On Optimizing Power Allocation And Energy Harvesting For MISO SWIPT Systems With Heterogeneous Users

Recently,the Internet of Things(IoT)is entering various industries,in wireless sensor networks(WSNs),a key compotent and enabler of IoT,the sensor nodes are normally powered by batteries that have very limited lifetime.The network lifetime can be extended by replacing or recharging the batteries,which however is inconvenient.As an alternative solution to prolonging the network lifetime,energy harvesting(EH)has become an appealing solution that potentially provides unlimited power supplies to wireless networks by scavenging energy from the environment.Nowadays,the idea is to apply EH in traditional wireless communications,that is,to develop simultaneous wireless information and power transfer(SWIPT).In this paper,we have studied two kinds of scenes,one is that the users and the base station(BS)are in one hop,and the other is that they are in two hops,where there is a relay between them.The main works of this paper can be summarized into the following aspects:1)Both of the scenes have considered the heterogeneous users,where there are three types of single-antenna users,ID users who can only receive information,EH users who can only receive energy,and ID/EH users who receive information and energy simultaneously from a multi-antenna BS via power splitting(PS).2)The relay is energy-constrained,which means there is no external power supply,it should harvest energy by adopting PS scheme.Furthermore,the relay is equipped with multiple receiving antennas,as well as multiple transmitting antennas in SWIPT for the first time.And the relay operates on full-duplex(FD)protocol,that is,the relay can also transmit signals while receive information and store energy.3)The zero forcing dirty paper coding(ZF-DPC)technique is first applied to the SWIPT system to reduce the computational complexity.In specific,we apply the semidefinite relaxation(SDR),zero-forcing(ZF),and maximum ratio transmission(MRT)techniques to solve the non-convex problems,which means they are Non-deterministic Polynomial-hard(NP-hard)problems.Then,we have done the simulation comparison above them.