Connected cyber-physical systems perform inference based on real-time inputs from multiple data streams. Uncertain communication delays across data streams challenge the temporal flow of the inference process. State-of-the-art (SotA) non-blocking inference methods rely on a reference-modality paradigm, requiring one modality input to be fully received before processing, while depending on costly offline profiling. We propose a novel, neuro-inspired non-blocking inference paradigm that primarily employs adaptive temporal windows of integration (TWIs) to dynamically adjust to stochastic delay patterns across heterogeneous streams while relaxing the reference-modality requirement. Our communication-delay-aware framework achieves robust real-time inference with finer-grained control over the accuracy-latency tradeoff. Experiments on the audio-visual event localization (AVEL) task demonstrate superior adaptability to network dynamics compared to SotA approaches.

The emergence of Agile Earth Observation Satellites (AEOSs) has marked a significant turning point in the field of Earth Observation (EO), offering enhanced flexibility in data acquisition. Concurrently, advancements in onboard satellite computing and communication technologies have greatly enhanced data compression efficiency, reducing network latency and congestion while supporting near real-time information delivery. In this paper, we propose a framework for addressing the Agile Earth Observation Satellite Scheduling Problem (AEOSSP) in a real-time remote monitoring scenario integrating onboard data processing. The AEOSSP consists of determining the optimal sequence of target observations to maximize overall observation profit, and, to this end, we define a set of priority indicators and develop a constructive heuristic method, further enhanced with a Local Search (LS) strategy. The results show that the proposed algorithm provides high-quality information by increasing the spatial resolution of the collected frames by up to 18.6% on average, while reducing the variance in the monitoring frequency of the targets within the instance by up to 84%, ensuring more up-to-date information across the entire set compared to First-In First-Out (FIFO) and genetic algorithm (GA)-based methods.

Motivated by the stringent and challenging need for 'greener communications' in increasingly power-hungry 5 G networks, this paper presents a detailed energy efficiency analysis for three different multi-antenna architectures, namely fullydigital arrays, hybrid arrays, and dynamic metasurface antennas (DMAs). By leveraging a circuital model, which captures mutual coupling, insertion losses, propagation through the waveguides in DMAs and other electromagnetic phenomena, we design a transmit Wiener filter solution for the three systems. We then use these results to analyze the energy efficiency, considering different consumption models and supplied power, and with particular focus on the impact of the physical phenomena. DMAs emerge as an efficient alternative to classical arrays across diverse tested scenarios, most notably under low transmission power, strong coupling, and scalability requirements.

Sensors are increasingly being deployed to monitor critical infrastructure. However, as the number of sensors being deployed increases, so does the amount of sensor data that must be transmitted, stored, and analyzed. Thus, a significant number of methods have been proposed to improve sensor data acquisition and analytics. However, the proposed strategies and methods generally focus exclusively on either sensor data acquisition or analytics, thus ignoring the possible optimization that can be performed by taking a holistic view. To explore this opportunity, this paper provides an overview of sensor data acquisition and analytics and an analysis of two very different use cases, specifically monitoring wind turbines and measuring utility consumption using smart meters. Based on this analysis, the Framework for joint Sensory Data Acquisition and Analytics (SENDAI) is proposed, an integrated framework that models sensor data acquisition and analytics together, thus enabling holistic reasoning about sensor data acquisition and analytics. To demonstrate how the information in SENDAI can be used to reason about sensor data acquisition and analytics together, we show how sensor data acquisition can be optimized to respond efficiently to query workloads.

This paper introduces a full solution for decentralized routing in Low Earth Orbit Satellite Constellations (LSatCs) based on continual Deep Reinforcement Learning (DRL), specifically designed for on-board implementation in satellites with limited computational and communication resources. This requires addressing multiple challenges, including the partial knowledge at the satellites and their continuous movement, and the time-varying sources of uncertainty in the system, such as traffic, communication links, or communication buffers. We follow a multi-agent approach, where each satellite acts as an independent decision-making agent, while acquiring a limited knowledge of the environment based on the feedback received from the nearby agents. The solution is divided into two phases. First, an offline learning phase relies on decentralized decisions and a global Deep Neural Network (DNN) trained with global experiences to learn the optimal paths. Then, the online phase with local, on-board, and pre-trained DNNs requires continual learning to evolve with the environment, which can be done in two different ways: (1) Model anticipation, where the predictable conditions of the constellation, resulting from its orbital dynamics, are exploited by each satellite sharing local model with the next satellite; and (2) Federated Learning (FL), where each agent’s model is merged first at the cluster level and then aggregated in a global Parameter Server (PS) at ground or at a geostationary orbit (GEO) satellite. Results from simulations with State-of-the-Art (SoA) constellations such show that the proposed approach converges in less than a second to similar end-to-end (E2E) latency than the shortest-path centralized approach with full knowledge of the network. Moreover, the Centered Kernel Alignment (CKA) metric quantifies the necessary alignment of the models when the dynamics of the environment change.

