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.

This paper introduces FedSVD, a novel unsupervised federated learning framework for real-time anomaly detection in IoT networks. By leveraging Singular Value Decomposition (SVD) and optimization on the Grassmann manifolds, FedSVD enables accurate detection of both known and unknown intrusions without relying on labeled data or centralized data sharing. Tailored for deployment on low-power devices like the NVIDIA Jetson AGX Orin, the proposed method significantly reduces communication overhead and computational cost. Experimental results show that FedSVD achieves performance comparable to deep learning baselines while reducing inference latency by over 10x, making it suitable for latency-sensitive IoT applications.

Integrated sensing and communication is a key feature in next-generation wireless networks, enabling joint data transmission and environmental radar sensing on shared spectrum. In multi-user scenarios, simultaneous transmissions cause mutual interference on overlapping frequencies, leading to spurious target detections and degraded sensing accuracy. This paper proposes an interference detection and exploitation algorithm for sensing using spectrally interleaved orthogonal frequency division multiplexing. A statistically rigorous procedure is introduced to detect interference while controlling the familywise error rate. We propose an algorithm that estimates the angle by exploiting interference, while estimating the delay by avoiding the interference. Numerical experiments demonstrate that the proposed method reliably detects interference, and that the delay and angle estimation error approaches the Cramér–Rao lower bound.

The use of Dynamic Random Access Memory (DRAM) for storing Machine Learning (ML) models plays a critical role in accelerating ML inference tasks in the next generation of communication systems. However, periodic refreshment of DRAM results in wasteful energy consumption during standby periods, which is significant for resource-constrained Internet of Things (IoT) devices. To solve this problem, this work advocates two novel approaches: 1) wireless memory activation and 2) wireless memory approximation. These enable the wireless devices to efficiently manage the available memory by considering the timing aspects and relevance of ML model usage; hence, reducing the overall energy consumption. Numerical results show that our proposed scheme can realize smaller energy consumption than the always-on approach while satisfying the retrieval accuracy constraint.

Quantum teleportation is a foundational protocol for sending quantum information through entanglement distribution and classical communication. Assuming ideal classical communication, the reliability of quantum teleportation is limited by the fidelity of the shared EPR pairs. This reliability can be improved through two mechanisms: entanglement purification and quantum error correction (QEC). Using both techniques in concert requires flexible QEC rates, since purification alters the structure of errors induced by imperfect-EPR teleportation, and fixed-rate codes cannot be uniformly effective across purification regimes or reliability targets. In this work, we supplement purification with punctured QEC codes, providing a family of code variants that can be adapted to error-channel characteristics and reliability targets. Punctured codes improve teleportation reliability across a broader range of purification regimes, enabling target reliability to be met without hardware-level code switching. This is corroborated by numerical results, showing that different punctured codes achieve the lowest logical error probability in different operating regimes, and that selecting among them reduces logical error relative to fixed-rate encoded teleportation. This reduction relaxes the requirement on the initial EPR fidelity or purification needed to achieve a target reliability. Overall, puncturing enables adaptation to varying entanglement conditions and reliability requirements while reusing a single stabilizer structure.

This paper focuses on communication, radar search, and tracking task scheduling in multi-cell integrated sensing and communication (ISAC) networks under quality-of-service constraints. We propose a medium access control framework that multiplexes these tasks while optimizing radar scan patterns through an interference-aware scheduling algorithm. Specifically, the proposed framework employs time-domain task scheduling and beam selection, formulated as an assignment problem, to mitigate inter-task and inter-cell interference, respectively. Simulations show that our solution guarantees target communication throughput, sensing target detection probability, and sensing signal-to-interference-plus-noise ratio with improved resource efficiency over baseline schemes, highlighting the benefits of coordinated scheduling in multi-cell ISAC.

The rapid growth of the Internet of Things (IoT) presents sustainability challenges, including increased maintenance requirements and overall higher energy consumption. This motivates self-sustainable IoT ecosystems based on Energy Harvesting (EH). This paper treats IoT deployments in which IoT devices (IoTDs) rely solely on EH to sense and transmit information about events/alarms to a base station (BS). The objective is to effectively manage the duty cycling of the IoTDs to prolong battery life and maximize the relevant data delivered to the BS. The BS can also selectively wake up specific IoTDs to gather extra information following initial detection. We propose a K-nearest neighbors (KNN)-based duty cycling management to optimize energy efficiency and detection accuracy by considering spatial correlations among IoTDs’ activity and their EH process. We evaluate machine learning approaches, including reinforcement learning (RL) and decision transformers (DT), to maximize information captured from events while managing energy consumption. All three approaches (KNN, RL, and DT) achieve significant energy savings over state-of-the-art methods. Moreover, the RL-based solution approaches the performance of a genie-aided benchmark as the number of IoTDs increases.

Frequency-shifting reconfigurable intelligent surfaces (FS-RISs) represent a novel operation mode relying upon metamaterials capable of shifting the carrier frequency of transmitted signals. While the technology is still not mature for wider deployment, previous work has revealed its significant potential for improvements in wireless communication. This work expands the application domain of FS-RIS towards localization within cellular networks operating at sub-6 GHz. The FS-RIS upconverts signals transmitted by the base station to the millimetre-wave spectrum, redirecting them to the user and enabling precise position estimation through narrow, highly directive beams. To eliminate the overhead associated with traditional reference symbols, a superimposed training (ST) scheme is employed. This novel approach achieves accurate localization using only a single FS-RIS, similar to existing solutions based on multiple base stations or traditional RISs. The derived Cramér-Rao lower bound (CRLB) demonstrates that the proposed ST scheme surpasses the performance of conventional regular pilot approaches in localization accuracy while maintaining the data rate. Simulation results further validate the proposed method, showcasing significant performance gains regarding bit-error rate (BER) and position estimation error. These findings underscore the transformative potential of FS-RISs in enhancing spectrum utilization, optimizing interference management, and advancing localization applications in next-generation networks.

Non-geostationary orbit (NGSO) constellations, represented by Low and Medium Earth Orbit (LEO and MEO) satellites, require resource-Allocation frameworks that jointly address payload flexibility and communication performance. This work presents a hierarchical three-stage framework for resource allocation: demand-driven satellite cell coloring (SCC), topology-Aware satellite-cell association (S2C), and multi-satellite beam hopping (BH). Our framework halves the downlink power required to reach 10% unserved capacity (UC) and reduces the peak load on inter-satellite links (ISLs) by approximately 40% compared to a baseline strategy, while controlling cell handovers. The results further show that architectural parameters dominate performance: increasing the number of beams, refining cell granularity, and scaling constellation density enable approximately × , × , and × reductions in power consumption, respectively. These reductions in the required downlink (DL) power and ISL load for a given performance target can be directly translated into system-level gains at payload or constellation size level. The proposed framework provides a scalable foundation for end-To-end optimization of next-generation NGSO satellite networks.

Goal-oriented communication entails the timely transmission of updates related to a specific goal defined by the application. In a distributed setup with multiple sensors, each individual sensor knows its own observation and can determine its freshness, as measured by Age of Incorrect Information (AoII). This local knowledge is suited for distributed medium access, where the transmission strategies have to deal with collisions. We present Dynamic Epistemic Logic for Tracking Anomalies (DELTA), a medium access protocol that limits collisions and minimizes AoII in anomaly reporting over dense networks. Each sensor knows its own AoII, while it can compute the belief about the AoII for all other sensors,based on their Age of Information (AoI), which is inferred from the acknowledgments. This results in a goal-oriented approach based on dynamic epistemic logic emerging from public information. We analyze the resulting DELTA protocol both from a theoretical standpoint and with Monte Carlo simulations, showing that it is significantly more efficient and robust than classical random access, while outperforming state-of-the-art scheduled schemes by at least 30%, even with imperfect feedback.

The problem of uplink transmissions in massive connectivity is commonly dealt with using schemes for grant-free random access. When a large number of devices transmit almost synchronously, the receiver may not be able to resolve the collision. This could be addressed by assigning dedicated pilots to each user, leading to a contention-free random access (CFRA), which suffers from low scalability and efficiency. This paper explores contention-based random access (CBRA) schemes for asynchronous access in massive multiple-input multiple-output (MIMO) systems. The symmetry across the accessing users with the same pilots is broken by leveraging the delay information inherent to asynchronous systems and the angle information from massive MIMO to enhance activity detection (AD) and channel estimation (CE). The problem is formulated as a sparse recovery in the delay-angle domain. The challenge is that the recovery signal exhibits both row-sparse and cluster-sparse structure, with unknown cluster sizes and locations. We address this by a cluster-extended sparse Bayesian learning (CE-SBL) algorithm that introduces a new weighted prior to capture the signal structure and extends the expectation maximization (EM) algorithm for hyperparameter estimation. Simulation results demonstrate the superiority of the proposed method in joint AD and CE.

There is a broad consensus that artificial intelligence (AI) will be a defining component of the sixth-generation (6G) networks. As a specific instance, AI-empowered sensing will gather and process environmental perception data at the network edge, giving rise to integrated sensing and edge AI (ISEA). Many applications, such as autonomous driving and industrial manufacturing, are latency-sensitive and require end-to-end (E2E) performance guarantees under stringent deadlines. However, the 5G-style ultra-reliable and low-latency communication (URLLC) techniques designed with communication reliability and agnostic to the data may fall short in achieving the optimal E2E performance of perceptive wireless systems. In this work, we introduce an ultra-low-latency (ultra-LoLa) inference framework for perceptive networks that facilitates the analysis of the E2E sensing accuracy in distributed sensing by jointly considering communication reliability and inference accuracy. By characterizing the tradeoff between packet length and the number of sensing observations, we derive an efficient optimization procedure that closely approximates the optimal tradeoff. We validate the accuracy of the proposed method through experimental results, and show that the proposed ultra-Lola inference framework outperforms conventional reliability-oriented protocols with respect to sensing performance under a latency constraint.

Reconfigurable Intelligent Surfaces (RISs) are potential enablers of future wireless communications and sensing applications and use-cases. The RIS is envisioned as a dynamically controllable surface that is capable of transforming impinging electromagnetic waves in terms of angles and polarization. Many models have been proposed to predict the wave-transformation capabilities of potential RISs, where power conservation is ensured by enforcing that the scattered power equals the power impinging upon the aperture of the RIS, without considering whether the scattered field adds coherently of destructively with the source field. In effect, this means that power is not conserved, as elaborated in this paper. With the goal of investigating the implications of global and local power conservation in RISs, this work considers a single-layer metasurface based RIS. A complete end-to-end communications channel is given through polarizability modeling and conditions for power conservation and channel reciprocity are derived. The implications of the power conservation conditions upon the end-to-end communications channel are analyzed.

We investigate accuracy and freshness of status updates from a large number of energy-harvesting devices that monitor two-state Markov processes and access the medium using slotted ALOHA without feedback. Using a Markovian framework, we analyze the average value of a generic state-dependent penalty function that grows whenever there is a state estimation error. The age of incorrect information (AoII) is an example of such penalty function. We propose an accurate and easy-to-compute approximation for the average penalty. Numerical results show the benefits of optimizing the transmission probabilities according to the process state transitions and current battery levels to minimize the average penalty. Minimizing a state-independent penalty function can be highly suboptimal when one of the process states is critical, i.e., entails a high penalty if wrongly estimated. Furthermore, minimizing the average penalty does not guarantee a low probability of misdetecting a critical state period.

Frequency-shifting reconfigurable intelligent surfaces (FS-RISs) can be developed using a new generation of meta-materials that shift the original frequency spectrum of sent signals, it is also known as frequency conversion. Their application in wireless communications and localization remains in the early stages of development. This work proposes the FSRIS to provide localization services in an existing cellular network operated at sub- 6 GHz. It can up-convert the signal transmitted by the base station (BS) to the millimetre Wave and forward it to the user, allowing this to estimate its position thanks to the use of narrow directive beams. The proposed method significantly outperforms the existing solution based on multiple BSs in terms of the Cramér-Rao lower bound, which is analytically derived. An efficient data-aided beam-sweeping is provided to reduce the beam-training delay at FS-RIS without sacrificing the data rate. This work introduces a novel approach to utilizing FS-RIS in wireless systems, with the potential to reshape how we view spectrum and interference management, paving the way for more efficient communication and localization applications.

Age of Incorrect Information (AoII) is particularly relevant in systems where real time responses to anomalies are required, such as natural disaster alerts, cybersecurity warnings, or medical emergency notifications. Keeping system control with wrong information for too long can lead to inappropriate responses. In this paper, we study the Peak AoII (PAoII) for multisource status reporting by independent devices over a collision channel, following a zero-threshold ALOHA access where nodes observing an anomaly immediately start transmitting about it. If a collision occurs, nodes reduce the transmission probability to allow for a resolution. Finally, wrong or lost feedback messages may lead a node that successfully updated the destination to believe a collision happened. The PAoII for this scenario is computed in closed-form. We are eventually able to derive interesting results concerning the minimization of PAoII, which can be traded against the overall goodput and energy efficiency, but may push the system to the edge of congestion collapse.

Artificial intelligence (AI) is expected to be a defining component in sixth-generation (6G) wireless networks. One specific use is AI-empowered sensing, where sensor data will be processed at the network edge, giving rise to integrated sensing and edge AI (ISEA). Many sensing applications, such as autonomous driving and industrial manufacturing, are latencysensitive and require end-to-end (E2E) performance guarantees under stringent deadlines. However, data-agnostic ultrareliable and low-latency communication (URLLC) techniques designed for 5G fall short in achieving the optimal E2E sensing performance. In this work, we introduce an ultra-low-latency (ultra-LoLa) inference framework for perceptive networks that facilitates the analysis of E2E sensing accuracy in distributed sensing by jointly considering communication reliability and inference accuracy. By characterizing the tradeoff between packet length and the number of sensing observations, we derive an efficient optimization procedure that closely approximates the optimal balance. The experimental results show that the proposed approach outperforms conventional reliability-oriented protocols with respect to sensing performance under a latency constraint.

We investigate a lossy source compression problem in which both the encoder and decoder are equipped with a pre-trained sequence predictor. We propose an online lossy compression scheme that, under a$0-1$loss distortion function, ensures a deterministic, per-sequence upper bound on the distortion (outage) level for any time instant. The outage guarantees apply irrespective of any assumption on the distribution of the sequences to be encoded or on the quality of the predictor at the encoder and decoder. The proposed method, referred to as online conformal compression (OCC), is built upon online conformal prediction-a novel method for constructing confidence intervals for arbitrary predictors. Numerical results show that OCC achieves a compression rate comparable to that of an idealized scheme in which the encoder, with hindsight, selects the optimal subset of symbols to describe to the decoder, while satisfying the overall outage constraint.

