TY - JOUR
T1 - Optimal detector design for molecular communication systems using an improved swarm intelligence algorithm
AU - Ntouni, Georgia D.
AU - Paschos, Alexandros E.
AU - Kapinas, Vasileios M.
AU - Karagiannidis, George K.
AU - Hadjileontiadis, Leontios J.
N1 - Funding Information:
The research works of Georgia D. Ntouni and George K. Karagiannidis have been supported by the ‘Research Projects for Excellence IKY/Siemens’. The authors thank Prof. Traianos V. Yioultsis from the Aristotle University of Thessaloniki, Department of Electrical and Computer Engineering, for his useful discussion.
Publisher Copyright:
© The Institution of Engineering and Technology 2017.
PY - 2018/3/1
Y1 - 2018/3/1
N2 - The authors optimise the detection process of diffusion-based molecular communication systems utilising the weighted sum detector with appropriate weight values. Interestingly, no optimisation technique has ever been proposed for the calculation of the weights. To this end, they build on the standard particle swarm optimisation (PSO) technique and propose a robust iterative optimisation algorithm, called acceleration-aided PSO (A- APSO). While modified swarm-based optimisation algorithms focus on slight variations of the standard mathematical formulas, in A- APSO, the acceleration variable of the particles in the swarm is also involved in the search space of the optimisation problem. Particularly, they implement the A- APSO algorithm to evaluate the detector’s weights that minimise the closed-form expression of the error probability. Their findings reveal that, when employing the A- APSO weights, the error performance is superior to that achieved by utilising the weight values already existing in the literature or those evaluated with the standard PSO algorithm.
AB - The authors optimise the detection process of diffusion-based molecular communication systems utilising the weighted sum detector with appropriate weight values. Interestingly, no optimisation technique has ever been proposed for the calculation of the weights. To this end, they build on the standard particle swarm optimisation (PSO) technique and propose a robust iterative optimisation algorithm, called acceleration-aided PSO (A- APSO). While modified swarm-based optimisation algorithms focus on slight variations of the standard mathematical formulas, in A- APSO, the acceleration variable of the particles in the swarm is also involved in the search space of the optimisation problem. Particularly, they implement the A- APSO algorithm to evaluate the detector’s weights that minimise the closed-form expression of the error probability. Their findings reveal that, when employing the A- APSO weights, the error performance is superior to that achieved by utilising the weight values already existing in the literature or those evaluated with the standard PSO algorithm.
UR - http://www.scopus.com/inward/record.url?scp=85042946742&partnerID=8YFLogxK
U2 - 10.1049/mnl.2017.0489
DO - 10.1049/mnl.2017.0489
M3 - Article
AN - SCOPUS:85042946742
SN - 1750-0443
VL - 13
SP - 383
EP - 388
JO - Micro and Nano Letters
JF - Micro and Nano Letters
IS - 3
ER -