Ying Zhu's research while affiliated with Beijing University of Posts and Telecommunications and other places

... Recently, applications of FD technology in DSS systems have received significant attention [26], [28], [29]. Liao et al. [26] have presented the application scenarios with FD-enabled CR and highlighted key open research directions considering FD-CR as an important enabler for enhancing the spectrum usage in future wireless networks. ...

... Amongst the common spectrum sensing approaches, i.e. matched filtering, cyclostationary feature detector and energy detection, WDC concept is applied to cyclostationary feature detector due to it's high level of accuracy but at the cost of computational complexity as well as it has been recommended by IEEE 802.22 standard [7]. WDC, however, is not a benefit in case of the energy detector spectrum sensing technique due to it's design simplicity and low computational complexity. ...

... During recent years, the energy-efficient data transmission in WSNs has been a hot topic among both industrial and academic communities [15,16,17,20]. Energy-efficient designs are essential for some WSNs, especially for networks with energy-constraint sensors. ...

... Through simultaneous transmission and reception using the same frequency band at the same time, full-duplex relay can outperform half-duplex relay in terms of spectrum efficiency if the self-interference can be efficiently cancelled or suppressed [13], [14]. Over the recent years, plenty of works have been devoted to the study of the advanced self-interference cancellation and/or suppression techniques [15]- [20], which can reduce the effect of self-interference on wireless full-duplex transmission. ...

... This renders another level of efficiency for large relay assisted networks in which a limited number of relays have limited time to exchange data between nodes. For example, relay-assisted cognitive radio networks [23][24][25][26] in which some specific relays (usually the base stations) are used to exchange data between all nodes, and the more free time for the relays, the more efficient is the network. ...

... Approach Merits Demerits FFT detector [38] Nyquist WB Noncoherent Require high sampling rate Wavelet detector [39,43] Nyquist WB Edge detector High computational complexity Filter-bank detector [40] Nyquist WB High performance High computational complexity Compressive sensing [41,[44][45][46][47][48][49] Subnyquist WB Low sampling rate Dynamic behaviors for sparsity level [27][28][29][30][31][32] Sensing Fast sensing as compared to energy detection Higher accuracy requires a longer length of known sequences that results in lower efficiency of the spectrum Receiver statistics [33][34][35] Monitor Monitoring during reception Sensitive to receiver impairments Energy ratio [36,37] Monitor Insensitive to receiver impalements May require long monitoring cycle users in the same region, hard decisions work just as well as soft ones. However, implementing soft judgments in large networks becomes a difficult task because it adds a lot of complexity to the fusion rules while combining a lot of data. ...

... 9 On the basis of this method, 2 transmitter nodes to exchange information via a relay send their data (X A , X B ) to the relay, simultaneously, and the relay extracts a network coded signal (in the simplest way, the modulo-sum mapping of data, X R = X A ⊕ X B ) from the received superimposed signals and sends this code to the source nodes in the second time slot. Receiving the network code, each node with the knowledge of its own information can extract the information of the other node (for example, X B = X R ⊕ X A ). Performance of PLNC in CRN has been studied in previous studies 10, [16][17][18][19][20] 22 However, in these recent works, 2 different PUs coverage areas with 2 separate orthogonal frequency sets are considered, where SUs in each area operate on the frequency range of the opposite area. ...

... In cognitive radio, Channel State Information (CSI) is divided into two types: "unoccupied (idle)" and "occupied (busy)", which are represented by binary "0" and "1" in this paper. The channel correlation indicates the probability of consistent states of two channels, i.e., the higher the correlation, the greater the probability that the two channels will be in the same or opposite state (depending on sign of correlation) [26]. In this work, Channel Correlation Factor (CCF) is used to represent channel correlation, which is defined as: ...