Green communication for cognitive radio networks based on game and utility-pricing theories

Abstract

The most crucial challenge in the functioning of the wireless networks is the efficient utilization of radio resources. A significant element of resource handling is power regulation. With increasing requirement of wireless data transmission services, it is essential to devise energy harvesting techniques for mobile devices. In this research, a new methodology has been proposed for distributed power regulation in cognitive radio, networks of CR are grounded on non-cooperation game phenomenon and pricing technique. QoS (Quality of service) of the user of CR is anticipated as a beneficial activity through pricing as well as dissemination of energy generating as an unbeneficial game wherein the consumers increase their overall efficacy. The price is defined as an actual function of transmission power to upraise the pricing of the most distant consumers. The proposed mathematical model shows that the proposed game model has a Nash equilibrium and is also unique. Furthermore, in order to make the proposed algorithm valid for green communication within the wireless network, the best response technique was proposed. Finally, simulation results showed that the proposed energy harvesting technique, grounded on a unique function of the utilization, reduces the consumption of transmission power and greatly improves the convergence speed; which are suitable for the vision of the 5G networks.

Efficient power allocation algorithm in downlink cognitive radio networks

“…This case provides an optimal RA solution; that is, no mutual interference exists between the CRs and PUs [33]. Power interference (PI) algorithm [6]: This algorithm also uses Algorithm 1 for SA and allocates power to the subcarriers by the following means: (i) water‐filling‐based interference mode, (ii) Cap‐limited water filling [34], and (iii) power‐level readjustment to exploit the entire allowable interference constraint. PPA: This is the proposed algorithm in which SA is performed using Algorithm 1, while powers are assigned using Algorithm 2. GC‐NPGP algorithm [35]: This algorithm allocates power based on noncooperative game theory and a pricing system. Its utility function includes an exponential pricing function and a linear pricing function with a fixed pricing factor to allocate power to CR users accordingly.…”

“…Its utility function includes an exponential pricing function and a linear pricing function with a fixed pricing factor to allocate power to CR users accordingly. The liner pricing with a fixed pricing factor was adopted from [35] to ensure a fair comparison with the proposed PPA.…”

“…iv. GC-NPGP algorithm [35]: This algorithm allocates power based on noncooperative game theory and a pricing system. Its utility function includes an exponential pricing function and a linear pricing function with a fixed pricing factor to allocate power to CR users accordingly.…”

An Efficient Game Theory-Based Power Control Algorithm for D2D Communication in 5G Networks

“…The non-cooperative game formulated describes the algorithm for power control to develop a new user device utility iteration function to improve the game outcome. Consequently, the derived power control from the user devices utility function will prove the existence and convergence of NE in the algorithm [18] and [19]. This is an act of satisfaction with the convergence to occur as soon as possible and develop the new algorithm for power control.…”

Performance Investigation of Cooperative Green Underground Wireless Systems

“…Consider a cooperative diversity structure with a source, a relay, and a destination. Both the destination and the relay received an MPSK symbol from the source [31]. The relay retransmits the MPSK symbol to the destination using the DF protocol [21].…”