The novel concept of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is investigated, where incident signals can be transmitted and reflected to users located ...at different sides of the surface. In particular, the fundamental coverage range of STAR-RIS aided two-user communication networks is studied. A sum coverage range maximization problem is formulated for both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA), where the resource allocation at the access point and the transmission and reflection coefficients at the STAR-RIS are jointly optimized to satisfy the communication requirements of users. For NOMA, we transform the non-convex decoding order constraint into a linear constraint and the resulting problem is convex, which can be optimally solved. For OMA, we first show that the optimization problem for given time/frequency resource allocation is convex. Then, we employ the one dimensional search-based algorithm to obtain the optimal solution. Numerical results reveal that the coverage can be significantly extended by the STAR-RIS compared with conventional RISs.
The development of 5G terrestrial mobile communications technology has been a driving force for revolutionizing satellite mobile communications. Satellite mobile communications, which carry many ...unique features, such as large coverage and support for reliable emergency communications, should satisfy the requirements for convergence between terrestrial mobile communications and satellite mobile communications for future broadband hybrid S-T communications. On the other hand, CR is an attractive technique to support dynamic single-user or multi-user access in hybrid S-T communications. This article first discusses several key issues in applying cognitive radio to future broadband satellite communications toward 5G. Then we present an overview of future broadband hybrid S-T communications systems, followed by an introduction to a typical application scenario of futuristic CR-broadband hybrid S-T communication systems toward 5G. Moreover, we propose a space segment design based on a spectrum-sensing-based cooperative framework, in consideration of the presence of MUs. An experiment platform for the proposed CR-based hybrid S-T communications system is also demonstrated.
Vehicular ad hoc network (VANET) is expected one of promising network forms for intelligent transportation system which supports road safety applications, in-vehicle entertainment and arriving ...automatic driving. Establishing and maintaining stable connections in VANETs are challenging on account of the high mobility of vehicles, dynamic vehicle topology, and time-varying vehicle density. Clustering can provide scalability and reliability for VANETs by grouping vehicles with hierarchical structures. However, keeping cluster stable became a hard nut to crack due to high vehicle speed and unpredictable driving pattern. Recent rapid development of artificial intelligence (AI) provided an innovative solution for this situation. In this paper, a Naive Bayes Classifier based driving habit prediction scheme for stable clustering is proposed, briefly named NBP. According to driving speed and overtaking decisions, vehicles are classified into two alignments with different driving habit. Specifically, Naive Bayes classifier perform driving habit prediction through several relative independent factors, such as relative velocity, vehicle type, number of lanes traveled. The cluster head candidates will be chosen from alignment with mild driving pattern which will benefit for stable clusters. Combined with clustering design, the proposed method has been proven effective for stable clustering in VANET based on the real data of highways in California.
As the bandwidth increases, the high-speed sampling rate becomes the bottleneck for the development of wideband spectrum sensing. Wideband spectrum sensing with sub-Nyquist sampling attracts more ...attention and modulated wideband converter (MWC) is an attractive sub-Nyquist sampling system. For the purpose of breaking the system structure limit, an advanced sub-Nyquist sampling framework is proposed to simplify the MWC system structure, adopting the single sampling channel structure with a frequency shifting module to acquire the sub-Nyquist sampling values. In order to recover the signal support information, the sensing matrix must be built according to the only one mixing function. Most existing support recovery methods rely on some prior knowledge about the spectrum sparsity, which is difficult to acquire in practical electromagnetic environment. To address this problem, we propose an adaptive residual energy detection algorithm (ARED), which bypasses the need for the above-mentioned prior knowledge. Simulation results show that, without requiring the aforementioned prior knowledge, the ARED algorithm based on the advanced sub-Nyquist sampling framework has the similar performance as MWC and even higher than MWC in some cases using only one sampling channel.
As one of the most critical technologies in signal detection, weak signal detection has received a great deal of attention in many areas. Traditional methods perform well when the number of signal is ...single and the form of signal is simple, which are quite different from the situation in some real applications (e.g., the marine communication scenario). In this paper, we consider the problem of high-precision frequency detection of multiple variable-frequency signals with overlapping frequencies (MVFS-O) in the low signal-to-noise ratio scenario, whose key point is how to detect the parameters (frequency range and frequency modulation rate) of each signal. To solve the problem, a novel chaos detection scheme based on Duffing difference oscillator is proposed. In this scheme, the wide frequency detection range of the critical state is used to determine the frequency range of the signals quickly, and the Lyapunov exponent is used to discriminate the output state of the system qualitatively. Furthermore, combining with the time points of the periodic state of the system, we can obtain the FMR of the corresponding signal by analyzing overlapping frequency ratio. Numerical results are provided to verify the practical effectiveness of the proposed scheme.
