We investigate a binary classification problem in an edge intelligence system where false negatives are more costly than false positives. The system features a compact, locally deployed model, supplemented by a larger, remote model that is accessible via the network, albeit at an offloading cost. For each sample, our system first uses the locally deployed model for inference. Based on the output of the local model, the sample may be offloaded to the remote model. This work aims to understand the fundamental trade-off between classification accuracy and the offloading costs within such a hierarchical inference (HI) system. To optimise this system, we propose an online learning framework that continuously adapts a pair of thresholds on the local model's confidence scores. These thresholds determine the prediction of the local model and whether a sample is classified locally or offloaded to the remote model. We present a closed-form solution for the setting where the local model is calibrated. For the more general case of uncalibrated models, we introduce H2T2, an online two-threshold hierarchical inference policy, and prove it achieves sublinear regret. H2T2 is model-agnostic, requires no training, and learns during the inference phase using limited feedback. Simulations on real-world datasets show that H2T2 consistently outperforms naive and single-threshold HI policies, sometimes even surpassing single-threshold offline optima. The policy also demonstrates robustness to distribution shifts and adapts effectively to mismatched classifiers.

Motivated by the growing importance of flexible automation in industrial environments, this article investigates the impact of wireless solutions in collaborative robotics, toward which we provide a quality of control (QoC)-based abstraction and methodology that comprehensively captures the interplay between network-induced delays, reliability, and robotic workload parameters for wireless collaborative robotics (WCR). For such a setting, we formulate a joint control-communication co-design based optimization framework to maximize the QoC across all robots, for 5G resource dimensioning. This is crucial for identifying optimal co-design parameters maximizing the QoC for limited 5G bandwidth across different topologies of robotic connectivity, prior to the deployment of these WCRs, or when selecting appropriate connectivity priority levels. We compare the performance of our proposed algorithm to different state of the art schemes in the literature. Our simulation results highlight the latency-reliability tradeoff and its implications on the control performance. We also demonstrate that our abstraction can be utilized for control-communication co-design, identifying optimal latency-reliability points in conjunction with the maximum velocity the robots operate with, while highlighting the energy gains due to co-design as well.

Rapid advancements in digitalization and artificial intelligence (AI) have catalyzed the adoption of digital twin technologies in the construction sector, enabling real-time synchronization between virtual models and physical systems. Simultaneously, on-site robotic automation has shown promise for reducing physical workloads, enhancing productivity, and contributing to sustainability goals that are key values of Industry 5.0. However, current digital twin implementations rarely incorporate multirobot construction systems, often relying on single-robot approaches or purely offline simulations. This gap hinders the realization of truly integrated construction environments that combine sensing, data analytics, wireless communications, and multirobot coordination. In response, this article proposes ConstrucTwin, a digital twin-driven multirobot construction framework designed to support complex construction tasks in real-world settings. By combining a 5G communication estimation-involved architecture and a cross-level planning strategy, ConstrucTwin streamlines interactions between physical robots and their digital counterparts. Essential tasks such as motion and task-level planning, as well as remote human-in-the-loop (HIL) oversight, are orchestrated within a single unified architecture. Through case studies involving rebar cage and brick wall construction, we demonstrate how an integrated approach to vision-based servoing and multirobot coordination enhances execution speed, precision, and scalability. The results underscore the system’s potential to advance human-centric, resilient, and sustainable construction, thereby aligning with the broader vision of Industry 5.0.

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.

This paper highlights the value of Digital Twins (DTs) in optimizing and maintaining Cyber-Physical Systems (CPSs) across domains like manufacturing and communication. It proposes the interaction between an industry DT and a 6G DT and identifies Situational Awareness (SA) as a key step toward supporting critical services. A use case involving 6G communication with mobile User Equipment (UE) in manufacturing is analyzed to identify critical operating scenarios and how SA can predict and mitigate the risk of failure in these scenarios. Based on this use case, relevant parameters for improving the SA of the entire CPS are identified in both DTs and an example of interaction is given for optimizing the joint task planning and execution. Additionally, the benefits of cross-domain SA are demonstrated through 5G testbed measurements. The use case illustrates the broader applicability of our proposal.

This paper investigates a multi-user MIMO down-link network operating in the finite blocklength (FBL) regime. We propose an efficient power allocation scheme that balances overall system performance and user fairness. Specifically, we design a power allocation scheme to maximize the overall FBL throughput of the system. To address the non-convexity of the formulated problem, we employ a successive convex approximation (SCA) -based approach, transforming the non-convex problem into a series of convex subproblems to obtain the optimal power allocation. Subsequently, we introduce a fairness-oriented power allocation scheme that maximizes the minimum user FBL throughput. By combining these two approaches, we develop a unified power allocation scheme that effectively balances overall system performance and user fairness. Simulation results demonstrate that the proposed scheme efficiently addresses the trade-off between system-wide FBL performance and user fairness, providing a flexible solution for diverse application scenarios.

Time series anomaly detection (TSAD) is essential for ensuring the safety and reliability of intelligent and autonomous vehicles. In edge-cloud systems, this task is challenging due to limited on-board resources and real-time constraints. Deep learning (DL) models offer high accuracy but are too computationally demanding for embedded devices, whereas lightweight models are efficient but less precise. To address this trade-off, we propose a hierarchical cascaded framework for unsupervised multivariate TSAD, consisting of two lightweight Gaussian Mixture Models (GMMs) on the edge and a fully connected Variational Autoencoder (FC-VAE) in the cloud. An adaptive offloading mechanism based on online regret minimization dynamically decides when to escalate inputs, balancing inference cost and detection accuracy. Experiments on real-world sensor data from Scania's autonomous mining trucks show that the proposed method achieves accuracy within 1% of a cloud-only FC-VAE while reducing computation cost by over 85%. These results demonstrate that cost-aware hierarchical inference enables scalable and efficient real-time anomaly detection in edge-centric intelligent transportation 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$\times 45$. 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.

Despite years of research and the introduction of advanced features in 5G systems, no commercially available 5G device has yet met the stringent latency and reliability requirements defined for ultra-reliable and low-latency communication (URLLC). This raises fundamental questions about whether and how URLLC can be practically implemented under current technological constraints. To answer this question, in this work, we explore the combination of several features defined by the 3GPP standardization, including link adaptation, retransmission schemes, and scheduling algorithms. We evaluate the performance of these techniques to investigate the extent to which the requirements of URLLC can be achieved by using the NS-3 5G LENA simulator. Our results demonstrate that the shortest latency at which a reliability level of 99.999% can be achieved is 4 milliseconds, provided that the wireless channel is of exceptionally high quality. This level of performance is enabled by combining a retransmission scheme with an effective scheduling algorithm. However, in the presence of poor channel quality, it becomes necessary to relax the reliability constraint and/or provide more resources to the transmission to maintain system feasibility.

In this article, we consider an Industrial Internet of Things (IIoT) network operating under a carrier sense multiple access with collision avoidance (CSMA/CA) protocol. Latency-sensitive packets generated at multiple stations randomly are transmitted to a destination for decision making. To meet the strict timeliness constraint, the transmissions are carried by finite blocklength (FBL) codes, while the truncated hybrid automatic repeat request (HARQ) scheme is exploited to improve the reliability. For such unsaturated CSMA/CA networks, for the first time, we characterize the timeliness of packets utilized for decision-making via the age upon decisions (AuD) metric which emphasizes the information freshness at decision moments in comparison to age of information (AoI). To explicitly quantify the AuD performance, we develop an equivalent and tractable unsaturated Markov transfer model for the considered network and investigate the transmission probability and collision probability, respectively. Subsequently, the probability density functions of interarrival time and service time of the successfully transmitted packets are derived. We further derive a closed-form expression for the average AuD under a geometric decision process accordingly. Based on these characterizations, we aim at improving average AuD by jointly allocating the blocklength and transmit power. The formulated nonconvex problem is decomposed into subproblems, and we prove its joint convexity across all feasible intervals. Via simulations, we evaluate the performance of the considered network and conclude a series of design guidelines.

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.

Deep learning (DL) applications have rapidly evolved to address increasingly complex tasks by leveraging large-scale, resource-intensive models. However, deploying such models on low-power devices is not practical or economically scalable. While cloud-centric solutions satisfy these computational demands, they present challenges in terms of communication costs and latencies for real-time applications when every computation task is offloaded. To mitigate these concerns, hierarchical inference (HI) frameworks have been proposed, enabling edge devices equipped with small ML models to collaborate with edge servers by selectively offloading complex tasks. Existing HI approaches depend on immediate offloading of data upon selection, which can lead to inefficiencies due to frequent communication, especially in time-varying wireless environments. In this work, we introduce Batch HI, an approach that offloads samples in batches, thereby reducing communication overhead and improving system efficiency while achieving similar performance as existing HI methods. Additionally, we find the optimal batch size that attains a crucial balance between responsiveness and system time, tailored to specific user requirements. Numerical results confirm the effectiveness of our approach, highlighting the scenarios where batching is particularly beneficial.

Low-power wireless Internet of Things (IoT) devices employ Time Slotted Channel Hopping (TSCH) Medium Access Control to achieve predictable timing behaviour. TSCH aims at collision-free scheduling by exploiting diversity over time (slots) and frequency (channels). However, existing works on performance and worst-case analysis are based on deterministic models, which lead to rather pessimistic non-realistic results, i.e. tools for probabilistic performance analysis of TSCH schedulers are still lacking. In this context, we devised a Stochastic Network Calculus model that enables to calculate end-to-end delays for specific traffic flows and (deadline) violation probability, building on Moment Generating Functions. We instantiate this SNC model and provide bounds for three widely used TSCH schedulers, namely Minimal Scheduling Function, Orchestra, and a custom collision-free scheduler, with different parameters such as radio duty-cycle, radio link quality, and traffic arrival rate. We demonstrate that our proposed model closely follows the simulation results, under different network scenarios.