We investigate the coexistence of massive and critical Internet of Things (IoT) services in the context of the unsourced multiple access (UMA) framework introduced by Polyanskiy (2017), where all users employ a common codebook and the receiver returns an unordered list of decoded codewords. This setup is suitably modified to introduce heterogeneous traffic. Specifically, to model the massive IoT service, we assume that a standard message originates independently from each IoT device as in the standard UMA setup. To model the critical IoT service, we assume the generation of alarm messages that are common for all devices. This setup requires a significant redefinition of the error events, i.e., misdetections and false positives. We further assume that the number of active users in each transmission attempt is random and unknown. We derive a random-coding achievability bound on the misdetection and false positive probabilities of both standard and alarm messages on the Gaussian multiple access channel. Using our bound, we demonstrate that orthogonal network slicing enables massive and critical IoT to coexist under the requirement of high energy efficiency. On the contrary, we show that nonorthogonal network slicing is energy inefficient due to the residual interference from the alarm signal when decoding the standard messages.

In mobile networks, Open Radio Access Network (ORAN) provides a framework for implementing network slicing that interacts with the resources at the lower layers. Both monitoring and Radio Access Network (RAN) control is feasible for both 4G and 5G systems. In this work, we consider how data-driven resource allocation in a 4G context can enable adaptive slice allocation to steer the experienced latency of Virtual Reality (VR) traffic towards a requested latency. We develop an xApp for the near realtime RAN Intelligent Controller (RIC) that embeds a heuristic algorithm for latency control, aiming to: (1) maintain latency of a VR stream around a requested value; and (2) improve the available RAN allocation to offer higher bit rate to another user. We have experimentally demonstrated the proposed approach in an ORAN testbed. Our results show that the data-driven approach can dynamically follow the variation of the traffic load while satisfying the required latency. This results in 15.8 % more resources to secondary users than a latency-equivalent static allocation.

This paper investigates the theoretical basis for using ground relaying in multi-antenna satellites exposed to blocking situations. Inactive and unobstructed User Equipments (UEs) located on the ground are the relaying nodes of UEs that are not in the satellite’s field of view. Exact closed-form relationships of the Signal-to-Noise Ratio (SNR) and the outage probability are obtained for the case where each user is connected to two transmitting antennas at the satellite. The channel between the satellite and ground is modeled as a shadowed-Rician (SR), whereas a Fluctuating Two-Ray (FTR) fading model is used for the mmWaves ground link between relay and UE, as well as cases with perfect and imperfect Channel State Information (CSI). The simulation results showed that if perfect CSI is not available in the pre-coding and only the signal phase is estimated, the performance loss is minimal and the system can reach its ideal performance by spending limited power. In any case, the closed-forms for the ideal state are a good proxy to predict the performance under non-ideal conditions.

Taking inspiration from linguistics, the communications theoretical community has recently shown a significant recent interest inpragmatic, or goal-oriented, communication. In this paper, we tackle the problem of pragmatic communication with multiple clients with different, and potentially conflicting, objectives. We capture the goal-oriented aspect through the metric of Value of Information (VoI), which considers the estimation of the remote process as well as the timing constraints. However, the most common definition of VoI is simply the Mean Square Error (MSE) of the whole system state, regardless of the relevance for a specific client. Our work aims to overcome this limitation by including different summary statistics, i.e., value functions of the state, for separate clients, and a diversified query process on the client side, expressed through the fact that different applications may request different functions of the process state at different times. A query-aware Deep Reinforcement Learning (DRL) solution based on statically defined VoI can outperform naive approaches by 15-20%.

In high-resolution Earth observation imagery, Low Earth Orbit (LEO) satellites capture and transmit images to ground to create an updated map of an area of interest. Such maps provide valuable information for meteorology and environmental monitoring, but can also be employed for real-time disaster detection and management. However, the amount of data generated by these applications can easily exceed the communication capabilities of LEO satellites, leading to congestion and packet dropping. To avoid these problems, the Inter-Satellite Links (ISLs) can be used to distribute the data among multiple satellites and speed up processing. In this paper, we formulate a satellite mobile edge computing (SMEC) framework for real-time and very-high resolution Earth observation and optimize the image distribution and compression parameters to minimize energy consumption. Our results show that our approach increases the amount of images that the system can support by a factor of$12\times $and$2\times $when compared to directly downloading the data and to local SMEC, respectively. Furthermore, energy consumption was reduced by 11% in a real-life scenario of imaging a volcanic island, while a sensitivity analysis of the image acquisition process demonstrates that energy consumption can be reduced by up to 90%.

The use of parallel communication paths to provide reliable, low-latency service is a significant trend in cellular networks, as it can provide a way to satisfy the exacting Quality of Service (QoS) requirements of 5G-enabled applications. In particular, coding data across multiple paths can significantly improve reliability and reduce overall latency, compensating for stragglers and lost packets with the redundant information from other paths. However, the design trade-offs in optimizing these systems are non-trivial, particularly when considering Age of Information (AoI). In this work, we derive the latency and Peak Age of Information (PAoI) distributions for such a multipath coded system, drawing design insights on how to optimize either. While preemption is always the optimal choice to minimize AoI in a single-path, uncoded queuing system, the trade-off in this case is more complex, as dropping a late packet on one path might affect the reliability of the whole block. Our results show that the parameters to minimize the PAoI lead to poor latency performance, and optimizing both at once might require significant resource overprovisioning.

