Multicast Spatial Filter Beamforming with Resource Allocation Using Joint Multi-objective Optimization Approaches in Wireless Powered Communication Networks

Optimum transmission strategy must be adopted in radio frequency energy-harvesting networks. For the purpose the study considered various radio applications in which the nodes operate on the batteries thereby minimizing the energy consumption and consequently obtaining high throughput and satisfactory delay. This paper analysed the best model for minimizing transmission energy which reduces the total consumption of energy needed to send required number of bits. Hence the study exploited proximal gradient convex optimization algorithm and spatial filter-based beam formers for minimizing the transmission power and reducing the computational time. These minimizations might be achieved by optimizing the signal to noise ratio. In general, receiving signals radiating from a particular location and directing the signal reception or transmission seems to be a challenging task. To overcome this the proposed spatial filter-based beamforming, a signal processing technique receives signals that are radiating from specific location and also attenuate signals from different locations. Moreover, it can easily direct signal reception or transmission. The simulation results depicted that the proposed algorithm is found to be energy efficient that describes the trade-off existing between the required harvested powers. This study employed Multi-objective Hungarian algorithm for the detection of channels that have low transmission power and less computational time for efficient resource allocation. The performance evaluation of the proposed system has been validated and compared with state of art methods like Joint optimization, fixed time allocation and bisectional search. The experimental results show that the proposed system outperforms the existing systems in terms of signal to noise ratio, transmission of energy and resource allocation.