Augmented reality applications are bitrate intensive, delay-sensitive, and computationally demanding. To support them, mobile edge computing systems need to carefully manage both their networking and computing resources. To this end, we present a proof of concept resource management scheme that adapts the bandwidth at the base station and the GPU frequency at the edge to efficiently fulfill roundtrip delay constrains. Resource adaptation is performed using a Multi-Armed Bandit algorithm that accounts for the monotonic relationship between allocated resources and performance. We evaluate our scheme by experimentation on an OpenAirInterface 5G testbed where the considered application is OpenRTiST. The results indicate that our resource management scheme can substantially reduce both bandwidth usage and power consumption while delivering high quality of service. Overall, this work demonstrates that intelligent resource control can potentially establish systems that are not only more efficient but also more sustainable.

We investigate the role of causal models in the context of obtaining scheduling policies that minimize delay violations. We consider multi-user queuing systems with random delay constraints, packet sizes and arrivals in Gilbert-Elliot wireless channels. Owing to Judea Pearl's landmark work on causality to achieve a higher level of cognitive ability, we demonstrate the role of counterfactual reasoning, leading to the well-investigated optimal EDF policy for wired channels. Due to the randomness associated with the wireless channels, finding an optimal policy is not straightforward, leading to RL-based approaches. We present CPPO (counterfactual-PPO) and CA2C (counterfactual-A2C) algorithms that use counterfactual examples generated using causal models during the training process. We argue how stochastic gradient based policy gradient RL algorithms benefit during training due to incorporation of counterfactuals. We show that these algorithms provably lead to lower variance indicating a robust learning performance. Our results demonstrate a ~ 60% increase in the number of cases where CA2C and CPPO outperform their non-counterfactual counterparts with reduced variance and negligible computation overhead.

Ultra-reliable low latency communication (URLLC) systems are pivotal for applications demanding high reliability and low latency, such as autonomous vehicles. In such contexts, channel prediction becomes essential to maintaining communication quality, as it allows the system to anticipate and mitigate the effects of fast-fading channels, thereby reducing the risk of packet loss and latency spikes. This letter presents a novel framework that integrates neural networks with extreme value theory (EVT) to enhance channel prediction, focusing on predicting extreme channel events that challenge URLLC performance. We propose an EVT-based adaptive quantile loss function that integrates EVT into the loss function of the deep recurrent neural networks (DRNNs) with gated recurrent units (GRUs) to predict extreme channel conditions efficiently. The numerical results indicate that the proposed GRU model, utilizing the EVT-based adaptive quantile loss function, significantly outperforms the traditional GRU. It predicts a tail portion of 7.26%, which closely aligns with the empirical 7.49%, while the traditional GRU model only predicts 2.4%. This demonstrates the superior capability of the proposed model in capturing tail values that are critical for URLLC systems.

The growing demand for stringent quality of service (QoS) guarantees in 5G networks requires accurate characterisation of delay performance, often measured using Delay Violation Probability (DVP) for a given target delay. Widely used retransmission schemes like Automatic Repeat reQuest (ARQ) and Hybrid ARQ (HARQ) improve QoS through effective feedback, incremental redundancy (IR), and parallel retransmission processes. However, existing works to quantify the DVP under these retransmission schemes overlook practical aspects such as decoding complexity, feedback delays, and the resulting need for multiple parallel ARQ/HARQ processes that enable packet transmissions without waiting for previous feedback, thus exploiting valuable transmission opportunities. This work proposes a comprehensive multi-server delay model for ARQ/HARQ that incorporates these aspects. Using a finite blocklength error model, we derive closed-form expressions and algorithms for accurate DVP evaluation under realistic 5G configurations aligned with 3GPP standards. Our numerical evaluations demonstrate notable improvements in DVP accuracy over the state-of-the-art, highlight the impact of parameter tuning and resource allocation, and reveal how DVP affects system throughput. 

Addressing the broadband gap between rural and urban regions requires rural-focused wireless research and innovation. In the meantime, rural regions provide rich, diverse use cases of advanced wireless, and they offer unique real-world settings for piloting applications that advance the frontiers of wireless systems (e.g., teleoperation of ground and aerial vehicles). To fill the broadband gap and to leverage the unique opportunities that rural regions provide for piloting advanced wireless applications, we design and implement the ARA wireless living lab for research and innovation in rural wireless systems and their applications in precision agriculture, community services, and so on. ARA focuses on the unique community, application, and economic context of rural regions, and it features the first-of-its-kind, real-world deployment of long-distance, high-capacity terrestrial wireless x-haul and access platforms as well as Low-Earth-Orbit (LEO) satellite communications platforms across a rural area of diameter over 30 km. The high-capacity x-haul platforms operate at the 11 GHz, 14 GHz, 71–86 GHz, and 194 THz bands and offer communication capacities of up to 160 Gbps at per-hop distances up to 15+ km. The wireless access platforms feature 5G-and-beyond MIMO systems operating at the 460–776 MHz, 3.4–3.6 GHz, and 27.5–28.35 GHz bands and with 14, 192, and 384 antenna elements per sector respectively, and they offer up to 3+ Gbps wireless access throughput and up to 10+ km effective cell radius. With both software-defined radios and programmable COTS systems, and through effective orchestration of these wireless resources with fiber as well as compute resources embedded end-to-end across User Equipment (UE), Base Stations (BS), edge, and cloud, including support for Bring Your Own Device (BYOD), ARA offers programmability, performance, robustness, and heterogeneity at the same time, thus enabling rural-focused co-evolution of wireless and applications while helping advance the frontiers of wireless systems in domains such as Open RAN, NextG, and agriculture applications. The resulting solutions hold the potential of reducing the rural broadband cost by a factor of 10 or more, thus making rural broadband as affordable as urban broadband. Here we present the design principles and implementation strategies of ARA, characterize its performance and heterogeneity, and highlight example wireless and application experiments uniquely enabled by ARA.