The increasing demand for latency-sensitive applications has necessitated the development of sophisticated algorithms that efficiently manage packets with end-to-end delay targets traversing the networked infrastructure. Network components must consider minimizing the packets' end-to-end delay violation probabilities (DVP) as a guiding principle throughout the transmission path to ensure timely deliveries. Active queue management (AQM) schemes are commonly used to mitigate congestion by dropping packets and controlling queuing delay. Today's established AQM schemes are threshold-driven, identifying congestion and trigger packet dropping using a predefined criteria which is unaware of packets' DVPs. In this work, we propose a novel framework, Delta, that combines end-to-end delay characterization with AQM for minimizing DVP. In a queuing theoretic environment, we show that such a policy is feasible by utilizing a data-driven approach to predict the queued packets' DVPs. That enables Delta AQM to effectively handle links with arbitrary stationary service time processes. The implementation is described in detail, and its performance is evaluated and compared with state of the art AQM algorithms. Our results show the Delta outperforms current AQM schemes substantially, in particular in scenarios where high reliability, i.e. high quantiles of the tail latency distribution, are of interest.

With the emergence of new application areas, such as cyber-physical systems and human-in-the-loop applications, there is a need to guarantee a certain level of end-to-end network latency with extremely high reliability, e.g., 99.999%. While mechanisms specified under IEEE 802.1AS time-sensitive networking (TSN) can be used to achieve these requirements for switched Ethernet networks, implementing TSN mechanisms in wireless networks is challenging due to their stochastic nature. To conform the wireless link to a reliability level of 99.999%, the behavior of extremely rare outliers in the latency probability distribution, or the tail of the distribution, must be analyzed and controlled. This work proposes predicting the tail of the latency distribution using state-of-the-art data-driven approaches, such as mixture density networks (MDN) and extreme value mixture models, to estimate the likelihood of rare latencies conditioned on the network parameters, which can be used to make more informed decisions in wireless transmission. Actual latency measurements of a commercial private and a software-defined 5G network are used to benchmark the proposed approaches and evaluate their sensitivities concerning the tail probabilities. Our benchmarks highlight how the proposed methods, aided by noise regularization, achieve an acceptable accuracy in the extreme 99.9999% latency probabilities.

This paper presents ExPECA, an edge computing and wireless communication research testbed designed to tackle two pressing challenges: comprehensive end-to-end experimentation and high levels of experimental reproducibility. Leveraging OpenStack-based Chameleon Infrastructure (CHI) framework for its proven flexibility and ease of operation, ExPECA is located in a unique, isolated underground facility, providing a highly controlled setting for wireless experiments. The testbed is engineered to facilitate integrated studies of both communication and computation, offering a diverse array of Software-Defined Radios (SDR) and Commercial Off-The-Shelf (COTS) wireless and wired links, as well as containerized computational environments. We exemplify the experimental possibilities of the testbed using OpenRTiST, a latency-sensitive, bandwidth-intensive application, and analyze its performance. Lastly, we highlight an array of research domains and experimental setups that stand to gain from ExPECA's features, including closed-loop applications and time-sensitive networking.

Since its first release in the late 1990s, Wi-Fi has been updated to keep up with evolving user needs. Recently, Wi-Fi and other radio access technologies have been pushed to their edge when serving Augmented Reality (AR) applications. AR applications require high throughput, low latency, and high reliability to ensure a high-quality user experience. The 802.11be amendment – which will be marketed as Wi-Fi 7 – introduces several features that aim to enhance its capabilities to support challenging applications like AR. One of the main features introduced in this amendment is Multi-Link Operation (MLO) which allows nodes to transmit and receive over multiple links concurrently. When using MLO, traffic is distributed among links using an implementation-specific traffic-to-link allocation policy. This paper aims to evaluate the performance of MLO, using different policies, in serving AR applications compared to Single-Link (SL). Experimental simulations using an event-based Wi-Fi simulator have been conducted. Our results show the general superiority of MLO when serving AR applications. MLO achieves lower latency and serves a higher number of AR users compared to SL with the same frequency resources. In addition, increasing the number of links can improve the performance of MLO. Regarding traffic-to-link allocation policies, we found that policies can be more susceptible to channel blocking, resulting in possible performance degradation.

As wireless systems evolve, the problems of radio resource management (RRM) become harder to solve. Once the additional constraint of energy-efficient utilization of resources is factored in, these problems become even more challenging. Thus, experts started developing solutions based on complex artificial intelligence (AI) models that, unfortunately, suffer from a performance-explainability trade-off. In this work, we propose an explainable AI (XAI) methodology for addressing this tradeoff. Our methodology can be used to generate feature importance explanations of AI models through three XAI methods: (i) Kernel SHapley Additive exPlanations (SHAP), (ii) Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models (CERTIFAI), and (iii) Anchors. For Anchors, we formulate a new feature importance score based on the feature’s presence within the rules built by the method. We then use the generated explanations to improve the understanding of the model and reduce its complexity through a feature selection process. By applying our methodology to a reinforcement learning (RL) agent designed for energy-efficient RRM, we were able to reduce its complexity by approximately 27%−62% according to various metrics, without losing performance. Additionally, we show the possibility to replace the AI-based inference process with an Anchors-based inference process with similar performance and higher interpretability for humans.

End-to-end learning for wireless communications has recently attracted much interest in the community, owing to the emergence of deep learning-based architectures for the physical layer. Neural network-based autoencoders have been proposed as potential replacements of traditional model-based transmitter and receiver structures. Such a replacement primarily provides an unprecedented level of flexibility, allowing to tune such emerging physical layer network stacks in many different directions. The semantic relevance of the transmitted messages is one of those directions. In this paper, we leverage a specific semantic relationship between the occurrence of a message (the source), and the channel statistics. Such a scenario could be illustrated for instance, in vehicular communications where the distance is to be conveyed between a leader and a follower. We study two autoencoder approaches where these special circumstances are exploited. We then evaluate our autoencoders, showing through the simulations that the semantic optimization can achieve significant improvements in the BLERs (up till 93.6%) and RMSEs (up till 87.3%) for vehicular communications leading to considerably reduced risks and needs for message retransmissions.

We study the problem of finding efficient sampling policies in an edge-based feedback system, where sensor samples are offloaded to a back-end server that processes them and generates feedback to a user. Sampling the system at maximum frequency results in the detection of events of interest with minimum delay but incurs higher energy costs due to the communication and processing of redundant samples. On the other hand, lower sampling frequency results in higher delay in detecting the event, thus increasing the idle energy usage and degrading the quality of experience. We quantify this trade-off as a weighted function between the number of samples and the sampling interval. We solve the minimisation problem for exponential and Rayleigh distributions, for the random time to the event of interest. We prove the convexity of the objective functions by using novel techniques, which can be of independent interest elsewhere. We argue that adding an initial offset to the periodic sampling can further reduce the energy consumption and jointly compute the optimum offset and sampling interval. We apply our framework to two practically relevant applications and show energy savings of up to$36\%$when compared to an existing periodic scheme.

We consider a finite-state Discrete-Time Markov Chain (DTMC) source that can be sampled for detecting the events when the DTMC transits to a new state. Our goal is to study the trade-off between sampling frequency and staleness in detecting the events. We argue that, for the problem at hand, using Age of Information (AoI) for quantifying the staleness of a sample is conservative and therefore, study another freshness metricage penalty, which is defined as the time elapsed since the first transition out of the most recently observed state. We study two optimization problems: minimize average age penalty subject to an average sampling frequency constraint, and minimize average sampling frequency subject to an average age penalty constraint; both are Constrained Markov Decision Problems. We solve them using the Lagrangian MDP approach, where we also provide structural results that reduce the search space. Our numerical results demonstrate that the computed Markov policies not only outperform optimal periodic sampling policies, but also achieve sampling frequencies close to or lower than that of an optimal clairvoyant (non-causal) sampling policy, if a small age penalty is allowed.

Interactive applications with automated feedback will largely influence the design of future networked infrastructures. In such applications, status information about an environment of interest is captured and forwarded to a compute node, which analyzes the information and generates a feedback message. Timely processing and forwarding must ensure the feedback information to be still applicable; thus, the quality-of-service parameter for such applications is the end-to-end latency over the entire loop. By modelling the communication of a feedback loop as a two-hop network, we address the problem of allocating network resources in order to minimize the delay violation probability (DVP), i.e. the probability of the end-to-end latency exceeding a target value. We investigate the influence of the network queue states along the network path on the performance of semi-static and dynamic scheduling policies. The former determine the schedule prior to the transmission of the packet, while the latter benefit from feedback on the queue states as time evolves and reallocate time slots depending on the queue’s evolution. The performance of the proposed policies is evaluated for variations in several system parameters and comparison baselines. Results show that the proposed semi-static policy achieves close-to-optimal DVP and the dynamic policy outperforms the state-of-the-art algorithms.