Achieving flexible and efficient wireless resource sharing across diverse applications and services is among the key goals of the sixth-generation of mobile systems (6G). This work investigates the performance of a real-time system coexisting with a broadband service in a frame-based wireless channel. Specifically, we consider a remote tracking device that monitors an information source and transmits updates to a base station (BS) for real-time source reconstruction, and potential remote actuation. We revise the common idealized assumptions in real-time remote tracking studies, such as instantaneous feedback and pervasive wireless resources, as they do not hold in practical scenarios. We consider a monitoring device and a broadband user communicating with the BS via a grant-free access mechanism over wireless resources defined for either orthogonal or non-orthogonal access, with feedback scheduled at the end of each frame. We analyze system performance using goal-oriented performance metrics for real-time remote reconstruction, alongside throughput and energy efficiency for the broadband user. Our results show that the ‘Idealistic’ model considered in conventional studies achieves better performance but incurs disproportionately high overhead compared to the Frame-Based model. Moreover, within the Frame-Based model, orthogonal resource sharing is preferable for maximizing broadband throughput, while non-orthogonal sharing significantly improves energy efficiency.

This paper investigates how to achieve both low-power operations of sensor nodes and accurate state estimation using Kalman filter for internet of things (IoT) monitoring employing wireless sensor networks under radio resource constraint. We consider two policies used by the base station to collect observations from the sensor nodes: (i) an oblivious policy, based on statistics of the observations, and (ii) a decentralized policy, based on autonomous decision of each sensor based on its instantaneous observation. This work introduces a wake-up receiver and wake-up signaling to both policies to improve the energy efficiency of the sensor nodes. The decentralized policy designed with random access prioritizes transmissions of instantaneous observations that are highly likely to contribute to the improvement of state estimation. Our numerical results show that the decentralized policy improves the accuracy of the estimation in comparison to the oblivious policy under the constraint on the radio resource and consumed energy when the correlation between the processes observed by the sensor nodes is low. We also clarify the degree of correlation in which the superiority of two policies changes.

Tactile Internet (TI) facilitates virtual immersion in a remote environment by transferring the sense of touch, thereby posing stringent latency requirements. It is challenging to meet those requirements, especially for large data volumes and in the uplink. This work presents a latency reduction scheme for uplink data transmission while ensuring high reliability based on considerations of the timing contribution from both compression and communication. The approach is termed Parallel Compression and Transmission (PCT) and the key idea is to send part of the data uncompressed while simultaneously compressing the remaining data. This is formulated into a non-convex optimization problem that minimizes the latency under a statistical constraint on reliability. The results show that the proposed approach can lead to latency reduction of more than 30% under a reliability level of 99% in the considered setup, outlining the promise of the concept of parallel compression and computation towards latency reduction.

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 article considers methods for delivering ultrareliable low-latency communication (URLLC) to enable mission-critical Internet of Things (IoT) services in wireless environments with unknown channel distribution. The methods rely upon the historical channel gain samples of a few locations in a target area. We formulate a nontrivial transmission control adaptation problem across the target area under the URLLC constraints. Then we propose two solutions to solve this problem. The first is a power scaling scheme in conjunction with the deep reinforcement learning (DRL) algorithm with the help of the channel knowledge map (CKM) without retraining, where the CKM employs the spatial correlation of the channel characteristics from the historical channel gain samples. The second solution is model agnostic meta-learning (MAML)-based meta-reinforcement learning algorithm that is trained from the known channel gain samples following distinct channel distributions and can quickly adapt to the new environment within a few steps of gradient update. Simulation results indicate that the DRL-based algorithm can effectively meet the reliability requirement of URLLC under various Quality-of-Service (QoS) constraints. Then the adaptation capabilities of the power scaling scheme and meta-reinforcement learning algorithm are also validated.

Compared to the generations up to 4G, whose main focus was on broadband and coverage aspects, 5G has expanded the scope of wireless cellular systems toward embracing two new types of connectivity: massive machine-type communications (mMTCs) and ultrareliable low-latency communications (URLLCs). This article discusses the possible evolution of these two types of connectivity within the umbrella of 6G wireless systems. This article consists of three parts. The first part deals with the connectivity for a massive number of devices. While mMTC research in 5G predominantly focuses on the problem of uncoordinated access in the uplink for a large number of devices, the traffic patterns in 6G may become more symmetric, leading to closed-loop massive connectivity. One of the drivers for this type of traffic pattern is distributed/decentralized learning and inference. The second part of this article discusses the evolution of wireless connectivity for critical services. While latency and reliability are tightly coupled in 5G, 6G will support a variety of safety-critical control applications with different types of timing requirements, as evidenced by the emergence of metrics related to information freshness and information value. In addition, ensuring ultrahigh reliability for safety-critical control applications requires modeling and estimation of the tail statistics of the wireless channel, queue length, and delay. The fulfillment of these stringent requirements calls for the development of novel artificial intelligence (AI)-based techniques, incorporating optimization theory, explainable AI (XAI), generative AI, and digital twins (DTs). The third part analyzes the coexistence of massive connectivity and critical services. Specifically, we consider scenarios in which a massive number of devices need to support traffic patterns of mixed criticality. This is followed by a discussion about the management of wireless resources shared by services with different criticality.

This paper studies decision-making for goal-oriented effective communication. We consider an end-to-end status update system where a sensing agent (SA) observes a source, generates and transmits updates to an actuation agent (AA), while the AA takes actions to accomplish a goal at the endpoint. We integrate the push- and pull-based update communication models to obtain apush-and-pullmodel, which allows the transmission controller at the SA to decide whether to push an update to the AA and the query controller at the AA to pull updates by initiating queries at specific time instants. To gauge effectiveness, we utilize agrade of effectiveness(GoE) metric incorporating the updates’ freshness, usefulness, and the timeliness of actions as qualitative attributes. We then derive effect-aware policies to maximize the expected discounted sum of the updates’ effectiveness subject to induced costs. The effect-aware policy at the SA considers the potential effectiveness of communicated updates at the endpoint, while at the AA, it accounts for the probabilistic evolution of the source and importance of the generated updates. Our results show the proposed push-and-pull model outperforms models solely based on push- or pull-based updates both in terms of efficiency and effectiveness. Additionally, using effect-aware policies at both agents enhances effectiveness compared to periodic and/or probabilistic, effect-agnostic policies at either or both agents.

We consider a wireless networked control system (WNCS) with imperfect bidirectional links for real-time applications such as smart grids. To maintain the stability of WNCS, captured by the probability that plant state violates preset values, at minimal cost, heterogeneous physical processes are monitored by multiple sensors. This status information, such as dynamic plant state and Markov Process-based context information, is then received/estimated by the controller for remote control. However, scheduling multiple sensors and designing the controller with limited resources is challenging due to their coupling, delay, and transmission loss. We formulate a Constrained Markov Decision Problem (CMDP) to minimize violation probability with cost constraints. We reveal the relationship between the goal and different updating actions by analyzing the significance of information that incorporates goal-related usefulness and contextual importance. Subsequently, a goal-oriented deterministic scheduling policy is proposed. Two sensing-assisted control strategies and a control-aware estimation policy are proposed to improve the violation probability-cost tradeoff, integrated with the scheduling policy to form a goal-oriented co-design framework. Additionally, we explore retransmission in downlink transmission and qualitatively analyze its preference scenario. Simulation results demonstrate that the proposed goal-oriented co-design policy outperforms previous work in simultaneously reducing violation probability and cost.

A reconfigurable intelligent surface (RIS) is commonly made of low-cost passive and reflective meta-materials with excellent beam steering capabilities. It is applied to enhance wireless communication systems as a customizable signal reflector. However, RIS can also be adversely employed to disrupt the existing communication systems by introducing new types of vulnerability to the physical layer. We consider the RIS-In-The-Middle (RITM) attack, in which an adversary uses RIS to jeopardize the direct channel between two transceivers by providing an alternative one with higher signal quality. This adversary can eavesdrop on all exchanged data by the legitimate users, but also perform a false data injection to the receiver. This work devises anti-attack techniques based on a non-reciprocal channel produced by a defensive RIS (D-RIS). The proposed precoding and combining methods and the channel estimation procedure for a non-reciprocal link are effective against potential adversaries while keeping the existing advantages of the RIS. We analyse the robustness of the system against attacks in terms of achievable secrecy rate and probability of detecting fake data. We believe that this defensive role of RIS can be a basis for new protocols and algorithms in the area.

We present a communication scheme using guessing random additive noise decoding (GRAND) to improve flexibility and reliability of the existing compressed error (CE) framework. The CE framework uses information feedback to construct follow-up transmissions by compressing previous noise realizations, offering high reliability at a code rate close to the forward channel capacity. The channel decoding algorithm GRAND allows us to efficiently maintain this performance in noisy feedback settings by shifting redundancy for forward message protection to the feedback channel. Our scheme, GRAND-CE, is therefore appropriate for cases where forward and feedback channel usage costs are asymmetric, e.g. uplink communications. GRAND-CE offers super-exponential error rate performance as channel use increases, with finite usage of a noisy feedback channel. Unlike the traditional forward error correction model, the receiver performs error correction encoding and the sender handles decoding. We also propose a technique for pipelining sequential transmissions to maintain fixed forward transmission length and good feedback channel coding performance.

Estimating the probability of rare channel conditions is a central challenge in ultra-reliable wireless communication, where random events, such as deep fades, can cause sudden variations in the channel quality. This paper proposes a sample-efficient framework for predicting the statistics of such events by utilizing spatial dependency between channel measurements acquired from various locations. The proposed framework combines radio maps with non-parametric models and extreme value theory (EVT) to estimate rare-event channel statistics under a Bayesian formulation. The framework can be applied to a wide range of problems in wireless communication and is exemplified by rate selection in ultra-reliable communications. Notably, besides simulated data, the proposed framework is also validated with experimental measurements. The results in both cases show that the Bayesian formulation provides significantly better results in terms of throughput compared to baselines that do not leverage measurements from surrounding locations. It is also observed that the models based on EVT are generally more accurate in predicting rare-event statistics than non-parametric models, especially when only a limited number of channel samples are available. Overall, the proposed methods can significantly reduce the number of measurements required to predict rare channel conditions and guarantee reliability.

Efficiently retrieving relevant data from massive Internet of Things (IoT) networks is essential for downstream tasks such as machine learning. This paper addresses this challenge by proposing a novel data sourcing protocol that combines semantic queries and random access. The key idea is that the destination node broadcasts a semantic query describing the desired information, and the sensors that have data matching the query then respond by transmitting their observations over a shared random access channel, for example to perform joint inference at the destination. However, this approach introduces a tradeoff between maximizing the retrieval of relevant data and minimizing data loss due to collisions on the shared channel. We analyze this tradeoff under a tractable Gaussian mixture model and optimize the semantic matching threshold to maximize the number of relevant retrieved observations. The protocol and the analysis are then extended to handle a more realistic neural network-based model for complex sensing. Under both models, experimental results in classification scenarios demonstrate that the proposed protocol is superior to traditional random access, and achieves a near-optimal balance between inference accuracy and the probability of missed detection, highlighting its effectiveness for semantic query-based data sourcing in massive IoT networks.

We study a scenario in which multiple uncoordinated devices aim to achieve reliable transmissions within a given time frame. The devices are intermittently active and access a shared pool of channel resources in a grant-free manner by utilizing multiple transmissions (K-repetition coding). This allows them to achieve diversity and improve the reliability within a certain latency constraint. We focus on two access methods: one where devices choose K slots at random and another one where the access patterns are deterministic and follow a specific code design, namely the Steiner System. We analyze the problem under two signal models that involve different complexity for the receiver. First, collision model is considered, where only interference-free transmissions can be used and combined. Second, a model treating interference as noise is analyzed, where the receiver is capable of utilizing all K replicas, applying maximum ratio combining (MRC). For both signal models, we investigate receivers with and without successive interference cancellation (SIC). We develop approximations and bounds for the outage probabilities that very closely match simulation results. Overall, we show that deterministic access patterns have the potential to significantly outperform random selection in terms of reliability. Furthermore, deterministic access patterns offer a simplified system design.

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 introduce a massive multiple access scheme designed to meet different trade-offs between reliability, scalability, and latency. To maximize the number of successfully decoded users, the scheme builds upon coded random access, incorporating both grant-free and grant-based procedures, along with a massive acknowledgment phase conducted at the base station. The main design premise is the establishment of two distinct latency deadlines: the first one guaranteeing high reliability (e.g., between 99% and 99.99%), and the second one enforcing ultra-high reliability, even above 99.9999%. This dual-latency approach, supplemented with massive MIMO, enables the system to support a higher number of active users per frame while meeting stringent reliability requirements. Throughout the paper, we present a theoretical analysis and derive performance bounds to guide and support effective system design. The approach opens the door for the development of critical services that bridge the gap between massive machine-type communication (mMTC) and ultra-reliable and low-latency communication (URLLC), providing a more flexible and efficient framework for next-generation systems.

Energy efficiency and information freshness are key requirements for sensor nodes serving Industrial Internet of Things (IIoT) applications, where a sink node collects informative and fresh data before a deadline, e.g., to control an external actuator. Content-based wake-up (CoWu) activates a subset of nodes that hold data relevant for the sink’s goal, thereby offering an energy-efficient way to attain objectives related to information freshness. This paper focuses on a scenario where the sink collects fresh information on top-kvalues, defined as data from the nodes observing thekhighest readings at the deadline. We introduce a new metric called top-kQuery Age of Information (k-QAoI), which allows us to characterize the performance of CoWu by considering the characteristics of the physical process. Further, we show how to select the CoWu parameters, such as its timing and threshold, to attain both information freshness and energy efficiency. The numerical results reveal the effectiveness of the CoWu approach, which is able to collect top-kdata with higher energy efficiency while reducingk-QAoI when compared to round-robin scheduling, especially when the number of nodes is large and the required size ofkis small.

