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Table 1 Notations

From: CSIT: channel state and idle time predictor using a neural network for cognitive LTE-Advanced network

Parameter Meaning
Τ Past observation length in terms of number of slot(s)
y β k Output of the neuronβ of the k th layer
v βα Connection weights, connecting the neuron β of the k th layer to the neuron α in the (k −1)th layer
z β k Weighted sum of inputs coming from the output neuron in the (k − 1) th layer
b β Bias input in the neuronβ
O Neuron in the output layer
s τ + 1 The actual slot(s) state from the MLP-based CSIT predictor
s τ + 1 ^ The desired slot(s) state
i τ + 1 Actual predictor idle time slot(s)
i τ + 1 ^ Desired idle time slot(s)
φ'() Activation function, i.e., ‘purelin’ for the output layer and ‘log sigmoid’ for the hidden layer
δ β k Local gradient of the neuron β in the k th layer
e s Error between the desired and actual slot(s) state of the predictor
e i Error between the desired and the actual idle time slot(s) of the predictor
N is Total number of idle time slot(s) in the system
N i Total number of idle time slot(s) sensed by CUsense
N ip Total number of idle time slot(s) sensed by CUpredict
X Unit energy required for sensing a slot(s)
Y Total number of slot(s) predicted to be idle
Z Number of slots a CUsense sensed in a finite duration of time slot(s)
N s Total number of slot(s) to be sensed
C Sensing threshold for the event PLR to occur
p m Probability of the m th slot(s) appearing to be idle
Δ s Time duration of the slot(s)
C 0 Referred transmission capacity of the SU
η i K This corresponds to how fast the a SU can find the idle slot(s) among total of Ns