We consider a mobile edge computing (MEC) network supporting low-latency, critical offloading workloads. The task offloading from the user to the server is operated under a truncated Hybrid Automatic Repeat reQuest (HARQ) process, i.e., we consider finite retransmission attempts. Both the HARQ type-I and type-II schemes are studied. For each scheme, we first characterize the total error probability and the total energy cost, while the impact of finite blocklength (FBL) on the stochastic retransmission behavior is considered. Following the characterizations, we are interested in optimal frameworks for each considered HARQ type, where the number of potential retransmission attempts is optimized together with the duration of each transmission, while the CPU frequency at the edge node is adjusted via voltage scaling. The objective is to minimize the total energy cost with error probability threshold. We show that the resulting stochastic optimization problems can be solved by means of convex optimization. We furthermore demonstrate that sharp minima exist among the energy consumption, underlying the importance of near-optimal parameter choice in the studied scenarios. Our results underline the importance of trading off communication and computational characteristics in delay-critical MEC setups with FBL codes.

Physical layer authentication (PLA) has recently been discussed in the context of URLLC due to its low complexity and low overhead. Nevertheless, these schemes also introduce additional sources of error through missed detections and false alarms. The trade-offs of these characteristics are strongly dependent on the deployment scenario as well as the processing architecture. Thus, considering a feature-based PLA scheme utilizing channel-state information at multiple distributed radio-heads, we study these trade-offs analytically. We model and analyze different scenarios of centralized and decentralized decision-making and decoding, as well as the impacts of a single-antenna attacker launching a Sybil attack. Based on stochastic network calculus, we provide worst-case performance bounds on the system-level delay for the considered distributed scenarios under a Sybil attack. Results show that the arrival-rate capacity for a given latency deadline is increased for the distributed scenarios. For a clustered sensor deployment, we find that the distributed approach provides 23% higher capacity when compared to the centralized scenario.

Driven by standardization and commercialization, digital infrastructures evolve in waves. Over the last few years, a particular focus has been on realizing ultra-reliable low-latency wireless communications (URLLC), anticipated mostly for rather specific use cases in industrial automation. Even though initial such systems finally exist today - with future network releases advancing URLLC capabilities even more - the broad market impact to date is low. We argue in this paper that an essential missing component for corresponding dependable applications like closed-loop control or human-in-the-loop are nearby compute capabilities provided within the infrastructure, aka edge computing capabilities. Only in conjunction can such future infrastructures support dependable applications to a full extent. Nevertheless, this also leads to unique challenges which will be central to the evolution of networked infrastructures during the current decade. Out of this evolution of networked infrastructures, we finally argue that a new type of networked application class will emerge, resembling the representation of various aspects of reality in the infrastructure at any point in time. We dub this development the Internet of Reality, and discuss further challenges in this context.

There is a growing interest in analysing freshness of data in networked systems. Age of Information (AoI) has emerged as a relevant metric to quantify this freshness at a receiver, and minimizing this metric for different system models has received significant research attention. However, a fundamental question remains: what is the minimum achievable AoI in any single-server-single-source queuing system for a given service-time distribution? We address this question for the average peak AoI (PAoI) statistic by considering generate-at-will source model, service preemptions, and request delays. Our main result is on the characterization of the minimum achievable average PAoI, and we show that it is achieved by a fixed-threshold policy among the set of all causal policies. We use the characterization to provide necessary and sufficient condition for preemptions to be beneficial for a given service-time distribution. Our numerical results, obtained using well-known distributions, demonstrate that the heavier the tail of a distribution the higher the performance gains of using preemptions.

Age of Information (AoI) has proven to be a useful metric in networked systems where timely information updates are of importance. In the literature, minimizing “average age” has received considerable attention. However, various applications pose stricter age requirements on the updates which demand knowledge of the AoI distribution. Furthermore, the analysis of AoI distribution in a multi-hop setting, which is important for the study of Wireless Networked Control Systems (WNCS), has not been addressed before. Toward this end, we study the distribution of AoI in a WNCS with two hops and devise a problem of minimizing the tail of the AoI distribution with respect to the frequency of generating information updates, i.e., the sampling rate of monitoring a process, under first-come-first-serve (FCFS) queuing discipline. We argue that computing an exact expression for the AoI distribution may not always be feasible; therefore, we opt for computing upper bounds on the tail of the AoI distribution. Using these upper bounds, we formulate Upper Bound Minimization Problems (UBMP), namely, Chernoff-UBMP and α-relaxed Upper Bound Minimization Problem (α-UBMP), where α > 1 is an approximation factor, and solve them to obtain “good” heuristic rate solutions for minimizing the tail. We demonstrate the efficacy of our approach by solving the proposed UBMPs for three service distributions: geometric, exponential, and Erlang. Simulation results show that the rate solutions obtained are near optimal for minimizing the tail of the AoI distribution for the considered distributions.

In future industrial scenarios, Wireless Sensor Networks (WSN) are envisioned to support the traffic of Networked Control Systems (NCS). WirelessHART is a prevalent WSN protocol that uses the Time Slotted Channel Hopping (TSCH) medium access to cope with the delay and reliability requirements of NCS in the harsh industrial environment. In TSCH, time slots and frequencies can be scheduled by a network coordinator to provide Quality of Service (QoS). In contrast to previous works that consider the end-to-end delay requirement of a flow of packets, we focus on a finite sequence of time-critical packets. These packets may belong to a time-critical message whose latency could significantly impact the NCS. Given an end-to-end delay deadline, our objective is to minimize the Delay Violation Probability (DVP) for a finite sequence of packets by dynamically scheduling the time slots in each frame. This is a challenging task as DVP depends on the instantaneous state of the network and requires its transient analysis. In this work, we model the wireless NCS as a two-queue lossy wireless network and propose the first transient analysis of DVP for a finite sequence of time-critical packets. Noting that DVP cannot be directly used for dynamic resource allocation, we propose a heuristic algorithm by relating DVP with the network's throughput. The proposed heuristic maximizes the expected throughput, is computed by solving a finite-horizon Markov Decision Process (MDP), and can be implemented at the network coordinator. Using simulation we demonstrate that the MDP-based heuristic achieves lower DVP compared to the classical MaxWeight and Weighted-Fair Queuing.

There is a growing interest in analysing the freshness of data in networked systems. Age of Information (AoI) has emerged as a popular metric to quantify this freshness at a given destination. There has been a significant research effort in optimizing this metric in communication and networking systems under different settings. In contrast to previous works, we are interested in a fundamental question, what is the minimum achievable AoI in any single-server-single-source queuing system for a given service-time distribution? To address this question, we study a problem of optimizing AoI under service preemptions. Our main result is on the characterization of the minimum achievable average peak AoI (PAoI). We obtain this result by showing that a fixed-threshold policy is optimal in the set of all randomized-threshold causal policies. We use the characterization to provide necessary and sufficient conditions for the service-time distributions under which preemptions are beneficial.

In this article, we investigate the transient behavior of a sequence of packets/bits traversing a multi-hop wireless network under static routing. Our work is motivated by novel applications from the domain of process automation, Machine-Type Communication (MTC) and cyber-physical systems, where short messages are communicated and statistical guarantees need to be provided on a per-message level. In order to optimize such a network, apart from understanding the stationary system dynamics, an understanding of the short-term dynamics (i.e. transient behavior) is also required. To this end, we derive novel Wireless Transient Bounds (WTB) for end-to-end delay and backlog in a multi-hop wireless network using stochastic network calculus approach. We start by analyzing a single end-to-end path, i.e. a line topology, and then we show how the obtained results can be applied to a mesh network with static routing using a concept called 'leftover service'. WTB depends on the initial backlog at each node as well as the instantaneous channel states. We numerically compare WTB with Kernel-Based-Transient Bound (KBTB), which can be obtained by adapting existing stationary bound, as well as simulated end-to-end delay of the investigated network. While KBTB and stationary bounds are not able to capture the short-term system dynamics well, WTB provides relatively tight upper bound and has a decay rate that closely matches the simulation. This is achieved by WTB only with a slight increase in the computational complexity, by a factor of O(T + N), where T is the duration of the arriving sequence and N is the number of hops in the network. We believe that the presented analysis and the bounds are necessary tools for future work on transient network optimization for many important emerging applications, e.g., massive MTC, critical MTC, edge computing and autonomous vehicle.

We study whether using non-orthogonal multiple access (NOMA) in the uplink of a mobile network can reduce the queueing delay compared to orthogonal multiple access (OMA) when the system requires communications at very low latency and high reliability. We first consider an ideal system model with perfect channel state information (CSI) at the transmitter and long codewords, where we determine the optimal decoding orders when the decoder uses successive interference cancellation (SIC) and derive closed-form expressions for the optimal rate when joint decoding is used. While joint decoding performs well even under tight delay constraints, NOMA with SIC decoding often performs worse than OMA. For low-latency systems, we must also consider the impact of finite-length channel coding, as well as rate adaptation based imperfect CSI. We derive closed-form approximations for the corresponding outage or error probabilities and find that those effects create a larger performance penalty for NOMA than for OMA. Thus, NOMA with SIC decoding may often be unsuitable for low-latency systems.

Over the last decade there has been a large research interest in exploiting terminal positions for various cellular network services and communication aspects. However, the relevance of terminal coordinates for resource allocation is relatively unexplored to date. In this work, we thus take a first step in that direction by studying coordinates-based resource allocation in an arguably favorable, and straightforward set-up. In particular, we consider the usage of supervised machine learning for resource allocation. Our results show that for the studied scenario, coordinates-based resource allocation can achieve a comparable performance to a CSI-based comparison scheme. While the main limiting factors are channel uncertainty as well as the accuracy of the terminal coordinates, in particular more complex machine learning schemes like Random Forests are able to provide some robustness despite the above mentioned noisy features.

