In order to overcome the shortcomings of orthogonal frequency division multiplexing (OFDM) and prolong the battery life of devices in the Internet of Things, a joint subcarrier and subsymbol allocation-based simultaneous wireless information and power transfer scheme for multiuser generalized frequency division multiplexing (GFDM) system is proposed in this paper. According to the 2-D time-frequency block structure of GFDM, we investigate the problem to maximize sum information decoding (ID) rate by optimizing subcarrier and subsymbol allocation, power allocation and power splitting ratio under the constraints of total transmit power and harvested energy. To solve the nonconvex problem, an iterative algorithm is developed to obtain its optimal solution. The performances of sum ID rate and harvested energy are simulated and evaluated. Simulation results show that the proposed algorithm converges fast. Moreover, the proposed algorithm can not only allocate the subcarriers, subsymbols, and power based on different channel conditions of users, but also outperform the conventional OFDM in sum ID rate on the premise of satisfying the minimum harvested energy of each user.

The vertical industry member and the Internet of Things (IoT) business are spurring heated discussions on the evolution of future mobile communication. The emerging non-terrestrial network (NTN) plays increasingly important role in addressing service and coverage demand boom, whilst they still envisage technical and performance bottlenecks. Besides, traditional communication paradigm is constrained by Shannon theorem and fails to exploit the semantic features. In this article, we review the classic 6G NTN IoT networks and semantic communication in terms of structure, challenges, and applications. Then we observe that the superiority of semantic communication in low signal-to-noise ratio (SNR) regime is complementary to 6G NTN. We further propose an adaptive semantic-empowered 6G NTN IoT structure supporting communication paradigm switching. Finally, the open issues are discussed.

As a key feature of the next generation wireless networks (5G), heterogeneous networks (HetNets) architecture is embraced as the fundamental network technology to meet the immensely diverse service requirements and characteristics of various devices. By introducing underlaying device-to-device (D2D) communication into HetNets, it is possible to offload the unevenly distributed load from the overloaded macrocell to the uncongested femtocell in multitier HetNets. However, the performance of load balancing (LB) scheme heavily depends on the D2D relay selection method as well as the potential resource reuse interference brought by underlay D2D relaying. In order to improve resources utilization ratio and mitigate the resource reuse interference, an LB strategy based on D2D relaying is proposed. To accommodate more new users into the already overloaded macrocell, the macro user equipment with poor link quality will be first transferred into nearby uncongested femtocells by D2D relaying. Then, the released macrocell resource can be allocated to new users who cannot access femtocell due to location restriction. Furthermore, we also propose a two-stage relay selection and resource allocation scheme which not only minimizes the potential interference caused by resource reuse but also guarantees the transmission requirement of different users. Extensive simulation results demonstrate that by taking advantage of D2D relaying, the proposed LB algorithm can effectively adjust the unbalanced load between macrocell and femtocells, which achieves improved performance of the whole HetNets.

In this paper, a novel reconfigurable intelligent surface (RIS) aided communication system with multiple unmanned aerial vehicles (UAVs) is investigated. To achieve better system performance, we employ the non-orthogonal multiple access (NOMA) technique, and consider imperfect successive interference cancellation (SIC) at each user equipment (UE). To maximize the system throughput, we jointly optimize the three-dimensional (3D) trajectories of UAVs, the power allocation strategy, and the phase shift of the RIS. Due to the movement of UEs and UAVs, the formulated problem is difficult to solve. To address the formulated problem, we propose a two-step approach, named throughput maximization by trajectories design, power allocation and phase shift optimization (TMTDPAPO).Specifically, a K-means based UE clustering algorithm is adopted to group UEs. Then, a double deep Q-network (DDQN) approach is utilized to deal with the formulated problem. Extensive simulations are conducted, and results demonstrate that: (1) the TM-TDPAPO algorithm is effective in improving the system throughput, especially, the proposed approach achieves approximately 57% higher throughput gain compared to the conventional DQN algorithm; (2) by applying the imperfect SIC at each UE, the proposed scheme yields 23% performance gain than orthogonal multiple access (OMA) case; (3) the considered system with the deployment of RIS benefits from 29% throughput gain over the system without RIS.

Coordinated beamforming is very efficient at managing interference in ultra dense network. However, the optimal strategy remains as a challenge task to obtain due to the coupled nature among densely and autonomously deployed cells. In this paper, the deep reinforcement learning is investigated for predicting coordinated beamforming strategy. Formulated as a sum-rate maximization problem, the optimal solution turns out as a balanced combination of selfish and altruistic beamforming. As the balancing coefficients depend on the beamforming vectors of all the cells, iterations are inevitable to get the final solution. To address this problem and improve efficiency, deep reinforcement learning (DL) is proposed to predict the balancing coefficients. Specifically, the agent, on behalf of a base station-user pair, will rely on Deep Q-network to learn the highly complex mapping between the balancing coefficients and signal-interference environment of each user. Subsequently, the beamforming vectors are obtained efficiently through the learned balancing coefficients. Due to the distinguished feature in exploration of the beamforming parameterization, the complexity problem brought by predicting the beamforming matrix directly is avoided. The performance of the proposed scheme is investigated by experiments with arguments regarding multiple input and multiple output configuration, shadow fading and state design. Simulation results indicate the facts that: 1) the theoretically infinite strategy space can be discretized with limited levels and granularity;2)it is feasible to approximate the complex mapping by Q-learning for wireless channel consisting both the large and small scale fading, 3) the balancing coefficients only concerns large scale fading, so the coordinated beamforming can be decomposed to two sub-problems with different time scales: parameterization at large time scales and instant beamforming based on balancing coefficients.