Journal of Communications and Information Networks
Print ISSN 2096-1081 CN 10-1375/TN
Editor-in-Chief: Wei Zhang
As a key low-power communication technique, backscatter communications exploits the reflected or backscattered signals to transmit data, where the backscattered signals can be the reflection of ambient radio frequency (RF) signals, the RF signals from the dedicated carrier emitter or the signal photons in the non-classical quantum entangled pairs, etc. In the past 70 years, various kinds of backscatter communication systems have been developed, which will enable the low-power communications as required in the Internet of things (IoTs)and green communications. This article provides a historical view on the development and the research achievements on backscatter communications, including the fundamental principles, the applications, the challenges, and the potential research topics. This article will benefit the researchers and engineers concerning the area of backscatter communications, especially for applications in IoTs.
Future wireless communication networks tend to be intelligentized to accomplish the missions that cannot be preprogrammed. In the new intelligent communication systems, optimizing the network performance has become a challenge due to the ever-increasing complexity of the network environment. New theories and technologies for intelligent wireless communications have obtained widespread attention, among which deep reinforcement learning (DRL) is an excellent machine learning technology. DRL has great potential in enhancing the intelligence of wireless communication systems while overcoming the above challenge. This paper presents a review on applications of DRL in intelligent wireless communications with focuses on millimeter wave(mmWave), intelligent caching and unmanned aerial vehicle (UAV) scenarios. We first introduce the concepts and basic principles of single/multi- agent DRL techniques. Then, we review the related works where DRL algorithms are used to address emerging issues in wireless communications. These issues include mmWave communication, intelligent caching, UAV aided communication, and handover/access control in HetNets. Finally, critical challenges and future research directions of applying DRL in intelligent wireless communications are outlined.
In recent years, intelligent communication has drawn huge research efforts in both academia and industry. With the advent of 5G technology, intelligent wireless terminals and intelligent communication networks are increasingly under intensive study. Artificial intelligence enhances the network capability with automatic and adaptive adjustment. Reinforcement learning (RL) and deep reinforcement learning (DRL) are two powerful techniques in artificial intelligence which can learn the optimal decision according to the environment feedback. In this paper, we focus on the latest research progress on RL and DRL applied in three emerging technologies including mobile edge computing (MEC), software defined network (SDN) and network virtualization in 5G. The prospect of further research and development in the future is preliminarily forecasted.
Large intelligent surface/antennas(LISA), a two-dimensional artificial structure with a large number of reflective-surface/antenna elements, is a promising reflective radio technology to construct programmable wireless environments in a smart way. Specifically, each element of the LISA adjusts the reflection of the incident electromagnetic waves with unnatural properties, such as negative refraction, perfect absorption, and anomalous reflection, thus the wireless environments can be softwaredefined according to various design objectives. In this paper, we introduce the reflective radio basics, including backscattering principles, backscatter communication, reflective relay, the fundamentals and implementations of LISA technology. Then, we present an overview of the state-of-the-art research on emerging applications of LISA-aided wireless networks. Finally, the limitations, challenges, and open issues associated with LISA for future wireless applications are discussed.
Hybrid precoding is a cost-effective approach to support directional transmissions for millimeter-wave (mmWave) communications, but its precoder design is highly complicated. In this paper, we propose a new hybrid precoder implementation, namely the double phase shifter (DPS) implementation, which enables highly tractable hybrid precoder design. Efficient algorithms are then developed for two popular hybrid precoder structures, i. e. , the fully-and partially-connected structures. For the fully-connected one, the RF-only precoding and hybrid precoding problems are formulated as a least absolute shrinkage and selection operator problem and a low-rank matrix approximation problem, respectively. In this way, computationally efficient algorithms are provided to approach the performance of the fully digital one with a small number of radio frequency (RF) chains. On the other hand, the hybrid precoder design in the partially-connected structure is identified as an eigenvalue problem. To enhance the performance of this cost-effective structure, dynamic mapping from RF chains to antennas is further proposed, for which a greedy algorithm and a modified K-means algorithm are developed. Simulation results demonstrate the performance gains of the proposed hybrid precoding algorithms over existing ones. It shows that, with the proposed DPS implementation, the fully-connected structure enjoys both satisfactory performance and low design complexity while the partially-connected one serves as an economic solution with low hardware complexity.