In this paper, we elaborate on the frequency resource allocation problem of Wireless Networked Control Systems (WNCS). We consider a multi user wireless environment (e.g., factory) where the users are remote industrial Internet of Things (IIoT) devices competing for network resources and a centralized network base station needs to assign the resources to each device accordingly in order to keep the overall control system stability. We design a joint network and control scheduler solution, where we can estimate the degradation of the control system for a given network state and use this information to assign the frequency resource to each device. We show that the proposed solution outperforms traditional scheduling baselines, including genetic algorithms, assuming polynomial complexity in worst case scenario and generalizing for different control and network configurations.

In this paper, we demonstrate the real-time performance of Dedicated Short Range Communication (DSRC) through the design and development of a full-stack testbed based on open-source software that includes the IEEE 802.11p and the IEEE 1609 protocol suite. Vehicles broadcast periodic Basic Safety Messages (BSM). Reference mobility and data traffic models from 3GPP are used for scenario generation using ns-3. We show how to meet the timing requirements of the 802.11p lower layers using low-cost, commercial off-the-shelf components, based on open-source GNU Radio and tested with Ettus USRP B210 Software Defined Radio (SDR), 6 GHz Tx/RxRF frontends and GPS Disciplined Oscillator (GPSDO). The testbed is used to characterize the different contributors to the total delay budget and the Age of Information (AoI), a key metric for vehicle-to-vehicle (V2V). We observe that the AoI is impacted not only by the communication parameters but also by the mobility and traffic conditions. Our results show that more frequent BSM transmission reduces the average peak age but with a higher value relative to the inter-BSM period due to the collisions and packet losses.

With the advent of 5G technology, the notion oflatencygot a prominent role in wireless connectivity, serving as a proxy term for addressing the requirements for real-time communication. As wireless systems evolve toward 6G, the ambition to immerse the digital into physical reality will increase. Besides making the real-time requirements more stringent, this immersion will bring the notions of time, simultaneity, presence, and causality to a new level of complexity. A growing body of research points out that latency is insufficient to parameterize all real-time requirements. Notably, one such requirement that received significant attention is information freshness, defined through the Age of Information (AoI) and its derivatives. In general, the metrics derived from a conventional black-box approach to communication network design are not representative of new distributed paradigms, such as sensing, learning, or distributed consensus. The objective of this article is to investigate the general notion of timing in wireless communication systems and networks, and its relation to effective information generation, processing, transmission, and reconstruction at the senders and receivers. We establish a general statistical framework oftimingrequirements in wireless communication systems, which subsumes both latency and AoI. The framework is made by associating a timing component with the two basic statistical operations: decision and estimation. We first use the framework to present a representative sample of the existing works that deal with timing in wireless communication. Next, it is shown how the framework can be used with different communication models of increasing complexity, starting from the basic Shannon one-way communication model and arriving at communication models for consensus, distributed learning, and inference. Overall, this article fills an important gap in the literature by providing a systematic treatment of various timing measures in wireless communication and sets the basis for design and optimization for the next-generation real-time systems.

Age of Information (AoI) has become an important concept in communications, as it allows system designers to measure the freshness of the information available to remote monitoring or control processes. However, its definition tacitly assumes that new information is used at any time, which is not always the case: the instants at which information is collected and used may be dependent on a certain query process, and resource-constrained environments such as most Internet of Things (IoT) use cases require precise timing to fully exploit the limited available transmissions. In this work, we consider apull-based communication modelin which the freshness of information is only important when the receiver generates a query: if the monitoring process is not using the value, the age of the last update is irrelevant. We optimize the Age of Information at Query (QAoI), a metric that samples the AoI at relevant instants, better fitting the pull-based resource-constrained scenario, and show how this can lead to very different choices. Our results show that QAoI-aware optimization can significantly reduce the average and worst-case perceived age for both periodic and stochastic queries.

Multipath communication is a critical technology to provide Quality of Service (QoS) to interactive and Internet of Things (IoT) monitoring and control applications. In this work, we model the exemplary case with two paths and consider different strategies that exploit redundancy and coding to improve the timing performance of wireless communications. We consider two disparate scenarios, in which the data blocks are generated via a Markovian and a deterministic process, respectively. We consider simple scheduling and coding schemes, considering both lossless and lossy encoding, and modeling the resulting process as a fork-join queue with different arrival processes. We analyze the full distribution of two relevant metrics for the two-path case: the packet delay and the Peak Age of Information (PAoI), which measures the freshness of the information at the receiver. The results show interesting trade-offs between the update frequency, latency, PAoI, and level of compression, with interesting implications for system designers.

Age of Information (AoI) has gained significant attention from the research community because of its applications to Internet of Things (IoT) monitoring and control. In this work, we treat multihop connections over queuing networks with tributary flows and non-preemptive service: packets cannot be discarded because they are utilized for other system objectives, such as data analytics. Without preemption, the key tool for optimizing AoI is then the scheduling policy between the different data flows at each intermediate node. This is the subject of our analysis, along with the impact of packet erasure on the age. We derive upper and lower bounds for the average AoI considering several queuing policies in arbitrary network topologies, and present the results in different scenarios. Network topology, tributary traffic load, and link characteristics such as packet erasure generate complex trade-offs, which affect the optimal operation point and the age performance. The scheduling strategy at each node can also affect performance and fairness among users, particularly at critical bottleneck links, which have a significant impact on the overall performance of the whole network.

