OFDM Multiple Antennas Cognitive Radio Relay Synchronization CDMA Channel Estimation Spectrum Sharing Interference Cancellation Full duplex Spectrum Sensing Neural Networks Resource Allocation Stochastic Geometry Equalizer Bi-Directional Feedback Femtocell Heterogeneous Networks Energy Harvesting Device-to-Device (D2D) Cell Search Spectral efficiency NOMA FBMC interference management Dynamic TDD Cross-link interference SINR mismatch problem HetNet Idle cells Ultra-dense small cell networks achievable sum rate outage probability multi-spectral full-duplex relay selection diversity bursty traffic model mode selection Handoff CP-OFDM non-orthogonal multiple access self-interference cancellation Singular Vale Decomposition interference mitigation full-duplex cellular interference coordination Cognitive relay networks Time spreading beamforming Link adaptation Heterogeneous channel estimation capability coexistence MU-MIMO full-spreading NOMA GFDM Bi-directional full-duplex OQAM sensing duration Correlated MIMO Simultaneous Sensing and Transmission transmission capacity (TC) Two-way communications in-band full-duplex system automatic repeat request (ARQ) UWB flexible duplex TS-W-OFDM Windowing time-frequency efficiency eigen decomposition pilot signal 5G networks prototype filter Aggregate interference Long Term Evolution-Advanced interference to noise ratio resource size control link reliability mixed numerology Vehicle-to-vehicle communication LTE-based V2V Coexistence scenarios Resource management Filtered OFDM Cooperative systems Complexity tabu-search Deep learning Reliability Vehicle-to-Vehicle C-V2V HST Mobility Preamble 5G Grant-free Transmission Asynchronous Transmission Railway MLP massive connectivity
Status : Published 
Date : 2019-10 
Title : An Enhanced Tabu Search based Receiver for Full-spreading NOMA Systems 
Authors : Insik Jung, Hyunsoo Kim, Jinkyo Jung, Sooyong Choi, and Daesik Hong 
Journal : IEEE Access 
Abstract : Full-spreading non-orthogonal multiple access (FS-NOMA) is one category of the candidate technologies designed to support massive connectivity in wireless communication systems. Before it can handle the massive volume of user connections, it is important for the FS-NOMA to develop a receiver that successfully decodes target data from non-orthogonally overlapped receiving signals. However, the decoding performance of conventional interference-cancellation (IC)-based receivers is far from optimal because of error-propagation problems. To improve the decoding performance, we propose a novel FSNOMA receiver based on the tabu-search (TS) algorithm which is a sort of machine-learning algorithm. Specifically, a novel TS mechanism and a diversification scheme are proposed to overcome the inherent adverse conditions of FS-NOMA systems which lead the TS algorithm to local optima. Simulation results demonstrate that the proposed TS-based receiver has decoding performance that is superior to that of the conventional IC-based receiver. The results also show that the proposed receiver accommodates a higher number of user connections with a given packet drop rate threshold. 
URL : https://ieeexplore.ieee.org/document/889...uthoralert 

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» [IEEE Access] Insik Jung, Hyunsoo Kim, Jinkyo Jung, Sooyong Choi, and Daesik Hong, "An Enhanced Tabu Search based Receiver for Full-spreading NOMA Systems", IEEE Access, Oct. 2019 Published  2019-10