Low earth orbit (LEO) satellite networks are pivotal for sixth-generation (6G) wireless systems, yet their high-speed mobility induces frequent packet loss, causing severe head-of-line blocking delays under traditional retransmission mechanisms. While streaming forward erasure correction (FEC) can mitigate retransmissions, existing packet loss models fail to capture the unique dynamics of LEO networks, causing difficulties in the design and analysis of FEC schemes. This paper addresses this problem through the following contributions. First, based on real-world Starlink measurements, we reveal the inadequacy of conventional loss models such as those based on Markov chains. Second, we propose a Markovian arrival process (MAP) to model LEO packet loss. Using an expectation-maximization (EM) algorithm to fit Starlink traces, we demonstrate its superior accuracy over existing models. Third, based on MAP modeling, we show that the decoding delay of a typical streaming FEC scheme with fixed repair insertion intervals can be analyzed by approximating it as the busy period of a MAP/D/1 queue. Using matrix-analytic methods, we provide a numerical recipe to compute this delay. Simulations validate the precision of the model in predicting delay, offering practical guidelines for FEC design in LEO networks.

Efficient resource utilization is vital for terrestrial and non-terrestrial networks (TN-NTN) integration. However, different spatio-temporal resource scales in TN and NTN networks pose challenges for joint resource allocation. To tackle this problem, we propose a joint user scheduling and multi-domain resource allocation scheme in the downlink network, to improve coverage for ground users (GUs). Specifically, the scheme is designed in two time-scales, including large-scale satellite beam-hopping (i.e., spatial resource allocation) and small-scale time-frequency resource allocation. For beam-hopping, we first analyze the coverage of terrestrial base stations (TBS) for GUs, and accordingly propose a joint design of user scheduling and beam-hopping. For time-frequency resource allocation, to cope with the complexity induced by multi-domain and multi-scale resources, we propose a two-step approach which first obtains a preliminary allocation with worst-case co-frequency interference assumption, then employs the genetic algorithm to re-allocate redundant resources, thereby increasing the proportion of successfully served GUs. We evaluate the performance of the proposed scheme through simulations with different user demands and network service settings. Results show that the proposed scheme provides considerable improvement over existing schemes, which can efficiently reduce co-frequency interference and provide service for more GUs.

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 RicianKfactor, and temporal dispersion is characterized. The resulting channel models and parameter characteristics offer valuable insights for maritime wireless system design and deployment.

In future air-to-ground integrated networks, the scattering effects from ground-based scatterers, such as buildings, cannot be neglected in millimeter-wave and higher frequency bands, and have a significant impact on channel characteristics. However, current scattering measurement studies primarily focus on single incident angles within the incident plane, leading to insufficient characterization of scattering properties. In this paper, we present scattering measurements conducted at 28 GHz on various real-world building surfaces with multiple incident angles and three-dimensional (3D) receiving angles. The measured data are analyzed in conjunction with parameterized scattering models in ray tracing and numerical simulations. Results indicate that for millimeter-wave channel modeling near building surfaces, it is crucial to account not only for surface materials but also for the scattering properties of the building surfaces with respect to the incident angle and receiving positions in 3D space.

The growing popularity of Low-Earth Orbit (LEO) satellites, which are increasingly cheaper to manufacture and launch, has revolutionized the Internet market. Given the impact of packet loss on the quality of service (QoS) and optimization strategies of communication systems, it is of great significance to model packet loss in LEO satellite networks. The existing packet loss models considered in the literature have different assumptions and characteristics, but it remains unclear whether they are suitable for LEO satellite networks. This paper aims to evaluate several packet loss models. Through the evaluation of several metrics, the advantages and disadvantages of each model are discussed. We then introduce the Markovian Arrival Process (MAP) as a choice for packet loss modeling, and the experimental results show that the performance is better than that of existing models.

In this letter, the issue of recursive state estimation for time-varying systems is ironed out under time-correlated fading channels and multiple description coding scheme. The measurements are transmitted through wireless channels, where the coefficients exhibit time-correlated fading. Accordingly, a class of auxiliary variables is introduced to construct an augmented system reflecting the state and fading coefficients. Furthermore, a two-description coding scheme is adopted to encode time-correlated fading measurements into two equally important descriptions. A recursive state estimator capable of guaranteeing an upper bound on the estimation error covariance is firstly designed, which innovatively considers both time-correlated channel fading and multiple description coding approach, and is suitable for online computing. Then, such an upper bound is minimized through appropriate estimator gain parameters. Finally, a numerical example is utilized to demonstrate the effectiveness of the proposed state estimation algorithm.