Low Earth orbit (LEO) satellite constellations rely on inter-satellite links (ISLs) to provide global connectivity. However, one significant challenge is to establish and maintain inter-plane ISLs, which support communication between different orbital planes. This is due to the fast movement of the infrastructure and to the limited computation and communication capabilities on the satellites. In this paper, we make use of antenna arrays with either Butler matrix beam switching networks or digital beam steering to establish the inter-plane ISLs in a LEO satellite constellation. Furthermore, we present a greedy matching algorithm to establish inter-plane ISLs with the objective of maximizing the sum of rates. This is achieved by sequentially selecting the pairs, switching or pointing the beams and, finally, setting the data rates. Our results show that, by selecting an update period of 30 seconds for the matching, reliable communication can be achieved throughout the constellation, where the impact of interference in the rates is less than 0.7% when compared to orthogonal links, even for relatively small antenna arrays. Furthermore, doubling the number of antenna elements increases the rates by around one order of magnitude.

Low Earth Orbit (LEO) satellite constellations combine great flexibility and global coverage with short propagation delays when compared to satellites deployed in higher orbits. However, the fast movement of the individual satellites makes inter-satellite routing a complex and dynamic problem. In this paper, we investigate the limits of unipath routing in a scenario where ground stations (GSs) communicate with each other through a LEO constellation. For this, we present a lightweight and topology-aware routing metric that favors the selection of paths with high data rate inter-satellite links (ISLs). Furthermore, we analyze the overall routing latency in terms of propagation, transmission, and queueing times and calculate the maximum traffic load that can be supported by the constellation. In our setup, the traffic is injected by a network of GSs with real locations and is routed through adaptive multi-rate inter-satellite links (ISLs). Our results illustrate the benefits of exploiting the network topology, as the proposed metric can support up to 53% more traffic when compared to the selected benchmarks, and consistently achieves the shortest queueing times at the satellites and, ultimately, the shortest end-to-end latency.

Age of Information (AoI) has become an important concept in communications, as it allows system designers to measure the freshness of the information available to remote monitoring or control processes. However, its definition tacitly assumes that new information is used at any time, which is not always the case. Instead instants at which information is collected and used are dependent on a certain query process. We propose a model that accounts for the discrete time nature of many monitoring processes, by considering a pull-based communication model in which the freshness of information is only important when the receiver generates a query. We then define the Age of Information at Query (QAoI), a more general metric that fits the pull-based scenario, and show how its optimization can lead to very different choices from traditional push-based AoI optimization when using a Packet Erasure Channel (PEC).

Joint design of control and communication in Wireless Networked Control Systems (WNCS) is a promising approach for future wireless industrial applications. In this context, Age of Information (AoI) recently has been proposed as a metric that is more representative than communication latency in conduct of systems with a sense-compute-actuate cycle. Nevertheless, AoI is commonly defined for a single communication direction, Downlink or Uplink, which does not capture the closed-loop dynamics. In this paper, we extend the concept of AoI by defining a new metric, Age of Loop (AoL), relevant for closed-loop WNCS problems. The AoL is defined as the time elapsed since the piece of information causing the latest action or state (depending on the selected time origin) was generated. We use the proposed metric to learn the WNCS latency and freshness bounds, and apply such learning methodology to minimize the long-term WNCS cost with the least amount of bandwidth. We show that, using the AoL, we can learn the control system requirement and use this information to optimize network resources.

Age of Information (AoI) is a critical metric for several Internet of Things (IoT) applications, where sensors keep track of the environment by sending updates that need to be as fresh as possible. The development of edge computing solutions has moved the monitoring process closer to the sensor, reducing the communication delays, but the processing time of the edge node needs to be taken into account. Furthermore, a reliable system design in terms of freshness requires the knowledge of the full distribution of the Peak AoI (PAoI), from which the probability of occurrence of rare, but extremely damaging events can be obtained. In this work, we model the communication and computation delay of such a system as two First Come First Serve (FCFS) queues in tandem, analytically deriving the full distribution of the PAoI for the M/M/1 - M/D/1 and the M/M/1 - M/M/1 tandems, which can represent a wide variety of realistic scenarios.

With numerous ongoing deployments owned by private companies and startups, dense satellite constellations deployed in low Earth orbit (LEO) will play a major role in the near future of wireless communications. In addition, the 3rd Generation Partnership Project (3GPP) has ongoing efforts to integrate satellites into 5G and beyond-5G networks. Nevertheless, numerous challenges must be overcome to fully exploit the connectivity capabilities of satellite constellations. These challenges are mainly a consequence of the low capabilities of individual small satellites, along with their high orbital speeds and small coverage due to the low altitude of deployment. In particular, inter-plane inter-satellite links (ISLs), which connect satellites from different orbital planes, are greatly dynamic and may be considerably affected by the Doppler shift. In this paper, we present a framework and the corresponding algorithms for the dynamic establishment of the inter-plane ISLs in LEO constellations. Our results show that the proposed algorithms increase the sum of rates in the constellation 1) by up to 115% with respect to the state-of-the-art benchmark schemes in an interference-free environment and 2) by up to 71% when compared to random resource allocation in a worst-case scenario for interference.

