Reducing the energy consumption of Internet services requires knowledge about the specific traffic and energy consumption characteristics, as well as the associated end-to-end topology and the energy consumption of each network segment. Here, we propose a shift from segment-specific to service-specific end-to-end energy-efficiency modeling to align engineering with activity-based accounting principles. We use the model to assess a range of the most popular instant messaging and video play applications to emerging augmented reality and virtual reality applications. We demonstrate how measurements can be conducted and used in service-specific end-to-end energy consumption assessments. Since the energy consumption is dependent on user behavior, we then conduct a sensitivity analysis on different usage patterns and identify the root causes of service-specific energy consumption. Our main findings show that smartphones are the main energy consumers for web browsing and instant messaging applications, whereas the LTE wireless network is the main consumer for heavy data applications such as video play, video chat and virtual reality applications. By using small cell offloading and mobile edge caching, our results show that the energy consumption of popular and emerging applications could potentially be reduced by over 80%.

The tradeoff between energy efficiency (EE) and spectral efficiency (SE) provides a new perspective for the transmit design in satellite communications (SATCOM), i.e., one can jointly optimize the system EE and SE of the SATCOM system. In addition, jointly considering that it is challenging to obtain accurate channel state information at the transmitter, we study the tradeoff between EE and SE for the robust beamforming in multi-beam SATCOM in this paper. Specifically, with the quality of service and total power constraints taken into account, we study the robust beamforming aimed at maximizing the resource efficiency, which is defined as the weighted sum of the system EE and SE, to achieve the tradeoff between EE and SE with more flexibility. For this problem, we first approximate the ergodic user rate and ergodic signal-to-interference-plus-noise ratio, both with closed forms. Then, the semidefinite relaxation is applied to tackle the optimization of beamforming vectors via relaxing the rank constraints. To handle the sum of fractions involved in the optimization objective, a nested iterative algorithm exploiting the quadratic transformation and majorization-minimization method is proposed. Finally, the eigenvalue decomposition is utilized to obtain the beamformers. Simulation results indicate the effectivity of our proposed algorithm in achieving the EE-SE tradeoff and illustrate the superiority of the robust approach compared with the conventional baselines.

The vast amounts of mobile communication data collected by mobile operators can provide important insights regarding epidemic transmission or traffic patterns. By analyzing historical data and extracting user location information, various methods can be used to predict the mobility of mobile users. However, existing prediction algorithms are mainly based on the historical data of all users at an aggregated level and ignore the heterogeneity of individual behavior patterns. To improve prediction accuracy, this paper proposes a weighted Markov prediction model based on mobile user classification. The trajectory information of a user is extracted first by analyzing real mobile communication data, where the complexity of a user’s trajectory is measured using the mobile trajectory entropy. Second, classification criteria are proposed based on different user behavior patterns, and all users are classified with machine learning algorithms. Finally, according to the characteristics of each user classification, the step threshold and the weighting coefficients of the weighted Markov prediction model are optimized, and mobility prediction is performed for each user classification. Our results show that the optimized weighting coefficients can improve the performance of the weighted Markov prediction model.

Unmanned aerial vehicle (UAV)-assisted multiaccess edge computing (MEC) technology has garnered significant attention and has been successfully implemented in specific scenarios. The optimization of the network energy consumption in the relevant scenarios is essential for the whole system performance due to the constrained energy capacity of UAVs. However, the dynamic changes in MEC network resources make the energy consumption optimization a challenge. In this paper, a multi-UAV-multiuser MEC model is established to assess the system energy consumption, and the optimization problem of multi-UAV cooperation strategies is formulated base on the model. Then, a multi-agent deep deterministic policy gradient (MADDPG) algorithm based on deep reinforcement learning (DRL) is employed to resolve the above optimization problem. Each UAV is equivalent to a single agent that cooperates with other agents to train actors and critic evaluation networks to accomplish collaborative decision-making. In addition, a prioritized experience replay (PER) scheme is used to improve the convergence of the training process. Simulation results show that the impact of changes in different network resources on the network energy consumption by comparing the performance of different algorithms. The findings presented in this paper serve as a valuable reference for future work on system performance optimization, specifically in terms of energy efficiency.

Unmanned aerial vehicle (UAV)-assisted multiaccess edge computing (MEC) networks can effectively broaden the application scope of the Internet of Things (IoT) in complex scenarios such as maritime operations, military communications, and emergency commands. However, uncertain factors, such as weather changes and temporary airspace control, pose great challenges to UAV flight safety. Obstacles resulting from these uncertain factors may intersect with UAVs with preplanned flight paths, leading to accidents. Therefore, generating the optimal flight trajectory to avoid these obstacles is key in the successful operation of this fuzzy system. In this paper, we present a heuristic trajectory generation scheme for complex offshore environments that can generate optimal trajectories according to complex terrain conditions and avoid uncertain obstacles. First, we build a complex terrain model based on a three-dimensional (3D) offshore environment to simulate the conditions in UAV-assisted MEC networks. Second, we propose a network performance optimization objective function that is based on UAV characteristics. Third, we improve the existing ant colony optimization (ACO) algorithm by introducing chaotic mapping, polarizing the pheromone recording rule and implementing a simulated annealing screening mechanism to efficiently generate trajectories. Finally, we design an efficient obstacle avoidance algorithm for different combinations of obstacle regions. The simulation results show that our proposed trajectory generation scheme can efficiently avoid obstacles and significantly improve the total trajectory loss rate compared with that of baseline schemes.

The energy savings of 10 Gbps vertical-cavity surface-emitting lasers (VCSELs) for use in energy-efficient optical network units (ONUs) is critically examined in this work. We experimentally characterize and analytically show that the fast settling time and low power consumption during active and power-saving modes allow the VCSEL-ONU to achieve significant energy savings over the distributed feedback laser (DFB) based ONU. The power consumption per customer using VCSEL-ONUs and DFB-ONUs, is compared through an illustrative example of 10G-EPON for Video-on-Demand delivery. Using energy consumption models and numerical analyses in sleep and doze mode operations, we present an impact study of network and protocol parameters, e.g. polling cycle time, network load, and upstream access scheme used, on the achievable energy savings of VCSEL-ONUs over DFB-ONUs. Guidance on the specific power-saving mode to maximum energy savings throughout the day, is also presented.