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Unmanned aerial vehicles (UAVs) are considered a promising example of an automatic emergency task in a dynamic marine environment. However, the maritime communication performance between UAVs and offshore platforms has become a severe challenge. Due to the complex marine environment, the task allocation and route planning efficiency of multiple UAVs in an intelligent ocean are not satisfactory. To address these challenges, this paper proposes an intelligent marine task allocation and route planning scheme for multiple UAVs based on improved particle swarm optimization combined with a genetic algorithm (GA-PSO). Based on the simulation of an intelligent marine control system, the traditional particle swarm optimization (PSO) algorithm is improved by introducing partial matching crossover and secondary transposition mutation. The improved GA-PSO is used to solve the random task allocation problem of multiple UAVs and the two-dimensional route planning of a single UAV. The simulation results show that compared with the traditional scheme, the proposed scheme can significantly improve the task allocation efficiency, and the navigation path planned by the proposed scheme is also optimal.

Massive multiple-input multiple-output (MIMO) relay can significantly improve the capacity and throughput of wireless networks, thus has been a sought-after technique for future communication systems. However, the development of massive MIMO relay systems faces several major challenges. For example, the knowledge of instantaneous channel state information (CSI) is needed to estimate signals and optimize systems. Traditional estimation schemes need to transmit pilot sequences, which occupy the spectrum resources. In this paper, we propose a tensor-based method for joint signal and channel estimation for multiuser massive MIMO relay systems without using pilot sequences, and develop two tensor-based semi-blind receivers. Through multidimensional signaling scheme, the signals received by each user are formulated as the block Tucker2-PARAFAC (TP) tensor model. Then, two semi-blind receivers are proposed to jointly estimate the information signals and channel matrices. One is based on the tensor-based closed-form receiver, the other is based on the tensor-based iterative receiver. The proposed closed-form approach can also be used to initialize the iterative receiver for improving the convergence speed. In particular, the proposed schemes are practicable for both time division duplexing (TDD) and frequency division duplexing (FDD) modes. Uniqueness, identifiability and complexity are analyzed for our receivers. Compared with existing receivers, our receivers offer superior bit error rate (BER) and normalized mean square error (NMSE) performance. Numerical examples are shown to demonstrate the effectiveness of the proposed tensor-based receivers. INDEX TERMS Massive MIMO, cooperative communication, block Tucker2 model, PARAFAC model, signal and channel estimation.

Hybrid precoding achieves a compromise between the sum rate and hardware complexity of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, most prior works on multi-user hybrid precoding only consider the full-connected structure. In this paper, a novel multi-user hybrid precoding algorithm is proposed for the sub-connected structure. Based on the improved successive interference cancellation (SIC), the analog precoding matrix optimization problem is decomposed into multiple analog precoding sub-matrix optimization problems. Further, a near-optimal analog precoder is designed through factorizing the precoding sub-matrix for each sub-array. Furthermore, digital precoding is designed according to the block diagonalization (BD) technology. Finally, the water-filling power allocation method is used to further improve the communication quality. The extensive simulation results demonstrate that the sum rate of the proposed algorithm is higher than the existing hybrid precoding methods with the sub-connected structure, and has higher energy efficiency compared with existing approaches. Moreover, the proposed algorithm is closer to the state-of-the-art optimization approach with the full-connected structure. In addition, the simulation results also verify the effectiveness of the proposed hybrid precoding design of the uniform planar array (UPA).

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