Real-time wireless networked control of an Autonomous Guided Vehicle (AGV) from an edge cloud controller is an attractive approach to reduce hardware costs of AGVs, e.g., for industrial applications. We specify a networked control protocol for AGV and investigate how system performance and stability are affected by the reliability of the wireless link with fading. Particularly, there is a trade-off between the AGV speed, the control stability, and the channel quality. Our model takes into account end-to-end latency, which includes control loops and communication. Considering the model complexity, we employ a Reinforcement Learning (RL) approach in order to find the optimal speed of AGV to complete a mission path in shortest time. The proposed solution achieves system stability at par with widely used baseline state-of-the-art controllers, while reducing the AGV mission time by more than 30%.

Age of Information is a new metric used in real-time status update tracking applications. It measures at the destination the time elapsed since the generation of the last received packet. In this paper, we consider the co-existence of critical and noncritical status updates in a two-hop system, for which the network assigns different scheduling priorities. Specifically, the high priority is reserved to the packets that traverse the two nodes, as they experience worse latency performance. We obtain the distribution of the age and its natural upper bound termed peak age. We provide tight upper and lower bounds for priority updates and the exact expressions for the non-critical flow of packets with a general service distribution. The results give fundamental insights for the design of age-sensitive multi-hop systems.

The classical definition of network delay has been recently augmented by the concept of information timeliness, or Age of Information (AoI). We analyze the network delay and the AoI in a multi-hop satellite network that relays status updates from satellite 1, receiving uplink traffic from ground devices, to satellite K, using K-2 intermediate satellite nodes. The last node, K, is the closest satellite with connectivity to a ground station. The satellite formation is modeled as a queue network of M/M/1 systems connected in series. The scenario is then generalized for the case in which all satellites receive uplink traffic from ground, and work at the same time as relays of the packets from the previous nodes. The results show that the minimum average AoI is experienced at a decreasing system utilization when the number of nodes is increased. Furthermore, unloading the first nodes of the chain reduces the queueing time and therefore the average AoI. These findings provide insights for designing multihop satellite networks for latency-sensitive applications.

Dense constellations of Low Earth Orbit (LEO) small satellites are envisioned to make extensive use of the inter-satellite link (ISL). Within the same orbital plane, the inter-satellite distances are preserved and the links are rather stable. In contrast, the relative motion between planes makes the inter-plane ISL challenging. In a dense set-up, each spacecraft has several satellites in its coverage volume, but the time duration of each of these links is small and the maximum number of active connections is limited by the hardware. We analyze the matching problem of connecting satellites using the inter-plane ISL for unicast transmissions. We present and evaluate the performance of two solutions to the matching problem with any number of orbital planes and up to two transceivers: a heuristic solution with the aim of minimizing the total cost; and a Markovian solution to maintain the on-going connections as long as possible. The Markovian algorithm reduces the time needed to solve the matching up to 1000x and 10x with respect to the optimal solution and to the heuristic solution, respectively, without compromising the total cost. Our model includes power adaptation and optimizes the network energy consumption as the exemplary cost in the evaluations, but any other QoS-oriented KPI can be used instead.

A Low Earth Orbit (LEO) mega constellation (MC) is a satellite network of hundreds of spacecrafts organized in several orbital planes, which can be deployed at different inclinations and altitudes. Beyond the connection to the ground segment, the direct inter-satellite communication, both intraplane and inter-plane, is also used. One of the challenges in LEO MCs is the development of efficient routing strategies that scale up with the size of the network. We propose a simple distributed routing algorithm, GomHop, to find the best nexthop for forwarding a packet in a connection-less network. Each satellite runs the on-board algorithm autonomously, with no need for the ground segment to calculate, maintain and distribute routing tables. GomHop minimizes the number of required interplane hops, which are typically more challenging and expensive for the network, and relies mostly on intra-plane hops. The simulation results show that the overall performance is very close to the centralized optimal solution provided by the Dijkstra algorithm, with GomHop having a smaller amount of inter-plane hops and low complexity, in contrast to the O(N2) of Dijkstra.

The established view on massive IoT access is that the IoT devices are activated randomly and independently. This is a basic premise also in the recent information-theoretic treatment of massive access by Polyanskiy [1]. In a number of practical scenarios, the information from IoT devices in a given geographical area is inherently correlated due to a commonly observed physical phenomenon. We introduce a model for massive access that accounts for correlation both in device activation and in the message content. To this end, we introduce common alarm messages for all devices. A physical phenomenon can trigger an alarm causing a subset of devices to transmit the same message at the same time. We develop a new error probability model that includes false positive errors, resulting from decoding a non-transmitted codeword. The results show that the correlation allows for high reliability at the expense of spectral efficiency. This reflects the intuitive trade-off: an access from a massive number can be ultra-reliable only if the information across the devices is correlated.

