Unmanned aerial vehicle (UAV) assisted emergency communication is an important technique for future B5G/6G scenario. The UAV is usually considered as a mobile relay to forward information from the macro base station (MBS) to the users in emergency area. In this paper, the MBS power allocation, the UAV service zone selection, and the user scheduling are jointly investigated to enhance the sum spectrum efficiency. We formulate the MBS power allocation and UAV service zone selection problem as an Markov Decision Process (MDP) in the delay ignored system (DIS) and propose a deep reinforcement learning (DRL) algorithm based on Q-learning and Convolutional Neural Networks (CNN). Then the proposed DRL-based scheme is extended in time delay system (TDS) to estimate the current optimal action with the outdated channel information. We also formulate the user scheduling as a 0-1 optimization problem and solve it by dividing into sub-problems. Simulation results demonstrate that the proposed DRL-based resource scheduling scheme can effectively improve the spectrum efficiency compared with the existing schemes.

This letter proposes a joint interference suppression scheme in heterogeneous networks (HetNets) with dense small cells (SCs) and users. Different from the majority of existing studies, we adopt the co-tier intra-cell interference alignment (IA), while the co-tier inter-cell and cross-tier interference is suppressed by centralized power control in the macro base station (MBS). Specifically, the power control problem is modeled as a Markov Decision Process (MDP) with the aim of maximizing the sum spectrum efficiency. Considering the exponential growth of the output layer neurons faced by general deep reinforcement learning (DRL) algorithms, we propose a deep deterministic policy gradient (DDPG)-based algorithm to solve the problem. Simulation results demonstrate that the proposed algorithm is able to achieve better performance and wider application scope comparing with existing algorithms.

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.

Semantic communication is a novel paradigm that conveys intention or goal from the source to the destination. It can greatly improve communication efficiency, especially for the applications that require extremely low latency and high reliability, such as augmented reality (AR), virtual reality (VR) or extended reality (XR). An adaptive semantic-bit communication structure based on resource efficiency enhancement for XR is proposed, in which part of the XR users employ semantic communication, while others employ the conventional way. We utilize adaptive communication and power allocation to maximize the system-level achievable performance indicated by equivalent semantic rate. The formulated problem is addressed by a two-step optimization. First, we propose a signal-to-interference-plus-noise ratio (SINR)-based paradigm selection scheme as the semantic communication outperforms the conventional way in low and moderate SINR regimes. Then we propose a genetic algorithm-based power allocation to solve the non-convex optimization. Simulation results demonstrate that the proposed scheme achieves a higher equivalent semantic rate against the baseline schemes.

The unmanned aerial vehicle (UAV)-aided vehicular communication can be greatly facilitated by joint optimization of UAV trajectory design and vehicle assignment. While most existing works are based on the full observation of system state, we consider the partial observability with predicting the vehicles’ trajectories. The vehicle trajectory prediction and joint optimization problem are modeled as a Partially-Observable Markov Decision Process (POMDP). To deal with the non-Markovian of the POMDP, we construct a new deep recurrent Q-network (DRQN) framework based on deep Q-network (DQN) algorithm and Long Short Term Memory (LSTM) layer. Simulation results demonstrate that the proposed DRQN-based scheme is fast convergent and outperforms the baseline schemes in terms of the sum spectral efficiency.

In this letter, the radio resource optimization in multibeam geostationary earth orbit (GEO) satellite communication (Satcom) is studied. We propose a deep reinforcement learning (DRL) algorithm based on the state-of-the-art twin delayed deep deterministic policy gradient (TD3) to jointly allocate the subchannel and power. Then we integrate independent training, prioritized experience replay, scaling factor, and noise rebound to address the bound action problem of TD3. Simulation results show that the proposed DRL-based algorithm outperforms the baseline schemes in terms of the sum log spectral efficiency.