompass a wide range of critical IoT applications such as healthcare, industrial automation and vehicular communications. The obtained results have been published in 7 IEEE Transactions/Letters journals and 5 conference papers, while several others are under review or being prepared to be submitted to IEEE journals. |
| Exploitation Route | The proposed schemes in the project can be used in various mission-critical IoT applications that require ultra-reliable wireless connectivity and low cost, low power devices, for example, healthcare, wearables, smart home, industrial automation, vehicular communications and any other device-to-device communications. |
| Sectors | Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Electronics,Energy,Healthcare,Manufacturing, including Industrial Biotechology,Transport |