This paper proposes a new approach for physical layer authentication where transmissions are authenticated based on the single-input/multiple-output channel-states observed at multiple distributed antenna-arrays. The receiver operating characteristics (ROC) are derived in terms of closed form expressions for the false alarm and missed detection probability in order to evaluate the effectiveness compared to single-array authentication. To this end, we study the worst-case missed detection probability based on the optimal attacker position. Finally, we apply our previously developed queueing analytical tools, based on stochastic network calculus, in order to assess the delay performance impacts of the physical layer authentication scheme in a mission-critical communication scenario. Our results show that the distributed approach significantly outperforms single-array authentication in terms of worst-case missed detection probability and that this can help mitigating the delay performance impacts of authentication false alarms.

Since Age of Information (AoI) has been proposed as a metric that quantifies the freshness of information updates in a communication system, there has been a constant effort in understanding and optimizing different statistics of the AoI process for classical queueing systems. In addition to classical queuing systems, more recently, systems with no queue or a unit capacity queue storing the latest packet have been gaining importance as storing and transmitting older packets do not reduce AoI at the receiver. Following this line of research, we study the distribution of AoI for the GI/GI/1/1 and GI/GI/1/2* systems, under non-preemptive scheduling. For any single-source-single-server queueing system, we derive, using sample path analysis, a fundamental result that characterizes the AoI violation probability, and use it to obtain closed-form expressions for D/GI/1/1, M/GI/1/1 as well as systems that use zero-wait policy. Further, when exact results are not tractable, we present a simple methodology for obtaining upper bounds for the violation probability for both GI/GI/1/1 and GI/GI/1/2* systems. An interesting feature of the proposed upper bounds is that, if the departure rate is given, they overestimate the violation probability by at most a value that decreases with the arrival rate. Thus, given the departure rate and for a fixed average service, the bounds are tighter at higher utilization.

We study the detection and delay performance impacts of a feature-based physical layer authentication (PLA) protocol in mission-critical machine-type communication (MTC) networks. The PLA protocol uses generalized likelihood-ratio testing based on the line-of-sight (LOS), single-input multiple-output channel-state information in order to mitigate impersonation attempts from an adversary node. We study the detection performance, develop a queueing model that captures the delay impacts of erroneous decisions in the PLA (i.e., the false alarms and missed detections), and model three different adversary strategies: data injection, disassociation, and Sybil attacks. Our main contribution is the derivation of analytical delay performance bounds that allow us to quantify the delay introduced by PLA that potentially can degrade the performance in mission-critical MTC networks. For the delay analysis, we utilize tools from stochastic network calculus. Our results show that with a sufficient number of receive antennas (approximately 4-8) and sufficiently strong LOS components from legitimate devices, PLA is a viable option for securing mission-critical MTC systems, despite the low-latency requirements associated to corresponding use cases. Furthermore, we find that PLA can be very effective in detecting the considered attacks, and in particular, it can significantly reduce the delay impacts of disassociation and Sybil attacks.

We use stochastic network calculus to investigate the delay performance of a multiuser MISO system with zero-forcing beamforming. First, we consider ideal assumptions with long codewords and perfect CSI at the transmitter, where we observe a strong channel hardening effect that results in very high reliability with respect to the maximum delay of the application. We then study the system under more realistic assumptions with imperfect CSI and finite blocklength channel coding. These effects lead to interference and to transmission errors, and we derive closed-form approximations for the resulting error probability. Compared to the ideal case, imperfect CSI and finite length coding cause massive degradations in the average transmission rate. Surprisingly, the system nevertheless maintains the same qualitative behavior as in the ideal case: as long as the average transmission rate is higher than the arrival rate, the system can still achieve very high reliability with respect to the maximum delay.

Future mobile networks will provide support for real-time control applications. The tight real-time and reliability constraints of these applications introduce novel challenges for mobility management. Legacy individual handover schemes do not sufficiently address these issues, as they do not consider physical interactions between mobile nodes. A novel group handover scheme is proposed which allows for the simultaneous handover of a group of nodes. Both the individual and the group handover are modeled as discrete-time Markov chains. Based on these models expressions for the stochastic handover delay are derived. The results are numerically evaluated in a vehicle platooning scenario. The group handover is shown to significantly reduce the handover delay in comparison to the individual handover. Furthermore, the group handover is shown to scale well when the number of vehicles increases. These improvements are shown to come at the cost of an increased messaging overhead.

We analyze a MISO downlink channel where a multi-antenna transmitter communicates with a large number of single-antenna receivers. Using linear beamforming or nonlinear precoding techniques, the transmitter can serve multiple users simultaneously during each transmission slot. However, increasing the number of users, i.e., the multiplexing gain, reduces the beamforming gain, which means that the individual data rates decrease. We use stochastic network calculus to analyze the queueing delay that occurs due to the time-varying data rates. Our results show that the optimal number of users, i.e., the optimal trade-off between multiplexing gain and beamforming gain, depends on incoming data traffic and its delay requirements.

Random Access Channel (RACH) has been identified as one of the major bottlenecks for accommodating massive number of Machine-to-Machine (M2M) devices in LTE networks, especially in the case of bursty arrivals of connection requests. As a consequence, the burst resolution problem has sparked a large number of works analyzing and optimizing the expected performance of RACH. In this paper, we go beyond the study of performance in expectation by investigating the probabilistic performance limits of RACH with access class barring. We model RACH as a queuing system, and apply stochastic network calculus to derive probabilistic performance bounds for burst resolution time, i.e., the time it takes to connect a burst of M2M devices to the base station. We illustrate the accuracy of the proposed methodology and its potential applications in performance assessment and system dimensioning.

Existing wireless communication systems are not able to meet the stringent requirements for critical machine-to-machine communications regarding ultra-reliability and low-latency. Since increasing the communication reliability often comes at the price of increasing the latency as well, new mechanisms must be proposed that consider both challenges together. A promising approach, according to analytical work, is to increase the reliability by using cooperative diversity, where all stations within range help each other in the transmission process. Theoretical analyses, however, only provide a limited insight regarding the actual performance due to the strong assumptions they make to model such complex systems. In this paper, we thus evaluate the practical feasibility of ultra-reliable low-latency communication through cooperation by designing a data link protocol that incorporates a best relay selection mechanism. We implement our protocol in a real-world testbed, consisting of software-defined radios, to gain a better understanding of how future ultra-reliable low-latency systems should be designed and implemented. Our measurement campaigns show that at a given low target latency of 1 ms, we achieve a packet error rate between 10−5 and 10−7 with a standard 802.11a physical layer.

With the rise of critical machine-to-machine applications, next generation wireless communication systems must meet challenging requirements with respect to latency and reliability. A key question in this context relates to channel state estimation, which allows the transmitter to adapt the code rate to the channel state. In this paper, we characterize the tradeoff between the training sequence length and data codeword length: shorter channel estimation leaves more time for the payload transmission but reduces the estimation accuracy and causes more decoding errors. Using lower coding rates can mitigate this effect, but may result in a higher backlog of data at the transmitter. In order to optimize the training sequence length and the rate adaptation scheme with respect to the delay performance, we employ queuing analysis on top of accurate models of the physical layer. We obtain an analytically tractable solution to the problem by deriving a closed-form approximation for the decoding error probability due to imperfect channel knowledge and finite-blocklength channel coding. The optimized training sequence length and rate adaptation strategy can reduce the delay violation probability by an order of magnitude, compared with suboptimal strategies that do not consider the delay constraints.

The abundance of spectrum in the millimeter-wave (mm-wave) bands makes it an attractive alternative for future wireless communication systems. Such systems are expected to provide data transmission rates in the order of multi-gigabits per second in order to satisfy the ever-increasing demand for high rate data communication. Unfortunately, mm-wave radio is subject to severe path loss, which limits its usability for long-range outdoor communication. In this paper, we propose a multi-hop mm-wave wireless network for outdoor communication, where multiple full-duplex buffered relays are used to extend the communication range, while providing end-to-end performance guarantees to the traffic traversing the network. We provide a cumulative service process characterization for the mm-wave propagation channel with self-interference in terms of the moment generating function of its channel capacity. Then, we then use this characterization to compute probabilistic upper bounds on the overall network performance, i.e., total backlog and end-to-end delay. Furthermore, we study the effect of self-interference on the network performance and propose an optimal power allocation scheme to mitigate its impact in order to enhance network performance. Finally, we investigate the relation between relay density and network performance under a sum power constraint. We show that increasing relay density may have adverse effects on network performance, unless the self-interference can be kept sufficiently small.

In this paper, we investigate the problem of mitigating interference between so-called antenna domains of a cloud radio access network (C-RAN). In contrast to previous work, we turn to an approach utilizing primarily the optimal assignment of users to central processors in a C-RAN deployment. We formulate this user assignment problem as an integer optimization problem and propose an iterative algorithm for obtaining a solution. Motivated by the lack of optimality guarantees on such solutions, we opt to find lower bounds on the problem and the resulting interference leakage in the network. We thus derive the corresponding Dantzig-Wolfe decomposition, formulate the dual problem, and show that the former offers a tighter bound than the latter. We highlight the fact that the bounds in question consist of linear problems with an exponential number of variables and adapt the column generation method for solving them. In addition to shedding light on the tightness of the bounds in question, our numerical results show significant sum-rate gains over several comparison schemes. Moreover, the proposed scheme delivers similar performance as weighted minimum mean squared-error (MMSE) with a significantly lower complexity (around 10 times less).

