Parameter tuning of Artificial Bee Colony algorithm for energy efficiency optimization in massive MIMO systems

Energy Efficiency Optimization of Massive MIMO Systems Based on the Particle Swarm Optimization Algorithm

“…In this paper, we proposed an algorithm based on PSO to study how to select the numbers of BS antennas (M) and terminal users (K) to maximize the EE in a single-cell massive MIMO with perfect and imperfect CSI, and a multicell scenarios with imperfect CSI. The results reveal that the algorithm presented in this paper possesses the lowest complexity and the highest optimal EE value in a singlecell scenario with perfect CSI when compared with the iterative algorithm [19] and ABC algorithm [26]. In a single-cell scenario with imperfect CSI, the proposed algorithm in this paper can achieve the optimal EE value as that obtained by the iterative algorithm in [19], but the time used by this algorithm is only one-twelfth of that required for the iterative algorithm.…”

“…Against this backdrop, our core innovations in this work are as follows. [19] and the artificial bee colony (ABC) algorithm [26], the computational time of the algorithm presented in this paper is at least 10 times higher, which reduces the computational complexity and improves the computational efficiency. What is more, the proposed algorithm requires fewer transmit antennas to achieve the optimal energy efficiency, which reduces the complexity and cost of system implementation In this paper, bold italic uppercase symbols describe matrices, e.g., H, and bold italic uppercase symbols with subscripts describe vectors, e.g., H i .…”

“…From Figure 2, we can see that the optimal EE value is 3:074e + 07 bit/Joule, and the corresponding num-bers of BS antennas and terminal users are M = 163 and K = 104. In particular, the running time of the whole optimization algorithm is only 23.485 s. As shown in Table 4, under the same conditions, [19] uses an iterative algorithm to obtain the optimal EE value of 3:07e + 07 bit/Joule when M = 165 and K = 104, and the running time of the algorithm is 617.669 s. [26] uses the ABC algorithm to achieve the optimal EE value of 3:0614e + 07 bit/Joule, and the corresponding numbers of BS antennas and terminal users are M = 166 and K = 106. The running time of the algorithm is 163.032 s. Compared with [19,26], the EE value obtained by the algorithm presented in this work is the best, the number of BS antennas (M) is the smallest and the running time is the shortest.…”

“…Figure 3 shows the achievable EE values of the massive MIMO system based on the PSO algorithm and the corresponding values of M and K in the single-cell scenarios with imperfect CSI. [26] does not consider the single-cell scenarios with imperfect CSI, but the scenario in [19] is the same as the one considered in this section. Therefore, the simulation results can only be compared with those in [19].…”

Impact of Parameter Tuning on the Cricket Chirping Algorithm

“…Intelligent Systems and Applications, 2017, 9, 58-68 optimal meta-heuristic algorithms for solving optimization problem [13][14][15][16][17][18][19][20][21][22][23]. Different authors have fine-tuned the parameters for different problems such as ABC was tuned for energy efficiency optimization in massive MIMO systems, Gaussian noise elimination on digital images [24][25][26], GA for real world transportation problem, Energy-Minimizing Vehicle Routing Problem, fire tube boiler [27][28][29], Shuffled Frog Leaping Algorithm Applied to Optimizing Water Distribution Networks [30,31], Self-Tuning PID(Proportional Integral Derivative) for PMSM (Permanent Magnet Synchronous Motor) Vector Control based on improved KMTOA (kineticmolecular theory optimization algorithm ) [32], PID controller parameter tuning for Superheated Steam Temperature of Power Station Boiler [33], improved PSO tuned PID and Sliding Mode (SMC) classical controllers for the motion control problem of the robotic manipulator [34] etc.…”