On outage probability and throughput performance of cognitive radio inspired NOMA relay system

To improve the throughput and the outage probability of the Cognitive Radio (CR) inspired system, a novel Non-Orthogonal Multiple Access (NOMA) can be deployed to adapt multiple services in 5G wireless communication. In this scheme, after the reception of the superposition coded symbol with a predefined power allocation factors from the primary source, the relay decodes and forwards with a new superposition coded symbol (i.e. allocating another power factors) to the destination. By employing twin antenna design at the relay, the bandwidth efficiency in such scheme will be improved. Assuming Rayleigh fading channels, the closed-form expressions in terms of throughput and the outage performance are derived. Through numerical results, they showed that the outage performance of the proposed scheme using a Single Antenna (SA) scheme at the relay is better than a Twin Antenna (TA) scheme because SA scheme is not affected by its own antenna interference

Optimization of the overall success probability of the energy harvesting cognitive wireless sensor networks

Wireless energy harvesting can improve the performance of cognitive wireless sensor networks (WSNs). This paper considers radio frequency (RF) energy harvesting from transmissions in the primary spectrum for cognitive WSNs. The overall success probability of the energy harvesting cognitive WSN depends on the transmission success probability and energy success probability. Using the tools from stochastic geometry, we show that the overall success probability can be optimized with respect to: 1) transmit power of the sensors; 2) transmit power of the primary transmitters; and 3) spatial density of the primary transmitters. In this context, an optimization algorithm is proposed to maximize the overall success probability of the WSNs. Simulation results show that the overall success probability and the throughput of the WSN can be significantly improved by optimizing the aforementioned three parameters. As RF energy harvesting can also be performed indoors, hence, our solution can be directly applied to the cognitive WSNs that are installed in smart buildings