The escalating number of wireless users requiring different services, such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC), has led to exploring non-orthogonal multiplexing methods like heterogeneous non-orthogonal multiple access (H-NOMA). This method allows users demanding divergent services to share the same resources. However, implementing the H-NOMA scheme faces major resource management challenges due to unpredictable interference caused by the random access mechanism of mMTC users. To address this issue, this paper proposes a joint optimization and cooperative multi-agent (MA) deep reinforcement learning-based resource allocation mechanism, aimed at maximizing the energy efficiency (EE) of H-NOMA-based networks. Specifically, this work initially establishes an optimization framework capable of determining the optimal power allocation for any specific sub-channel assignment (SA) setting for all users. Based on that, a cooperative MA double deep Q network (CMADDQN) scheme is carefully designed at the base station to conduct SA among users. In addition, a distributed full learning-based approach using MADDQN for both SA and power allocation is also designed for comparison purposes. Simulation results show that the proposed joint optimization and machine learning method outperforms the solely-learning-based approach and other benchmark schemes in terms of convergence rate and EE performance.

Low-latency communication plays an increasingly important role in delay-sensitive applications by ensuring the real-time information exchange. However, due to the constraint on the maximum instantaneous power, guaranteeing bounded latency is challenging. In this paper, we investigate the reliability-latency-rate tradeoff in low-latency communication systems with finite-blocklength coding (FBC). Specifically, we are interested in the fundamental tradeoff between error probability, delay-violation probability (DVP), and service rate. Based on the effective capacity (EC), we present the gain-conservation equations to characterize the reliability-latency-rate tradeoffs in low-latency communication systems. In particular, we investigate the low-latency transmissions over an additive white Gaussian noise (AWGN) channel and a Nakagami-$m$fading channel. By defining the service rate gain, reliability gain, and real-time gain, we conduct an asymptotic analysis to reveal the fundamental reliability-latency-rate tradeoff of ultra-reliable and low-latency communications in the high signal-to-noise-ratio (SNR) regime. To analytically evaluate and optimize the quality-of-service-constrained throughput of low-latency communication systems adopting FBC, an EC-approximation method is conceived to derive the closed-form expression of that throughput. Our results may offer some insights into the efficient scheduling of low-latency wireless communications, in which statistical latency and reliability metrics are crucial.

Data markets facilitate decentralized data exchange for applications such as prediction, learning, or inference. The design of these markets is challenged by varying privacy preferences and data similarity among data owners. Related works have often overlooked how data similarity impacts pricing and data value through statistical information leakage. We demonstrate that data similarity and privacy preferences are integral to market design and propose a query-response protocol using local differential privacy (LDP) for a two-party data acquisition mechanism. In our regression data market model, we analyze strategic interactions between privacy-aware owners and the learner as a Stackelberg game over the asked price and privacy factor. Finally, we numerically evaluate how data similarity affects market participation and traded data value.

Autonomous reconfigurable intelligent surfaces (RISs) offer the potential to simplify deployment by reducing the need for real-time remote control between a base station (BS) and an RIS. However, we highlight two major challenges posed by autonomy. The first is implementation complexity, as autonomy requires hybrid RISs (HRISs) equipped with additional onboard hardware to monitor the propagation environment and perform local channel estimation (CHEST), a process known as probing. The second challenge, termed probe distortion, reflects a form of the observer effect: during probing, an HRIS can inadvertently alter the propagation environment, potentially disrupting the operations of other communicating devices sharing the environment. Although implementation complexity has been extensively studied, probe distortion remains largely unexplored. To further assess the potential of autonomous RISs, this paper comprehensively and pragmatically studies the fundamental trade-offs posed by these challenges collectively. In particular, we examine the robustness of an HRIS-assisted massive multiple-input multipleoutput (mMIMO) system by considering its critical components and stringent conditions. The latter include: 1) two extremes of implementation complexity, represented by minimalist operation designs of two distinct HRIS hardware architectures, and 2) an oblivious BS that fully embraces probe distortion. To make our analysis possible, we propose a physical-layer orchestration framework that aligns HRIS and mMIMO operations. We present empirical evidence that autonomous RISs remain promising under stringent conditions and outline research directions to deepen probe distortion understanding.

The integration of Non-Terrestrial Networks (NTNs) with Low Earth Orbit (LEO) satellite constellations into 5G and Beyond is essential to achieve truly global connectivity. A distinctive characteristic of LEO mega-constellations is that they constitute a global infrastructure with predictable dynamics, which enables the pre-planned allocation of radio resources. However, the different bands that can be used for ground-to-satellite communication are affected differently by atmospheric conditions such as precipitation, which introduces uncertainty on the attenuation of the communication links at high frequencies. Based on this, we present a compelling case for applying integrated sensing and communications (ISAC) in heterogeneous and multi-layer LEO satellite constellations over wide areas. Specifically, we propose a sensing-assisted communications framework and frame structure that not only enables the accurate estimation of theatmosphericattenuation in the communication links through sensing but also leverages this information to determine the optimal serving satellites and allocate resources efficiently for downlink communication with users on the ground. The results show that, by dedicating an adequate amount of resources for sensing and solving the association and resource allocation problems jointly, it is feasible to increase the average throughput by 59% and the fairness by 700% when compared to solving these problems separately.

Goal-oriented communication has become one of the focal concepts in sixth-generation communication systems owing to its potential to provide intelligent, immersive, and real-time mobile services. The emerging paradigms of goal-oriented communication constitute closed loops integrating communication, computation, and sensing. However, challenges arise for closed-loop timing analysis due to multiple random factors that affect the communication/computation latency, as well as the heterogeneity of feedback mechanisms across multi-modal sensing data. To tackle these problems, we aim to provide a unified timing analysis framework for closed-loop goal-oriented communication (CGC) systems over fading channels. The proposed framework is unified as it considers computation, compression, and communication latency in the loop with different configurations. To capture the heterogeneity across multi-modal feedback, we categorize the sensory data into the periodic-feedback and event-triggered, respectively. We formulate timing constraints based on average and tail performance, covering timeliness, jitter, and reliability of CGC systems. A method based on saddlepoint approximation is proposed to obtain the distribution of closed-loop latency. The results show that the modified saddlepoint approximation is capable of accurately characterizing the latency distribution of the loop with analytically tractable expressions. This sets the basis for low-complexity co-design of communication and computation.

Radio frequency (RF) wireless power transfer (WPT) is a promising technology for sustainable support of massive Internet of Things (IoT). However, RF-WPT systems are characterized by low efficiency due to channel attenuation, which can be mitigated by precoders that adjust the transmission directivity. This work considers a multi-antenna RF-WPT system with multiple non-linear energy harvesting (EH) nodes with energy demands changing over discrete time slots. This leads to the charging scheduling problem, which involves choosing the precoders at each slot to minimize the total energy consumption and meet the EH requirements. We model the problem as a Markov decision process and propose a solution relying on a low-complexity beamforming and deep deterministic policy gradient (DDPG). The results show that the proposed beamforming achieves near-optimal performance with low computational complexity, and the DDPG-based approach converges with the number of episodes and reduces the system’s power consumption, while the outage probability and the power consumption increase with the number of devices.

The paper examines a scenario wherein sensors are deployed within an Industrial Networked Control System, aiming to construct a digital twin (DT) model for a remotely operated Autonomous Guided Vehicle (AGV). The DT model, situated on a cloud platform, estimates and predicts the system’s state, subsequently formulating the optimal scheduling strategy for execution in the physical world. However, acquiring data crucial for efficient state estimation and control computation poses a significant challenge, primarily due to constraints such as limited network resources, partial observation, and the necessity to maintain a certain confidence level for DT estimation. We propose an algorithm based on Value of Information (VoI), seamlessly integrated with the Extended Kalman Filter to deliver a polynomial-time solution, selecting the most informative subset of sensing agents for data. Additionally, we put forth an alternative solution leveraging a Graph Neural Network to precisely ascertain the AGV’s position with a remarkable accuracy of up to 5 cm. Our experimental validation in an industrial robotic laboratory environment yields promising results, underscoring the potential of high-accuracy DT models in practice.

The novel frequency-shifting reconfigurable intelligent surface (FS-RIS) is built by a new generation of meta-materials, which can perform an operation that changes the frequency spectrum of the originally transmitted signal. In this work, we exploit the FS-RIS to fight against the RIS-In-The-Middle (RITM) attack, in which an adversary uses a RIS to replace the direct channel between two legitimate transceivers. The FS-RIS can prevent not only the eavesdropping of all exchanged data by legitimate users but also avoid the false data injection to the receiver by an adversary RIS. This work devises anti-attack techniques based on a non-reciprocal channel produced by a defensive FS-RIS. The proposed combination of precoding/combining and subcarrier shifting procedures for a non-reciprocal link are robust against potential adversaries while keeping the existing advantages of the RIS. We analyse the robustness of the system against attacks in terms of achievable secrecy rate and probability of detecting fake data. We believe that this defensive role of RIS can be a basis for new protocols and algorithms in the area.

This paper considers energy-efficient connectivity for Internet of Things (IoT) devices in a coexistence scenario between two distinctive communication models: pull- and push-based communication models. In pull-based communication, the base station (BS) decides when to retrieve a specific type of data from the IoT devices equipped with wake-up receivers, while in push-based communication, the IoT device decides when and which data to transmit. To efficiently manage both types of traffic, this paper applies content-based wake-up (CoWu) and designs a medium access control (MAC) frame. This enables the BS to activate a subset of pull-based nodes and collect the relevant data to fulfill its tasks, while receiving data from the push-based communication nodes. This paper analyzes the basic trade-off through the MAC layer operations: allocating longer duration for collecting data from pull-based nodes can lead to high retrieval accuracy while decreasing the probability of data transmission success for push-based nodes, and vice versa. Numerical results show that CoWu can manage communication requirements for both pull-based and push-based nodes while realizing high energy efficiency (up to 38%) of IoT devices, compared to the baseline.

This paper introduces EcoPull, a sustainable Internet of Things (IoT) framework powered by Tiny Machine Learning (TinyML) models for efficient image retrieval from multiple devices. The devices are equipped with two types of TinyML models: i) a behavior model and ii) an image compressor model. The behavior model filters out irrelevant images based on the current task, minimizing unnecessary data transmission and reducing communication resource competition among devices. The image compressor model enables devices to communicate with the edge server (ES) using latent representations of images, thereby reducing communication bandwidth usage. While integrating TinyML models into IoT devices does increase energy consumption due to the inference process, this cost is carefully accounted for in our design. Numerical results show that the proposed framework can achieve over 77% and 43% energy savings compared to the simple offloading and a state-of-the-art baseline while still maintaining the quality of the retrieved images at the ES.

We propose an efficient solution to the state estimation problem in multi-scan multi-sensor multiple extended target sensing scenarios. We first model the measurement process by a doubly inhomogeneous-generalized shot noise Cox process and then estimate the parameters using a jump Markov chain Monte Carlo sampling technique. The proposed approach scales linearly in the number of measurements and can take spatial properties of the sensors into account, herein, sensor noise covariance, detection probability, and resolution. Numerical experiments using radar measurement data suggest that the algorithm offers improvements in high clutter scenarios with closely spaced targets over state-of-the-art clustering techniques used in existing multiple extended target tracking algorithms.

We study a pull-based communication system where a sensing agent updates an actuation agent using a query control policy, which is adjusted in the evolution of an observed information source and the usefulness of each update for achieving a specific goal. For that, a controller decides whether to pull an update at each slot, predicting what is probably occurring at the source and how much effective impact that update could have at the endpoint. Thus, temporal changes in the source evolution could modify the query arrivals to capture important updates. The amount of impact is determined by a grade of effectiveness (GoE) metric, which incorporates both freshness and usefulness attributes of the communicated updates. Applying an iterative algorithm, we derive query decisions that maximize the longterm average GoE for the communicated packets, subject to cost constraints. Our analytical and numerical results show that the proposed query policy exhibits higher effectiveness than existing periodic and probabilistic query policies for a wide range of query arrival rates.

The setup considered in the paper consists of sensors in a Networked Control System that are used to build a digital twin (DT) model of the system dynamics. The focus is on control, scheduling, and resource allocation for sensory observation to ensure timely delivery to the DT model deployed in the cloud. Low latency and communication timeliness are instrumental in ensuring that the DT model can accurately estimate and predict system states. However, acquiring data for efficient state estimation and control computing poses a non-trivial problem given the limited network resources, partial state vector information, and measurement errors encountered at distributed sensors. We propose the REinforcement learning and Variational Extended Kalman filter with Robust Belief (REVERB), which leverages a reinforcement learning solution combined with a Value of Information-based algorithm for performing optimal control and selecting the most informative sensors to satisfy the prediction accuracy of DT. Numerical results demonstrate that the DT platform can offer satisfactory performance while reducing the communication overhead up to five times.

The novel frequency shifting reconfigurable intel-ligent surface (FS-RIS) is built by a new generation of meta-materials and circuits, which is able to perform an operation that changes the frequency spectrum of the signal that was originally transmitted. The use of this FS- RIS for communication is still in its infancy. In this work, a millimeter wave (mmWave) communication link aided by FS-RIS is proposed. The FS-RIS is able to down-convert the carrier frequency of the mm Wave signal and spread the control information to the cell by using a more reliable and stable link, due to the fact that lower bands have a lower attenuation. A novel multi-frequency beam-management procedure (BMP) relying on FS-RIS is given. The proposed method is faster than existing methods since it makes use of wider beams without suffering from range penalization thanks to the better propagation in sub-6 GHz bands, unlike the hierarchical BMP. Overall, this paper provides a new way to exploit FS-RIS in wireless communications, which has the potential to change the way we look at spectrum and interference management.

This paper presents an experimental validation for prediction of rare fading events using channel distribution information (CDI) maps that predict channel statistics from measurements acquired at surrounding locations using spatial interpolation. Using experimental channel measurements from 127 locations, we demonstrate the use case of providing statistical guarantees for rate selection in ultra-reliable low-latency communication (URLLC) using CDI maps. By using only the user location and the estimated map, we are able to meet the desired outage probability with a probability between 93.6–95.6% targeting 95%. On the other hand, a model-based baseline scheme that assumes Rayleigh fading meets the target outage requirement with a probability of 77.2%. The results demonstrate the practical relevance of CDI maps for resource allocation in URLLC.

