In this letter, we investigate the outage probability (OP) of amplify-and-forward hybrid satellite-terrestrial relay networks with a nonorthogonal multiple access (NOMA) scheme. By assuming that a single antenna satellite communicates with multiple multiantenna users simultaneously through the help of a single antenna relay and the NOMA scheme, we first derive the closed-form OP expressions for each NOMA user. Then, asymptotic OP expressions at the high signal-to-noise ratio regime are also obtained to evaluate the achievable diversity order and coding gain. Finally, simulations are provided to the validity of theoretical results, the superiority of introducing the NOMA scheme in satellite-terrestrial relay networks, and the effect of key parameters on the performance of NOMA users.

Allowing multiple users access simultaneously, a power-domain nonorthogonal multiple access (NOMA) scheme can provide enhanced resource utilization efficiency and has been viewed as a promising strategy in the satellite networks, to provide high quality-of-service for large number of users within limited spectrum and time resources. Taking into account the random location information of served users, imperfect channel state information (CSI), and antenna-pointing error, we study the ergodic capacity of a NOMA-based uplink satellite network in this article. Specially, we first develop a general framework for the NOMA-based system by applying standard recommendations, configurations, and channel characteristics. Then, the analytical expression for the achievable ergodic capacity of the considered network is derived. Finally, numerical simulations are provided to attest the validity of theoretical results and show the impact of various key parameters, such as location information, link quality, transmission power, imperfect CSI, and antenna-pointing error, on the considered network.

In this letter, we investigate the outage probability (OP) and ergodic capacity of downlink hybrid satellite terrestrial relay networks (HSTRNs) with a cooperative non-orthogonal multiple access (C–NOMA) scheme, in which a user with better channel condition acts as a relay node and forwards information to other users, thus alleviating the masking effect of users with poor channel conditions in heavy shadowing. Specifically, the exact analytical expression for the OP of the considered system is derived. Furthermore, the ergodic capacity expression is also developed to facilitate the performance evaluation of the proposed framework. Finally, simulations are provided to show the impact of key parameters on the considered system and the superiority of introducing the C–NOMA scheme to the HSTRNs.

Non-orthogonal multiple access (NOMA) scheme, which has the ability to superpose information in the power domain and serve multiple users on the same time/frequency resource, is regarded as an effective solution to increase transmit rate and fairness. In this paper, we introduce the NOMA scheme in a downlink land mobile satellite (LMS) network and present a comprehensive performance analysis for the considered system. Specifically, we first obtain the power allocation coefficients by maximizing the sum rate while meeting the predefined target rates of each NOMA user. Then, we derive the theoretical expressions for the ergodic capacity and the energy efficiency (EE) of the considered system. Moreover, the outage probability (OP) and average symbol error rate (ASER) performances of NOMA users are derived analytically. To gain further insights, we derive the asymptotic OP at the high signal-to-noise ratio (SNR) regime to characterize the diversity orders and coding gains of NOMA users. Finally, simulation results are provided to validate the theoretical analysis as well as the superiority of employing the NOMA scheme in the LMS system, and show the impact of key parameters such as fading configurations and user selection strategy on the performance of NOMA users.

In vehicle-to-everything (V2X) communication network, cellular device-to-device (D2D) communication can not only improve data rate and spectral utilization but also reduce the traffic load and power consumption. However, cellular D2D-based V2X technology has also a potential deficiency to meet various requirements of V2X communication, particularly in energy efficiency (EE). Power allocation provides an important approach to optimize the EE. In this paper, a new approach for power allocation with EE optimization (EEO) is proposed in cellular D2D-based V2X communication network. The mathematical framework of the new approach is formulated and proved and an algorithm is also proposed. Numerical simulation results are provided to demonstrate the feasibility of the proposed algorithm and the superiority over existing well-known algorithms.

As a novel paradigm, Vehicular Edge Computing (VEC) can effectively support computation-intensive or delay-sensitive applications in the Internet of Vehicles era. Computation offloading and resource management strategies are key technologies that directly determine the system cost in VEC networks. However, due to vehicle mobility and stochastic arrival computation tasks, designing an optimal offloading and resource allocation policy is extremely challenging. To solve this issue, a deep reinforcement learning-based intelligent offloading and power allocation scheme is proposed for minimizing the total delay cost and energy consumption in dynamic heterogeneous VEC networks. Specifically, we first construct an end-edge-cloud offloading model in a bidirectional road scenario, taking into account stochastic task arrival, time-varying channel conditions, and vehicle mobility. With the objective of minimizing the long-term total cost composed of the energy consumption and task delay, the Markov Decision Process (MDP) can be employed to solve such optimization problems. Moreover, considering the high-dimensional continuity of the action space and the dynamics of task generation, we propose a deep deterministic policy gradient-based adaptive computation offloading and power allocation (DDPG-ACOPA) algorithm to solve the formulated MDP problem. Extensive simulation results demonstrate that the proposed DDPG-ACOPA algorithm performs better in the dynamic heterogeneous VEC environment, significantly outperforming the other four baseline schemes.