Journal of Communications and Information Networks
Print ISSN 2096-1081 CN 10-1375/TN
Editor-in-Chief: Wei Zhang
Physical layer security (PLS) in wireless communication systems has attracted extensive research attentions in recent years. Unlike cryptography-based methods applied in upper-layer in network, PLS methods are applied in physical layers and can provide information-theoretic security by utilizing the randomness of signals and wireless channels. In this survey, we provide a comprehensive review in the domain of physical layer authentication (PLA) in wireless communication systems, including the concepts, several key techniques of typical PLA architectures as well as future challenges and research trends in more sophisticated communication systems. The survey begins with an overview of the background and basic concepts of PLA, such as the general model of wireless security communication system, typical frameworks of key-based/less PLA systems, and the common attack models. We then discuss the major concerns and key techniques that are applied in PLA systems, where three types of authentication schemes are considered, i. e. , the authentication based on channel information, radio-frequency and identity watermarks. Basic models and representative research results about key approaches and techniques applied to the authentication systems above are subsequently covered. Finally, the associated challenges and potential research trends of PLA in future communication systems are presented at the end of the survey paper.
Driven by the rapid growth in information services provided by the Internet and the appearance of new multimedia applications, millimeter wave is foreseen as a key enabler towards the Internet of intelligent vehicles (IoIV) for urban traffic safety enhancement. In this regard, cluster-based channel modeling has become an important research topic in the realm of emergency communications. To fully understand the cluster-based channel model, a series of vehicle-to-infrastructure(V2I) channel simulations at 22. 6 GHz are conducted by a three-dimensional ray tracing (RT) simulator. The clustering and tracking algorithm is proposed and analyzed from three aspects by the obtained simulation results. The multiple signal classification estimation spectrum is applied to restrain the influence of antenna sidelobes and identify targets at first. Based on the fundamentals, the clusters can be identified and subsequently tracked using the proposed approach. The impacts of antenna sidelobes, angle resolution of beam rotation, and non-line-of-sight propagation path on the performance of clustering and tracking are evaluated. The multi-component-level RT results are adopted as comparison benchmarks, which reflect the ground truth. This work aims to provide a full picture of the clustering characteristics for designing and analyzing emergency communication systems.
Along with natural disasters, the destruction of communication infrastructures leads to the congestion or failure of communication networks. Unmanned aerial vehicles (UAVs), which are with a high flexibility, can be employed as temporary base stations to establish emergency networks. To relieve the backhaul burden of UAVs, some imperative contents can be cached by terrestrial cache-enabled rescuers (CERs) and provide for victims with device-to-device (D2D) transmissions. To support the effectiveness and timeliness of emergency communication, the delay-bounded quality-of-service (QoS) requirement and network throughput are desired to be comprehensively considered, which imposes a new challenge for caching placement and CER deployment. In this paper, we focus on joint caching placement and CER deployment to maximize the effective capacity subject to delay-bounded QoS requirement. The overall non-convex problem is transformed into the caching placement and the CER deployment sub-problems. Then, we develop the QoS-aware caching placement scheme with fixed CER deployment density and obtain the QoS-aware CER deployment density with fixed caching placement. Based on the block-coordinate descent method, we also propose the joint caching placement and CER deployment scheme, which can not only effectively enhance average effective capacity but also guarantee the delay-bounded QoS requirement. Also, numerical simulations are conducted to show the performances of the proposed schemes.
Benefiting from the inherent superiorities in flexibility and mobility, the use of unmanned aerial vehicles (UAVs) as flying base stations for wireless coverage has been of significant interest, especially for rescue services. This work concerns the reliable emergency communication based on commercial micro-sized UAVs due to their high availability and low cost. To decrease the weight overloads and to improve the power efficiency, a UAV body conformal and omnidirectional antenna is first presented based on the characteristic mode analysis. To extend the wireless coverage and to improve the communication quality of the UAV network, a UAV flight formation is then proposed and analyzed. In addition, the propagation analyses for the proposed UAV transmitter designs are performed in a realistic hilly canyon region. Simulations and comparisons are presented to demonstrate the effectiveness of the proposed designs in emergency communications and performance enhancement through the UAV flight formation.
Visible light communication (VLC) is a promising research field in modern wireless communication. VLC has its irreplaceable strength including rich spectrum resources, no electromagnetic disturbance, and high-security guarantee. However, VLC systems suffer from the non-linear effects that exist in almost every part of the system. As a part of artificial intelligence, machine learning (ML) is showing its potential in non-linear mitigating for its natural ability to fit all kinds of transfer functions, which may dramatically push the research in VLC. This paper introduces the application of ML in VLC, describes five recent research of deep learning applications in VLC, and analyses the performance.
