An integrated sensing and communication (ISAC) scheme for a millimeter wave (mmWave) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) Vehicle-to-Infrastructure (V2I) system is presented, in which both the access point (AP) and the vehicle are equipped with large antenna arrays and employ hybrid analog and digital beamforming structures to compensate the path loss, meanwhile compromise between hardware complexity and system performance. Based on the sparse scattering nature of the mmWave channel, the received signal at the AP is organized to a four-order tensor by the introduced novel frame structure. A CANDECOMP/PARAFAC (CP) decomposition-based method is proposed for time-varying channel parameter extraction, including angles of departure/arrival (AoDs/AoAs), Doppler shift, time delay and path gain. Then leveraging the estimates of channel parameters, a nonlinear weighted least-square problem is proposed to recover the location accurately, heading and velocity of vehicles. Simulation results show that the proposed methods are effective and efficient in time-varying channel estimation and vehicle sensing in mmWave MIMO-OFDM V2I systems.
With the development of wireless communication, the 6G mobile communication technology has received wide attention. As one of the key technologies of 6G, terahertz （THz） communication technology has the characteristics of ultra-high bandwidth, high security and low environmental noise. In this paper, a THz duplexer with a half-wavelength coupling structure and a sub-harmonic mixer operating at 216 GHz and 204 GHz are designed and measured. Based on these key devices, a 220 GHz frequency-division multiplexing communication system is proposed, with a real-time data rate of 10.4 Gbit/s for one channel and a transmission distance of 15 m. The measured constellation diagram of two receivers is clearly visible, the signal-to-noise ratio (SNR) is higher than 22 dB, and the bit error ratio (BER) is less than 10-8. Furthermore, the high definition (HD) 4K video can also be transmitted in real time without stutter.
Out-door billboard advertising plays an important role in attracting potential customers. However, whether a customer can be attracted is influenced by many factors, such as the probability that he/she sees the billboard, the degree of his/her interest, and the detour distance for buying the product. Taking the above factors into account, we propose advertising strategies for selecting an effective set of billboards under the advertising budget to maximize commercial profit. By using the data collected by Mobile Crowdsensing (MCS), we extract potential customers’ implicit information, such as their trajectories and preferences. We then study the billboard selection problem under two situations, where the advertiser may have only one or multiple products. When only one kind of product needs advertising, the billboard selection problem is formulated as the probabilistic set coverage problem. We propose two heuristic advertising strategies to greedily select advertising billboards, which achieves the expected maximum commercial profit with the lowest cost. When the advertiser has multiple products, we formulate the problem as searching for an optimal solution and adopt the simulated annealing algorithm to search for global optimum instead of local optimum. Extensive experiments based on three real-world data sets verify that our proposed advertising strategies can achieve the superior commercial profit compared with the state-of-the-art strategies.
With the emergence of mobile crowdsensing (MCS), merchants can use their mobile devices to collect data that customers are interested in. Now there are many mobile crowdsensing platforms in the market, such as Gigwalk, Uber and Checkpoint, which publish and select the right workers to complete the task of some specific locations (for example, taking photos to collect the price of goods in a shopping mall). In mobile crowdsensing, in order to select the right workers, the platform needs the actual location information of workers and tasks, which poses a risk to the location privacy of workers and tasks. In this paper, we study privacy protection in MCS. The main challenge is to assign the most suitable worker to a task without knowing the task and the actual location of the worker. We propose a bilateral privacy protection framework based on matrix multiplication, which can protect the location privacy between the task and the worker, and keep their relative distance unchanged.
The development of cloud computing has made container technology a hot research issue in recent years. The container technology provides a basic support for micro service architecture, while container networking plays an important role in application of the container technology. In this paper, we study the technical implementation of the Flannel module, a network plug-in for Docker containers, including its functions, implementation principle, utilization, and performance. The performance of Flannel in different modes is further tested and analyzed in real application scenarios.