Wearable Cognitive Assistance (WCA) has the potential to revolutionize daily life with real-time, context-aware guidance, but current performance models often overlook the dynamic nature of human-system interactions, leading to inefficiencies in resource allocation and system responsiveness. In this work, we investigate the implications of the correlated nature of human-system interactions through the development of a novel data-driven methodology for the modeling of task execution times in WCA.We apply this methodology to a WCA application previously shown to exhibit second-order effects between system responsiveness and human performance. Our resulting model presents an improvement in up to 30% with respect to traditional first-order approaches, highlighting the importance of capturing complex behavioral dynamics. These findings raise important questions about the design and optimization of WCA systems and the tools that target them: What are the implications of this correlation for resource allocation and system design in real-world deployments? How can our methodology inform the development of more accurate and adaptive models for WCA applications? By exploring these questions, this research aims to contribute to the development of more efficient and effective WCA systems.

Industrial cyber-physical systems require dependable network communication with formal end-to-end reliability guarantees. Striving towards this goal, recent efforts aim to advance the integration of 5 G into Time-Sensitive Networking (TSN). However, we show that IEEE 802.1Qbv TSN schedulers that are unattuned to 5 G packet delay variations may jeopardize any reliability guarantees provided by the 5 G system. We demonstrate this on a case where a 99.99 % reliability in the inner 5G network diminishes to below 10 % when looking at end-to-end communication in TSN. In this paper, we overcome this shortcoming by introducing Full Interleaving Packet Scheduling (FIPS) as a wireless-friendly IEEE 802.1Qbv scheduler. To the best of our knowledge, FIPS is the first to provide formal end-to-end QoS guarantees in wireless TSN. FIPS allows a controlled batching of TSN streams, which improves schedulability in terms of the number of wireless TSN streams by a factor of up to × 45. Even in failure cases, FIPS isolates the otherwise cascading QoS violations to the affected streams and protects all other streams. With formal end-to-end reliability, improved schedulability, and fault isolation, FIPS makes a substantial advance towards dependability in wireless TSN.

Many network faults are flooding the telecommunication companies in the form of Trouble Tickets (TT). Automation in managing these TTs is vital in increasing customer satisfaction. We develop a solution to address two challenges regarding TTs generated from fixed and mobile access network domains: prediction of resolution times and technician dispatch needs. Our study utilizes datasets from Telenor, a Swedish telecommunication operator, encompassing 35,000 access switches and 8,000 base stations. It incorporates 40,000 switch TTs and 22,000 mobile TTs during 2019-2023. None of the previous works studied multiple telecommunication domains or considered the time evolution of TTs. This work comprehensively studies several prediction models for the mentioned use cases and network domains. Our models successfully outperform the company baseline and best proposed state-of-the-art models. Within 1-hour confidence interval, our method can correctly predict shortest ranges of resolution times for 90% of switch TTs and 80% of mobile TTs. We also predict the necessity of dispatching workforce to the place with weighted F1 scores of respectively, 88% and 89% for switch and mobile TTs which shows high average accuracy of our system in prediction across both dispatch and non-dispatch TT classes to assist operation. With these scores, our model is capable of allocating resources automatically, enhancing customer satisfaction. We also studied the TTs evolution, for example, for switch TTs, within 15 minutes of creation time, prediction improves by 57% and 50%, for resolution and dispatch prediction, respectively.

With the emergence of time-critical applications in modern communication networks such as vehicle-to-everything (V2X) systems, there is a growing demand for proactive network adaptation and quality of service (QoS) prediction. However, a fundamental question remains largely unexplored: How can we quantify and achieve more predictable communication systems in terms of performance? To address this gap, this paper introduces a theoretical framework for defining and analyzing predictability in communication systems, with a focus on the impact of observations for performance forecasting. We establish a mathematical definition of predictability based on the total variation distance between the forecast and marginal performance distributions. A system is deemed unpredictable when the forecast distribution, providing the most comprehensive characterization of future states using all accessible information, is indistinguishable from the marginal distribution, which depicts the system's behavior without any observational input. This framework is applied to multi-hop systems under Markovian conditions, with a detailed analysis of Geo/Geo/1/K queuing models in both single-hop and multi-hop scenarios. Additionally, we apply the framework to a random-walk-based model of QoS for connected vehicles experiencing changing channel conditions. We derive exact and approximate expressions for predictability in these systems, as well as upper bounds based on spectral analysis of the underlying Markov chains. Our results have implications for the design of efficient monitoring and prediction mechanisms in future communication networks aiming to provide dependable services.

With the increasing focus on flexible automation, which emphasizes systems capable of adapting to varied tasks and conditions, exploring future deployments of cloud and edge-based network infrastructures in robotic systems becomes crucial. This work, examines how wireless solutions could support the shift from rigid, wired setups toward more adaptive, flexible automation in industrial environments. We provide a quality of control (QoC) based abstraction for robotic workloads, parameterized on loop latency and reliability, and jointly optimize system performance. The setup involves collaborative robots working on distributed tasks, underscoring how wireless communication can enable more dynamic coordination in flexible automation systems. We use our abstraction to optimally maximize the QoC ensuring efficient operation even under varying network conditions. Additionally, our solution allocates the communication resources in time slots, optimizing the balance between communication and control costs. Our simulation results highlight that minimizing the delay in the system may not always ensure the best QoC but can lead to substantial gains in QoC if delays are sometimes relaxed, allowing more packets to be delivered reliably.