Search

Abstract Signal processing over the molecular domain is critical for analysing, modifying, and synthesising chemical signals in molecular communication systems. However, the lack of chemical signal processing blocks and the wide use of electronic devices to process electrical signals in existing molecular communication platforms can hardly meet the biocompatible, non-invasive, and size-miniaturised requirements of applications in various fields, e.g., medicine, biology, and environment sciences. To tackle this, here we design and construct a liquid-based microfluidic molecular communication platform for performing chemical concentration signal processing and digital signal transmission over distances. By specifically designing chemical reactions and microfluidic geometry, the transmitter of our platform is capable of shaping the emitted signals, and the receiver is able to threshold, amplify, and detect the chemical signals after propagation. By encoding bit information into the concentration of sodium hydroxide, we demonstrate that our platform can achieve molecular signal modulation and demodulation functionalities, and reliably transmit text messages over long distances. This platform is further optimised to maximise data rate while minimising communication error. The presented methodology for real-time chemical signal processing can enable the implementation of signal processing units in biological settings and then unleash its potential for interdisciplinary applications.

Unmanned aerial vehicles (UAVs) with the potential of providing reliable high-rate connectivity, are becoming a promising component of future wireless networks. A UAV collects data from a set of randomly distributed sensors, where both the locations of these sensors and their data volume to be transmitted are unknown to the UAV. In order to assist the UAV in finding the optimal motion trajectory in the face of the uncertainty without the above knowledge whilst aiming for maximizing the cumulative collected data, we formulate a reinforcement learning problem by modelling the motion-trajectory as a Markov decision process with the UAV acting as the learning agent. Then, we propose a pair of novel trajectory optimization algorithms based on stochastic modelling and reinforcement learning, which allows the UAV to optimize its flight trajectory without the need for system identification. More specifically, by dividing the considered region into small tiles, we conceive state-action-reward-state-action (Sarsa) and Q-learning based UAV-trajectory optimization algorithms (i.e., SUTOA and QUTOA) aiming to maximize the cumulative data collected during the finite flight-time. Our simulation results demonstrate that both of the proposed approaches are capable of finding an optimal trajectory under the flight-time constraint. The preference for QUTOA vs. SUTOA depends on the relative position of the start and the end points of the UAVs.

Wireless devices, including smartphones/PDAs and the network equipment that connects them, consume a significant amount of power. The global greenhouse gas emissions produced by the operation of information and communication technology (ICT) infrastructure is comparable to that produced by the aviation industry. This situation is getting worse since billions of wireless devices, such as the Internet of Things devices, are expected to be newly added to the Internet in the coming decade. Consequently, there are multiple research and development projects that aim at addressing this issue, including some IEEE standards intended to reduce the environmental impact of the ICT industry. These standards include energy-efficient communications and networking. Although these emerging techniques have attracted considerable attention and have been studied recently, there are still many open research challenges to be addressed.

Wireless power transmission is envisioned to be a promising technology for prolonging the lifetime of wireless devices in energy-constrained networks. This paper presents a general power beacon (PB) assisted multi-source transmission, where a practical source selection scheme with information transmission (IT) mode or non-IT mode is developed to maximize the transmission reliability. In the IT mode, a zero-forcing beamformed signal with no interference to the destination is transmitted at the multi-antenna PB to supply wireless energy for the sources, and bring non-negative effect to the destination. Among multiple sources, the energy-sufficient source with the best channel quality is selected for wireless information transmission, while the other sources remain for energy harvesting. In the non-IT mode, the equal power transmission is adopted at PB to focus on energy delivery. Using Markov chain theory, the energy arrival and departure of each finite-capacity storage at the source is characterized mathematically, and the comprehensive analytical expressions of the energy outage probability (EOP), the connection outage probability (COP), and the average transmission delay (ATD) are formulated and derived. Our results reveal that the EOP, COP, and ATD can be significantly improved via increasing the number of sources deployed in the proposed network with finite transmit power of PB. We also prove that the multi-source network will never experience energy outage with infinite transmit power of PB.

Full-duplex (FD) technology is currently under consideration for adoption in a range of legacy communications standards due to its attractive features. On the other hand, cellular networks are becoming increasingly heterogeneous as operators deploy a mix of macrocells and small cells. With growing tendency toward network densification, small cells are expected to play a key role in realizing the envisioned capacity objectives of emerging 5G cellular networks. From a practical perspective, small cells provide an ideal platform for deploying FD technology in cellular networks due to its lower transmit power and lower cost for implementation compared with the macrocell counterpart. Motivated by these developments, in this paper, we analyze a two-tier heterogeneous cellular network, wherein the first tier comprises half-duplex macrobase stations and the second tier consists of the FD small cells. Through a stochastic geometry approach, we characterize and derive the closed-form expressions for the outage probability and the rate coverage. Our analysis explicitly accounts for the spatial density, the self-interference cancellation capabilities, and the interference coordination based on enhanced inter-cell interference coordination techniques. Performance evaluation investigates the impact of different parameters on the outage probability and the rate coverage in various scenarios.

The development of compressive sensing (CS) technology has inspired data gathering in wireless sensor networks to move from traditional raw data gathering towards compression based gathering using data correlations. While extensive efforts have been made to improve the data gathering efficiency, little has been done for data that is gathered and recovered data with unknown and dynamic sparsity. In this work, we present an adaptive compressive sensing data gathering scheme to capture the dynamic nature of signal sparsity. By only re-sampling a few measurements, the current sparsity as well as the new sampling rate can be accurately determined, thus guaranteeing recovery performance and saving energy. In order to recover a signal with unknown sparsity, we further propose an adaptive step size variation integrated with a sparsity adaptive matching pursuit algorithm to improve the recovery performance and convergence speed. Our simulation results show that the proposed algorithm can capture the variation in the sparsities of the original signal and obtain a much longer network lifetime than traditional raw data gathering algorithms.

The development of wireless power transfer (WPT) technology has inspired the transition from traditional battery-based wireless sensor networks (WSNs) towards wireless rechargeable sensor networks (WRSNs). While extensive efforts have been made to improve charging efficiency, little has been done for routing optimization. In this work, we present a joint optimization model to maximize both charging efficiency and routing structure. By analyzing the structure of the optimization model, we first decompose the problem and propose a heuristic algorithm to find the optimal charging efficiency for the predefined routing tree. Furthermore, by coding the many-to-one communication topology as an individual, we further propose to apply a genetic algorithm (GA) for the joint optimization of both routing and charging. The genetic operations, including tree-based recombination and mutation, are proposed to obtain a fast convergence. Our simulation results show that the heuristic algorithm reduces the number of resident locations and the total moving distance. We also show that our proposed algorithm achieves a higher charging efficiency compared with existing algorithms.