By offloading tasks from the mobile device (MD) to its nearby deployed access points (APs), each of which is connected to one server for task processing, computation offloading can strike a balance between MD’s task execution delay and energy consumption in mobile edge computing (MEC) systems. Considering communication and computation dynamics in MEC systems, we aim to design online computation offloading mechanisms in this paper to minimize the time average expected task execution delay under the constraint of average energy consumption. Firstly, with known current channel gains between the MD and APs as well as available computing capability at MEC servers, we leverage the Lyapunov optimization framework to make an optimal one-slot decision on MD’s transmit power allocation and MEC server selection. On this basis, we then consider a more realistic scenario, where it is difficult to capture current available computing capability at MEC servers, and combine the multi-armed bandit framework for an online learning based MEC server selection algorithm. Finally, through theoretical analyses and extensive simulations, we demonstrate the near-optimality and feasibility of our proposed algorithms, and present that our proposed algorithms fully explore the interplay between communication and computation with enriched user experience and reduced energy consumption.

Maritime channel modeling is crucial for designing robust nearshore communication systems, yet reliable models that account for the dynamic marine environment with varying sea waves, wind conditions, and vessel motions remain scarce. This article investigates land-to-ship maritime wireless channel characteristics at 5.8 GHz based upon an extensive measurement campaign, with concurrent hydrological and meteorological information collection. First, a novel large-scale path loss model with physical foundation and high accuracy is proposed for dynamic marine environments. Then, we introduce the concept of sea-wave-induced fixed-point (SWIFT) fading, a peculiar phenomenon in maritime scenarios that captures the impact of sea surface fluctuations on received power. An enhanced two-ray model incorporating vessel rotational motion is propounded to simulate the SWIFT fading, showing good alignment with measured data, particularly for modest antenna movements. Next, the small-scale fading is studied by leveraging a variety of models including the two-wave with diffuse power (TWDP) and asymmetric Laplace distributions, with the latter performing well in most cases, while TWDP better captures bimodal fading in rough seas. Furthermore, maritime channel sparsity is examined via the Gini index and Rician K factor, and temporal dispersion is characterized. The resulting channel models and parameter characteristics offer valuable insights for maritime wireless system design and deployment.

Jitter effects caused by airflow and vibrations could be harmful to UAV communications, e.g., it may cause mismatch between the transmit beam and actual user direction. However, jitter introduces additional randomness to the channel, which may have merit in sparse channels that are relatively static and deep faded. For this consideration, we pick the commonly used air-to-sea two ray propagation model and investigate the impact of UAV jitter on the channel fading statistics. Assuming a Gaussian random model for the jitter angle, we derive analytical expressions for the jitter-beneficial probability (the probability that UAV jitter increases the receive power), and the average receive power under jitter, respectively. Accordingly, the jitter-beneficial conditions are analyzed and verified via simulations.

Random jitter poses significant impacts on unmanned aerial vehicle (UAV) base-station (BS) networking, as it may cause beam misalignment during the communication transmission for aerial UAV BS. To address this issue, in this paper, we present a robust transmission scheme to combat the jitter effects. Based on a three-dimensional jitter model, we first analyze the random variation in the angle of departure of the channel, and its impact on the communication rate. Using the insight of the analysis, we then propose a jitter-aware antenna selection scheme for UAV BS, which can identify optimal antenna set to maximize the average achievable rate given the statistics of jittering. Simulations demonstrate that through jitter-aware antenna selection optimization, the performance of aerial UAV base-station networking can be significantly improved, and the optimal number of antennas for transmission depends on factors such as the elevation angle and the jitter variances.

The construction of channel gain map (CGM) is essential for realizing environment-aware wireless communications expected in 6 G, for which a fundamental problem is how to predict the channel gains at unknown locations effectively by a finite number of measurements. As using a single prediction model is not effective in complex propagation environments, we propose a subregional learning-based CGM construction scheme, with which the entire map is divided into subregions via data-driven clustering, then individual models are constructed and trained for every subregion. In this way, specific propagation feature in each subregion can be better extracted with finite training data. Moreover, we propose to further improve prediction accuracy by uneven subregion sampling, as well as training data reuse around the subregion boundaries. Simulation results validate the effectiveness of the proposed scheme in CGM construction.

Packet transmissions over multi-hop lossy links are important in the future space-air-ground integrated networks. This paper considers sparse coded transmissions with reduced complexity compared to the well-known random linear network coding, which is known to be efficient in multi-hop lossy links. We propose a reinforcement learning framework for dynamically adjusting coding parameters on-line to improve the performance based on decoder feedback. A key advantage of the dynamic scheme is that it does not require a prior knowledge of the environment nor an analysis model. Extensive evaluation shows that it adapts well in scenarios where link conditions are unknown and/or changing, and achieves performance close to that of the optimal fixed schemes found by exhaustive search.