Low Earth Orbit (LEO) satellite networks such as Starlink allow global and affordable internet access. This not only connects remote residents, but also enables new potentials such as connecting non-stationary agricultural systems in rural areas for high-precision spot farming. One challenge is that tractors and farming robots need to transmit large amounts of data from cameras and laser scanners in real-time, which easily overwhelms the uplink capabilities of a single LEO connection. In this paper, we examine the Starlink performance of multiple terminals within a single service cell to lay ground for multipath link bundling as a possible approach to increase link capabilities. For this purpose, we present two measurement setups consisting of four and seven Starlink dishes placed in one service cell. We conduct multiple measurements and evaluate UDP and TCP throughput, latency, and packet loss. We find that UDP throughput scales almost linearly with the number of dishes, indicating significant bundling potential up to an expected limit. In contrast, TCP BBRv1’s bundling potential is limited, due to inefficiencies in utilizing the links. Moreover, we find that some packet loss events are synchronized within a service cell. For future and independent research, we make our dataset publicly available.
Energy consumption reduction in 5G networks and beyond is a significant challenge. Discontinuous Reception (DRX) established by 3GPP is the current state of the art energy-saving strategy. With DRX, users that neither receive nor transmit for a certain duration are set in a sleep cycle. These users periodically wake up in order to consult the Physical Downlink Control Channel in case of potential transmissions. DRX timings are negotiated during the initial connection. Despite the inherent variability of user traffic patterns, DRX configuration parameters remain static, leading to energy waste. In addition, DRX does not consider delay constraints, which may incur QoS degradation due to inadequate sleep duration. This paper proposes Meta Sleep Scheduler (MSS) as an alternative to DRX. User sleep duration is dynamically adjusted to meet application time constraints while reducing the time users spend in high power states. Similarly to DRX, MSS is compatible with all radio resources allocation schedulers. MSS achieves an average reduction of up to 90% in user standby power consumption compared to DRX, while maintaining or surpassing DRX’s Quality of Experience.
Edge caching in Next-Generation Networks (NGNs) deploys cache units in Small-Cell Base Stations (SBSs), enabling User Equipment (UE) to retrieve content locally and allowing neighboring SBSs to share cached data. However, the large-scale deployment of SBSs and the diverse content preferences of UEs create significant challenges for cache strategy design by hindering accurate semantic representation during SBS collaboration, which in turn leads to redundant caching and suboptimal decisions in network environments. To address these issues, we propose a value decomposition-based reward allocation method to optimize network costs. Specifically, we design an agent model capable of explicitly exchanging information to enhance the semantic learning capabilities of SBSs. We then introduce a Neural Attention Additive Q-learning (NA2Q) model within the Advantage Actor-Critic (A2C) framework, which decomposes joint action values to capture the nonlinear relationships arising from SBS interactions. The model employs a Variational Auto-Encoder (VAE) to construct latent local semantics for each agent and integrates a self-attention mechanism to evaluate each agent’s credit. Finally, a credit-based reward allocation mechanism dynamically assesses SBS contributions, addressing traditional methods’ shortcomings in modeling inter-agent dependencies. Experimental results demonstrate that the proposed method significantly improves cache hit rates while reducing backhaul traffic, outperforming baseline methods.
For various wireless sensing and IoT applications, LoRa has emerged as an energy-efficient and long range solution for wireless data transfers. However, real-world deployments face multiple challenges, including packet collisions and variable link qualities, which generally lead to packet corruptions and data loss. Even though LoRa relies on forward error correction to restore corrupted packets, its usage comes with a significant energy overhead and only provides limited capabilities. In our paper, we present CEC-LoRa as an alternative to LoRa’s forward error correction feature. Its key innovation is that CEC-LoRa allows to restore corrupted packets without any error correction codes. Our approach exploits two symbiotic properties in LoRa’s encoding and chirp-based modulation scheme. On the one hand, misinterpreted chirps are often confused with neighboring symbols. On the other hand, neighboring symbol values only differ by one, keeping high similarity between misinterpreted and correct symbols. We leverage these properties by pre-computing a set of all plausible LoRa packets based on the expected payload permutations and compare them with the received packet. This allows us to identify the closest match and thus the most probable candidate packet. While the pre-computations incur a quite substantial energy overhead, CEC-LoRa shifts this from the energy-constrained end devices to the receiver side. The benefits greatly outweigh the energy demand, though: CEC-LoRa reduces the number of corrupted packets by 86.5 % on average without any additional overhead on the end device and while still being fully compliant to the LoRa specification. By including symbol information, CEC-LoRa can reduce the number of corrupt packets even further, by 99.5 % on average. This translates into SNR gains in the range of 0.79 dB to 0.93 dB.