The reflective intelligent surface (RIS) that is com-monly considered has linear properties and does not change the frequency of the impinging signal. This is different for the emerging RIS designs, capable of doing non-linear operations on the reflected signal. We show how nonlinear RIS operation can significantly reduce the overhead for channel estimation and localization. We treat communication and sensing for cyclic-prefixed orthogonal frequency-division multiplexing (OFDM)-based system. Introducing controllable artificial Doppler-shifts through non-linear RISs enables identification of the propagation path by the frequency shift. In this way, both the direct path and multiple RIS-assisted paths can be estimated by transmission of a single OFDM-symbol. Thus, with only a minimal penalty in terms of channel estimation accuracy, this scheme achieves performance that for linear RISs would require coding across multiple OFDM-symbols. This paper considers the linear minimum mean squared error (LMMSE) channel estimator and presents four different methods to choose the pilot sequence and the artificial Doppler-shifts, in order to minimize the mean squared error (MSE) of the channel estimation.

One of the primary goals of future wireless systems is to foster sustainability, for which, radio frequency (RF) wireless power transfer (WPT) is considered a key technology enabler. The key challenge of RF-WPT systems is the extremely low end-to-end efficiency, mainly due to the losses introduced by the wireless channel. Distributed antenna systems are undoubtedly appealing as they can significantly shorten the charging distances, thus, reducing channel losses. Interestingly, radio stripe systems provide a cost-efficient and scalable way to deploy a distributed multi-antenna system, and thus have received a lot of attention recently. Herein, we consider an RF-WPT system with a transmit radio stripe network to charge multiple indoor energy hotspots, i.e., spatial regions where the energy harvesting devices are expected to be located, including near-field locations. We formulate the optimal radio stripe deployment problem aimed to maximize the minimum power received by the users and explore two specific predefined shapes, namely the straight line and polygon-shaped configurations. Then, we provide efficient solutions relying on geometric programming to optimize the location of the radio stripe elements. The results demonstrate that the proposed radio stripe deployments outperform a central fully-digital square array with the same number of elements and utilizing larger radio stripe lengths can enhance the performance, while increasing the system frequency may degrade it.

The third generation partnership project (3GPP) has outlined ultra-reliable low latency communication (URLLC) essential to ensure enhanced network dependability under stringent latency constraints. A transmission scheme known as configured grant (CG) transmission, defined in 3GPP Release 15, allows devices to compete for resources and transmit their data without explicit permission in a specified time budget. In this study, we introduce an enhanced modeling approach for CG transmission that takes into account the repetition and retransmission, allowing a thorough evaluation of network performance. The numerical results shed light on configuring CG transmission settings to achieve a predetermined probability of success for successful packet decoding. Importantly, the system model in the paper closely follows 3GPP-based Transmission Time Interval (TTI) models, which helps to map the observed results to real system performance.

Vehicle-to-Everything (V2X) networks will constitute a prominent application in future generations of cellular networks, definitely transforming our conception of transportation systems. A major challenge in V2X networks is the vast amount of data generated by the large number of sensors in the vehicles, which saturates the wireless links. As it is not possible to meet the throughput, timing, and reliability requirements for the total bulk of generated data, one needs to filter out data based on the actual communication goal. In this paper we present an architecture and diverse options to implement filtering and source coding for goal-oriented vehicular communications. We illustrate how filtering and source coding contribute to meeting the strict delay requirements while maintaining energy-efficient operation. Our results show that goal-oriented communications, performed as the combination of Bloom filtering and goal-oriented source coding, can greatly contribute not only to reduce the energy consumption by up to 30% but also to decrease delay, which in turn increases the supported amount of delay-sensitive traffic by up to 819.2%.

This article establishes a market for trading Internet of Things (IoT) data that is used to train machine learning (ML) models. The data, either raw or processed, is supplied to the market platform through a network, and the price of such data is controlled based on the value it brings to the ML model under the adversity of the correlation property of data. Eventually, a simplified distributed solution for a data trading mechanism is derived that improves the mutual benefit of devices and the market. Our key proposal is an efficient algorithm for data markets that jointly addresses the challenges of availability and heterogeneity in participation, as well as the transfer of trust and the economic value of data exchange in IoT networks. The proposed approach establishes the data market by reinforcing collaboration opportunities between devices with correlated data to limit information leakage. Therein, we develop a network-wide optimization problem that maximizes the social value of coalition among the IoT devices of similar data types; at the same time, it minimizes the cost due to network externalities, i.e., the impact of information leakage due to data correlation, as well as the opportunity costs. Finally, we reveal the structure of the formulated problem as a distributed coalition game and solve it following the simplified split-and-merge algorithm. Simulation results show the efficacy of our proposed mechanism design toward a trusted IoT data market, with up to 32.72% gain in the average payoff for each seller.

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.

The emergence of mega-constellations of interconnected satellites has a major impact on the integration of cellular wireless and non-terrestrial networks, while simultaneously offering previously inconceivable data gathering capabilities. This paper studies the problem of running a federated learning (FL) algorithm within low Earth orbit satellite constellations connected with intra-orbit inter-satellite links (ISL), aiming to efficiently process collected data in situ. Satellites apply on-board machine learning and transmit local parameters to the parameter server (PS). The main contribution is a novel approach to enhance FL in satellite constellations using intra-orbit ISLs. The key idea is to rely on predictability of satellite visits to create a system design in which ISLs mitigate the impact of intermittent connectivity and transmit aggregated parameters to the PS. We first devise a synchronous FL, which is extended towards an asynchronous FL for the case of sparse satellite visits to the PS. An efficient use of the satellite resources is attained by sparsification-based compression the aggregated parameters within each orbit. Performance is evaluated in terms of accuracy and required data transmission size. We observe a sevenfold increase in convergence speed over the state-of-the-art using ISLs, and 10× reduction in communication load through the proposed in-network aggregation strategy.

Reconfigurable intelligent surface (RIS) has emerged as a highly promising infrastructure benefiting from its capability to manipulate the propagation environment and facilitate efficient aggregation of wireless transmission signals. However, a major challenge in these systems is the significant overhead incurred by the acquisition of channel state information. This paper proposes a pre-designed beam-based transmission protocol that aims to reduce the burden of pilot overhead by designing a RIS reflective pattern codebook as an alternative to channel estimation (CE). We adopt an electromagnetic-compliant RIS model and develop a theoretical approximate expression for the channel gain, incorporating the impact of location mismatch. This approximation facilitates the construction of a location-based reflective pattern codebook, wherein the chosen locations linked with the codewords represent optimal location sampling points derived from theoretical results. By utilizing the constructed codebook, our proposed transmission protocol enables the system to search reflective patterns instead of real-time optimization. To validate our approach, extensive numerical simulations are conducted. The results demonstrate the accuracy of the approximate channel gain expression and highlight the superior coverage achieved by the proposed location-based reflective pattern codebook as well as the achievable spectral efficiency of our transmission protocol.

Grant-free non-orthogonal multiple access has been regarded as a viable approach to accommodate access for a massive number of machine-type devices with small data packets. The sporadic activation of the devices creates a multiuser setup where it is suitable to use compressed sensing in order to detect the active devices and decode their data. We consider asynchronous access of machine-type devices that send data packets of different frame sizes, leading todata length diversity. We address the composite problem of activity detection, channel estimation, and data recovery by posing it as a structured sparse recovery, having three-level sparsity caused by sporadic activity, symbol delay, and data length diversity. We approach the problem through approximate message passing with a backward propagation algorithm (AMP-BP), tailored to exploit the sparsity, and in particular the data length diversity. Moreover, we unfold the proposed AMP-BP into a network, termed learned AMP-BP (LAMP-BP), which enhances detection performance. The results show that the proposed LAMP-BP outperforms existing methods in activity detection and data recovery accuracy.

This work investigates the design of Hybrid Automatic Repeat Request (HARQ) strategies for downlink Rate-Splitting Multiple Access (RSMA). The existence of private and common stream as well as their conditioning for Successive Interference Cancellation (SIC), gives rise to an expanded set of opportunities for retransmission of failed packets. Specifically, we devise a scheme in which the retransmissions are scheduled through the common stream, which offers a higher success probability. With this, the common stream needs to carry both new and retransmitted bits, which leads to a layered HARQ (L-HARQ) strategy which is capable of trading off throughput and reliability. Simulation results demonstrate that the devised HARQ scheme outperforms RSMA with conventional HARQ, where each retransmission is handled independently through its own stream. It also helps in closing the throughput gap between HARQ and Adaptive Modulation and Coding (AMC) in the high Signal-to-Noise Ratio (SNR) regime while also achieving a decreased Packet Error Rate (PER) and a lower latency.

Frequency shifting-reconfigurable intelligent surfaces (FS-RISs) can be built by a new generation of meta-materials and circuits, which can perform an operation that changes the original frequency spectrum of the transmitted signal. Its exploitation for wireless communications is still in its infancy. In this work, a millimetre wave (mmWave) cellular network aided by FS-RIS is proposed to satisfy the new challenging communication services. The FS-RIS is able to down-convert the carrier frequency of the mmWave signal and spread the control information to the cell by using a more reliable and stable link, relying on better propagation within the lower bands, and a novel multi-frequency beam-management procedure is given. The proposed method can rapidly discover all users in the entire cell by using different sizes of beams. It avoids range penalization due to the better propagation in sub-6 GHz bands, unlike the hierarchical BMP. Additionally, the proposed technique only requires one base station (BS), and hence the delay overhead caused by the information exchange among several BSs is fully avoided, unlike the case of coordinated multi-point (CoMP). Overall, this paper provides a new way to exploit FS-RIS in wireless systems, which can potentially change the way we look at spectrum and interference management for the efficient exploitation of communication applications.

Location information is often used as a proxy to guarantee the performance of a wireless communication link. However, localization errors can result in a significant mismatch with the guarantees, particularly detrimental to users operating the ultra-reliable low-latency communication (URLLC) regime. This paper unveils the fundamental statistical relations between location estimation uncertainty and wireless link reliability, specifically in the context of rate selection for ultra-reliable communication. We start with a simple one-dimensional narrowband Rayleigh fading scenario and build towards a two-dimensional scenario in a rich scattering environment. The wireless link reliability is characterized by the meta-probability, the probability with respect to localization error of exceeding the outage capacity, and by removing other sources of errors in the system, we show that reliability is sensitive to localization errors. The ϵ-outage coherence radius is defined and shown to provide valuable insight into the problem of location-based rate selection. However, it is generally challenging to guarantee reliability without accurate knowledge of the propagation environment. Finally, several rate-selection schemes are proposed, showcasing the problem’s dynamics and revealing that properly accounting for the localization error is critical to ensure good performance in terms of reliability and achievable throughput.

There are three generic services in 5G: enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). To guarantee the performance of heterogeneous services, network slicing is proposed to allocate resources to different services. Network slicing is typically done in an orthogonal multiple access (OMA) fashion, which means different services are allocated non-interfering resources. However, as the number of users grows, OMA-based slicing is not always optimal, and a non-orthogonal scheme may achieve better performance. This work aims to analyse the performances of different slicing schemes in uplink, and a promising scheme based on rate-splitting multiple access (RSMA) is studied. RSMA can provide a more flexible decoding order and theoretically has the largest achievable rate region than OMA and non-orthogonal multiple access (NOMA) without time-sharing. Hence, RSMA has the potential to increase the rate of users requiring different services. In addition, it is not necessary to decode the two split streams of one user successively, so RSMA lets suitable users split messages and designs an appropriate decoding order depending on the service requirements. This work shows that for network slicing RSMA can outperform NOMA counterpart, and obtain significant gains over OMA in some regions.

Rate-splitting multiple access (RSMA) has attracted a lot of attention as a general and powerful multiple access scheme. In the uplink, instead of encoding the whole message into one stream, a user can split its message into two parts and encode them into two streams before transmitting a superposition of these two streams. The base station (BS) uses successive interference cancellation (SIC) to decode the streams and reconstruct the original messages. Focusing on the packet transmission reliability, we investigate the features of RSMA in the context of hybrid automatic repeat request (HARQ), a well-established mechanism for enhancing reliability. This work proposes a HARQ scheme for uplink RSMA with different retransmission times for a two-user scenario and introduces a power allocation strategy for the two split streams. The results show that compared with non-orthogonal multiple access (NOMA) and frequency division multiple access (FDMA), RSMA outperforms them in terms of error probability and power consumption. The results show that RSMA with HARQ has the potential to improve the reliability and efficiency of wireless communication systems.

Sensing is envisioned as a key network function of thesixth-generation(6G) mobile networks.Artificial intelligence(AI)-empowered sensing fuses features of multiple sensing views from devices distributed in edge networks for the edge server to perform accurate inference. This process, known asmulti-view pooling, creates a communication bottleneck due to multi-access by many devices. To alleviate this issue, we propose a task-oriented simultaneous access scheme for distributed sensing calledOver-the-Air Pooling(AirPooling). The existingOver-the-Air Computing(AirComp) technique can be directly applied to enable Average-AirPooling, which exploits the waveform superposition property of a multi-access channel to implement fast over-the-air averaging of pooled features. However, despite being most popular in practice, the over-the-air maximization, called Max-AirPooling, is not AirComp realizable given the fact that AirComp addresses only a limited subset of functions. We tackle the challenge by proposing the novel generalized AirPooling framework that can be configured to support both Max- and Average-AirPooling by controlling a configuration parameter and extended to even other pooling functions. The former is realized by adding to AirComp the designed pre-processing at devices and post-processing at the server. To characterize theEnd-to-End(E2E) sensing performance in object recognition, the theory of classification margin is applied to relate the classification accuracy and the AirPooling error, which allows the latter to be a tractable surrogate of the former. Furthermore, the analysis reveals an inherent tradeoff of Max-AirPooling between the accuracy of the pooling-function approximation and the effectiveness of noise suppression. Using the tradeoff, we make an attempt to optimize the configuration parameter of Max-AirPooling, yielding a sub-optimal closed-form method of adaptive parametric control. Experimental results obtained on real-world datasets show that AirPooling provides sensing accuracies close to those achievable by the traditional digital air interface but dramatically reduces the communication latency, by up to an order of magnitude.

In this paper, to tackle the blockage issue in massive multiple-input-multiple-output (mMIMO) systems, a reconfigurable intelligent surface (RIS) is seamlessly deployed to support devices with ultra-reliable and low-latency communications (URLLC). The transmission power of the base station and the phase shifts of the RIS are jointly devised to maximize the weighted sum rate while considering the spatially correlation and channel estimation errors. Firstly, the relationship between the channel estimation error and spatially correlated RIS’s elements is revealed by using the linear minimum mean square error. Secondly, based on the maximum-ratio transmission precoding, a tight lower bound of the rate under short packet transmission is derived. Finally, the NP-hard problem is decomposed into two optimization problems, where the transmission power is obtained by geometric programming and phase shifts are designed by using gradient ascent method. Besides, we have rigorously proved that the proposed algorithm can rapidly converge to a sub-optimal solution with low complexity. Simulation results confirm the tightness between the analytic results and Monte Carlo simulations. Furthermore, the two-timescale scheme provides a practical solution for the short packet transmission.

