In this paper, we study the user pairing in a downlink non-orthogonal multiple access (NOMA) network, where the base station allocates the power to the pairwise users within the cluster. In the considered NOMA network, a user with poor channel condition is paired with a user with good channel condition, when both their rate requirements are satisfied. Specifically, the quality of service for weak users can be guaranteed, since the transmit power allocated to strong users is constrained following the concept of cognitive radio. A distributed matching algorithm is proposed in the downlink NOMA network, aiming to optimize the user pairing and power allocation between weak users and strong users, subject to the users’ targeted rate requirements. Our results show that the proposed algorithm outperforms the conventional orthogonal multiple access scheme and approaches the performance of the centralized algorithm, despite its low complexity. In order to improve the system’s throughput, we design a practical adaptive turbo trellis coded modulation scheme for the considered network, which adaptively adjusts the code rate and the modulation mode based on the instantaneous channel conditions. The joint design work leads to significant mutual benefits for all the users as well as the improved system throughput.

With the advantages of low cost and flexible deployment, low earth orbit satellite (LEO-Sat) communications have received widespread attentions, such as providing global wireless access with enhanced data rates in future 6G networks. However, due to the feature of high-mobility, LEO-Sat communications are facing severe Doppler effects, which heavily degrades the link reliability. The existing approaches for coping with Doppler effects, such as phase locked loop, mainly carry out the prediction and compensation for Doppler frequency shifts, which demands high implementation complexity, as well as accurate channel estimation. Against this background, this article portrays a new paradigm of reliable LEO-Sat communications with the aid of orthogonal time frequency space (OTFS) modulation scheme, which can average out the channel dynamics through modulating information in the delay-Doppler (DD) domain, thereby overcoming severe Doppler effects. In particular, we start with studying the Doppler effect characteristics of LEO-Sat communications, which serves as the motivations of using OTFS. Then, we conceive a case study to show the feasibility of OTFS enabled LEO-Sat communication by evaluating the reliability performance. Finally, we discuss a range of promising research opportunities and potential challenges.

Internet of Things (IoT) technology suffers from the challenge that rare wireless network resources are difficult to meet the influx of a huge number of terminal devices. Cache-enabled Device-to-Device (D2D) communication technology is expected to relieve network pressure with the facts that the requesting contents can be easily obtained from nearby users. However, how to design an effective caching policy becomes very challenging due to the limited content storage capacity and the uncertainty of user mobility pattern. In this paper, we study the jointly cache content placement and delivery policy for the cache-enabled D2D networks. Specifically, two potential recurrent neural network approaches (the echo state network (ESN) and long-short term memory (LSTM) network) are employed to predict users’ mobility and content popularity, so as to determine which content to cache and where to cache. When the local cache of the user cannot satisfy its own request, the user may consider establishing a D2D link with the neighboring user to implement the content delivery. In order to decide which user will be selected to establish the D2D link, we propose the novel schemes based on deep reinforcement learning to implement the dynamic decision-making and optimization of the content delivery problems, aiming at improving the quality of experience of overall caching system. The simulation results suggest that the cache hit ratio of the system can be well improved by the proposed content placement strategy, and the proposed content delivery approaches can effectively reduce the request content delivery delay and energy consumption.

Air ground integrated mobile cloud computing (MCC) provides unmanned aerial vehicles (UAVs) the capability to act as an aerial relay with more flexibility and resilience. In the cloud computing architecture, the data generated by ground users (GUs) can be offloaded to the remote server for fast processing. However, the heterogeneity of mobile tasks makes the data size distributed among GUs unbalanced. Besides, the energy efficiency of UAVs movement should be carefully considered for sustainable flight and obstacle avoidance. In general, such a joint trajectory issue can hardly be formulated as a convex optimization in unpredictable and dynamic environments. This paper proposes a potential game combined multi-agent deep deterministic policy gradient (MADDPG) approach to optimize multiple UAVs' trajectory with the consideration of GUs' offloading delay, energy efficiency as well as obstacle avoidance system. In specific, we first model the issue as a mixed integer non-linear problem (MINP), in which the service assignment between multi-user and multi-UAV is solved by potential game. The convergence to a Nash Equilibrium (NE) can be achieved by distributive service assignment update with infinite iteration. Then, we optimize the trajectory with obstacle avoidance at each UAV by MADDPG approach, which has a great advantage of centralized-training and decentralized-execution to reduce the global synchronized communication overhead. UAVs movement can be optimized in continuity rather than other deep reinforcement learning (DRL) approaches generating discrete simple actions. Experiments demonstrate the proposed game-combined learning algorithm can minimize the offloading delay, enhance UAVs’ energy efficiency and avoid the obstacles at the same time.

As an important technique of enabling global access for 6G, low earth orbit satellite (LEO-Sat) communication is still facing the challenges of large path-loss, and severe Doppler effect. This paper studies the reliability performance of a downlink LEO-Sat communication system, where the orthogonal time frequency space (OTFS) scheme is employed to combat severe Doppler effect. Further, the unmanned aerial vehicle (UAV) based cooperative transmission is developed, in order to compensate for the large margin of path-loss caused by the long transmission distance. In particular, the closed-form expression for the outage probability of the OTFS based LEO-Sat transmission is derived, where the novel moment matching approach is used to closely approximate the PDF of a sum of shadowing Rician (SR) variables. Further, the condition of using UAV cooperation is obtained so that the positive reliability gain is guaranteed. Finally, both the theoretical analysis and simulation results show that, the OTFS scheme can significantly outperform the traditional OFDM scheme, while demanding to carefully address the trade-off between reliability improvement and implementation complexity determined by modulation size.

The optimal energy efficient power allocation is investigated for the uplink multi-carrier non-orthogonal multiple access (MC-NOMA) system. Considering the user fairness, the power allocation is formulated as a maximization problem of the system weighted energy efficiency, which is defined as a ratio of the weighted sum rate to the total power consumption. By utilizing Dinkelbach algorithm and Lagrange dual decomposition approach, the optimal closed-form power allocation expressions are derived. The convergence and optimality of the proposed scheme can be proved. The simulation results demonstrate performance enhancement of the proposed algorithm compared to the existing works, i.e., the enhance water-filling scheme and orthogonal multiple access (OMA) system.