Journal of Communications and Information Networks
Print ISSN 2096-1081 CN 10-1375/TN
Editor-in-Chief: Wei Zhang
In millimeter wave (mmWave) systems, desirable spectral efficiency can be realized by the hybrid beamformer in which a large sized analog beamformer and a small sized baseband digital beamformer are cascade-connected together to reduce the system cost. However, most of works focus on the beamforming (or combining) method design with an impractical analog beamformer where the infinite-resolution phase shifts (PSs)are adopted. In this paper, a more practical hybrid beamformer with low-resolution PSs is considered and the hybrid receiver beamforming method under the multiuser uplink communication circumstance is studied. Since the system performance is mainly constrained by the interuser interference, we propose a novel two-stage hybrid receiver beamforming against the inter-user interference with comparable computational complexity than that of the recently presented method. Moreover, to enhance the performance of systems with many users, we propose a novel multiuser selection algorithm by exploiting the multiuser diversity gain. Finally, from the simulation results, we can verify the superiority of the proposed method over the comparing method in error probability and spectrum efficiency.
One of the biggest obstacles to the application of orbital angular momentum (OAM) in the microwave field is its divergence problem. This paper presents a full analysis of generating and focusing OAM waves using original and improved Fabry-Perot (F-P) cavities. Utilizing combination of microstrip antenna and original F-P cavity, the gain of the OAM antenna is enhanced from 4. 0 dBi to 9. 3 dBi and the corresponding divergence angle is decreased from 41?to 24?. To further improve the performance of the OAM antenna, the improved F-P cavity is introduced. The simulated results show that the gain is further enhanced to 12. 0 dBi and the divergence angle is further decreased to be 15?.
With no dark zone in the propagation path and the same divergence angles, different plane spiral orbital angular momentum (PSOAM) beams can be superposed easily in the transverse plane. In this paper, a stacked resonator antenna based on substrate integrated waveguide is proposed, which is a compact planar waveguide structure. To obtain the structured EM wave which is superposed by the PSOAM waves (±3, ±4, ±5, ±6) at 10 GHz, eight ring resonators are placed symmetrically on the two sides of the center plane and excited simultaneously. Simulation results, including distributions of the electric field, far-field results and purity analysis, are given, indicating that PSOAM beams can be generated with good performances. By controlling the initial phases of different PSOAM waves, beam steering can be realized with the same radiation patterns, which has great potentials in communication and radar detection.
Coherent beam combining(CBC)is recently used to generate high power vortex beams which are strongly required in specific applications. In this paper, based on the extended Huygens-Fresnel principle, the exact theoretical forms for the intensity distribution of CBC Bessel-Gaussian beams (BGBs) in turbulent ocean are derived. To show the superiority of CBC BGBs in turbulent channels, the comparison in the intensity evolution of CBC BGBs with ideal BGBs is performed. It is found that the beam spreading of CBC BGBs is smaller than that of ideal BGBs under the same oceanic turbulence conditions. Moreover, the effect of the beam parameters and channel parameters on the quality of CBC BGBs is also analyzed. The results show that the oceanic turbulence with a higher rate of dissipation of kinetic energy per unit mass of fluid, lower dissipation rate of the mean-squared temperature, or smaller ratio of temperature and salinity contributions to the refractive index spectrum has smaller impact on CBC BGBs. Moreover, the increasing number of the beamlets, the increasing waist width of each beamlet, and the decreasing radius of the beam distribution cause the optical energy to be more concentrated, and thus leading to a longer non-diffraction propagation distance.
The ubiquitous Wi-Fi devices and recent research efforts on wireless sensing have led to intelligent environments which can sense people’s locations and activities in a device-free manner. However, current works are mostly designed for single human environment owing to the complexity of multiple human environments, the limited bandwidth of Wi-Fi and in turn, greatly hinder this technology from the real implementation. To realize such device-free sensing in multiple human environments, the first step-stone is to estimate how many targets or in other words crowd counting in a closed environment, which is not only the basis for multiple human environmental sensing but also leads to many potential applications such as crowd control. To this end, we propose DeepCount—a solution using deep learning approach to infer the number of people in an indoor environment with Wi-Fi signals. Our scheme is based on the key intuition that, although with great complexity, the deep learning approaches can somehow be able to build a complex function to fit the correlation between the number of people and channel state information values. Furthermore, to alleviate the inadequate amount of data required and improve the adaptability of the deep learning approach, we add an online transfer learning approach, which utilizes the entering/leaving results to fine-tune the deep learning model. The prototype of DeepCount is implemented and evaluated on the commercial Wi-Fi device. By the massive training samples, our deep learning model is able to estimate the number of crowd up to 5 with the mean accuracy of 82. 3% by this end-to-end learning approach. Meanwhile, by using the amendment mechanism of the activity recognition model to judge door switch to get the variance of the crowd to amend deep learning predicted results, the accuracy is up to 87% in a rather effective manner.