Intelligent metasurfaces are one of the favorite technologies for integrating sixth-generation (6G) networks, especially the reconfigurable intelligent surface (RIS), which has been extensively researched in various applications. Although many applications and studies of electromagnetic manipulation under the linear RIS topology were performed, applying coding sequences to the element switching enables the space-frequency scattering feature, referred to as Space-Time-Coding metasurface (STCM) topology. This type of topology causes impairments to the established communication methods by generating undesirable interference both in frequency and space, which is worsened when using wideband signals. Nevertheless, it can potentially bring forward useful features for sensing and localization. This work exploits STCM sensing capabilities in target detection, localization, and classification using narrowband downlink pilot signals at the base station (BS). The results of this novel approach reveal the ability to retrieve scattering points (SP) localization within the sub-centimeter and sub-decimeter accuracy depending on the SPs positions in space. We also analyze the associated detection and classification probabilities, which show reliable performance for both in the whole analyzed environment, especially when using the STCM information. We conclude that this method presents a promising approach for future integrated sensing and communications (ISAC) protocols by providing a tool to perform sensing and localization services using legacy communication signals.

The amount of data generated by Earth observation satellites can be enormous, which poses a great challenge to the satellite-to-ground connections with limited rate. This paper considers problem of efficient downlink communication of multi-spectral satellite images for Earth observation using change detection. The proposed method for image processing consists of the joint design of cloud removal and change encoding, which can be seen as an instance of semantic communication, as it encodes important information, such as changed multi-spectral pixels (MPs), while aiming to minimize energy consumption. It comprises a three-stage end-to-end scoring mechanism that determines the importance of each MP before deciding its transmission. Specifically, the sensing image is (1) standardized and passed through a high-performance cloud filtering via the Cloud-Net model, (2) passed to the proposed scoring algorithm that uses Change-Net to identify MPs that have a high likelihood of being changed, compress them and forward the result to the ground station, and (3) reconstructed at ground gateway based on reference image and received data. The experimental results indicate that the proposed framework is effective in optimizing energy usage while preserving high-quality data transmission in satellite-based Earth observation applications.

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.

We leverage standards-compliant beam training measurements from commercial-of-the-shelf (COTS) 802.11ad/ay devices for localization of a moving object. Two technical challenges need to be addressed: (1) the beam training measurements are intermittent due to beam scanning overhead control and contention-based channel-time allocation, and (2) how to exploit underlying object dynamics to assist the localization. To this end, we formulate the trajectory estimation as a sequence regression problem. We propose a dual-decoder neural dynamic learning framework to simultaneously reconstruct Wi-Fi beam training measurements at irregular time instances and learn the unknown dynamics over the latent space in a continuous-time fashion by enforcing strong supervision at both the coordinate and measurement levels. The proposed method was evaluated on an in-house mmWave Wi-Fi dataset and compared with a range of baseline methods, including traditional machine learning methods and recurrent neural networks.

The integration of Low Earth Orbit (LEO) satellite constellations into 5G and Beyond is essential to achieve efficient global connectivity. As LEO satellites are a global infrastructure with predictable dynamics, a pre-planned fair and load-balanced allocation of the radio resources to provide efficient downlink connectivity over large areas is an achievable goal. In this paper, we propose a distributed and a global optimal algorithm for satellite-to-cell resource allocation with multiple beams. These algorithms aim to achieve a fair allocation of time-frequency resources and beams to the cells based on the number of users in connected mode (i.e., registered). Our analyses focus on evaluating the trade-offs between average per-user throughput, fairness, number of cell handovers, and computational complexity in a downlink scenario with fixed cells, where the number of users is extracted from a population map. Our results show that both algorithms achieve a similar average per-user throughput. However, the global optimal algorithm achieves a fairness index over 0.9 in all cases, which is more than twice that of the distributed algorithm. Furthermore, by correctly setting the handover cost parameter, the number of handovers can be effectively reduced by more than 70% with respect to the case where the handover cost is not considered.

A novel opportunistic semantic and bit communication strategy is proposed for uplink non-orthogonal multiple access (NOMA). Specifically, a secondary far user (F-user) employs either semantic communication (SemCom) or bit-based communication (BitCom) to participate in NOMA with a primary near user (N-user) employing the BitCom. For each fading channel state, the secondary F-user has to select the most suitable communication method, thus striking a good tradeoff between its own achieved performance and the interference imposed on the primary N-user. The optimal communication policy at the F-user over fading channels is derived for maximizing the ergodic (equivalent) semantic rate achieved at the F-user, subject to the minimum ergodic bit rate constraint of the N-user. Numerical results show that the proposed opportunistic scheme can achieve higher communication performance for NOMA than the baseline schemes merely employing SemCom or BitCom. In addition, SemCom can better guarantee the performance of the F-user admitted in NOMA than BitCom when the communication requirement of the primary N-user is high.

The Age of Incorrect Information (AoII) is a metric that can combine the freshness of the information available to a gateway in an Internet of Things (IoT) network with the accuracy of that information. As such, minimizing the AoII can allow the operators of IoT systems to have a more precise and up-to-date picture of the environment in which the sensors are deployed. However, most IoT systems do not allow for centralized scheduling or explicit coordination, as sensors need to be extremely simple and consume as little power as possible. Finding a decentralized policy to minimize the AoII can be extremely challenging in this setting. This paper presents a heuristic to optimize AoII for a slotted ALOHA system, starting from a threshold-based policy and using dual methods to converge to a better solution. This method can significantly outperform state-independent policies, finding an efficient balance between frequent updates and a low number of packet collisions.

The uplink from a very small aperture terminal (VSAT) towards multiple satellites is considered, in this paper. VSATs can be equipped with multiple antennas, allowing parallel transmission to multiple satellites. A low-complexity precoder based on imperfect positional information of the satellites is presented. The probability distribution of the position uncertainty and the statistics of the channel elements are related by the characteristic function of the position uncertainty. This knowledge is included in the precoder design to maximize the mean signal-to-leakage-and-noise ratio (SLNR) at the satellites. Furthermore, the performance w.r.t. the inter-satellite distance is numerically evaluated. It is shown that the proposed approach achieves the capacity for perfect position knowledge and sufficiently large inter-satellite distances. In case of imperfect position knowledge, the performance degradation of the robust precoder is relatively small.

The use of Reconfigurable Intelligent Surface (RIS) technology to extend coverage and allow for better control of the wireless environment has been proposed in several use cases, including Ultra-Reliable Low-Latency Communications (URLLC) communications. However, the extremely challenging latency constraint makes explicit channel estimation difficult, so positioning information is often used to configure the RIS and illuminate the receiver device. In this work, we analyze the effect of imperfections in the positioning information on the reliability, deriving an upper bound to the outage probability. We then use this bound to perform power control, efficiently finding the minimum power that respects the URLLC constraints under positioning uncertainty. The optimization is conservative, so that all points respect the URLLC constraints, and the bound is relatively tight, with an optimality gap between 1.5 and 4.5 dB.

In a setting where the forward and feedback channel are noisy BSCs, we show how capacity is nearly achievable in a scheme with only source coding on the forward channel. In representative settings with noisy feedback, GRAND makes the scheme not only possible but practical. The sender transmits uncoded messages, and the receiver provides a noise effect guess as in GRAND, which is channel-encoded and sent to the receiver. With noiseless, finite-length feedback our scheme provides the type of super exponential error behavior associated in previous work with infinite-capacity feedback channels. With noisy feed-back, which is the more common setting in most systems, our scheme permits forward throughput that is effectively the same as in a noiseless feedback case. Moreover, the feedback channel usage remains limited. We propose a target error rate as a useful design parameter.

We treat the problem of client selection in a Federated Learning (FL) setup, where the learning objective and the local incentives of the participants are used to formulate a goal-oriented communication problem. Specifically, we incorporate the risk-averse nature of participants and obtain a communication-efficient on-device performance, while relying on feedback from the Parameter Server (PS). A client has to decide its transmission plan on when not to participate in FL. This is based on its intrinsic incentive, which is the value of the trained global model upon participation by this client. Poor updates not only plunge the performance of the global model with added communication cost but also propagate the loss in performance on other participating devices. We cast the relevance of local updates as semantic information for developing local transmission strategies, i.e., making a decision on when to "not transmit". The devices use feedback about the state of the PS and evaluate their contributions in training the learning model in each aggregation period, which eventually lowers the number of occupied connections. Simulation results validate the efficacy of our proposed approach, with up to 1.4× gain in communication links utilization as compared with the baselines.

As IoT devices become increasingly advanced and equipped with sensors such as cameras and microphones, the collection of massive data streams, produced in real-time, becomes challenging. In many cases only a small fraction of the collected data might be relevant, e.g., if cameras are used to search for a specific object. In this paper, we introduce and analyze a set of random access protocols in which the transmitting IoT devices are activated by semantic queries. This can be seen as a semantic data sourcing random access: each device computes a matching score that characterizes the relevance of its current observation and, if the matching score exceeds a threshold, the device transmits its observation over a random access collision channel to an edge node. We study two random access transmission policies. The first is the classical slotted ALOHA policy, while the other is able to exploit semantic correlation between the device observations. Furthermore, we show how the protocol can be integrated with machine learning-based query and matching score functions to capture the semantic content of, say, images. The numerical results show that the proposed protocol is able to effectively filter the device observations, such that mostly relevant data is received. Overall, the protocol is promising for collecting data in real-time from massive IoT networks based on the semantic content of sensor observations.

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 stringent timing and reliability requirements in mission-critical applications require a detailed statistical characterization of end-to-end latency. Teleoperation is a representative use case, in which a human operator (HO) remotely controls a robot by exchanging command and feedback signals. We present a framework to analyze the latency of a closed-loop teleoperation system consisting of three entities: an HO, a robot located in remote environment, and a Base Station (BS) with Mobile edge Computing (MEC) capabilities. A model of each component is used to analyze the closed-loop latency and optimize the compression strategy. The closed-form expression of the distribution of the closed-loop latency is difficult to estimate, such that suitable upper and lower bounds are obtained. We formulate a non-convex optimization problem to minimize the closed-loop latency. Using the obtained upper and lower bound on the closed-loop latency, a computationally efficient procedure to optimize the closed-loop latency is presented. The simulation results reveal that compression of sensing data is not always beneficial, while system design based on average performance leads to under-provisioning and may cause performance degradation. The applicability of the proposed analysis is much wider than teleoperation, including a large class of systems whose latency budget consists of many components.

Consider two data providers that want to contribute data to a certain learning model. Recent works have shown that the value of the data of one of the providers is dependent on the similarity with the data owned by the other provider. It would thus be beneficial if the two providers can calculate the similarity of their data, while keeping the actual data private. In this work, we devise multiparty computation-protocols to compute similarity of two data sets based on correlation, while offering controllable privacy guarantees. We consider a simple model with two participating providers and develop methods to compute exact and approximate correlation, respectively, with controlled information leakage. Both protocols have computational and communication complexities that are linear in the number of data samples. We also provide general bounds on the maximal error in the approximation case, and analyse the resulting errors for practical parameter choices.

We investigate a novel multi-user mobile Virtual Reality (VR) arcade system for streaming scalable 8K 360° video with low interactive latency, while providing high remote scene immersion fidelity and application reliability. This is achieved through the integration of embedded multi-layer 360° tiling, edge computing, and wireless multi-connectivity that comprises sub-6 GHz and mmWave (millimeter wave) links. The sub-6 GHz band is used for broadcast of the base layer of the entire 360° panorama to all users, while the directed mmWave links are used for high-rate transmission of VR-enhancement layers that are specific to the viewports of the individual users. The viewport-specific enhancements can comprise compressed and raw 360° tiles, decoded first at the edge server. We aim to maximize the smallest immersion fidelity for the delivered 360 content across all VR users, given rate, latency and computing constraints. We characterize analytically the rate-distortion trade-offs across the spatiotemporal 360° panorama and the computing power required to decompress 360° tiles. The proposed solution consists of geometric programming algorithms and an intermediate step of graph-theoretic VR user to mmWave access point assignment. The results reveal a significant improvement (8-10 dB) in delivered VR user immersion fidelity and spatial resolution (8K vs. 4K) compared to a state-of-the-art method based on sub-6 GHz transmission only. We also show that an increasing number of raw 360° tiles are sent, as the mmWave network link data rate or the edge server/user computing power increase. Finally, we demonstrate that in order to hypothetically deliver the same immersion fidelity, the reference method would incur a much higher (2.5-4.5x) system latency.

We present Tesla-Rapture, a gesture recognition system for sparse point clouds generated by mmWave Radars. State of the art gesture recognition models are either too resource consuming or not sufficiently accurate for the integration into real-life scenarios using wearable or constrained equipment such as IoT devices (e.g., Raspberry PI), XR hardware (e.g., HoloLens), or smart-phones. To tackle this issue, we have developed Tesla, a Message Passing Neural Network (MPNN) graph convolution approach for mmWave radar point clouds. The model outperforms the state of the art on three datasets in terms of accuracy while reducing the computational complexity and, hence, the execution time. In particular, the approach, is able to predict a gesture almost 8 times faster than the most accurate competitor. Our performance evaluation in different scenarios (environments, angles, distances) shows that Tesla generalizes well and improves the accuracy up to 20% in challenging scenarios, such as a through-wall setting and sensing at extreme angles. Utilizing Tesla, we develop Tesla-Rapture, a real-time implementation using a mmWave Radar on a Raspberry PI 4 and evaluate its accuracy and time-complexity. We also publish the source code, the trained models, and the implementation of the model for embedded devices.

Age of Information (AoI) has recently received much attention due to its relevance for IoT sensing and monitoring. In this paper, we consider the problem of minimizing the AoI in a system in which a set of sources are observed by multiple sensors in a many-to-many relationship, and the probability that a sensor observes a source depends on the source’s state. This model represents many practical scenarios, such as when multiple cameras or microphones are deployed to monitor objects moving in certain areas. We formulate the scheduling problem as a Markov Decision Process, and show how the age-optimal scheduling policy can be obtained. We further consider partially observable variants of the problem, and devise approximate policies for large state spaces. The evaluations show that the approximate policies work well in the considered scenarios, while the fact that sensors can observe multiple sources is beneficial, especially when there is high uncertainty of the source states.