Various industrial control applications have stringent end-to-end latency requirements in the order of a few milliseconds. Software-defined networking (SDN) is a promising solution in order to meet these stringent requirements under varying traffic patterns, as it enables the flexible management of flows across the network. Thus, SDN allows to ensure that traffic flows use congestion-free paths, reducing the delay to forwarding and processing delays at the SDN nodes. However, accommodating new flows at runtime is under such a setting challenging as it may require the migration of existing flows, without interrupting ongoing traffic. In this paper, we consider the problem of dynamic flow migration and propose a polynomial time algorithm that can find a solution if direct flow migration is feasible. We furthermore propose an algorithm for computing both direct and indirect flow migration and prove its correctness. Numerical results obtained on a FatTree network topology show that flow migration is typically necessary for networks with a moderate number of flows, while direct flow migration is feasible in around 60% of the cases.

Feature-based authentication schemes that verify wireless transmitter identities based on physical-layer features allow for fast and efficient authentication with minimal overhead. Hence, they are interesting to consider for safety-critical applications where low latency and high reliability is required. However, as erroneous authentication decisions will introduce delays, we propose to study the impact of feature-based schemes on the system-level performance. In this paper, we therefore study the queuing performance of a line-of-sight wireless link that employs a feature- based authentication scheme based on the complex channel gain. Using stochastic networks calculus, we provide bounds on the delay performance which are validated by numerical simulations. The results show that the delay and authentication performance is highly dependent on the SNR and Rice factor. However, under good channel conditions, a missed-detection rate of 10E-8 can be achieved without introducing excessive delays in the system.

In this paper, we investigate the performance of the wiretap Rayleigh fading channel in the presence of statistical delay constraints. We invoke tools from stochastic network calculus to derive probabilistic bounds on the delay. This method requires a statistical characterization of the wiretap fading service process, which we derive in closed form. We then validate these analytical bounds via simulations. Interestingly, the analysis of the wiretap fading channel reveals close structural similarities with the interference channel in terms of service process characterization, which is derived in our prior work. In our numerical evaluations, we show that the delay performance of the wiretap fading channel is in particular sensitive to bursty arrival processes due to the high variance of the service process.

Exhaustive testing of wireless communication protocols on prototypical hardware is costly and time-consuming. An alternative approach is network simulation, which, however, often strongly abstracts from the actual hardware. Especially in the wireless domain, such abstractions often lead to inaccurate simulation results. Therefore, we propose a code-transparent discrete event simulator that enables a direct simulation of existing code for wireless prototypes. With a focus on lower layers of the communication stack, we enable a parametrization of the simulation timings based on real-world measurements to increase the simulation accuracy. Our evaluation shows that we achieve close results for throughput (deviation below 3% for UDP and latency (corrected deviation about 13% compared to real-world setups, while providing the benefits of code-transparent simulation, i.e., to flexibly simulate large topologies with existing prototype code. Moreover, we demonstrate that our approach finds implementation defects in existing hardware prototype software, which are otherwise difficult to track down in real deployments.

In this paper, we extend our previous work on user assignment in Cloud-RAN, where we proposed an algorithm for user assignment (UA). We motivate the inherent fairness issue that is present in the latter UA scheme, since some users in the system will never get served. For that purpose, we propose two schemes to be used in conjunction with aforementioned UA scheme, to improve its fairness. The first scheme aims at improving the minimum throughput (MT), by selecting users with lowest throughput, as input to the UA algorithm, and to be (potentially) scheduled in the next time slot. The second scheme is based on round-robin (RR) scheduling, where the set of potentially scheduled users (for the next slot), is done by excluding all the previously served users, in that round. Moreover, the subset of actual users to be served, is determined using the UA algorithm. We evaluate their fairness and sum-rate performance, via extensive simulations. While one might have expected a tradeoff between the sum-rate performance and fairness, our results showed that MT improves both metric, when compared to the original UA algorithm (without fairness), for some choice of parameter values.

To meet the ever-increasing demands on higher throughput and better network delay performance, 60 GHZ networking is proposed as a promising solution for the next generation of wireless communications. To successfully deploy such networks, its important to understand their performance first. However, due to the unique fading characteristic of the 60 GHz channel, the characterization of the corresponding service process, offered by the channel, using the conventional methodologies may not be tractable. In this work, we provide an alternative approach to derive a closed-form expression that characterizes the cumulative service process of the 60 GHz channel in terms of the moment generating function (MGF) of its instantaneous channel capacity. We then use this expression to derive probabilistic upper bounds on the backlog and delay that are experienced by a flow traversing this network, using results from the MGF-based network calculus. The computed bounds are validated using simulation. We provide numerical results for different networking scenarios and for different traffic and channel parameters and we show that the 60 GHz wireless network is capable of satisfying stringent quality-of-Service (QoS) requirements, in terms of network delay and reliability. With this analysis approach at hand, a larger scale 60 GHz network design and optimization is possible.

We study here the problem of Antenna Domain Formation (ADF) in cloud RAN systems, whereby multiple remote radio-heads (RRHs) are each to be assigned to a set of antenna domains (ADs), such that the total interference between the ADs is minimized. We formulate the corresponding optimization problem, by introducing the concept of interference coupling coefficients among pairs of radio-heads. We then propose a low-overhead algorithm that allows the problem to be solved in a distributed fashion, among the aggregation nodes (ANs), and establish basic convergence results. Moreover, we also propose a simple relaxation to the problem, thus enabling us to characterize its maximum performance. We follow a layered coordination structure: after the ADs are formed, radio-heads are clustered to perform coordinated beamforming using the well known Weighted-MMSE algorithm. Finally, our simulations show that using the proposed ADF mechanism would significantly increase the sum-rate of the system (with respect to random assignment of radio-heads).

Low-power wireless devices suffer notoriously from Cross- Technology Interference (CTI). To enable co-existence, researchers have proposed a variety of interference mitigation strategies. Existing solutions, however, are designed to work with the limitations of currently available radio chips. In this paper, we investigate how to exploit physical layer properties of 802.15.4 signals to better address CTI. We present CrossZig, a cross-layer solution that takes advantage of physical layer information and processing to improve low-power communication under CTI. To this end, CrossZig utilizes physical layer information to detect presence of CTI in a corrupted packet and to apply an adaptive packet recovery which incorporates a novel cross-layer based packet merging and an adaptive FEC coding. We implement a prototype of CrossZig for the low-power IEEE 802.15.4 in a software-defined radio platform. We show the adaptability and the performance gain of CrossZig through experimental evaluation considering both micro-benchmarking and system performance under various interference patterns. Our results demonstrate that CrossZig can achieve a high accuracy in error localization (94.3% accuracy) and interference type identification (less than 5% error rate for SINR ranges below 3 dB). Moreover, our system shows consistent performance improvements under interference from various interfering technologies.

Provision of high data rates with always-on connectivity to high mobility users is one of the motivations for design of fifth generation (5G) systems. High system capacity can be achieved by coordination between large number of antennas, which is done using the cloud radio access network (CRAN) design in 5G systems. In terms of baseband processing, allocation of appropriate resources to the users is necessary to achieve high system capacity, for which the state of the art uses the users' channel state information (CSI); however, they do not take into account the associated overhead, which poses a major bottleneck for the effective system performance. In contrast to this approach, this paper proposes the use of machine learning for allocating resources to high mobility users using only their position estimates. Specifically, the `random forest' algorithm, a supervised machine learning technique, is used to design a learning-based resource allocation scheme by exploiting the relationships between the system parameters and the users' position estimates. In this way, the overhead for CSI acquisition is avoided by using the position estimates instead, with better spectrum utilization. While the initial numerical investigations, with minimum number of users in the system, show that the proposed learning-based scheme achieves 86% of the efficiency achieved by the perfect CSI-based scheme, if the effect of overhead is factored in, the proposed scheme performs better than the CSI-based approach. In a realistic scenario, with multiple users in the system, the significant increase in overhead for the CSI-based scheme leads to a performance gain of 100%, or more, by using the proposed scheme, and thus proving the proposed scheme to be more efficient in terms of system performance.

Recently, the wireless networking community is getting more and more interested in novel protocol designs for safety-critical applications. These new applications come with unprecedented latency and reliability constraints which poses many open challenges. A particularly important one relates to the question how to develop such systems. Traditionally, development of wireless systems has mainly relied on simulations to identify viable architectures. However, in this case the drawbacks of simulations - in particular increasing run-times - rule out its application. Instead, in this paper we propose to use probabilistic model checking, a formal model-based verification technique, to evaluate different system variants during the design phase. Apart from allowing evaluations and therefore design iterations with much smaller periods, probabilistic model checking provides bounds on the reliability of the considered design choices. We demonstrate these salient features with respect to the novel EchoRing protocol, which is a token-based system designed for safety-critical industrial applications. Several mechanisms for dealing with a token loss are modeled and evaluated through probabilistic model checking, showing its potential as suitable evaluation tool for such novel wireless protocols. In particular, we show by probabilistic model checking that wireless token-passing systems can benefit tremendously from the considered fault-tolerant methods. The obtained performance guarantees for the different mechanisms even provide reasonable bounds for experimental results obtained from a real-world implementation.

The proliferation of wireless communications has lead to a high interest to establish this technology in industrial settings. The main arguments in favor of wireless are reduced costs in deployment and maintenance, as well as increased flexibility. In contrast to home and office environments, industrial settings include mission-critical machine-to-machine applications, demanding stringent requirements for reliability and latency in the area of 1–10−9 PDR and 1ms, respectively. One way to achieve both is cooperative Automatic Repeat reQuest (ARQ), which leverages spatial diversity. This paper presents a wireless multi-user Time Division Multiple Access system with cooperative ARQ for mission-critical communication. We evaluate two design options analytically, using an outage-capacity model, to investigate whether the relaying of messages should be performed centrally at a multi-antenna AP with perfect Channel State Information (CSI) or decentrally at simultaneously transmitting stations with average CSI. Results indicate that both options are able to achieve the targeted communication guarantees when a certain degree of diversity is implemented, showing a stable system performance even with an increasing number of stations.

