In millimeter wave (mmWave) communications, the feature of relatively large signal absorption and directional transmission render new challenges for wireless communications and signal processing. To further improve the performance of mmWave communications, a novel radar-aided mmWave communication (RAMC) approach is proposed, which can be used in vehicular communications. There are two parts in the proposed RAMC system, including the radar subsystem and the mmWave communication subsystem. In the radar subsystem, the bistatic co-prime multi-input and multi-output (MIMO) arrays are considered. With the radar antenna arrays, both the directions of departure (DoD) and the directions of arrival (DoA) are estimated. Additionally, the compressed sensing (CS)-based method is proposed to obtain the target positions. Using the estimated angle and position information, the channel estimation and feedback link of the mmWave communication subsystem can be eliminated. Moreover, a hybrid beamforming algorithm is proposed in the mmWave communication subsystem, which can overcome the shortage of the analog-only beamforming. Simulation results show that the better estimation performance can be achieved by the bistatic co-prime MIMO arrays than that by the traditional uniform linear arrays (ULA), and with the radar aided, the mmWave communication subsystem can reduce the beam search time, and the cell discovery time is improved significantly.

The existing positioning methods for the automatic guided vehicle (AGV) in the port can not achieve high location precision, Therefore, a novel multiple input multiple output (MIMO) antenna radar positioning scheme is proposed in this paper. The positioning problem for AGV is considered, and the joint estimation problem for direction of departure (DoD) and direction of arrival (DoA) is addressed in the multiple-input multiple-output (MIMO) radar system. With the radar detect the transponder and estimate the DoA/DoD, the relative location between the transponder and the AGV can be obtained. The corresponding Cramér–Rao lower bounds (CRLBs) for the target parameters are also derived theoretically. Finally, we compare the positioning accuracy of the traditional global position system (GPS) with the proposed MIMO radar system. Simulation results show that the proposed method can achieve better performance than the traditional GPS.

In this paper, we investigate the resource allocation for multi-user orthogonal frequency division multiple access (OFDMA) power line communication (PLC) systems, which satisfy both the total average transmit power constrained by PLC networks and the required interference limitations. First, we propose an optimal resource allocation scheme, with respect to joint consideration of the subcarrier and power constraint to maximize the throughput of the PLC system. Then, we divide the intractable problem into two individual subproblems to decrease the computational burden, which can avoid proceeding the mixed integer nonlinear programming (MINLP) problem. The proposed resource allocation scheme can ensure the optimality and efficiency of the system with rigorous proofs. The simulation results show that our proposed resource allocation scheme for the PLC system outperforms other algorithms, and is efficient with acceptable complexity.

The estimation problem for target velocity is addressed in this in the scenario with a distributed multi-input multi-out (MIMO) radar system. A maximum likelihood (ML)-based estimation method is derived with the knowledge of target position. Then, in the scenario without the knowledge of target position, an iterative method is proposed to estimate the target velocity by updating the position information iteratively. Moreover, the Carmér-Rao Lower Bounds (CRLBs) for both scenarios are derived, and the performance degradation of velocity estimation without the position information is also expressed. Simulation results show that the proposed estimation methods can approach the CRLBs, and the velocity estimation performance can be further improved by increasing either the number of radar antennas or the information accuracy of the target position. Furthermore, compared with the existing methods, a better estimation performance can be achieved.