Wireless Sensor Networks (WSNs) are being applied widely for data collection, especially to carry out the mission critical tasks. Therefore, one of the most challenging tasks of Mission Critical Sensors and Sensor Networks (MC-SSN) is the development of energy efficient (EE) routing protocols. Comparing with flat routing protocols, more EE can be achieved in hierarchical routing protocols. In this paper, we propose an enhanced balanced energy efficient network-integrated super-heterogeneous (E-BEENISH) routing protocol, by analyzing communication energy consumption of the clusters and large range of energy levels in heterogeneous WSNs. E-BEENISH is based on weighted election probabilities of each node to become a cluster head (CH) according to the remaining energy and the distance from the sink to node. Moreover, we also study the impact of heterogeneity of nodes in terms of energy. Studying the sensitivity of our stable election protocol, we conclude heterogeneity parameters capturing energy imbalance in the network, and find that E-BEENISH yields the longer stability region for suitable weight of energy and distance. Our results show by simulation that E-BEENISH can improve system lifetime by an order of magnitude compared to obtained using current clustering protocols, which is crucial for many applications.

The linear zero-forcing (ZF) precoding can achieve the near-optimal sum-rate performance when the favorable channel propagation is obtained in downlink massive multiple-input multiple-output (MIMO) systems. However, it involves high complexity with the matrix inversion. To significantly reduce the complexity of ZF precoding, we propose a weighted two-stage (WTS) precoding scheme with low complexity based on an iterative method. Specially, the proposed WTS precoding converts the complicated matrix inversion into two half iteration stages, and the result of each stage is weighted by a coefficient to further speed up the convergence and reduce the complexity. Theoretical analysis demonstrates that the proposed WTS precoding enjoys a fast convergence rate and low complexity. Simulation results indicate that the proposed WTS precoding can achieve better bit-error-rate (BER) and sum-rate performance with a smaller number of iterations than the recently proposed schemes.

Industrial Internet of Things (IIoT) networks, as the application of IoT networks to modern industry, are growing rapidly with the digital transformation accelerates. Mobile sensors are widely adopted in monitoring key information of massive IIoT networks in an ad hoc fashion or retrofitting on to an existing infrastructure. Hence, owing to the characteristics of scalability, unstable locations and highly unstructured, achieving perfect prediction for mobile multi-sensor is a challenging problem. In this paper, a dynamic space-time prediction algorithm combining temporal convolutional network and graph convolutional network (TCN-GCN) is proposed to provide reliable multiple nodes prediction for detection and maintenance in IIoT networks. In particularly, an adaptive learning graph process is exclusively designed according to the dynamic characteristics of mobile sensors. Moreover, the improved dilated time convolution network and dynamic graph convolution network are combined to effectively capture the time dependence and topology information. Furthermore, an advanced loss function is applied to avoid overfitting. Numerical simulations show that the proposed TCN-GCN prediction algorithm exhibits higher effectiveness in accuracy and complexity than convolutional long short-term memory (ConvLSTM), temporal graph convolutional network (T-GCN) and residual graph convolutional long short-term memory (Graph-ResLSTM).

Hybrid precoding has been recognized as promising and effective for practical 5G communication. It is generally challenging to obtain the sample for deep learning-based hybrid precoding due to its need of massive precoding vector and channel matrix. To effectively solve this issue, a novel coordinated hybrid precoding algorithm based on unsupervised learning graph attention networks (CHP-ULGAT) is first developed by making full use of the underlying topology formed by the channel matrix. Subsequently, a more realistic situation of existing an ultra-low execution time is considered. A sub-optimal coordinated hybrid precoding based on unsupervised learning convolutional neural networks (CHP-ULCNN) is proposed to further reduce complexity. Moreover, we present effective ways to design the multi-matrix operation and the loss function to address the practicability of the algorithm. Extensive simulation results show that the proposed hybrid-precoding algorithms have obvious advantages in spectral efficiency (SE) and energy efficiency (EE) improvement with ultra-low computational complexity, considering the different number of RF chains and deployment scenarios.

Heterogeneous networks (HetNets) that incorporate small cells save energy effectively because the small cells have lower transmission and operational power consumptions. However, the performance of HetNets depends heavily on reducing the interference. In this paper, we investigated interference cancellation (IC) with the aim of developing a decorrelating receiver. We focused on millimeter wave small cells systems in which multiuser multiple-input multiple-output transmission was utilized. We began by suppressing the interference in the multiple antennas mode by means of well-known zero-forcing and a minimum mean square error algorithm. Then, we investigated more active IC. To further reduce the impact of interference in HetNets, we developed a hybrid HetNets paradigm. Finally, we used simulations to achieve performance evaluation and comprehensive comparative analysis. Extensive numerical results verified the effectiveness of the proposed schemes. This research provides an efficient solution for suppressing multiple access interference and improving the throughput in hybrid HetNets.

In this paper, we consider the performance of an amplify-and-forward relaying-based multiple-input multiple-output space–time-block-coded cooperative communication system. We propose an efficient two-stage receiver design for a relay-assisted transmission scenario over frequency-selective channels. It is also demonstrated that the receiver is able to achieve the full diversity order of the system by incorporating linear processing techniques. With this method, the potential spatial diversity and multipath diversity can be obtained by using the diversity-combining technique, which frequency-selective fading channels can be equivalently transformed. Simulation results show that the proposed scheme significantly outperforms the minimum mean square error and ZF receivers, while maintaining nearly linear complexity.