We consider the effective capacity performance measure of persistent- and truncated-retransmission schemes that can involve any combination of multiple transmissions per packet, multiple communication modes, or multiple packet communication. We present a structured unified analytical approach, based on a random walk model and recurrence relation formulation, and give exact effective capacity expressions for persistent hybrid automatic repeat request (HARQ) and for truncated-retransmission schemes. For the latter, effective capacity expressions are given for systems with finite (infinite) time horizon on an algebraic (spectral radius-based) form of a special block companion matrix. In contrast to prior HARQ models, assuming infinite time horizon, the proposed method does not involve a non-trivial per case modeling step. We give effective capacity expressions for several important cases that have not been addressed before, e.g., persistent-HARQ, truncated-HARQ, network-coded ARQ, two-mode-ARQ, and multilayer-ARQ. We propose an alternative quality-of-service-parameter (instead of the commonly used moment generating function parameter) that represents explicitly the target delay and the delay violation probability. This also enables the closed-form expressions for many of the studied systems. Moreover, we use the recently proposed matrix-exponential distributed modeling of wireless fading channels to provide the basis for numerous new effective capacity results for HARQ.

In this paper, the blocklength-limited performance of a relaying system is studied, where channels are assumed to experience quasi-static Rayleigh fading while at the same time only the average channel state information (CSI) is available at the source. Both the physical-layer performance (blocklength-limited throughput) and the link-layer performance (effective capacity) of the relaying system are investigated. We propose a simple system operation by introducing a factor based on which we weight the average CSI and let the source determine the coding rate accordingly. We show that both the blocklength-limited throughput and the effective capacity are quasi-concave in the weight factor. Through numerical analysis, we investigate the relaying performance with average CSI while considering perfect CSI scenario and direct transmission as comparison schemes. We observe that relaying is more efficient than direct transmission in the finite blocklength regime. Moreover, this performance advantage of relaying under the average CSI scenario is more significant than under the perfect CSI scenario. Finally, the speed of convergence (between the blocklength-limited performance and the performance with infinite blocklengths) in relaying system is faster in comparison to the direct transmission under both the average CSI scenario and the perfect CSI scenario.

Various system tasks, such as interference coordination, handover decisions, admission control, and so on in current cellular networks require precise mid-term (spanning over a few seconds) performance models. Due to channel-dependent scheduling at the base station, these performance models are not simple to obtain. Furthermore, LTE cellular systems are interference limited; hence, the way interference is modeled is crucial for the accuracy. In this paper, we present a closed-form analytical model for the throughput expectation of proportional fair scheduling in orthogonal frequency division multiple access/LTE networks. The model takes into account a precise signal-to-interference-and-noise ratio (SINR) distribution as well as considering limitations with respect to modulation and coding, as encountered in LTE networks. Furthermore, the analysis is extended to ultradense deployments likely to happen in the 5th generation of cellular networks. The resulting analytical performance model is validated by means of simulations, considering realistic network deployments. Compared with related work, the model introduced in this paper demonstrates a significantly higher accuracy for mid-term throughput estimation.

Multi-hop wireless networks are increasingly becoming more relevant to current and emerging wireless network deployment. The need for understanding the performance of such networks in order to be able to provide quantifiable end-to-end quality of service is apparent. Until recently, only asymptotic results that describe the scaling of the delay in the size of the network under numerous conformity conditions were available. Recently, a new methodology for wireless networks performance analysis based on stochastic network calculus was presented [1]. This methodology enables the computation of end-to-end probabilistic delay bound of multi-hop wireless networks in terms of the underlying fading channel parameters. However, the approach assumes identically distributed channel gain which applies to a very specific class of networks. In this work, we seek to develop an end-to-end probabilistic delay bound for multi-hop wireless networks with non-identically distributed channel gains. We show that the delay bound for such networks can be computed recursively. We validate the resulting bound by means of simulation and discuss various numerical examples.

Upcoming low-latency machine-to-machine (M2M) applications are currently attracting a significant amount of interest from the wireless networking research community. The design challenge with respect to such future applications is to allow wireless networks to operate extremely reliably at very short deadlines for rather small packets. To date, it is unclear how to design wireless networks efficiently for such novel requirements. One reason is that existing performance models for wireless networks often assume that the rate of the channel code is equal to the Shannon capacity. However, this model does not hold anymore when the packet size and thus blocklength of the channel code is small. Although it is known that finite blocklength has a major impact on the physical layer performance, we lack higher-layer performance models which account in particular for the queueing effects under the finite blocklength regime.

Simulation of wireless systems is highly complex and can only be efficient if the simulation is executed in parallel. To this end, independent events have to be identified to enable their simultaneous execution. Hence, the number of events identified as independent needs to be maximized in order to increase the level of parallelism. Traditionally, dependencies are determined only by time and location of events: If two events take place on the same simulation entity, they must be simulated in timestamp order. Our approach to overcome this limitation is to also investigate data-dependencies between events. This enables event reordering and parallelization even for events at the same simulation entity. To this end, we design the simulation language PSimLa, which aids this process. In this paper, we discuss the PSimLa design and compiler as well as our data-dependency analysis approach in detail and present case studies of wireless network models, speeded up by a factor of 10 on 12 cores where time-based parallelization only achieves a 1.6x speedup.

We propose Spectrum Aware Virtual Coordinate (SAViC) for multi hop cognitive radio network (CRN) to facilitate geographic routing. The proposed virtual coordinates (VC) of any two secondary users reflect both geographic distance and opportunistic spectrum availability between them. As a result, geographic routing is able to detour the area affected by licensed users or cut through the area with more available spectrum. According to different spectrum occupation patterns of primary user, two versions of SAViC are designed based on the channel utility and primary user’s sojourning time respectively. Simulation shows the proposed virtual coordinate facilitates geographic routing to achieve high success rate of path construction. When duty cycle on the licensed channel is heterogeneous in the network, channel utility based virtual coordinate supports geographic routing to outperform a state-of-the-art geographic routing protocol by 40% on packet delivery ratio. When the channel utility is identical on each secondary node, and the sojourning time of primary users for secondary users are different from each other, SAViC based on primary user’s sojourning time achieves significantly shorter delay than other virtual coordinates.

To parallelize simulations, independent events have to be identified, which can be executed concurrently. The highest level of parallelism is achieved if the number of events identified as independent is maximized. Traditionally, this identification is based on time and location of events, only allowing parallelization if events on the same simulation entity are executed in timestamp order. To increase the level of parallelism, we propose a novel approach investigating another criterion for independence: If two events on the same simulation entity do not access the same data items in a conflicting manner, they can as well be executed in parallel. To this end, we propose static analysis of the model code for data access. To ease this process we develop the simulation language PSimLa similar to C++ but modified where necessary to increase analyzability without removing essential C++ features. First evaluation results show the potential of this approach and increase the confidence that data-dependency analysis can improve future parallel simulation.

We present a system that leverages physical layer features to combat Cross-Technology Interference (CTI) in low-power wireless networks. Our system incorporates: (i) a lightweight interference detection mechanism for low-power radios that recognizes the type of interference in the received signal, (ii) a lightweight error detection mechanism to estimate and characterize error patterns within interfered packets, and (iii) a CTI-aware protocol that dynamically adapts transmission and recovery mode to the current interference patterns. We implement a prototype of our system for the low-power IEEE 802.15.4 in software-defined radios (SDR). Our early results of the system components demonstrate that we can achieve a high accuracy in error detection and interference type identification. Moreover, we observed a significant performance improvement compared to the standard 802.15.4 systems without interference-awareness.

Token-passing wireless network protocols (TPWNP) (e.g., EchoRing), designed for hard real-time systems, typically need to provide ultra-low-latency coupled with ultrahigh-reliability guarantees. In this paper, we initiate a study to investigate the feasibility of distributed relay beamforming (DRBF) in a TP-WNP with the aim to enhance its reliability (and latency) performance even further. Specifically, we consider employing N i) amplify-and-forward (AF), ii) decode-and-forward (DF) relays in a wireless network running a TP-WNP. The relays operate in FDD mode, and do distributed transmit beamforming to realize low-latency, highly-reliable communication between each of the M source-destination pairs in the TP-WNP (a.k.a TDMA) system. The enablers/pre-requisites for the proposed DRBF-TDMA system are frequency, phase and timing synchronization among the relay nodes. To this end, we propose a novel distributed method for frequency synchronization among the AF/DF relay nodes operating in FDD mode. Furthermore, for oscillators with drift, we derive a rule of thumb which provides us the maximum relaying delay Tdelayup to which the proposed frequency synchronization method is effective. For phase and timing synchronization, we employ standard techniques from the literature. Our simulation results verify the analytical results, i.e., by means of proposed DRBF using N AF (DF) relays, upto a factor of N (N2) gains in received SNR can be achieved, at each of the M destination nodes in the proposed system.

Analyzing a communication protocol by means of simulation and real-world experimentation requires careful protocol implementation in both domains. Differences in the implementation may lead to significantly diverging performance results, which may affect the protocol design process adversely. A code-transparent simulation and experimentation framework for Wireless Access Research Platform (WARP) devices is proposed, which is called WARPsim. By extending the simulation engine appropriately, the same application code that runs on WARP devices can be used for simulation. This work studies the implications of this approach using the example of implementing time-critical Medium Access Control Layer (MAC) protocols on WARP devices. In the demonstration, various MAC protocols will be simulated using WARPsim, while changing protocol parameters, but also crucial aspects of the emulated hardware. A graphical representation integrated into the framework allows for an intuitive examination of the protocol behavior.