Low-resolution analog-to-digital converter (ADC) is a promising solution to reduce hardware cost and power consumption in generalized frequency division multiplexing (GFDM) systems. The severe nonlinear distortion of ADCs and the non-orthogonality of GFDM make receiver design a great challenge. In this paper, we propose a novel model-driven receiver architecture for GFDM with low-resolution ADCs. Orthogonal approximate message passing (OAMP) framework is combined with the classical linear estimator in this work to create a robust iterative receiver for GFDM systems with low-precision ADCs. The corresponding model-driven network is organized based on the proposed novel iterative algorithm according to the procedures of the receiver. The network of OAMP can reduce the gap between the approximate algorithm and the Bayesian optimal result due to the information loss of ADCs. The signal flow of the neural network is designed by unfolding the iterative algorithms for channel estimation and data detection. Numerical results are provided to show that the proposed OAMP-based receiver algorithm outperforms traditional receivers and the model-driven network can further improve the system performance on the basis of the corresponding novel algorithm.
In mobile satellite communication networks employing digital beam forming technology, beam alignment imposes great influence on link quality and network efficiency. Owing to complex coupling motion by low earth orbit (LEO) satellite and ship, direction of arrival (DOA) of target satellite varies rapidly and nonlinearly. It then causes difficulty to accurately track the DOA. In this work, an adaptive tracking algorithm is proposed by exploiting advantages of flexible parameter configuration of digital phased-array antenna. The alignment process basically consists of observation and tracking. In the observation stage, two-dimensional (2-D) multiple signal classification(MUSIC)is applied by the ship-borne digital phased-array antenna to estimate beam direction of satellite; in the tracking stage, an extended Kalman filter (EKF)based adaptive tracking is designed to achieve fast and accurate alignment. The proposed adaptive tracking improves performance by adaptively estimating tracking parameters in EKF firstly. The estimation results are then used as feedback to adaptively adjust digital phased-array antenna parameters to improve estimation accuracy of DOA. Simulation results under sea state 5 show that the proposed tracking algorithm improves tracking accuracy and stability over conventional ones.
Distributed underwater acoustic sensor networks (UASNs) are envisioned in real-time ocean current velocity estimation. However, UASNs at present are still dominated by post-processing partially due to the complexity of on-line detection for travel times and lack of dedicated medium access control (MAC) protocols. In this study, we propose a dedicated MAC protocol package for real-time ocean current velocity estimation using distributed UASNs. First, we introduce the process and requirements of ocean current velocity estimation. Then, we present a series of spatial reuse time division multiple access (TDMA) protocols for each phase of real-time ocean current field estimation using distributed UASNs, followed by numerical analysis. We divide UASNs into two categories according to their computing ability: feature-complete and feature-incomplete systems. The feature-complete systems that have abundant computing ability carry out the presented MAC protocol package in three phases, whereas the feature-incomplete ones do not have enough computing ability and the presented MAC protocol package is reduced to two phases plus an additional downloading phase. Numerical analysis shows that feature-complete systems using mini-slot TDMA have the best real-time performance, in comparison with feature-incomplete systems and other feature-complete counterparts. Feature-incomplete systems are more energy-saving than feature-complete ones, owing to the absence of in-network data exchange.
With the development of fishery industry, accurate estimation of the number of fish in aquaculture waters is of great importance to fish behavior analysis, bait feeding and fishery resource investigation. In this paper, we propose a method for fish density estimation based on the multi-scale context enhanced convolutional network, which could map a fish school image taken at any angle to a density map, and calculate the number of fish in the image finally. In order to eliminate the influence of camera perspective effect and image resolution on density estimation, multi-scale filters are utilized in a convolutional neural network to process fish image in parallel. And then, the context enhancement module is merged in the network structure to help the network understand the global context information of the image. Finally, different feature maps are merged together to construct the density map of fish school images, and finally get the number of fish in the image. In order to make the effectiveness of our method valid, we test the proposed method on DlouDataset. The results show that the proposed method has lower mean square error and mean absolute error, which is helpful to improve the accuracy of the fish counting in dense fish school images.