With the increasing use of Internet-of-Things–enabled sensors, it is important to have effective methods to query the sensors. For example, in a dense network of battery-driven temperature sensors, it is often possible to query (sample) only a subset of the sensors at any given time, since the values of the non-sampled sensors can be estimated from the sampled values. If we can divide the set of sensors into disjoint so-calledrepresentative sampling subsets, in which each represents all the other sensors sufficiently well, then we can alternate between the sampling subsets and, thus, increase the battery life significantly of the sensor network. In this article, we formulate the problem of finding representative sampling subsets as a graph problem on a so-calledsensor graphwith the sensors as nodes. Our proposed solution,SubGraphSample, consists of two phases. In Phase-I, we create edges in thesimilarity graphbased on the similarities between the time-series of sensor values, analyzing six different techniques based on proven time-series similarity metrics. In Phase-II, we propose six different sampling techniques to find the maximum number ofrepresentative sampling subsets. Finally, we proposeAutoSubGraphSample, which auto-selects the best technique for Phase-I and Phase-II for a given dataset. Our extensive experimental evaluation shows thatAutoSubGraphSamplecan yield significant battery-life improvements within realistic error bounds.

In this paper, we propose to investigate a single RF chain multi-antenna full-duplex (FD) relay built with$b$-bit analog phase shifters and passive self-interference cancellation. Next, assuming only passive self-interference cancellation at the FD relay, we derive the achievable data rate of a system comprised of a source, the proposed FD relay, and a destination. We then compare the achievable data rate of the proposed FD relaying system with the achievable data rate of the same system but with the FD relay replaced by an ideal passive RIS. Our results show that the proposed relaying system with 2-bit quantized analog phase shifters significantly outperforms the RIS-assisted system. In fact, the performance gains are so large, at least for small to intermediate numbers of antenna elements, that we believe it makes this result of interest to the wireless community. The proposed FD relay can also be built with reconfigurable holographic surfaces, one surface for the transmit-side and one for the receive-side. For such a scenario, we derive the energy efficiency of the relay-assisted system and compare it with the RIS-assisted system. Our numerical results show that the energy efficiency of the relay-assisted system built with reconfigurable holographic surfaces is significantly higher than the energy efficiency of the RIS-assisted system. Intuitively, the RIS system is at a disadvantage since there the total transmit power$P_{T}$is used entirely by the source, whereas in the FD relaying system the total transmit power$P_{T}$is shared by the source and the FD relay in addition to the noise-cleansing process performed by the decode-and-forwarding at the FD relay.

Age of information (AoI) is an effective measure of the information freshness for wireless networked control systems (WNCSs). However, the AoI performance for a closed loop of WNCS with two-way delays has remained unexplored, especially in the finite blocklength (FBL) regime. In this paper, we investigate the peak age of loop (PAoL) performances, including the average, variance and outage probability of PAoL, for WNCSs with FBL over fading channels. Their closed-form expressions are respectively derived regarding the blocklength and the maximum number of allowable transmissions. We prove that the average PAoL is less than the sum of the average peak AoI in uplink (UL) and downlink (DL) due to the coupling between UL and DL. We also show that there is a tradeoff between the average PAoL and the variance/outage probability of PAoL. Based on the comprehensive performance analysis, we study a PAoL-oriented communication and control co-design with an adaptation scheme for transmission power, blocklength and the maximum number of allowable transmissions. Simulation results verify the correctness of the analytical results and show that the proposed PAoL-oriented scheme significantly outperforms the UL only and DL only optimization schemes, with an up to 8-fold reduction in the average control cost.

We consider the scenario of inference at the wireless edge, in which devices are connected to an edge server and ask the server to carry out remote classification, that is, classify data samples available at edge devices. This requires the edge devices to upload high-dimensional features of samples over resource-constrained wireless channels, which creates a communication bottleneck. The conventional feature pruning solution would require the device to have access to the inference model, which is not available in the current split inference scenario. To address this issue, we propose the progressive feature transmission (ProgressFTX) protocol, which minimizes the overhead by progressively transmitting features until a target confidence level is reached. A control policy is proposed to accelerate inference, comprising two key operations: importance-aware feature selection at the server and transmission-termination control. For the former, it is shown that selecting the most important features, characterized by the largest discriminant gains of the corresponding feature dimensions, achieves a sub-optimal performance. For the latter, the proposed policy is shown to exhibit a threshold structure. Specifically, the transmission is stopped when the incremental uncertainty reduction by further feature transmission is outweighed by its communication cost. The indices of the selected features and transmission decision are fed back to the device in each slot. The control policy is first derived for the tractable case of linear classification, and then extended to the more complex case of classification using a convolutional neural network. Both Gaussian and fading channels are considered. Experimental results are obtained for both a statistical data model and a real dataset. It is shown that ProgressFTX can substantially reduce the communication latency compared to conventional feature pruning and random feature transmission strategies.

The widespread adoption of Reconfigurable Intelligent Surfaces (RISs) in future practical wireless systems is critically dependent on the integration of the RIS into higher-layer protocols beyond the physical (PHY) one, an issue that has received minimal attention in the research literature. In light of this, we consider a classical random access (RA) problem, where uncoordinated users’ equipment (UEs) transmit sporadically to an access point (AP). Differently from previous works, we ponder how a RIS can be integrated into the design of new medium access control (MAC) layer protocols to solve such a problem. We consider that the AP is able to control a RIS to change how its reflective elements are configured, namely, the RIS configurations. Thus, the RIS can be opportunistically controlled to favor the transmission of some of the UEs without the need to explicitly perform channel estimation (CHEST). We embrace this observation and propose a RIS-assisted RA protocol comprised of two modules: Channel Oracle and Access. During channel oracle, the UEs learn how the RIS configurations affect their channel conditions. During the access, the UEs tailor their access policies using the channel oracle knowledge. Our proposed RIS-assisted protocol is able to increase the expected throughput by approximately 60% in comparison to the slotted ALOHA (S-ALOHA) protocol.

The polarization domain provides an extra degree of freedom (DoF) for improving the performance of multiple-input multiple-output (MIMO) systems. This paper takes advantage of this additional DoF to alleviate practical issues of successive interference cancellation (SIC) in rate-splitting multiple access (RSMA) schemes. Specifically, we propose three dual-polarized downlink transmission approaches for a massive MIMO-RSMA network under the effects of polarization interference and residual errors of imperfect SIC. The first approach implements polarization multiplexing for transmitting the users’ data messages, which removes the need to execute SIC in the reception. The second approach transmits replicas of users’ messages in the two polarizations, which enables users to exploit diversity through the polarization domain. The third approach, in its turn, employs the original SIC-based RSMA technique per polarization, and this allows the BS to transmit two independent superimposed data streams simultaneously. An in-depth theoretical analysis is carried out, in which we derive tight closed-form approximations for the outage probabilities of the three proposed approaches. Accurate approximations for the ergodic sum-rates of the two first schemes are also derived. Simulation results validate the theoretical analysis and confirm the effectiveness of the proposed schemes. For instance, under low to moderate cross-polar interference, the results show that, even under high levels of residual SIC error, our dual-polarized MIMO-RSMA strategies outperform the conventional single-polarized MIMO-RSMA counterpart. It is also shown that the performance of all RSMA schemes is impressively higher than that of single and dual-polarized massive MIMO systems employing non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) techniques.

IoT systems typically involve separate data collection and processing, and face the scalability issue when the number of nodes increases. For some tasks, only the result of data fusion is needed. Then, the whole process can be realized in an efficient way, integrating the data collection and fusion in one step by over-the-air computation (AirComp). Its shortcoming, however, is signal distortion when channel gains of nodes are different, which cannot be well solved by transmission power control alone in times of deep fading. To address this issue, in this paper, we propose a multi-slot over-the-air computation (MS-AirComp) framework for the sum estimation in fading channels. Compared with conventional data collection (one slot for each node) and AirComp (one slot for all nodes), MS-AirComp is an alternative policy that lies between them, exploiting multiple slots to improve channel gains so as to facilitate power control. Avoiding to obtain instantaneous channel gains of all nodes at the sink is a key point. Specifically, the transmissions are distributed over multiple slots and a threshold of channel gain is set for distributed transmission scheduling. Each node transmits its signal only once, in the slot when its channel gain first gets above the threshold, or in the last slot when its channel gain remains below the threshold. Theoretical analysis gives the closed-form of the computation error in fading channels, based on which the optimal parameters are found. Noticing that computation error tends to be reduced at the cost of more transmission power, a method is suggested to control the increase of transmission power. Simulations confirm that the proposed method can effectively reduce computation error, compared with state-of-the-art methods.

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.

This paper proposes exploiting the spatial correlation of wireless channel statistics beyond the conventional received signal strength maps by constructing statistical radio maps to predict any relevant channel statistics to assist communications. Specifically, from stored channel samples acquired by previous users in the network, we use Gaussian processes (GPs) to estimate quantiles of the channel distribution at a new position using a non-parametric model. This prior information is then used to select the transmission rate for some target level of reliability. The approach is tested with synthetic data, simulated from urban micro-cell environments, highlighting how the proposed solution helps to reduce the training estimation phase, which is especially attractive for the tight latency constraints inherent to ultra-reliable low-latency (URLLC) deployments.

We treat the problem of power and rate adaptation for a point-to-point ultra-reliable low latency communication (URLLC) system over parallel fading channels. The model includes a stochastic traffic arrival process and the transmissions are conducted in the finite blocklength (FBL) regime. The problem is formulated as a long term total power minimization problem under reliability, latency, and peak power constraints. We first establish a proactive outdated data dropping queueing model and transform the reliability constraint into a queuing status constraint. Then we train a deep reinforcement learning (DRL) agent to employ Deep Deterministic Policy Gradient (DDPG) in order to allocate the transmit power on each sub-channel and control the decoding error probability to meet the URLLC constraints. Simulation results show that the proposed DDPG-based algorithm can reduce the transmit power consumption by 13%-26% compared to a baseline approach based on effective capacity. Furthermore, the trained network is scalable and robust towards different traffic arrival models, as well as variations of the average arrival rate.

Aiming at overcoming practical issues of successive interference cancellation (SIC), this paper proposes a dual-polarized rate-splitting multiple access (RSMA) technique for a downlink massive multiple-input multiple-output (MIMO) net-work. By modeling the effects of polarization interference, an in-depth theoretical analysis is carried out, in which we derive tight closed-form approximations for the outage probabilities and ergodic sum-rates. Simulation results validate the accuracy of the theoretical analysis and confirm the effectiveness of the proposed approach. For instance, under low to moderate cross-polar interference, our results show that the proposed dual-polarized MIMO-RSMA strategy outperforms the single-polarized MIMO-RSMA counterpart for all considered levels of residual SIC error.

Next-generation wireless systems are rapidly evolving from communication-only systems to multi-modal systems with integrated sensing and communications. In this paper a novel joint sensing and communication framework is proposed for enabling wireless extended reality (XR) at terahertz (THz) bands. To gather rich sensing information and a higher line-of-sight (LoS) availability, THz-operated reconfigurable intelligent surfaces (RISs) acting as base stations are deployed. The sensing parameters are extracted by leveraging THz’s quasi-opticality and opportunistically utilizing uplink communication waveforms. This enables the use of the same waveform, spectrum, and hardware for both sensing and communication purposes. The environmental sensing parameters are then derived by exploiting the sparsity of THz channels via tensor decomposition. Hence, a high-resolution indoor mapping is derived so as to characterize the spatial availability of communications and the mobility of users. Simulation results show that in the proposed framework, the resolution and data rate of the overall system are positively correlated, thus allowing a joint optimization between these metrics with no tradeoffs. Results also show that the proposed framework improves the system reliability in static and mobile systems. In particular, the highest reliability gains of 10% are achieved in a walking speed mobile environment compared to communication only systems with beam tracking.

Mega-constellations of small-size Low Earth Orbit (LEO) satellites are currently planned and deployed by various private and public entities. While global connectivity is the main rationale, these constellations also offer the potential to gather immense amount of data, e.g., for Earth observation. Power and bandwidth constraints together with motives like privacy, limiting delay, or resiliency make it desirable to process this data directly within the constellation. We consider the implementation of on-board federated learning (FL) orchestrated by an out-of-constellation parameter server (PS) and propose a novel communication scheme tailored to support FL. It leverages intraorbit inter-satellite links, the predictability of satellite movements and partial aggregating to massively reduce the training time and communication costs. In particular, for a constellation with 40 satellites equally distributed among five low Earth orbits and the PS in medium Earth orbit, we observe a 29× speed-up in the training process time and a 8× traffic reduction at the PS over the baseline.

We present a framework to analyse the latency budget in wireless systems with Mobile Edge Computing (MEC). Our focus is on teleoperation and telerobotics, as use cases that are representative of mission-critical uplink-intensive IoT systems with requirements on low latency and high reliability. The study is motivated by a general question: What is the optimal compression strategy in reliability and latency constrained systems? We address this question by studying the latency of an uplink connection from a multi-sensor IoT device to the base station. This is a critical link tasked with a timely and reliable transfer of potentially significant amount of data from the multitude of sensors. We introduce a comprehensive model for the latency budget, incorporating data compression and data transmission. The uplink latency is a random variable whose distribution depends on the computational capabilities of the device and on the properties of the wireless link. We formulate two optimization problems corresponding to two transmission strategies: (1) Outage-constrained, and (2) Latency-constrained. We derive the optimal system parameters under a reliability criterion. We show that the obtained results are superior compared to the ones based on the optimization of the expected latency.

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 work, we study the trade-off between the reliability and the investment cost of an unmanned aerial system (UAS) consisting of a set of unmanned aerial vehicles (UAVs) carrying radio access nodes, called portable access points (PAPs)), deployed to serve a set of ground nodes (GNs). Using the proposed algorithm, a given geographical region is equivalently represented as a set of circular regions, where each circle represents the coverage region of a PAP. Then, the steady-state availability of the UAS is analytically derived by modelling it as a continuous-time birth-death Markov decision process (MDP). Numerical evaluations show that the investment cost to guarantee a given steady-state availability to a set of GNs can be reduced by considering the traffic demand and distribution of GNs.