Artificial intelligence (AI) has shown great potential in wireless communications. AI-empowered communication algorithms have beaten many traditional algorithms through simulations. However, the existing works just use the simulated datasets to train and test the algorithms, which can not represent the power of AI in practical communication systems. Therefore, Peng Cheng Laboratory holds an AI competition, National Artificial Intelligence Competition (NAIC): AI+wireless communications, in which one of the topics is AI-empowered channel feedback system design using practical measurements. In this paper, we give a baseline neural network design, QuanCsiNet, for this competition, and the details of the channel measurements. QuanCsiNet shows excellent performance on channel feedback and the complexity of the neural networks is also given.
Anchor-based ad-hoc networks utilize hop measurements to generate a virtual coordinate system for topology inference and routing applications. A common problem with such coordinate system is its sensitivity to anchor placement. We present a general formulation to the anchor node selection problem. Then, we relax the optimization problem by deriving an upper-bound of the objective function. We finally propose an iterative algorithm that consists in choosing additional anchor nodes based on the connectivity information provided by the current anchor set. Numerical simulations indicate that our anchor selection method is robust to missing measurements and improves network topology inference and routing performance.
Orthogonal time frequency space (OTFS) modulation, collaborated with millimeter-wave (mmWave) massive multiple-input-multiple-output (MIMO), is a promising technology for next generation wireless communications in high mobility scenarios. However, one of the main challenges for mmWave massive MIMO-OTFS systems is the enormous computational complexity of channel estimation incurred by the huge OTFS symbol size and the large number of antennas. To address this issue, in this paper, a tensor-based orthogonal matching pursuit(OMP)channel estimation algorithm is proposed by exploiting the channel sparsity in the delayDoppler-angle domain. In particular, we firstly propose a novel pilot design for the OTFS symbol structure in the frequency-time domain. Then, based on the proposed pilot structure, we formulate the channel estimation as a sparse signal recovery problem, and the tensor decomposition and parallel support detection are introduced into the tensor-based OMP algorithm to reduce the signal processing dimension significantly. Numerical simulations are performed to verify the superiority and the robustness of the proposed tensor-based OMP algorithm.
We investigate two-user sum-rate capacity for Poisson channel considering practical photon-counting receiver, including finite sampling rate and dead time. The sum-rate capacity reduction due to photon-counting loss is characterized and compared with that of continuous Poisson channel. We show that the sum-rate capacity with non-perfect receiver approaches the capacity of continuous time Poisson channel as the sampling time and dead time both approach zero. For optimal transmission strategy, we demonstrate three possible transmission strategies, including only one active user and two active users. In addition, we study the special case of identical peak power constraint for each user. We adopt majorization method to demonstrate that the optimal duty cycle for the two users must be the same and unique. Furthermore, we analyze the sum-rate capacity for multiple input single output (MISO) multiple-access channel (MAC). We propose a sufficient condition on dead time where the sum-rate capacity of the Poisson MISO-MAC is equivalent to that of single input single output, and the equivalence would not hold for sufficient large peak power or dead time. The theoretical capacity results are validated by numerical results.
A wideband 2 × 2 sequential-phase fed circularly polarized (CP) microstrip antenna array (MAA) with sequentially rotated elements is proposed. Firstly, the antenna element loaded with shorting pins is studied as compared to the traditional counterpart. Then, a feeding network is designed to be connected together in a sequential rotation manner, resulting in forming a four-port network to allow symmetrical positioning of CP microstrip patch elements. After that, straight strips are added to the feeding network in order to increase the distance and reduce coupling between array elements. They also play a role in improving the impedance matching of the array. Finally, the proposed antenna array is designed and simulated. The results are presented that its impedance bandwidth is about 36. 3% at 24. 1～34. 8 GHz for |S< sub> 11< /sub> |＆lt; ?10 dB and axial-ratio (AR) bandwidth is about 20.4% at 24. 2～29. 7 GHz for AR＆lt; 3 dB. Besides, a peak gain of 11. 1 dBic is simulated over the operating frequency range, which is significantly improved compared to the classic and existing works.
This paper considers a massive single-input multiple-output (SIMO) system, where multiple singleantenna transmitters simultaneously communicate with a receiver equipped with a large number of antennas. Different from the conventional noncoherent transceivers which require a certain level of the statistical information on the channel fading, we propose a joint transceiver design method based on machine learning, requiring a limited number of channel realizations. In the proposed method, the multiple transmitters, the channel, and the receiver are represented with a deep neural network (NN), and an autoencoder is adopted to minimize the end-to-end transmission error probability. Besides, the relationship between the number of training samples and the transmission error probability is analyzed based on the confidence interval method. Simulation results show that the proposed NN-based transceiver achieves lower transmission error probability in typical scenarios, and is more robust against the channel parameters variation compared with the existing methods.