PHY layer authentication of a wireless sender has gained much interest recently. In this paper, we consider the famous Alice, Bob and Eve model and investigate (for the first time) the feasibility of using time-varying clock offsets for sender-node-authentication at Bob. Specifically, we exploit the fact (and de-facto problem) that clock offset between every node pair is unique; moreover, the two clock offsets between any two node pairs drift independently and randomly over time. Therefore, an explicit mechanism is needed to track the time-varying clock offsets. To this end, we model oscillator drift as brownian motion frequency and phase drift, and present a novel framework which is based on interplay between a hypothesis testing device and a bank of two Kaiman filters; one KF (KFh0) tracks Alice's clock while other KF (KFh1) tracks Eve's clock. Building on aforementioned framework, we then propose a novel sender-node-authentication method (so-called MHF method) by means of which Bob can automatically accept (reject) a received packet if it is sent by Alice (Eve). Finally, simulation results are presented which corroborate the efficiency of the proposed method.

In this paper we generalize analytical performance models for proportional fair scheduling in OFDMA/LTE networks. We address the issue of modelling multiple fading interferers present in practical deployments. Specifically, we elaborate on the stochastic modelling of SINR-distribution for which we derive the rate expectation of instantaneously scheduled resources. The resulting analytical performance model is validated by means of simulations considering realistic network deployments. Compared with related work, our model demonstrates a significantly higher accuracy for long-term rate estimation. We illustrate the utility of such high-precision models by studying the impact on terminal assignment in fractional frequency reuse. Simply by using a suitable estimation model, cell-edge throughput can be improved up to 50%.

We study stability and delay in dynamic networks under adversarial conditions. Adversarial conditions are mandatory in establishing deterministic performance guarantees in networks. Under this framework, we concentrate on the general stability region for a network, i.e. without specifying the routing algorithm. This is in contrast to related work for adversarial network conditions, where usually the backpressure routing algorithm is considered. Our work consists of four novel contributions: (1) We present a novel analysis model which is based on the theory of infinite two-player games, (2) Using this approach, we can characterize the stability region of networks under adversarial conditions for arbitrary routing schemes, (3) We determine conditions under which a delay bound for packet forwarding under adversarial conditions exists, (4) We provide a backtracking algorithm which determines in a model-checking fashion network stability. The backtracking algorithm is furthermore shown to reduce the computational effort significantly for practical scenarios.

To minimize interference in LTE networks, several inter-cell interference coordination (ICIC) techniques have been introduced. Among them, semi-static ICIC offers a balanced trade-off between applicability and system performance. The power allocation per resource block and cell is adapted in the range of seconds according to the load in the system. An open issue in the literature is the question how fast the adaptation should be performed. This leads basically to a trade-off between system performance and feasible computation times of the associated power allocation problems. In this work, we close this open issue by studying the impact that different durations of update times of semi-static ICIC have on the system performance. We conduct our study on realistic scenarios considering also the mobility of mobile terminals. Secondly, we also consider the implementation aspects of a semi-static ICIC. We introduce a very efficient implementation on general purpose graphic processing units, harnessing the parallel computing capability of such devices. We show that the update periods have a significant impact on the performance of cell edge terminals. Additionally, we present a graphic processing unit (GPU) based implementation which speeds up existing implementations up to a factor of 92x.

Jamming is a well-known reliability threat for mass-market wireless networks. With the rise of safety-critical applications this is likely to become a constraining issue in the future. Thus, the design of accurate jamming detection algorithms becomes important to react to ongoing jamming attacks. With respect to experimental work, jamming detection has been mainly studied for sensor networks. However, many safety-critical applications are also likely to run over 802.11-based networks where the proposed approaches do not carry over. In this paper we present a jamming detection approach for 802.11 networks. It uses metrics that are accessible through standard device drivers and performs detection via machine learning. While it allows for stand-alone operation, it also enables cooperative detection. We experimentally show that our approach achieves remarkably high detection rates in indoor and mobile outdoor scenarios even under challenging link conditions.

Various system tasks like interference coordination, handover decisions, admission control etc. in upcoming cellular networks require precise mid-term (spanning over a few seconds) performance models. Due to channel-dependent scheduling at the base station, these performance models are not simple to obtain. Furthermore, upcoming cellular systems will be interference-limited, hence, the way interference is modeled is crucial for the accuracy. In this paper we present an analytical model for the SINR distribution of the scheduled subcarriers of an OFDMA system with proportional fair scheduling. The model takes the precise SINR distribution into account. We furthermore refine our model with respect to uniform modulation and coding, as applied in LTE networks. The derived models are validated by means of simulations. In additon, we show that our models are approximate estimators for the performance of rate-based proportional fair scheduling, while they outperform some simpler prediction models from related work significantly.

Rate adaptation in vehicular networks is known to be more challenging than in WLANs due to the high mobility of stations. Nevertheless, vehicular networks are subject to certain recurring patterns particularly if stations communicate to roadside units. This has lead to the proposal of learning-based rate adaptation schemes which are trained for a certain propagation environment. In general, these schemes outperform other approaches at the price of being specific for a particular environment. In this paper we present RFRA, a novel rate adaptation scheme for vehicular networks. It is based on the machine-learning algorithm Random Forests which is known to be superior to most other learning approaches. Firstly, we show that RFRA outperforms other learning-based methods significantly. We also study the question how sensitive RFRA is to changes of the learned environment, especially with respect to the propagation characteristics. We show that, although this reduces the gain of our scheme, RFRA still provides a much higher performance than state-of-the-art rate adaptation schemes.

The concept of the effective service capacity is an analytical framework for evaluating QoS-constrained queuing performance of communication systems. Recently, it has been applied to the analysis of different wireless systems like point-to-point systems or multi-user systems. In contrast to previous work, we consider in this work slot-based systems where a scheduler determines a packet size to be transmitted at the beginning of the slot. For this, the scheduler can utilize outdated channel state information. Based on a threshold error model, we derive the effective service capacity for different scheduling strategies that the scheduler might apply. We show that even slightly outdated channel state information leads to a significant loss in capacity in comparison to an ideal system with perfect channel state information available at the transmitter. This loss depends on the `risk-level' the scheduler is willing to take which is represented by an SNR margin. We show that for any QoS target and average link state there exists an optimal SNR margin improving the maximum sustainable rate. Typically, this SNR margin is around 3 dB but is sensible to the QoS target and average link quality. Finally, we can also show that adapting to the instantaneous channel state only pays off if the correlation between the channel estimate and the channel state is relatively high (with a coefficient above 0.9).

Classical 802.11 rate adaptation algorithms rely on feedback from the receiver to correctly choose a sending rate, typically in the form of acknowledgments (ACKs). In the absence of such frames, novel techniques are required for rate selection. We present a novel On-Demand Feedback Rate Adaptation algorithm (OFRA) that works with ACK-less traffic. Feedback information is sent on-demand using a control frame to explicitly inform the transmitter about which bit rate to use on subsequent data frames. This approach guarantees standard conformity and exhibits fast and accurate bit rate adaptation at the cost of a modest overhead increase. We evaluate the performance of OFRA against various state-of-the-art rate adaptation schemes by means of simulations. If ACK frames are to be transmitted, OFRA performs better than related work in most considered scenarios, and on par in the others. In the absence of ACKs, OFRA provides large goodput gains under good channel conditions and comparable goodput in other situations.

IEEE 802.11ac WLANs propose the use of 80 and 160MHz bandwidths. Over such bandwidths the large frequency variability of the channel motivates the dynamic allocation of resources per OFDM subcarrier. In this work we focus on power adaptation (power loading). Besides physical layer performance analysis, we account for protocol overhead and for practical implementation issues in WLANs. In particular, we consider the exploitation of coherence time in 80MHz TGac indoor channels trading-off algorithm accuracy for channel estimation overhead. We further study the trade-off between algorithm performance and computational complexity to conclude that optimal solutions do not necessarily perform better than sub-optimal low-complex ones. We show that pure power loading provides only a modest performance gain compared to static schemes and propose power loading combined with the deactivation of highly attenuated subcarriers as a candidate approach for improving WLAN.

The time and space varying nature of channel availability among cognitive radio nodes challenges connectivity and robustness of ad-hoc cognitive radio networks. Clustering of neighbouring cognitive radio nodes is a suitable approach to address this challenge. A cluster utilizes the same channel for payload communication among the nodes. As a consequence, clustering enables cooperative spectrum sensing, supports a coordinated channel switching and simplifies routing in ad-hoc cognitive radio networks. However, the sudden appearance of primary nodes can lead to the loss of connectivity within a cluster or between clusters. This impact can be mitigated to some extent by the way clusters are formed. In this work we discuss a distributed, low-complexity clustering algorithm that emphasizes the robustness of clusters by improving inter- and intra-cluster connectivity. The algorithm is proven to converge fast while numerical evaluation shows a significant improvement of robustness compared to related work.

