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Difference between revisions of "Make Cellular Internet of Things Receivers Smart"

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(Short Description)
(Short Description)
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==Short Description==
 
==Short Description==
Recent developments in the 3GPP standard organization produced cellular standards for the Internet of Things (IoT). It is forecast that by the end of the decade billions IoT devices will be connected to the Internet, many in remote areas or poorly covered locations. The 3GPP produced two standards for the cellular IoT with extended coverage support: 4G based NB-IoT and a 2G based Extended Coverage GSM for IoT (EC-GSM-IoT) [1]. The latter promises up to 20 dB coverage increase compared to legacy solutions. The extended coverage is achieved by blindly repeating data packets. The terminal combines the blind repetitions before attempting to decode. There exist a number of promising combining schemes such as I/Q-, LLR-, and hybrid-combining. However, not every combining scheme is equally well suited for arbitrary radio channel conditions.
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Recent developments in the 3GPP standard organization produced cellular standards for the Internet of Things (IoT). It is forecast that by the end of the decade billions IoT devices will be connected to the Internet, many in remote areas or poorly covered locations. The 3GPP produced two standards for the cellular IoT with extended coverage support: 4G based NB-IoT and a 2G based Extended Coverage GSM for IoT (EC-GSM-IoT) [1]. The latter promises up to 20 dB coverage increase compared to legacy solutions. The extended coverage is achieved by blindly repeating data packets. The terminal combines the blind repetitions before attempting to decode. There exist a number of promising combining schemes such as I/Q-, LLR-, and hybrid-combining. However, the optimal combining scheme varies with varying radio channel conditions.
  
 
In this project, the suitability of selected combining schemes under various radio channel conditions, such as AWGN and slow and fast fading LTV channels, shall be studied. Then, methods and algorithms to estimate the channel coherence time shall be developed. The estimated channel coherence time can then be used to optimally choose the best combining scheme for the observed channel conditions. The chosen algorithmic candidate can then be implemented in HDL and incorporated into the [[stoneEDGE]] project, which already includes signal conditioning, equalization, and channel decoding. The result can then be tested on a Kintex FPGA board with a [[PULP]] CPU in conjunction with an [[evalEDGE]] FMC module as analog front-end. This project is a perfect opportunity to solve a hot topic algorithmic problem and make cellular IoT receivers smart.
 
In this project, the suitability of selected combining schemes under various radio channel conditions, such as AWGN and slow and fast fading LTV channels, shall be studied. Then, methods and algorithms to estimate the channel coherence time shall be developed. The estimated channel coherence time can then be used to optimally choose the best combining scheme for the observed channel conditions. The chosen algorithmic candidate can then be implemented in HDL and incorporated into the [[stoneEDGE]] project, which already includes signal conditioning, equalization, and channel decoding. The result can then be tested on a Kintex FPGA board with a [[PULP]] CPU in conjunction with an [[evalEDGE]] FMC module as analog front-end. This project is a perfect opportunity to solve a hot topic algorithmic problem and make cellular IoT receivers smart.

Revision as of 19:00, 31 January 2017

Smart IoT receivers autonomously adapt their algorithms to the current radio channel conditions.

Short Description

Recent developments in the 3GPP standard organization produced cellular standards for the Internet of Things (IoT). It is forecast that by the end of the decade billions IoT devices will be connected to the Internet, many in remote areas or poorly covered locations. The 3GPP produced two standards for the cellular IoT with extended coverage support: 4G based NB-IoT and a 2G based Extended Coverage GSM for IoT (EC-GSM-IoT) [1]. The latter promises up to 20 dB coverage increase compared to legacy solutions. The extended coverage is achieved by blindly repeating data packets. The terminal combines the blind repetitions before attempting to decode. There exist a number of promising combining schemes such as I/Q-, LLR-, and hybrid-combining. However, the optimal combining scheme varies with varying radio channel conditions.

In this project, the suitability of selected combining schemes under various radio channel conditions, such as AWGN and slow and fast fading LTV channels, shall be studied. Then, methods and algorithms to estimate the channel coherence time shall be developed. The estimated channel coherence time can then be used to optimally choose the best combining scheme for the observed channel conditions. The chosen algorithmic candidate can then be implemented in HDL and incorporated into the stoneEDGE project, which already includes signal conditioning, equalization, and channel decoding. The result can then be tested on a Kintex FPGA board with a PULP CPU in conjunction with an evalEDGE FMC module as analog front-end. This project is a perfect opportunity to solve a hot topic algorithmic problem and make cellular IoT receivers smart.

Status: Available

Looking for interested students (Semester or Master Thesis)
Contact: Benjamin Weber

Prerequisites

Interest in mobile communication
Matlab
VHDL

Character

25% Theory
25% Algorithm Design
25% Implementation
25% Testing

Professor

Qiuting Huang

References

[1] 3GPP. Release 13. http://www.3gpp.org/release-13, 2016.