Institutional Repository
Technical University of Crete
EN  |  EL



My Space

Non-orthogonal multiple access in modern wireless networks with clustering techniques

Skyvalakis Konstantinos

Full record

Year 2018
Type of Item Diploma Work
Bibliographic Citation Konstantinos Skyvalakis, "Non-orthogonal multiple access in modern wireless networks with clustering techniques", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2018
Appears in Collections


Non-orthogonal multiple access is an old problem, which has recently attracted renewed interest. This work revisits the problem in the context of industrial RFIDs and scatter radio. The latter has emerged as a key enabling wireless technology for low-cost and large-scale ubiquitous sensing. Radio frequency identification (RFID) tags/sensors utilize scatter radio technology to transfer sensed information to readers, typically employing Gen2, the industrial RFID protocol. In this thesis, a new system model is developed for the simultaneous transmission of two Gen2 RFID tags, as well as a channel estimation algorithm, based on clustering techniques.In the system model part of the thesis, two collided RN16 packets from two RFIDtags are considered, which may not be perfectly synchronized. This work builds upon prior art and further considers the time offset that can occur in the transmission among the two tags. It is shown that there are three possible scenarios, based on the time offset of the most delayed tag and their connection is further highlighted. Work includes both theoretical analysis of these scenarios, as well as experimental evaluation from a testbed.The channel estimation part of the thesis employs the clustering algorithm calledAffinity Propagation, based on probabilistic graphical models and inference algorithms. Channel estimates are obtained, based on the clustered, received data patterns, in conjunction with a line fitting method. Having obtained the channel estimates, the offset of the most delayed tag is estimated. Finally, the collision scenario is inferred and tag data detection is performed. Future work will focus on different clustering algorithms, as well as memory-based, long-sequence detection.

Available Files