Systems of small distributed satellites in low Earth orbit (LEO) transmitting cooperatively to a multiple antenna ground station (GS) are investigated. These satellite swarms have the benefit of much higher spatial separation in the transmit antennas than traditional big satellites with antenna arrays, promising a massive increase in spectral efficiency. However, this would require instantaneous perfect channel state information (CSI) and strong cooperation between satellites. In practice, orbital velocities around 7.5 km/s lead to very short channel coherence times on the order of fractions of the inter-satellite propagation delay, invalidating these assumptions. In this paper, we propose a distributed linear precoding scheme and a GS equalizer relying on local position information. In particular, each satellite only requires information about its own position and that of the GS, while the GS has complete positional information. Due to the deterministic nature of satellite movement this information is easily obtained and no inter-satellite information exchange is required during transmission. Based on the underlying geometrical channel approximation, the optimal inter-satellite distance is obtained analytically. Numerical evaluations show that the proposed scheme is, on average, within 99.8 % of the maximum achievable rate for instantaneous CSI and perfect cooperation.

Spectrum slicing of the shared radio resources is a critical task in 5G networks with heterogeneous services, through which each service gets performance guarantees. In this paper, we consider a setup in which a Base Station (BS) should serve two types of traffic in the downlink, enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC), respectively. Two resource allocation strategies are compared: non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA). A framework for power minimization is presented, in which the BS knows the channel state information (CSI) of the eMBB users only. Nevertheless, due to the resource sharing, it is shown that this knowledge can be used also to the benefit of the URLLC users. The numerical results show that NOMA leads to a lower power consumption compared to OMA for every simulation parameter under test.

A key challenge for random access communications arising in the monitoring of physical phenomena is optimizing the access policy. This is particularly the case when the activity of each sensor is correlated, contrasting with the independence assumption underpinning standard slotted ALOHA schemes. In this paper, we propose a stochastic resource allocation algorithm to reduce outages via maximization of the expected number of sensors that are able to reliably communicate with an access point. Allowing for devices to transmit data over multiple consecutive frames, we show that the proposed algorithm converges with probability one to a locally optimal solution. Moreover, our algorithm significantly outperforms existing methods in terms of the average number of successful transmissions when utilizing successive interference cancellation.

Wireless virtual reality (VR), a key 3GPP use case of emerging cellular systems, imposes new visual and haptic requirements directly linked to the Quality of Experience (QoE) of VR users. These QoE requirements can only be met by wireless connectivity that offers high-rate and high-reliability low-latency communications (HR2LLC), unlike the low rates commonly associated with ultrareliable low-latency communication. The high rates for VR over short distances can only be supported by an enormous bandwidth, available in the terahertz (THz)-frequency bands. To explore the potential of THz for meeting HR2LLC requirements, a quantification of the risk for an unreliable VR performance is conducted through a novel and rigorous characterization of the tail of the end-to-end (E2E) delay. Then, a thorough analysis of the Tail-Value-at-Risk (TVaR) is performed to concretely characterize the behavior of extreme wireless events crucial to the real-time VR experience. In particular, the probability distribution function of the THz transmission delay is derived and then used to infer the system reliability scenarios with guaranteed Line of Sight (LoS) as a function of THz network parameters. Numerical results show that abundant bandwidth and low molecular absorption are necessary to improve the reliability. However, their effect remains secondary compared to the availability of LoS, which significantly affects the THz HR2LLC performance. In particular, for scenarios with guaranteed LoS, a reliability of 99.999% (with an E2E delay threshold of 20 ms) for a bandwidth of 15 GHz along with data rates of 18.3 Gbps can be achieved by the THz network, compared to a reliability of 96% for twice the bandwidth, when blockages are considered.

In this work, we present a novel traffic prediction and fast uplink (FU) framework for IoT networks controlled by binary Markovian events. First, we apply the forward algorithm with hidden Markov models (HMMs) in order to schedule the available resources to the devices with maximum likelihood activation probabilities via the FU grant. In addition, we evaluate the regret metric as the number of wasted transmission slots to evaluate the performance of the prediction. Next, we formulate a fairness optimization problem to minimize the Age of Information (AoI) while keeping the regret as minimum as possible. Finally, we propose an iterative algorithm to estimate the model hyperparameters (activation probabilities) in a real-time application and apply an online-learning version of the proposed traffic prediction scheme. Simulation results show that the proposed algorithms outperform baseline models, such as time-division multiple access (TDMA) and grant-free (GF) random-access in terms of regret, the efficiency of system usage, and AoI.

Internet of Things (IoT) is one of the main features in 5G. Low-power wide-area networking (LPWAN) has attracted enormous research interests to enable large-scale deployment of IoT, with the design objectives of low cost, wide coverage area, as well as low-power consumption. In particular, a long battery lifetime is essential since many of the IoT devices will be deployed in hard-to-access locations. Prediction of the battery lifetime depends on the accurate modeling of energy consumption. This article presents a comprehensive power consumption model for battery lifetime estimation, which is based on user equipment (UE) states and procedures, for two cellular IoT technologies: 1) Narrowband IoT (NB-IoT) and 2) long-term evolution for machines (LTE-M). A measurement testbed has been set up and the proposed model has been tested and validated via extensive measurements under various traffic patterns and network scenarios, achieving the modeling inaccuracy within 5%. The measurement results show that the battery lifetime of an IoT device can reach up to ten years as required by 3GPP, with proper configuration of the traffic profile, the coverage scenario, as well as the network configuration parameters.

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.

Massive random access plays a central role in supporting the Internet of Things (IoT), where a subset of a large population of users simultaneously transmit small packets to a central base station. While there has been much research on the design of protocols for massive access in the uplink, the problem of providing message acknowledgments back to the users has been somewhat neglected. Reliable communication needs to rely on two-way communication for acknowledgement and retransmission. Nevertheless, because of the many possible subsets of active users, providing acknowledgments requires a significant amount of bits. Motivated by this, we define the problem of massive ARQ (Automatic Retransmission reQuest) protocol and introduce efficient methods for joint encoding of multiple acknowledgements in the downlink. The key idea towards reducing the number of bits used for massive acknowledgments is to allow for a small fraction of false positive acknowledgments. We analyze the implications of this approach and the impact of acknowledgment errors in scenarios with massive random access. Finally, we show that these savings can lead to a significant increase in the reliability when retransmissions are allowed since it allows the acknowledgment message to be transmitted more reliably using a much lower rate.

In this paper, we adopt a three-stage based uplink channel estimation protocol with reduced pilot overhead for an reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter wave (mmWave) communication system, in which both the base station (BS) and the RIS are equipped with a uniform planar array (UPA). Specifically, in Stage I, the channel state information (CSI) of a typical user is estimated. To address the power leakage issue for the common angles-of-arrival (AoAs) estimation in this stage, we develop a low-complexity one-dimensional search method. In Stage II, a re-parameterized common BS-RIS channel is constructed with the estimated information from Stage I to estimate other users’ CSI. In Stage III, only the rapidly varying channel gains need to re-estimated. Furthermore, the proposed method can be extended to multi-antenna UPA-type users, by decomposing the estimation of a multi-antenna channel with$J$scatterers into estimating$J$single-scatterer channels for a virtual single-antenna user. An orthogonal matching pursuit (OMP)-based method is proposed to estimate the angles-of-departure (AoDs) at the users. Simulation results demonstrate that the proposed algorithm significantly achieves high channel estimation accuracy, which approaches the genie-aided upper bound in the high signal-to-noise ratio (SNR) regime.

We consider a novel ultra-narrowband (UNB) low-power wide-area network (LPWAN) architecture design for uplink transmission of a massive number of Internet of Things (IoT) devices over multiple multiplexing bands. An IoT device can randomly choose any of the multiplexing bands to transmit its packet. Due to hardware constraints, a base station (BS) is able to listen to only one multiplexing band. Our main objective is to maximize the packet decoding probability (PDP) by optimizing the placement of the BSs and frequency assignment of BSs to multiplexing bands. We develop two online approaches that adapt to the environment based on the statistics of (un)successful packets at the BSs. The first approach is based on a predefined model of the environment, while the second approach is measurement-based model-free approach, which is applicable to any environment. The benefit of the model-based approach is a lower training complexity, at the risk of a poor fit in a model-incompatible environment. The simulation results show that our proposed approaches to band assignment and BS placement offer significant improvement in PDP over baseline random approaches and perform closely to the theoretical upper bound.

Radio frequency wireless energy transfer (WET) is a promising solution for powering autonomous Internet of Things (IoT) deployments. In this work, we leverage energy beamforming for powering multiple user equipments (UEs) with stringent energy harvesting (EH) demands in an indoor distributed massive multiple-input multiple-output system. Based on semi-definite programming, successive convex approximation (SCA), and maximum ratio transmission (MRT) techniques, we derive optimal and sub-optimal precoders aimed at minimizing the radio stripes’ transmit power while exploiting information of the power transfer efficiency of the EH circuits at the UEs. Moreover, we propose an analytical framework to assess and control the electromagnetic field (EMF) radiation exposure in the considered indoor scenario. Numerical results show that i) the EMF radiation exposure can be more easily controlled at higher frequencies at the cost of a higher transmit power consumption, ii) training is not a very critical factor for the considered indoor system, iii) MRT/SCA-based precoders are particularly appealing when serving a small number of UEs, thus, especially suitable for implementation in a time domain multiple access (TDMA) scheduling framework, and iv) TDMA is more efficient than spatial domain multiple access (SDMA) when serving a relatively small number of UEs. Results suggest that additional boosting performance strategies are needed to increase the overall system efficiency, thus making the technology viable in practice.

5G technology allows heterogeneous services to share the wireless spectrum within the same radio access network. In this context, spectrum slicing of the shared radio resources is a critical task to guarantee the performance of each service. We analyze a downlink communication serving two types of traffic: enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC). Due to the nature of low-latency traffic, the base station knows the channel state information (CSI) of the eMBB users while having statistical CSI for the URLLC users. We study the power minimization problem employing orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) schemes. Based on this analysis, we propose a lookup table-based approach and a block coordinated descent (BCD) algorithm. We show that the BCD is optimal for the URLLC power allocation. The numerical results show that NOMA leads to lower power consumption than OMA, except when the average channel gain of the URLLC user is very high. For the latter case, the optimal approach depends on the channel condition of the eMBB user. Even when OMA attains the best performance, the gap with NOMA is negligible, showing the capability of NOMA to reduce power consumption in practically every condition.

This paper considers a wireless network with a base station (BS) conducting timely status updates to multiple clients via adaptive non-orthogonal multiple access (NOMA)/orthogonal multiple access (OMA). Specifically, the BS is able to adaptively switch between NOMA and OMA for the downlink transmission to optimize the information freshness of the network, characterized by the Age of Information (AoI) metric. For the simple two-client case, we formulate a Markov Decision Process (MDP) problem and develop the optimal policy for the BS to decide whether to use NOMA or OMA for each downlink transmission based on the instantaneous AoI of both clients. The optimal policy is shown to have a switching-type property with obvious decision switching boundaries. A suboptimal policy with lower computation complexity is also devised, which is shown to achieve near-optimal performance via numerical simulations. For the more general multi-client scenario, the optimal solution is the computationally intractable due to the large state and action spaces. As such, we devote to provide a feasible suboptimal policy with low computation complexity. Specifically, inspired by the proposed suboptimal policy of the two-client scenario, we formulate a nonlinear optimization problem to determine the optimal power allocated to each client by maximizing the expected AoI drop of the network in each time slot (i.e., minimizing the expected network-wide AoI of the next slot). The problem is shown to be non-convex, we manage to solve it by approximating it as a convex optimization problem. Simulation results validate the tightness of the adopted approximation. Specifically, the performance of the adaptive NOMA/OMA scheme by solving the convex optimization is shown to be close to that of the max-weight policy solved by exhaustive search. Besides, the adaptive NOMA/OMA scheme achieves significant performance improvement compared to the OMA scheme, especially when the number of clients in the network is large and the transmission SNR is high.

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.

We consider globally optimal precoder design for rate splitting multiple access in Gaussian multiple-input single-output downlink channels with respect to weighted sum rate and energy efficiency maximization. The proposed algorithm solves an instance of the joint multicast and unicast beamforming problem and includes multicast-and unicast-only beamforming as special cases. Numerical results show that it outperforms state-of-the-art algorithms in terms of numerical stability and converges almost twice as fast.

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).

The fifth generation (5G) of wireless systems has a platform-driven approach, aiming to support heterogeneous connections with very diverse requirements. The shared wireless resources should be sliced in a way that each user perceives that its requirements have been met. Heterogeneity challenges the traditional notion of resource efficiency, as the resource usage has to cater for, e.g., rate maximization for one user and a timeliness requirement for another user. This paper treats a model for radio access network (RAN) uplink, where a throughput-demanding broadband user shares wireless resources with an intermittently active user that wants to optimize the timeliness, expressed in terms of latency-reliability or Age of Information (AoI). We evaluate the trade-offs between throughput and timeliness for Orthogonal Multiple Access (OMA) as well as Non-Orthogonal Multiple Access (NOMA) with successive interference cancellation (SIC). We observe that NOMA with SIC, in a conservative scenario with destructive collisions, is just slightly inferior to that of OMA, which indicates that it may offer significant benefits in practical deployments where the capture effect is frequently encountered. On the other hand, finding the optimal configuration of NOMA with SIC depends on the activity pattern of the intermittent user, to which OMA is insensitive.

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.

Urban areas are negatively impacted by Carbon Dioxide (CO2 ) and Nitrogen Oxide (NOx) emissions. In order to achieve a cost-effective reduction of greenhouse gas emissions and to combat climate change, the European Union (EU) introduced an Emissions Trading System (ETS) where organizations can buy or receive emission allowances as needed. The current ETS is a centralized one, consisting of a set of complex rules. It is currently administered at the organizational level and is used for fixed-point sources of pollution such as factories, power plants, and refineries. However, the current ETS cannot efficiently cope with vehicle mobility, even though vehicles are one of the primary sources of CO2 and NOx emissions. In this study, we propose a new distributed Blockchain-based emissions allowance trading system called B-ETS. This system enables transparent and trustworthy data exchange as well as trading of allowances among vehicles, relying on vehicle-to-vehicle communication. In addition, we introduce an economic incentive-based mechanism that appeals to individual drivers and leads them to modify their driving behavior in order to reduce emissions. The efficiency of the proposed system is studied through extensive simulations, showing how increased vehicle connectivity can lead to a reduction of the emissions generated from those vehicles. We demonstrate that our method can be used for full life-cycle monitoring and fuel economy reporting. This leads us to conjecture that the proposed system could lead to important behavioral changes among the drivers

Teleoperation of an unmanned aerial vehicle (UAV) is a challenging mobile application with real-time control from a first-person view. It poses stringent latency requirements for both video and control traffic. This paper studies the video quality and latencies for UAV teleoperation over LTE using ns3 simulations. A key ingredient is the latency budget model. We observe that the latency of the video is higher and more sensitive to mobility than that of the control traffic. The latency is influenced by the traffic variation caused by the variable bit rate of the streaming application. High mobility tends to increase latency and lead to more outliers, being problematic in real-time control.