Federated learning (FL) and split learning (SL) are two prominent distributed learning modes. FL allows for parallel training but demands significant computational resources on devices to train deep neural network models. Conversely, SL reduces the computational burden on devices and can enhance learning performance, though it often leads to longer training time due to its sequential nature. In this paper, we introduce a novel distributed learning framework, hybrid split and federated learning (HSFL), which combines the advantages of both FL and SL over wireless networks. To achieve a lower training loss within a shorter latency, we start with the convergence analysis of HSFL, followed by a joint optimization problem of the learning mode selection, model splitting, and bandwidth allocation. To solve the problem, we propose a two-stage algorithm. First, we find the optimal bandwidth allocation and model splitting with a fixed learning mode. Then, we select the optimal learning mode based on the above optimal values. Experimental results validate the superior learning efficacy of our proposed algorithm.

Visibility region (VR) is known as a key channel characteristic appeared in extra-large massive MIMO (XL-MIMO) systems, which can be exploited to facilitate low-complexity transmission design. Existing VR recognition method requires an a priori location-Vrdataset, with which a user's VR can be estimated given its location. This dataset is constructed by selecting some beacon users (BUs) to estimate the VR at their locations via uplink training. Constrained by the available training resource and possible environmental variation, practical size of the dataset is usually limited; how to efficiently select BUs for better VR recognition accuracy is therefore important. To this end, we propose and compare three BU selection methods, including random selection, minimum spacing constrained (MSC) selection, and a more sophisticated method (denoted as dynamic boundary refining, DBR) which utilizes partial of BUs for exploring unknown environment, while selecting the other BUs for further refining already-estimated VR region boundaries. Simulation results show that with a small number of BUs, both MSC and DBR achieve similar VR recognition performance and outperform random selection; as the number of BUs becomes larger, DBR achieves the best recognition accuracy.

The utilization of unmanned aerial vehicles (UAVs) for efficient data collection has gained considerable attention. In this paper, we examine a scenario involving grant-free access from Internet of Things (IoT) devices, where the random access may cause packet collision, stemming from multiple devices concurrently transmitting data. To address this issue, we propose a deep reinforcement learning-based collision avoidance (DRL-CA) approach for UAV data collection, which optimizes the UAV trajectory. The approach assists UAVs in identifying and maximizing the acquisition of device packet in an environment characterized by probabilistic packet transmission and potential collisions among device packets while ensuring a timely arrival at the destination. Through simulations, our proposed method effectively mitigates unnecessary conflicts among device packets while achieving the optimization objective.

We investigate a new deployment form of reflective intelligent surface (RIS), which aims at enhancing the quality of service of a main communication system in a target region, while without the need of changing its transmission protocol and scheme (i.e., the RIS is “transparent” to the main system). To this end, we mathematically formulate a coverage enhancement problem, where a RIS is used transparently in the sense that the BS can be unaware of its existence, while the minimum channel link strength, measured from every BS antenna to any point in the target region, can be maximized. The formulated problem is non-convex with mixed discrete-continuous variables. To tackle this challenge, we recast it into a convex feasibility problem via spatial sampling and semi-definite relaxation. Based on a derived analytical upper bound on the link strength difference between any two location points, we further characterize the coverage-similarity region of a given location, and accordingly propose an improved spatial sampling scheme for efficient implementation. Simulation results show that the proposed transparent RIS design achieves better coverage performance than benchmark schemes. More importantly, it can effectively improve the communication performance without affecting the transmission scheme originally adopted by the main communication system.

As the 6G network is evolving towards a space-air-ground integrated scale with ubiquitous long-distance non-terrestrial network (NTN) links, packet-level streaming forward erasure correction (FEC), which can achieve low end-to-end in-order delivery delay over lossy links with long propagation delay, has drawn increasing interest. However, the existing streaming FEC has a problem that full-length encoding windows (EWs) including all non-acknowledged source packets are used when generating repair packets, which incurs high computational cost when the link’s bandwidth-delay product is large. To address the problem, this paper proposes a new low-complexity streaming FEC design, where a mixture of short and full-length EWs are used. We propose a novel method to analyze the decoding window width observed by arriving repair packets, which is based on the analysis of the busy period of a virtual queue using renewal theory. Later, using the analysis as the key enabler, a design problem is formulated and solved to optimize parameters including the EW width and the fraction of short-length repair packets such that the computational cost is reduced. Evaluations using real-life code implementations show that the proposed design can significantly reduce the computational cost, while maintaining the key benefits of the original streaming FEC.

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.

Linear frequency modulated continuous wave (LFMCW) radar has been proven to be a highly reliable sensor to improve traffic safety. However, the increased use of LFMCW radars creates new challenges in mutual interference for automotive applications. In this contribution, an efficient approach is developed to reduce mutual interference by applying randomized sub-band spectra technique. Compared with state-of-the-art methods, the proposal shows superior performances.