One of the key ingredients of Intelligent Transportation Systems (ITS) is delivery of broadcast status messages among vehicles for safety purposes. This requires an efficient Medium Access Control (MAC) that provides low average delay and high reliability. To this end, Carrier Sense Multiple Access (CSMA) has been commonly proposed for Vehicle Ad Hoc Networks (VANETs). Nevertheless, the hidden- node problem can jeopardize the reliability of CSMA, whereas the latency when accessing the channel can be unbounded. To overcome these limitations, resource allocation based on the geo-location of the vehicles can be applied in VANETs. For example, a distributed location based access (DLOC) algorithm has been proposed such that vehicles access orthogonal resource blocks based on their position, aiming at maximizing the distance of co-channel transmitters. In this paper we propose a stochastic geometry approach to analyze DLOC taking into account path loss and fading as well as the random location of transmitting vehicles. Analytical results include the average interference, average binary rate and capture probability, i.e. probability of successful message transmission. It is shown that increasing the number of RBs increases reliability but there is a trade off between reliability and average BR.

5G downlink Inter-Cell Interference Coordination (ICIC) has a twofold objective: to better exploit the benefits of ICIC in coherence with the rest of Radio Resource Management (RRM) principles in 5G; and to support new 5G service classes and their coexistence in a multi-service setting. We propose on-demand Power Boosting and Inter-cell Coordination (PB-IC) to meet high reliability and low latency requirements of critical data without compromising the spectral efficiency of the rest of traffic. PB-IC is demonstrated to provide similar latency gains as the classical frequency reuse, but much lower impact to the enhanced Mobile BroadBand (eMBB) traffic. Results demonstrate that the tail of the latency distribution is reduced as much as 100% in the 10^-4 percentile, achieving comparable gains to static frequency reuse. At the same time, the degradation in eMBB throughput is minimized, reaching a 14% loss instead of the 50% observed with static ICIC. We discuss the standardization effort required for having PB-IC supported, with considerations of the measurement requirements to the UE and the infrastructure backhaul latencies.

Big expectations are put into vehicular communications (V2X) for a safer and more intelligent driving. With human lives at risk, the system cannot afford to fail, which translates into very stringent reliability and latency requirements to the radio network. One of the challenges is to find efficient radio resource management (RRM) strategies for direct vehicle-to-vehicle (V2V) communication that can fulfil the requirements even with high traffic density. In cellular networks, a device-to-device (D2D) communication is usually split into two phases: the discovery process, for node awareness of each other; and the communication phase itself, where data exchange takes place. In the case of V2V, the discovery phase can utilize the status information that cars broadcast periodically as the beacons to detect the presence of neighbouring cars. For the delivery of specific messages (e.g., a hazardous event), direct communications can then be set up in a very fast way, based on the previously collected information. Several aspects have been revealed as essential to ensure the reliability and latency requirements of the discovery, such as the autonomous selection of the resources, the duplexing mode and the interference cancellation receivers

Network-Assisted Interference Cancellation and Suppression (NAICS) receivers have appeared as a promising way to curb inter-cell interference in future dense network deployments. This investigation compares the performance of a NAICS receiver with successive interference cancellation capabilities, known as Symbol-Level Interference Cancellation (SLIC), with respect to a baseline Minimum Mean Square Error-Interference Rejection Combining (MMSE-IRC) receiver. The study is carried out on a dense, clusterized small cell network, illustrating to which extent NAICS can overcome the additional interference associated with such deployments. Moreover, we analyse how much the data rates could be potentially improved by estimating the throughput with ideal cancellation of the dominant interferer. The results point to limited gains of up to 12% in the coverage data rates when NAICS is used, and that the instantaneous throughput might increase up to 100% in the most favourable case, falling significantly below the estimated 200% maximum gain with ideal cancellation.

Network-Assisted Interference Cancellation and Suppression (NAICS) receivers have appeared as a promising way to curb inter-cell interference in future dense network deployments. This investigation compares the performance of a NAICS receiver with successive interference cancellation capabilities, known as Symbol-Level Interference Cancellation (SLIC), with respect to a baseline Minimum Mean Square Error-Interference Rejection Combining (MMSE-IRC) receiver. The study is carried out on a dense, clusterized small cell network, illustrating to which extent NAICS can overcome the additional interference associated with such deployments. Moreover, we analyse how much the data rates could be potentially improved by estimating the throughput with ideal cancellation of the dominant interferer. The results point to limited gains of up to 12% in the coverage data rates when NAICS is used, and that the instantaneous throughput might increase up to 100% in the most favourable case, falling significantly below the estimated 200% maximum gain with ideal cancellation.

This paper addresses distributed interference management in downlink of multi-cell multiple- input multiple-output (MIMO) time division duplex (TDD) networks. Each user is associated with a user-centric cluster of base stations (BSs), which cooperatively serve the user through CoMP joint transmission. Clusters of different users possibly share some BSs such that they may overlap, being coupled by interference and transmit power constraints at each BS. Our objective is the design of BSs clustering and precoding matrices per-user in order to maximize the weighted sum-rate of the system by controlling the interference and the power spent at BSs. In contrast to previous works where all channel matrices in the system are needed, we propose a distributed procedure whereby only channel matrices towards a limited number of candidate BSs per user are required while interference is still controlled by using the signal received from an uplink transmission. For an LTE-compliant dense deployment of 2×2 MIMO BSs/users, results show gains of 6-16% in terms of sum-rate and 49-84% in terms of 5%-tile per-user rate (depending on the maximum cluster size) as compared to distributed BS-disjoint clustering schemes.

