In this paper, we propose a novel simultaneous wireless information and power transfer (SWIPT) technique that employs orthogonal frequency-division multiplexing (OFDM)-based waveforms. By using deterministic symbols at the subcarriers with appropriate phase rotation, the proposed scheme embeds information into the position of the power peak of the OFDM-based waveform and is suitable for low-complexity SWIPT integrated receivers. Low-complexity decision rules are also investigated to extract information at the output of the rectification circuit. We study the performance of the proposed scheme in terms of average harvested power and symbol error probability for various modulation orders. The proposed technique transforms conventional OFDM transmitters to SWIPT devices without costly architectural modifications and is promising for practical applications.

This paper studies the performance of a wireless powered backscatter communication network consisting of an exciter, several randomly distributed backscattering devices (BDs) and a receiver. We consider selection/scheduling schemes, where a BD is selected for information transmission. The proposed schemes are based on random, end-to-end (e2e) signal-to-noise ratio (SNR) and Euclidean distance. By using stochastic geometry tools, we derive analytical expressions for the outage probability of each selection scheme. Our results show that the scheme based on the e2e SNR achieves the best performance. We also present a cluster-based selection scheme and provide some simulation results. The derived analytical framework provides useful insights on the design of such networks.

In this work, we propose an opportunistic beamforming strategy, which enables beam selection through random- rotations of an intelligent reflecting surface (IRS). To boost performance over a time slot, the proposed scheme splits the training period into multiple mini-slots. In each mini-slot, the access point generates different sets of orthonormal beamforming vectors and the IRS employs random-rotations. We provide an analytical framework for the sum-rate capacity and it is shown that a trade-off between the sum-rate capacity and the length of the training period exists due to the time constraint on the communication process. Based on this, we also derive the optimal number of the training mini-slots. The proposed low- complexity scheme outperforms conventional counterparts (single training slot) and approximates the performance of conventional beamforming (with channel state information) even for small number of users. Finally, by utilizing extreme value theory tools, we analyze the system’s performance under an asymptotic scenario, where the number of the users significantly increases.

The rapid growth of the so-called Internet of Things is expected to significantly expand and support the deployment of resource-limited devices. Therefore, intelligent scheduling protocols and technologies such as wireless power transfer, are important for the efficient implementation of these massive low-powered networks. This paper studies the performance of a wireless powered communication network, where multiple batteryless devices harvest radio-frequency from a dedicated transmitter in order to communicate with a common information receiver (IR). We investigate several novel selection schemes, corresponding to different channel state information requirements and implementation complexities. In particular, each scheme schedules the$k$-th best device based on: a) the end-to-end (e2e) signal-to-noise ratio (SNR), b) the energy harvested at the devices, c) the uplink transmission to the IR, and d) the conventional/legacy max-min selection policy. We consider a non-linear energy harvesting (EH) model and derive analytical expressions for the outage probability of each selection scheme by using tools from high order statistics. Moreover, an asymptotic scenario in terms of the number of devices is considered and, by applying extreme value theory, the system’s performance is evaluated. We derive a complete analytical framework that provides useful insights for the design and realization of such networks.

This paper studies the performance of a wireless powered communication network consisting of a finite number of batteryless devices that harvest radio frequency energy. We consider novel selection/scheduling schemes, where the k-th best device is selected for information transmission. The proposed schemes correspond to different complexity and are based on: a) the end-to-end (e2e) signal-to-noise ratio (SNR), b) the energy harvested at the devices, and c) the conventional channel-based max-min selection policy. By considering a non-linear energy harvesting (EH) model, we derive analytical expressions for the outage probability of each selection scheme by using high order statistics. We also consider an asymptotic scenario, where the number of devices increases and analyze the behavior of the system by applying extreme value theory. Due to the saturation effects of the non-linear EH model, the performance of all the proposed schemes converges to an error floor. Our results show that the scheme based on the e2e SNR achieves the best performance and the one based on the EH the worst. The derived analytical framework provides useful insights on the design of such networks.