Exploration of TV white space(TVWS)is a promising solution to mitigate the spectrum shortage and provide opportunities for new applications. In this paper, we present a detailed analysis of spectrum utilisation over TVWS at different locations in London. Both short-term and long-term outdoor measurement campaigns are conducted over large scales to better understand the spectrum features and variations across multiple locations and time periods. Different from most fixed-location-only measurements, we also drive along the main streets of London with a portable moving node to measure the on-route spectrum density along with the corresponding geographical information, which allows us to study the features and variations of spectrum use through a continuous space. To better analyse the dynamic spectrum utilisation, a machine learning based analysis algorithm is developed over the real-world measurements. This approach allows us to characterise the similarity and variability in spectrum usage within and among different channels, locations, and time instances, which is critical for the secondary system deployment to efficiently exploit the white space.
With the rapid growth of unmanned aerial vehicle (UAV), there is now an increasing interest in the security of UAV systems. There exist various attacks which have threatened the security of UAV systems, in terms of the global navigation satellite system (GNSS) spoofing. Consisted of multiple UAVs, the flying ad hoc networks (FANET) have been studied to extend the employment and coverage of UAV systems. Based on the connectivity and information interaction of FANET, in this article, the blockchain technology is applied to detect GNSS signal attacks for UAV systems. In particular, we first introduce an overview of the GNSS security of UAV systems and the principles for blockchain technology. Based on the principles, a logical architecture is proposed, where blockchain is taken into consideration for GNSS spoofing detection. Performance analysis verifies that the proposed GNSS spoofing detection system can be used effectively. Finally, several challenges in blockchain are discussed, including security, cost, and regulation.
Orbital angular momentum (OAM) based radio vortex wireless communications have received much attention recently because it can significantly increase the spectrum efficiency. The uniform circular array (UCA) is a simple antenna structure for high spectrum efficiency radio vortex wireless communications. However, the OAM based electromagnetic waves are vortically hollow and divergent, which may result in the signal loss. Moreover, the divergence of corresponding OAM based electromagnetic wave increases as the order of OAM-mode increases. Therefore, it is difficult to use highorder OAM-mode, because the corresponding received signal-to-noise ratio(SNR)is very small. To overcome the difficulty of high-order OAM modes transmission, in this paper we propose a lens antenna based electromagnetic waves converging scheme, which maintains the angular identification of multiple OAM-modes for radio vortex wireless communications. We further develop a bifocal lens antenna to not only converge the electromagnetic wave, but also compensate the SNR loss on traditional electromagnetic waves. Simulation results show that the proposed bifocal lens can converge the OAM waves into cylinder-like beams, providing an efficient way to increase the spectrum efficiency of wireless communications.
Reliable and efficient overtaking maneuvers are important and challenging for autonomous vehicles. Higher precision position information is thus required, rather than that traditional global navigation satellite systems can provide. In this paper, we try to perform reliable autonomous overtaking controls of vehicles, mainly based on the“relative”position information, including the distance, angle and velocity between vehicles, which can be achieved by on-board radars. To reduce the complexity of maneuvers, a fuzzy inference system is applied to analyze the driving behavior of the preceding vehicle based on the obtained consecutive relative position information. An output of“safe”or“dangerous”will be sent to the decision part based on reinforcement learning frameworks. Various overtaking maneuvers including“conservative”and“aggressive”can be obtained accordingly. Furthermore, we propose another overtaking strategy that vehicles can share their maneuver information during overtaking process via wireless links. Numeric results validate our analysis, and can provide meaningful performance benchmarks for practical system implementations.