This study considers a downlink cell association and scheduling mechanism for an LTE-Advanced network with a centralized architecture, featuring a central baseband (BB) pool and distributed small cells at the remote site. In the proposed solution, the BB pool determines the user-cell association to increase the network performance. A simple suboptimal algorithm with reduced complexity is used on a per time instant basis. The challenges associated to the user measurement reports and the interference variablity are discussed. Compared to the usual case where users connect to the cell providing the highest received power and scheduling is performed independently within the cells, the proposed algorithm can provide up to a 70% median data rate gain.

In this paper, we investigate the operation of eICIC in a realistic deployment based on site specific data from a dense urban European capital area. Rather than the classical semi-static and common network-wide configuration, the importance of having highly dynamic and distributed mechanisms that are able to adapt to local environment conditions is revealed. We propose a promising opportunistic cell association algorithm and a generalized method for fast muting adaptation. The performance results show that the traditional semi-static eICIC configuration leads to modest gains in realistic deployments, whereas the set of proposed fast dynamic eICIC algorithms leads to capacity gains on the order of 35-120% depending on the local environment characteristics. In the analysis of the performance results for the site specific use case, it is furthermore highlighted how those deviate from typical findings from 3GPP standardized HetNet scenarios.

The support of mission-critical communication (MCC) opens the possibility to implement a broad range of novel applications. V2X communication for traffic safety and automation is, among others, one of these innovative applications expected to bring big benefits to society: accidents are prevented, driving times are reduced, and carbon dioxide is saved. In this regard, we first present a system model and fundamental definitions of reliability, latency and availability. Relying on these definitions, a systematic review of requirements for the huge variety of V2X applications is provided, including insights into the expected evolution towards autonomous driving. The many challenges introduced by V2X use cases are emphasized and compared to today's wireless system capabilities. Finally, we give our vision on the design of future radio technologies for the support of this kind of communications.

The arrival of mission critical applications in the context of vehicular, medical and industrial wireless communications calls for reliability constraints never seen before in cellular systems. Enhanced Inter-Cell Interference Coordination (eICIC) has been widely investigated in the context of LTE-A Heterogeneous Networks, but always with load balancing and resource partitioning purposes. Given the broad range of new use cases targeting ultra high reliability, we propose the use of on-demand eICIC for reducing the BLER of the retransmissions of critical users while minimizing the impact to the rest of the network. Combined with a ROot Cause Aware HARQ (ROCA-HARQ), which provides additional information when a transmission fails, the joint mechanism is relevant for any LTE/LTE-A deployment and can be easily implemented in a real network. System-level simulations show attractive BLER reductions up to 80% with little impact in throughput performance (loss in user throughput below 6%).

This article compares the potential performance gains from downlink scheduling and interference mitigation in an LTE-Advanced dense small cell network. The study combines theoretical considerations and system-level simulations with a dynamic traffic model and different offered loads. It is shown that intra-cell scheduling can provide a 22% throughput gain in a narrow traffic load region, while the plausible gains from an ideal inter-cell resource management mechanism can be greater than 50% for a wider range of traffic loads, reaching 300% for some of the cases. The results from this research provide valuable insight for the design of resource management algorithms targeted to small cell scenarios on dedicated carriers.

Enhanced Intercell Interference Coordination (eICIC) techniques boost the performance of co-channel deployments of macro and small cells. However, features like the Almost Blank Subframes (ABS) are intended to be semistatically configured, due to the limited amount of information and the intrinsic delay over the X2 inter-cell interface. In this paper we investigate dynamic solutions for the allocation of the muted resources with small cells in the form of Remote Radio Heads (RRH), connected to the macro cell through a low latency interface. A simple eICIC framework for intercell Radio Resource Management in macro-RRH cases is developed. Based on minimal information collection, we propose an algorithm that adjusts the ABS ratio on a fast basis, aiming at balancing the instantaneous load betweeen the macro and the RRH layer. Performance results with bursty traffic and low and high load conditions show that the dynamic algorithm provides gains of 40-50% both in 5%-ile and 50%-ile user throughput compared to the semi-static case.

Enhanced Inter Cell Interference Coordination (eICIC) for co-channel deployments of pico cells throughout a macro cell layout is studied. In particular, we analyze a scenario where only some macro cells have picos deployed, while other macro cells have no small cells. The challenge for such highly irregular scenarios is how to operate eICIC, and especially how to coordinate macro-cell muting. Our analysis shows that for eICIC to provide gain in such scenarios, it is recommended to use fully time aligned traditional Almost Blank Subframes (ABS) in the macro-cells with picos, while first tier surrounding macro cells shall use low-power ABS. For such cases, user throughput gains of 30%-40% are still achievable. Moreover, it is demonstrated that if macro muting patterns are not fully time aligned, it causes additional interference fluctuations in the network, resulting in less efficient link adaptation and radio-aware packet scheduling.

Enhanced Inter-Cell Interference Coordination (eICIC) techniques are targeted to improve the system and cell-edge throughput of Heterogeneous Networks (HetNets) in LTE-Advanced systems. In order to protect pico UEs from the strong macro interference, the macro eNB can either stop data transmission or simply reduce the transmission power during certain subframes. In this paper, we investigate the impact of reducing the transmission power in LTE HetNets. We evaluate the tradeoff among macro transmission power, cell load and system and cell-edge throughput, with bursty and non-bursty traffic. Moreover, we address some of the technical and standardization challenges related to having a time-varying transmission power. Based on the results, we provide guidance on how to best configure the network to achieve the full potential of the eICIC concept in dynamically changing environments.

