In this article, we investigate the performance of a piece-wise linear model of energy harvesting-based multiuser overlay spectrum sharing system. Herein, an energy-constrained secondary node acts as a cooperative relay to assist the information transmission between a primary transmitter and multiple primary receivers. In return for the cooperation, the secondary node enjoys access to the primary user's spectrum for its own information transfer. Specifically, by employing a time-switching based receiver, the secondary node harvests energy from the received radio-frequency signal of primary transmitter during a dedicated energy harvesting phase. In the subsequent information transfer phase, the secondary node splits the harvested power to forward the primary data as well as its own information intended for another secondary user. We analyze the impact of decoding primary's information at the secondary receiver on the performance of secondary network. Importantly, we propose an improved energy harvesting-based relaying scheme which makes an efficient use of available degrees of freedom and thereby enhances the performance of both primary and secondary networks significantly. For this analytical framework, we derive the expressions of outage probability for primary and secondary networks. Numerical and simulation results are obtained to extract various useful insights and to validate our theoretical developments.INDEX TERMS Cooperative relaying, non-linear energy harvesting, outage probability, performance analysis, spectrum sharing, simultaneous wireless information and power transfer (SWIPT).

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We introduce the α-κ-µ shadowed (α-KMS) fading distribution as a natural generalization of the versatile α-κ-µ and α-η-µ distributions. The α-KMS fading distribution unifies a wide set of fading distributions, as it includes the α-κ-µ, αη-µ, α-µ, Weibull, κ-µ shadowed, Rician shadowed, κ-µ and ηµ distributions as special cases, together with classical models like Rice, Nakagami-m, Hoyt, Rayleigh and one-sided Gaussian. Notably, the α-KMS distribution reduces to a finite mixture of α-µ distributions when the fading parameters µ and m take positive integer values, so that performance analysis over α-KMS fading channels can be tackled by leveraging previous (existing) results in the literature for the simpler α-µ case. As application examples, important performance metrics like the outage probability and average channel capacity are analyzed.Index Terms-α-µ fading, channel capacity, κ-µ shadowed fading, outage probability, stochastic channel modeling.

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