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.

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.

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.

Non-terrestrial networks (NTNs) using flying objects such as satellites play key roles in the next-generation wireless system (6G). The NTN links with long propagation delay and random packet losses pose a great challenge to the performance of Transmission Control Protocol (TCP), which many Internet applications rely on. Performance enhancing proxy (PEP) is an easy-to-deploy approach for improving TCP's performance. In this paper, we design and implement a novel PEP calledPEPescwhich has two distinctive features. First, it featuresretransmission-freeloss recovery, using an adaptive packet-level forward erasure correction method called streaming coding (SC). Second, as packet losses are recovered by SC, the congestion control problem is simplified to rate control and local acknowledgement between entities based on bandwidth estimation. Based on a queueing theoretic analysis of the design, we carefully devise a protocol and implement PEPesc as an open-source application. Extensive evaluations show that PEPesc can achieve much higherandsmoother goodput than the canonical TCP variants and than other existing open-source PEPs in applications includingiperfand HTTP-based adaptive streaming, and achieves similar performance in web browsing. Finally, we also present a deployment case over a real-world geostationary satellite link.

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.

Constrained application protocol (CoAP) for low-power low-rate data transport in Industrial Internet of Things (IIoT) networks is typically running with two modes, namely, confirmable mode and nonconfirmable mode, respectively. Confirmable mode relies on retransmission to ensure a guaranteed Quality of Service (QoS) in terms of packet loss rate at the cost of increased latency and power consumption. Whereas nonconfirmable mode consumes less power, it is known to be packet loss prone. To enrich the CoAP transport for IIoT networks, we propose a packet-level forward error correction (FEC) mechanism based on systematic coding with an adaptive code rate to provide energy efficient and reliable packet delivery. We mathematically analyze packet loss and cost of energy consumption of the proposed mechanism and compare it with the confirmable CoAP transport scheme in the Gilbert–Elliott channel model. We demonstrate that the proposed mechanism can enhance the performance of nonconfirmable CoAP to be comparable to confirmable CoAP in terms of packet loss rate, while outperforming it in energy consumption. The analytical and simulation results verify that the proposed mechanism is suitable for IIoT networks especially in high-erasure burstiness scenarios.

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.

In application scenarios such as Internet of Things, a large number of energy receivers (ERs) exist and line-of-sight (LOS) propagation could be common. Considering this, we investigate wireless energy transfer (WET) in extra-large massive MIMO Rician channels. We derive analytical expressions of the received net energy for different schemes, including 1) training-based WET, where the ER sends beacon signal for channel training and the energy transmitter (ET) uses the channel estimate for energy beamforming, 2) LOS beamforming, where the ET transmits to the LOS direction of the ER, and 3) energy harvesting, which allows an ER to harvest the training energy from the other ERs. We derive a path loss threshold for switching between training and LOS beamforming-based WET. We further show that the WET scheme selection of one ER is not affected by the other ERs, and the energy harvested from training is minimal in practice. With these insights, we propose an algorithm for the multi-ER scenario, which minimizes the power consumption by iteratively updating the WET scheme selection and power allocation for all ERs. Simulations show that the proposed algorithm achieves near-optimal performance as compared to exhaustive searching, while with much lower implementation complexity.

Distributed computing is the mainstream for large-scale machine learning and big data analytics, but its performance usually suffers from unpredictable stragglers, i.e., very slow nodes. Recently, coded computing has emerged as a new distributed computing paradigm that uses coding-theoretical approaches to mitigate the effect of stragglers. Most existing coding schemes only use the results from a certain number of fastest worker nodes to recover the output and completely ignore the partial work done by other worker nodes, leading to inferior performance. In this paper, for scenarios where each worker node transmits its local result to the master node only after it has finished the whole local computation, we introduce communication at full speed to characterize the full utilization of all the communication links between each worker node that has finished its local computation and the master node, and for scenarios where each worker node computes out the local result piece by piece and can forward each piece once available, we introduce computation at full speed to characterize the full utilization of the work done entirely or partially by all the worker nodes. Considering a general polynomial-based coding framework which encapsulates many advanced coding schemes for a variety of fundamental computing tasks, we propose a randomized approach where each worker node partitions its local result into pieces, generates and forwards random linear combinations of these pieces to the master node sequentially, and theoretically demonstrate that it can lead the coding framework to achieve communication at full speed. For some typical task scenarios, we further show that computation at full speed can be achieved by mapping the encoding operations in the randomized approach into a part of encoding on the input dataset. Experiments conducted on Alibaba Cloud as well as simulations show that our approaches can reduce the total runtime significantly.

In this paper, we investigate the multiple input multiple output (MIMO)-non-orthogonal multiple access (NOMA) transmission with the aid of location information. We first consider two users separated in both the distance and angle domains. With different access distances, NOMA could be used to serve the near-user and the far-user simultaneously, whereas spatial division multiple access (SDMA) would be applied if the two users have largely-separated angles of departure (AOD) that guarantees spatial orthogonality. Comparing the ergodic sum rates of these two multiple access (MA) schemes, we first characterize the preferable MA regions in the angle-distance plane. Analytical expression of the region boundary between NOMA and SDMA is derived. Moreover, NOMA-preferable regions are expressed in terms of the maximum distance difference and the minimum angle difference between the two users, respectively. On basis of these results, we further propose a location-based low-complexity user pairing algorithm for the general multiuser scenario. Numerical results confirm the accuracy of the derived region boundaries, and the simulations show that the proposed user pairing algorithm can effectively improve the resource utilization rate, compared to the conventional MA and user pairing schemes.

Data dissemination to multiple users within a predetermined deadline is a commonly required function in many emerging wireless scenarios. In this paper, we propose a data dissemination scheme enhanced by device-to-device (D2D) communications and random linear network coding (RLNC), where the broadcast and the D2D links are used simultaneously with on-the-fly RLNC to accelerate the dissemination. The probability distribution of the completion time of using either pure or systematic RLNC is analyzed assuming a finite field size, under a general model where the cooperation may occur probabilistically and multiple packets may be exchanged between the D2D users in each time slot. An optimization problem is formulated to design the cooperation parameters to minimize the expected energy consumption of the system. Numerical and simulation results show that the analysis is accurate, and that to minimize the energy consumption a tradeoff exists between allowing for longer broadcast and for more intensive cooperation. The cooperation parameters designed for multiple D2D pairs are shown to be effective for meeting the reliability requirements.

Relay transmissions play an important role in many types of communication systems. In this paper, we consider packet-level coded transmissions over lossy relay links with the finite-buffer and coding coefficients delivery cost constraints. We propose a low-complexity coding scheme where packets are encoded from sequentially formed random subsets of source packets called batches. The relay recodes only from the buffered packets belonging to the same batch to maintain the code sparsity, lowering the packet header overhead and the decoding complexity compared to the random linear network coding (RLNC). To analyze the performance, we first propose an absorbing Markov chain model to analyze the RLNC transmission over finite-buffer relay links. The finite-length analysis not only provides a lower bound on the completion time using any sparser random codes but also characterizes each individual batch's transmission of the proposed code. Based on the analysis, another Markov chain is proposed to determine the decoding failure probability and the expected completion time of the batch coding scheme. As shown through analysis and simulations, the proposed scheme achieves higher effective end-to-end rates than RLNC when the coding coefficients delivery cost is considered and is also with much lowered decoding complexity thanks to its sparseness.