Enhanced Inter-Cell Interference Coordination (eICIC) for co-channel deployment of pico-cells throughout a macro-cell layout is a promising solution to increase system capacity and network coverage in Long Term Evolution (LTE)-Advanced systems. The use of both pico-cell Range Extension (RE) and time domain eICIC (TDM muting) in this scenario has been proved to provide high gains compared to the traditional homogeneous network. Nevertheless, performance results in literature assume ideal Common Reference Signals (CRS) interference cancellation in the pico-UEs during Almost Blank Subframes (ABS), i.e., receivers are capable of perfectly suppressing all CRS interference from all neighbour cells. In this paper we investigate the impact of non-ideal CRS Interference Cancellation (IC) in eICIC systems. We propose a simple RSRP-based CRS IC criterion not requiring any extra signaling. The performance is evaluated and compared to the ideal case in the downlink by means of extensive system level simulations that follow the 3GPP guidelines. Results confirm that TDM eICIC can still provide significant gains when realistic CRS IC is considered.

Heterogeneous Networks (HetNet) have been recognized as a key enabler for providing high data rates. However, co-channel deployed HetNet will experience inter-layer interference, and hence calls for use of enhanced Inter-Cell Interference Coordination (eICIC) to solve such interference problems. In order to achieve the full performance benefits of eICIC, its corresponding configuration parameters shall be carefully adjusted. In this paper we study how sensitive the HetNet performance is for different eICIC parameters in different HetNet scenarios, and provide guidelines on how to set them. Among others, it is concluded how the spatial UE distribution, the used propagation models, and pico eNB transmit power settings influence the performance and the optimal eICIC parameter choice.

Different technical solutions are enabling the move from macro-only scenarios towards heterogeneous networks with a mixture of different base station types. In this paper we focus on multi-layer LTE-Advanced networks, and especially address aspects related to co-channel interference management. The network controlled time-domain enhanced inter-cell interference coordination (eICIC) concept is outlined by explaining the benefits and characteristics of this solution. Extensive system level performance results are presented with bursty and non-bursty traffic to demonstrate the eICIC concepts ability to dynamically adapt according to the traffic conditions.

This work analyzes the joint influence of channel fading and the data outsourcing process in the QoS metrics of a wireless system. As a first contribution, expressions of the channel effective bandwidth function (also known as effective capacity) for time-correlated Rayleigh fading under different adaptive rate policies are obtained. This result allows us to analytically evaluate the maximum constant information rate that can be transmitted over the channel under a probabilistic delay constraint. Later, this Capacity with Probabilistic Delay Constraint CDt,εis generalized for systems with non-constant sources and simple closed-form expressions able to capture the influence of source processes in the probability of over-delay are given. Figures on how much the maximum allowable rate decreases for shorter allowable delays or for more changing rate sources are shown and the expected delay violation is compared to simulations in order to validate our results.

Ergodic capacity is not a suitable information-theoretic measure for delay sensitive applications over fading channels. On the other hand, delay-limited capacity becomes zero for Rayleigh channels. In this case, the Capacity with Probabilistic Delay Constraint becomes useful. In, the maximum constant source rate that may be supported in a wireless system with a probability ¿ of exceeding certain delay bound Dt, CDt,¿, was evaluated for a Rayleigh channel. This contribution extends that work to an OFDM system over a generic Rayleigh frequency selective channel. An analytical expression of the capacity CDt,¿is obtained and figures on how much the maximum allowable rate decreases for higher time or frequency correlation in the channel response are shown. Finally, the expected delay violation is compared to simulations in order to validate our results.

The capacity with probabilistic delay constraint CDt,epsivexpresses the maximum source rate that may be supported in a wireless system with a probability epsiv of exceeding certain delay bound Dt. In a previous paper by the authors (see Proc. IEEE GLOBECOM, November 2007), CDt,epsivwas calculated for a Rayleigh channel carrying a constant rate flow. In this work, the potential of the effective bandwidth theory is shown by evaluating this constrained capacity for a variable rate source modeled as an ON-OFF process. Cross relations among ON-OFF process parameters, fading channel statistics, QoS constraints and maximum allowable mean source rate are displayed by the obtained closed-form expressions. CDt,epsivis measured in different scenarios to exhibit to which extent higher time-correlation of the channel response or longer ON-OFF periods are harmful to the delay behaviour, getting a lower value of the capacity CDt,epsiv.

In this work, the maximum constant rate that can be supported with a probability epsiv f exceeding certain delay boundDtis evaluated. Following the works in (Wu, 2003), the procedure to derive this capacity with probabilistic delay constraint CDt,epsiv is described. A simple closed form analytical result is given for block fading channel and different adaptive rate policies. The extension to the general correlated Rayleigh fading is also provided based on a semi-analytical strategy and the previous result for independent samples. As expected, CDt,epsiv tends to the ergodic capacity for long allowed delays (Dtrarr infin) and diminishes when the delay constraint is more strict. The expected delay violation probability is compared to simulations in order to validate our results.