Wideband Waveforming for Integrated Data and Energy Transfer: Creating Extra Gain Beyond Multiple Antennas and Multiple Carriers

Abstract

When wideband signals propagate in a richscatterer environment, we obtain abundant resolvable multiple transmission paths to form a number of virtual antennas. Therefore, substantial spatial gain can be attained by carefully waveforming in all these resolvable transmission paths without additional antennas. This resultant spatial gain is then exploited for improving the performance of integrateddata-and-energy-transfer (IDET) from a single transmitter to multiple receivers. We aim to maximise the downlink fairthroughput and sum-throughput, while satisfying the energy harvesting requirements by jointly optimising the waveformers at the transmitter and the power splitters at the receivers. A low-complexity fractional-programming (FP) based alternating algorithm is proposed to solve these non-convex optimisation problems. The non-convex wireless energy transfer (WET) constraints are transformed to be convex with a modified quadratic transform (MQT) method. As a result, the stationary points for both the fair-throughput and the sum-throughput maximisation problems are obtained. The numerical results demonstrate the advantage of our proposed algorithm over a minimummean-square-error (MMSE) scheme, a zero-forcing (ZF) scheme and a time-reversal (TR) scheme. Simulation results show that the wireless data transfer (WDT) performance of our scheme outperforms the single-input-single-output orthogonal-frequencydivision-multiple-access (SISO-OFDMA) when the output direct current (DC) power requirement is high. When we have a practical individual subcarrier power constraint, the WDT performance of our scheme outperforms multiple-input-singleoutput orthogonal-frequency-division-multiplex-access (MISO-OFDMA).

Enhancing Healthcare Efficacy Through IoT-Edge Fusion: A Novel Approach for Smart Health Monitoring and Diagnosis

“…This study [49]introduced a flexible, IoT-aware, modular system design with several potential medical applications. In this study [50], scholars present an alternating technique based on low-complexity fractional programming (FP) to effectively address the optimization problems that exhibit non-convex characteristics.…”