Computational Intelligence and Neuroscience

Research Article

Blind Channel and Data Estimation Using Fuzzy Logic-Empowered Opposite Learning-Based Mutant Particle Swarm Optimization

Table 1

Proposed fuzzy logic-empowered opposite learning mutant particle swarm optimization (FL-OLMPSO) algorithm.


S. no.	Steps

Level 1
1	Start
2	2.1. Initialization of data populaces D_p = { D_p1, D_p2, .................. D_pa} and velocity W_d
	2.2. Initialization of channel populaces D_k = { D_k1, D_k2, .................. D_ka} and velocity W_k
3	Compute the wellness of population utilizing the cost work given in (14)
4	Compute lower bound value (MB_p, MB_i) and upper bound value (HB_p, HB_i) from D_p and D_k separately
	Calculate the opposite populace
5	For FL-TOLMPSO	For FL-POLMPSO
	5.1. Opposite data population	5.1. Opposite data population
	OD_p = {OD_p1, OD_p2, .................. OD_pa}	OD_p = {OD_p1, OD_p2, .................. OD_pa/2}
	OD_pi = {OD_pi,1, OD_pi,2, .................. OD_pi,M}	OD_pi = {OD_pi,1, OD_pi,2, .................. OD_pi,M}
	OD_pi,j = MB_p + HB_a − D_pi,j	OD_pi,j = MB_p + HB_a − D_pi,j
	5.2. Opposite channel population	5.2. Opposite channel population
	OD_k = {OD_k1,OD_k2, .................. OD_ka}	OD_k = {OD_k1,OD_k2, .................. OD_ka/2}
	OD_ki = {OD_ki,1, OD_ki,2, .................. OD_ki,M}	OD_ki = {OD_ki,1, OD_ki,2, .................. OD_ki,M}
	OD_ki,j = MB_i + HB_i − D_ki,j	OD_ki,j = MB + HB_i − D_ki,j
6	Compute the fitness of both opposite populations (OD_p and OD_k) using the cost function given in equation (16)
7	Select the local best particle of the following: 7.1. Data population M_bdp from D_p and OD_p 7.2. Channel population L_bdk from D_k and OD_k
8	Select the global best particle of the following: 8.1. Data population N_bdp = min(M_bdp) 8.2. Channel population N_bdp = min(L_bdp)

Level 2: global best data vector is fixed and continuous FL-OLMPSO algorithm works on the channel population
9	Update velocities of each particle of channel population using FIS: W_him(n) = W_him(n−1) + FLC (LI, GI, W_him(n−1))
10	Update the position of each particle channel population Calculate the mutant operator (MO) M_oh(i) = D_kim(n) = D_kim(n−1) + M_oh(i) ∗ rand()
11	Compute the fitness of mutated particles of channel population using equation (16)
12	Update the channel population D_k
13	If (number of cycles > required NoC) go to step 14 Else go to step 9

Level 3: in this level, the discrete FL-OLMPSO algorithm is used for estimating the data symbols
14	The global best particle of the data population is chosen and update the velocity: W_him(n) = FLC (LI, GI, W_him(n−1))
15	Update position of each particle of data population Compute the mutant operator (MO) M_od(i) = D_pim(n) = D_pim(n−1) + M_od(i) ∗ rand()
16	Compute the fitness of particles of data population using (16)
17	Update the data population D_p
18	If (number of cycles > required NoC) go to step 20 Else go to step 14

Level 4: next sample of the received signal is taken and execution goes to level 2
19	Stop