At present, unmanned aerial vehicles (UAVs) have been widely used in communication systems, and the fifth-generation wireless system (5G) has further promoted the vigorous development of them. The ...trajectory planning of UAV is an important factor that affects the timeliness and completion of missions, especially in scenarios such as emergency communications and post-disaster rescue. In this paper, we consider an emergency communication network where a UAV aims to achieve complete coverage of potential underlaying device-to-device (D2D) users. Trajectory planning issues are grouped into clustering and supplementary phases for optimization. Aiming at trajectory length and sum throughput, two trajectory planning algorithms based on K-means are proposed, respectively. In addition, in order to balance sum throughput with trajectory length, we present a joint evaluation index. Then relying on this index, a third trajectory optimization algorithm is further proposed. Simulation results show the validity of the proposed algorithms which have advantages over the well-known benchmark scheme in terms of trajectory length and sum throughput.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
For the future sixth-generation (6G) wireless communication networks, improved metrics are expected to provide connectivity of massive devices, which brings new challenges for 6G networks extending ...to modern radio access for Internet of Things (IoT) applications. We consider a 6G enabled nonterrestrial network working in remote areas in this article, where an on-demand unmanned aerial vehicles (UAVs) provides the connectivity services. To improve the energy efficiency (EE), a method combining nonorthogonal multiple access (NOMA) and spatial modulation (SM) techniques is proposed and termed spatial NOMA (S-NOMA). Particularly, by employing multiple input multiple output (MIMO), SM only activates partial transmit antennas in per symbol interval, which can provide large data rate with less interantenna interference (IAI). Moreover, a power allocation optimization method subject to EE for S-NOMA scheme is proposed. Specifically, the antenna selection bits are determined by all users, which improves EE for all users instead of the selected one. Besides, the capacity expressions of S-NOMA are derived, then the EE performance of S-NOMA is analyzed. In addition, simulation results show that the proposed S-NOMA with energy-efficient power allocation performs better EE performance compared with the conventional NOMA.
The satellite-terrestrial cooperative network is considered an emerging network architecture, which can adapt to various services and applications in the future communication network. In recent ...years, the combination of satellite communication and Mobile Edge Computing (MEC) has become an emerging research hotspot. Satellite edge computing can provide users with full coverage on-orbit computing services by deploying MEC servers on satellites. This paper studies the task offloading of multi-user and multi-edge computing satellites and proposes a novel algorithm that joint task offloading and communication computing resource optimization (JTO-CCRO). The JTO-CCRO is decoupled into task offloading and resource allocation sub-problems. After the mutual iteration of the two sub-problems, the system utility function can be further reduced. For the task offloading sub-problem, it is first confirmed that the offloading problem is a game problem. The offloading strategy can be obtained from the Nash equilibrium solution. We confirm resource optimization sub-problem is a convex optimization problem that can be solved by the Lagrange multiplier method. Simulation shows that the JTO-CCRO algorithm can converge quickly and effectively reduce the system utility function.
The GNSS has been considered the first choice for providing PNT services to telecommunications, power industry, and transportation applications because of its advantages of wide coverage, high ...accuracy, and low cost. However, the GNSS is inherently vulnerable to intended or unintended radio interference, and this threat has become more serious because of the easy availability of GNSS jammers on the market. From the robustness and safety perspectives, PNT services should not solely rely on GNSS. APNT solutions must be made available in the case of a GNSS outage. In this article, we investigate APNT solutions for aerial vehicles for which highly standardized PNT services are required to ensure secure and safe transportation, including solutions that have been proposed by the FAA in North America. We discuss the basic principle of each solution and analyze and compare their characteristics under different application scenarios. Finally, we conclude the article by predicting future trends in APNT solutions.
As one of the most critical technology in array signal processing, direction of arrival (DoA) estimation has received a great deal of attention in many areas. Traditional methods perform well when ...the signal-to-noise ratio (SNR) is high and the receiving array is perfect, which are quite different from the situation in some real applications (e.g., the marine communication scenario). To get satisfying performance of DoA estimation when SNR is low and the array is inaccurate (mutual coupling exist), this paper introduces a scheme consisting of denoising autoencoder (DAE) and deep neural networks (DNN), referred to as DAE-DNN scheme. DAE is used to reconstruct a clean "repaired" input from its corrupted version to increase the robustness, and then divide the input into multiple parts in different sub-areas. DNN is used to learn the mapping between the received signals and the refined grids of angle in each sub-areas, then the outputs of each sub-areas are concatenated to perform the final DoA estimation. By simulations in different SNR regimes, we study the performance of DAE-DNN in terms of the different snapshots, batch size, learning rate, and epoch. Our results demonstrate that the proposed DAE-DNN scheme outperforms traditional methods in accuracy and robustness.
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