Next generation wireless local area networks, like the upcoming IEEE 802.11ac, strive for large frequency band-widths to cope with the rising traffic demands. Bandwidths of 80 MHz or even 160 MHz are being considered, where a significant frequency diversity among OFDM subcarriers is likely to exist. With a potentially large number of highly attenuated subcarriers it is not clear if the system should better avoid their usage for payload transmission. Such an approach can improve the error performance, however, with every disabled subcarrier the raw data rate is lowered. This trade-off has not been analyzed in the literature despite its significant impact. In this paper we present and evaluate, by means of simulations, schemes that switch off subcarriers and dynamically distribute power on the active ones (while using the same modulation) so as to increase the goodput of an 80 MHz IEEE 802.11 system. We further propose a close-to optimal approach that is light-weight in complexity. If applied on top of realistic channel models the latter outperforms non-adaptive schemes by up to 13 dB and other power loading approaches by more than 5 dB.

Predicting and analyzing runtime performance characteristics is a vital step in the development process of parallel discrete event simulations. For instance, model developers need to identify and eliminate performance bottlenecks within a simulation model in order to derive a model structure that aids parallel execution. Similarly, developers of parallel simulation frameworks require means of assessing the efficiency of the framework. In this paper, we present a performance prediction methodology that computes the best possible performance bound for expanded parallel discrete event simulations in the context of our Horizon simulation framework. The methodology builds upon a linear program which calculates an optimal event execution schedule for a given simulation and a set of CPUs. In order to mitigate the complexity of this NP-complete scheduling problem, we introduce performance optimizations and relaxations of the linear program.

Wireless local area networks are known to apply the same transmit power and modulation type over all employed OFDM subcarriers. Recent studies show that this can lead to significant performance degradations. As future WLANs will employ even larger bandwidths 80 MHz and above -we study in this paper loading strategies to improve the system performance. In particular, we study the performance of adaptive modulation, power loading and bit loading not only from the physical layer point of view but also by accounting for necessary protocol extensions to accommodate the required control overhead. Our studies reveal that, indeed, loading algorithms can provide significant performance improvements especially for 80 MHz systems. However, the selection of the most appropriate approach depends on channel, protocol and traffic parameters. Hence, this choice is not straightforward and has a substantial impact on the overall system's performance.

In communication networks, resource assignment problems appear in several different settings. These problems are often modeled by a maximum weight matching problem in bipartite graphs and efficient matching algorithms are well known. In several applications, the corresponding matching problem has to be solved many times in a row as the underlying system operates in a time-slotted fashion and the edge weights change over time. However, changing the assignments can come with a certain cost for reconfiguration that depends on the number of changed edges between subsequent assignments. In order to control the cost of reconfiguration, we propose the k-constrained bipartite matching problem for bipartite graphs, which seeks an optimal matching that realizes at most k changes from a previous matching. We provide fast approximation algorithms with provable guarantees for this problem. Furthermore, to cope with the sequential nature of assignment problems, we introduce an online variant of the k-constrained matching problem and derive online algorithms that are based on our approximation algorithms for the k-constrained bipartite matching problem. Finally, we establish the applicability of our model and our algorithms in the context of OFDMA wireless networks finding a significant performance improvement for the proposed algorithms.

The simulation models of wireless networks rapidly increase in complexity to accurately model wireless channel characteristics and the properties of advanced transmission technologies. Such detailed models typically lead to a high computational load per simulation event that accumulates to extensive simulation runtimes. Reducing runtimes through parallelization is challenging since it depends on detecting causally independent events that can execute concurrently. Most existing approaches base this detection on lookaheads derived from channel propagation latency or protocol characteristics. In wireless networks, these lookaheads are typically short, causing the potential for parallelization and the achievable speedup to remain small. This paper presents Horizon, which unlocks a substantial portion of a simulation model's workload for parallelization by going beyond the traditional lookahead. We show how to augment discrete events with durations to identify a much larger horizon of independent simulation events and efficiently schedule them on multi-core systems. Our evaluation shows that this approach can significantly cut down the runtime of simulations, in particular for complex and accurate models of wireless networks.

It is well known that channel-dependent OFDMA resource assignment algorithms provide a significant performance improvement compared to static (i.e. channel-unaware) approaches. Such dynamic algorithms constantly adapt resource assignments to current channel states according to some objective function. Due to these dynamics, it is difficult to predict the resulting performance for such schemes given a certain scenario (characterized by the number of terminals in the cell and their average channel gains). Hence, previous work on admission control for OFDMA systems neglects the performance improvement from channel-dependent resource assignments and bases analysis on the average channel gains instead. In this paper we provide for the first time an analytical framework for admission control in OFDMA systems applying channel-dependent resource assignments. The framework is based on fundamental transformations of the channel gains caused by the channel-dependent assignment algorithms. We provide closed-form expressions for these transformations and derive from them probability functions for the rate achieved per terminal and frame. These functions can then be used for admission control as demonstrated in this paper for Voice-over-IP streams in IEEE 802.16e systems.

One of the major challenges in designing cellular cognitive radio (CR) networks is the avoidance of secondary user (SU) interference to so called primary users (PUs) operating in the licensed bands. Usually, SU operation has to be interrupted periodically in order to detect PU activity and avoid the respective frequencies. Recently, dynamic frequency hopping (DFH) mechanisms have been suggested to enable reliable PU detection and continuous SU operation at the same time. Applying DFH in a multi-cell environment adds the challenge of mitigating co-channel interference (CCI). In this paper, we introduce a new DFH approach for cellular CR networks to allow reliable PU detection and continuous SU operation while avoiding CCI: double hopping (DH). We present a distributed frequency assignment heuristic for DH and compare it to the optimal assignment. We show that the performance of the sub-optimal distributed assignment is only slightly worse than the optimal performance, and, thus, outperforms existing distributed approaches by far.

Dynamic OFDMA systems provide a significant performance gain compared to static OFDM approaches. In reality this gain is reduced by an overhead due to signaling. Previous work has shown that this loss is particularly high if the number of sub-carriers is large. In this paper we present two approaches to reduce this overhead by exploiting the correlation in frequency. These two schemes are shown to provide a significant reduction of the signaling overhead.

Earlier paper have demonstrated that the achievable throughput of OFDM systems can benefit significantly from individual modulation/transmit power selection on a per sub-carrier basis according to the actual gain of individual sub-carriers (so called dynamic OFDM scheme). Usage of such approach requires, however, providing support for additional functionality like: acquisition of the subcarrier gains, signaling of the used modulation types between the sender and receiver, etc. Therefore dynamic OFDM is actively pursued for future radio interfaces, rather then considered as extension of existing OFDM based standards. In this paper we present for the first time a proposal how the widely accepted IEEE 802.11a/g systems might be extended to support the dynamic OFDM in a singleuser (point-to-point) setting while assuring backward compatibility. We address these issues by a) presenting a set of protocol modifications; and b) a performance evaluation of the suggested extension (referred further on to as single-user 802.11 DYN mode) demonstrating the potential of performance improvement.

In wireless local area networks often a station can potentially associate with more than one access point. Therefore, a relevant question is which access point to select "best" from a list of candidate ones. In IEEE 802.11, the user simply associates to the access point with the strongest received signal strength. However, this may result in a significant load imbalance between several access points, as some accommodate a large number of stations while others are lightly loaded or even idle. Moreover, the multi-rate flexibility provided by several IEEE 802.11 variants can cause low bit rate stations to negatively affect high bit rate ones and consequently degrade the overall network throughput. This paper investigates the various aspects of "best" access point selection for IEEE 802.11 systems. In detail, we first derive a decision metric the selection can be based on. Using this metric we propose two new selection mechanisms which are decentralized in the sense that the decision is performed by each station, given appropriate status information of each access point. In fact, only few bytes of status information have to be added to the beacon and probe response frames which does not impose significant overhead. In addition, we show that our mechanism improves station quality of service and better utilizes network resources compared to the conventional one implemented today in IEEE 802.11 devices

Within the last decade, the orthogonal frequency- division multiplexing (OFDM) transmission scheme has become part of several standards for wireless systems. Today, OFDM is even a candidate for fourth-generation wireless systems. It is well known that dynamic OFDMA systems potentially increase the spectral efficiency. They exploit diversity effects in time, space, and frequency by assigning system resources periodically to terminals. Informing the terminals about new assignments creates a signaling overhead. Up to now, this overhead has not been taken into account in studies on dynamic orthogonal frequency-division multiplexing access (OFDMA) systems. Yet this is crucial for a realistic notion of the performance achieved by dynamic approaches. In this paper, we close this gap. We introduce two forms of representing the signaling information and discuss how these affect system performance. The study of the signaling impact on the performance is conducted for an exemplary dynamic approach. We find that the throughput behavior of dynamic OFDMA systems is significantly influenced by the signaling overhead. In many situations, neglecting the overhead leads to wrong performance conclusions. Also, the performance difference between dynamic and static schemes is now much more sensible to the specific parameter set of the transmission scenario (e.g., frame length, subcarrier number, etc.). This leads to the proposal of access points which should adapt certain system parameters in order to provide optimal performance.

OFDM systems are known to overcome the impairment of the wireless channel by splitting the given system bandwidth into parallel sub-carriers, on which data symbols can be transmitted simultaneously. This enables the possibility of enhancing the system's performance by deploying adaptive (dynamic) mechanisms, namely power and modulation adaption and dynamic sub-carrier assignments. In multi-user communication systems (OFDM-FDMA), these mechanisms can be used to achieve a level of system fairness ensuring that each terminal receives at least an environment-specific minimum amount of data per down-link phase. However, it has been doubted by multiple previous investigations that dynamic power adaption provides enough performance gain in order to be applied in such systems, as it increases the computational load significantly. In this study we discuss the performance gain due to the different approaches and show that in specific communication scenarios enabling a dynamic power distribution provides a significant performance increase compared to dynamic schemes without power adaption