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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 Ï„ + 1

The actual slot(s) state from the MLP-based CSIT predictor

s Ï„ + 1 ^

The desired slot(s) state

i Ï„ + 1

Actual predictor idle time slot(s)

i Ï„ + 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