Spectrum sharing of wireless vehicle-to-vehicle (V2V) communication and automotive radar in intelligent transportation systems (ITS) can provide higher spectrum utilization efficiency. However, spectrum sharing between two systems might bring severe interference, which needs to be well solved. To address the communication-radar spectrum sharing (CRSS) and inter-system interference issue, we first model the radar interference estimation as an off-grid compressed sensing problem. Then, we propose an interference cancellation algorithm based on the atomic norm (AN) minimization theory. Simulation results show that the proposed algorithm facilitates the coexistence between radar and communication and improves the V2V communication performance efficiently in the presence of automotive radar interference.

In recent years, the ultra-dense low Earth orbit (LEO) satellite communication (UD-LSC) networks such as SpaceX and OneWeb are under rapid development to provide worldwide and broadband services. However, the deployment of thousands of LEO satellites into space leads to the shortage of the orbital and the frequency resources. Spectrum sharing between geostationary Earth orbit (GEO) satellite systems and LEO satellite systems seems to be a promising way to alleviate the problem of restricted spectrum resources. In this paper, a joint cooperative beam association and power allocation scheme for the UD-LSC network to share the same spectrum with a GEO satellite system is considered. By exploiting the cooperative transmission between multiple LEO satellites, we first propose a many-to-many match game-based beam association (MGBA) algorithm to obtain a stable matching between LEO satellites and beam cells, and then, we propose a successive convex approximation (SCA)-based power allocation (SPA) algorithm to iteratively acquire the sub-optimal power allocation matrix. Simulation results show that the proposed MGBA-SPA scheme outperforms other contrast schemes from the perspective of communication satisfaction, and it realizes the balance between the traffic request and the provided capacity of each ground beam cell.

The multitarget tracking (MTT) based on distributed fusion methods in an acoustic sensor array network (ASAN) is limited by the performance of measurement parameter estimators, such as the received signal strength (RSS) and the direction of arrival (DOA). For measurement parameters with low accuracy and resolution, the MTT may fail in subsequent steps, e.g., data association, because the loss of upstream information cannot be made up by downstream processing. Thus, we propose a new wideband MTT algorithm based on the dynamic Bayesian network (DBN), which treats the ASAN as an overall extended array, and directly estimates the target states from the raw acoustic data. The DBN fuses the near-field model, the acoustic propagation model. and the motion model. These submodels can be optimized by each other, improving the final estimation. Also, for each subband, target signals and the precision parameters of the sensor noise are treated as hidden random variables. Based on this, the weight of each subband can be automatically adjusted according to the accurate hidden variables. Besides, the optimization problem of the posterior probability is transformed into a graphical model learning problem. Moreover, for nonconjugate models, a novel algorithm based on Laplace approximations (LAs) with Newton’s method (NM) is developed, i.e., DBN-LA-NM, bypassing data association. In addition, the corresponding Cramer–Rao lower bound and convergence conditions are derived. The numerical simulation results show that the proposed algorithm outperforms existing MTTs based on the near-field model in terms of accuracy, convergence, and computational complexity. Field experiments further verify the feasibility of the proposed algorithm.

Satellite ground fusion network has the advantages of broad coverage of satellite network and high-speed transmission link of ground network. It can realize communication access in peak hours in complex scenarios and meet the growing communication needs of high-speed and large capacity. Whether the downlink can accurately recover and analyze the signal is the key to the whole link. This paper designs a downlink receiver processing flow based on the combination of the standard protocol and FPGA (Field Programmable Gate Array). The processing of received data mainly includes symbol-level processing and bit-level processing. The data processing of the whole receiving end is completed through the transmission processing of data between each module. This framework uses FPGA as the core of data processing. It uses its powerful parallel processing ability and rich resources to provide a baseband implementation platform for the downlink to ensure communication continuity, stability, and reliability.