We treat a setting in which two priority wireless service classes are offered in a given area by a drone small cell (DSC). Specifically, we consider broadband (BB) user with high priority and reliability requirements that coexists with random access machine-type-communications (MTC) devices. The drone serves both connectivity types with a combination of orthogonal slicing of the wireless resources and dynamic horizontal opportunistic positioning (D-HOP). We treat the DHOP as a computational geometry function over stochastic BB user locations which requires careful adjustment in the deployment parameters to ensure MTC service at all times. Using an information theoretic approach, we optimize DSC deployment properties and radio resource allocation for the purpose of maximizing the average rate of BB users. While respecting the strict dual service requirements we analyze how system performance is affected by stochastic user positioning and density, topology, and reliability constraints combinations. The numerical results show that this approach outperforms static DSCs that fit the same coverage constraints, with outstanding performance in the urban setting.

Mobile devices with embedded sensors for data collection and environmental sensing create a basis for a cost-effective approach for data trading. For example, these data can be related to pollution and gas emissions, which can be used to check the compliance with national and international regulations. The current approach for IoT data trading relies on a centralized third-party entity to negotiate between data consumers and data providers, which is inefficient and insecure on a large scale. In comparison, a decentralized approach based on distributed ledger technologies (DLT) enables data trading while ensuring trust, security, and privacy. However, due to the lack of understanding of the communication efficiency between sellers and buyers, there is still a significant gap in benchmarking the data trading protocols in IoT environments. Motivated by this knowledge gap, we introduce a model for DLT-based IoT data trading over the narrowband Internet-of-Things (NB-IoT) system, intended to support massive environmental sensing. We characterize the communication efficiency of three basic DLT-based IoT data trading protocols via NB-IoT connectivity in terms of latency and energy consumption. The model and analyses of these protocols provide a benchmark for IoT data trading applications.

Ultra-narrowband (UNB) communications has become a signature feature for many emerging low-power wide-area (LPWA) networks. Specifically, using extremely narrowband signals helps the network connect more Internet-of-things (IoT) devices within a given band. It also improves robustness to interference, extending the coverage of the network. In this article, we study the coexistence capability of UNB networks and their scalability to enable massive access. To this end, we develop a stochastic geometry framework to analyze and model UNB networks on a large scale. The framework captures the unique characteristics of UNB communications, including the asynchronous time-frequency access, signal repetition, and the absence of base station (BS) association. Closed-form expressions of the transmission success probability and network connection density are presented for several UNB protocols. We further discuss multiband access for UNB networks, proposing a low-complexity protocol. Our analysis reveals several insights on the geographical diversity achieved when devices do not connect to a single BS, the optimal number of signal repetitions, and how to utilize multiple bands without increasing the complexity of BSs. Simulation results are provided to validate the analysis, and they show that UNB communications enables a single BS to connect thousands of devices even when the spectrum is shared with other networks.

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.

Remote classification involves offloading complex object-recognition tasks from mobile devices to servers at the network edge. It brings to the mobile device the capability of discerning hundreds of object classes by using the computational and storage capabilities of the infrastructure. Remote classification is challenged by the finite and variable data rate of the wireless channel, which affects the capability to transfer high-dimensional features and thus limits the classification resolution. We introduce a set of metrics under the name of classification capacity that are defined as the maximum number of classes that can be discerned over a given communication channel while meeting a target probability for classification error. We treat both the cases of a channel where the instantaneous rate is known and unknown. The objective is to choose a subset of classes from a class library that offers satisfactory performance over a given channel. We treat two different cases of subset selection. First, a device can select the subset by pruning the class library until arriving at a subset that meets the targeted error probability while maximizing the classification capacity. Adopting a subspace data model, we prove the equivalence of classification capacity maximization to the problem of packing on the Grassmann manifold. The results show that the classification capacity grows exponentially with the instantaneous communication rate, and super-exponentially with the dimensions of each data cluster. This also holds for ergodic and outage capacities with fading if the instantaneous rate is replaced with an average rate and a fixed rate, respectively. In the second case, a device has a unique preference of class subset for every communication rate, which is modeled as an instance of uniformly sampling the library. Without class selection, the classification capacity and its ergodic and outage counterparts are proved to scale linearly with their corresponding communication rates instead of the exponential growth in the last case.

The dominance of distributed energy resources in microgrids and the associated weather dependence require flexible protection. They include devices capable of adapting their protective settings as a reaction to (potential) changes in the state of the system. Communication technologies have a key role in this system, since the reactions of the adaptive devices shall be coordinated. This coordination imposes strict requirements: communications must be available and ultrareliable with bounded latency in the order of milliseconds. This article reviews the state of the art in the field and provides a thorough analysis of the main related communication technologies and optimization techniques. We also present our perspective on the future of communication deployments in microgrids, indicating the viability of 5G wireless systems and multiconnectivity to enable adaptive protection.

Among the new types of connectivity unleashed by the emerging 5G wireless systems, Ultra-Reliable Low Latency Communication (URLLC) is perhaps the most innovative, yet challenging one. Ultra-reliability requires high levels of diversity, however, the reactive approach based on packet retransmission in HARQ protocols should be applied carefully to conform to the stringent latency constraints. The main premise of this paper is that the NOMA principle can be used to achieve highly efficient retransmissions by allowing concurrent use of wireless resources in the uplink. We introduce a comprehensive solution that accommodates multiple intermittently active users, each with its own HARQ process. The performance is investigated under two different assumptions about the Channel State Information (CSI) availability: statistical and instantaneous. The results show that NOMA can indeed lead to highly efficient system operation compared to the case in which all HARQ processes are run orthogonally.

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.

The study of optimal properties of centralized dynamic-time-division-duplex (D-TDD) employed at a wireless network consisting of multiple nodes is a highly challenging and partially understood problem in the literature. In this paper, we develop an optimal centralized D-TDD scheme for a wireless network comprised of K full-duplex nodes impaired by self-interference and additive white Gaussian noise. As a special case, we also propose the optimal centralized D-TDD scheme when part or all nodes in the wireless network are half-duplex. Thereby, we derive the optimal adaptive scheduling of the reception, transmission, simultaneous reception and transmission, and silence at every node in the network in each time slot such that the rate region of the network is maximized. The performance of the optimal centralized D-TDD can serve as an upper-bound to any other TDD scheme, which is useful in qualifying the relative performance of TDD schemes. The numerical results show that the proposed centralized D-TDD scheme achieves significant rate gains over existing centralized D-TDD schemes.

A dual-polarized intelligent reflecting surface (IRS) can contribute to a better multiplexing of interfering wireless users. In this paper, we use this feature to improve the performance of dual-polarized massive multiple-input multiple-output (MIMO) with non-orthogonal multiple access (NOMA) under imperfect successive interference cancellation (SIC). By considering the downlink of a multi-cluster scenario, the IRSs assist the base station (BS) to multiplex subsets of users in the polarization domain. Our novel strategy alleviates the impact of imperfect SIC and enables users to exploit polarization diversity with near-zero inter-subset interference. To this end, the IRSs are optimized to mitigate transmissions originated at the BS from the interfering polarization. The formulated optimization is transformed into quadratically constrained quadratic sub-problems, which makes it possible to obtain the optimal solution via interior-points methods. We also derive analytically a closed-form expression for the users’ ergodic rates by considering large numbers of reflecting elements. This is followed by representative simulation examples and comprehensive discussions. The results show that when the IRSs are large enough, the proposed scheme always outperforms conventional massive MIMO-NOMA and MIMO-OMA systems even if SIC error propagation is present. It is also confirmed that dual-polarized IRSs can make cross-polar transmissions beneficial to the users, allowing them to improve their performance through diversity.

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%.

In this work, we consider a novel type of Internet of Things (IoT) ultra-narrowband (UNB) network architecture that involves multiple multiplexing bands or channels for uplink transmission. An IoT device can randomly choose any of the multiplexing bands and transmit its packet. Due to hardware constraints, a base station (BS) is able to listen to only one multiplexing band. The hardware constraint is mainly due to the complexity of performing fast Fourier transform (FFT) at a very small sampling interval over the multiplexing bands in order to counter the uncertainty of IoT device frequency and synchronize onto transmissions. The objective is to find an assignment of BSs to multiplexing bands in order to maximize the packet decoding probability (PDP). We develop a learning-based algorithm based on a sub-optimal solution to PDP maximization. The simulation results show that our approach to band assignment achieves near-optimal performance in terms of PDP, while at the same time, significantly exceeding the performance of random assignment. We also develop a heuristic algorithm with no learning overhead based on the locations of the BSs that also outperforms random assignment and serves as a performance reference to our learning-based algorithm.

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.

In this paper, the problem of associating reconfigurable intelligent surfaces (RISs) to virtual reality (VR) users is studied for a wireless VR network. In particular, this problem is considered within a cellular network that employs terahertz (THz) operated RISs acting as base stations. To provide a seamless VR experience, high data rates and reliable low latency need to be continuously guaranteed. To address these challenges, a novel risk-based framework based on the entropic value-at-risk is proposed for rate optimization and reliability performance. Furthermore, a Lyapunov optimization technique is used to reformulate the problem as a linear weighted function, while ensuring that higher order statistics of the queue length are maintained under a threshold. To address this problem, given the stochastic nature of the channel, a policy-based reinforcement learning (RL) algorithm is proposed. Since the state space is extremely large, the policy is learned through a deep-RL algorithm. In particular, a recurrent neural network (RNN) RL framework is proposed to capture the dynamic channel behavior and improve the speed of conventional RL policy-search algorithms. Simulation results demonstrate that the maximal queue length resulting from the proposed approach is only within 1% of the optimal solution. The results show a high accuracy and fast convergence for the RNN with a validation accuracy of 91.92%.

Wireless connectivity is rapidly becoming ubiquitous and affordable. As a consequence, most wireless devices are nowadays equipped with multi-connectivity, that is, availability of multiple radio access technologies (RATs). Each of these RATs has different characteristics that can be suitably utilized for different connectivity tasks. For example, a long-range low-rate RAT can be used for topology management and coordination, whereas a short-range high-rate RAT for data transmission. In this paper, we introduce a distributed consensus protocol for the hierarchical organization of Wireless Mesh Networks (WMNs) with devices using multiple RATs. Our protocol considers three hierarchical roles after the initial setup: Master, cluster head (CH), and cluster member (CM). The Master coordinates the use of all RATs, whereas the CHs coordinate all but the RAT with the longest transmission range. The initial setup takes place immediately after powering on the devices, after which the devices self-organize in a distributed manner by means of a consensus to elect the Masters and CHs. The resulting interconnected structure is based on the connectivity graphs created with the different RATs. The distributed consensus protocol operates with a minimal amount of network information and demonstrates high networking performance.

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.

Minimization of the expected value of age of information (AoI) is a risk-neutral approach, and it thus cannot capture rare, yet critical, events with potentially large AoI. In order to capture the effect of these events, in this paper, the notion of conditional value-at-risk (CVaR) is proposed as an effective coherent risk measure that is suitable for minimization of AoI for real-time IoT status updates. In the considered monitoring system, an IoT device monitors a physical process and sends the status updates to a remote receiver with an updating cost. The optimal status update process is designed to jointly minimize the AoI at the receiver, the CVaR of the AoI at the receiver, and the energy cost. This stochastic optimization problem is formulated as an infinite horizon discounted risk-aware Markov decision process (MDP), which is computationally intractable due to the time inconsistency of the CVaR. By exploiting the special properties of coherent risk measures, the risk-aware MDP is reduced to a standard MDP with an augmented state space, for which we derive the optimal stationary policy using dynamic programming. In particular, the optimal history-dependent policy of the risk-aware MDP is shown to depend on the history only through the augmented system states and can be readily constructed using the optimal stationary policy of the augmented MDP. The proposed solution is shown to be computationally tractable and able to minimize the AoI in real-time IoT monitoring systems in a risk-aware manner.

In this paper, we propose an energy-efficient optimal altitude for an aerial access point (AAP), which acts as a flying base station to serve a set of ground user equipment (UE). Since the ratio of total energy consumed by the aerial vehicle to the communication energy is very large, we include the aerial vehicle's energy consumption in the problem formulation. After considering the energy consumption model of the aerial vehicle, our objective is translated into a non-convex optimization problem of maximizing the global energy efficiency (GEE) of the aerial communication system, subject to altitude and minimum individual data rate constraints. At first, the non-convex fractional objective function is solved by using sequential convex programming (SCP) optimization technique. To compare the result of SCP with the global optimum of the problem, we reformulate the initial problem as a monotonic fractional optimization problem (MFP) and solve it using the polyblock outer approximation (PA) algorithm. Numerical results show that the candidate solution obtained from SCP is the same as the global optimum found using the monotonic fractional programming technique. Furthermore, the impact of the aerial vehicle's energy consumption on the optimal altitude determination is also studied.

This paper presents a novel framework for traffic prediction of IoT devices activated by binary Markovian events. First, we consider a massive set of IoT devices whose activation events are modeled by an On-Off Markov process with known transition probabilities. Next, we exploit the temporal correlation of the traffic events and apply the forward algorithm in the context of hidden Markov models (HMM) in order to predict the activation likelihood of each IoT device. Finally, we apply the fast uplink grant scheme in order to allocate resources to the IoT devices that have the maximal likelihood for transmission. In order to evaluate the performance of the proposed scheme, we define the regret metric as the number of missed resource allocation opportunities. The proposed fast uplink scheme based on traffic prediction outperforms both conventional random access and time division duplex in terms of regret and efficiency of system usage, while it maintains its superiority over random access in terms of average age of information for massive deployments.

In this paper, we advocate applying the concept of wake-up radio to distributed estimation in wireless sensor networks. With distributed estimation, where sensing data of multiple nodes are used for estimating a target observation, the energy consumption can be reduced by making only a subset of nodes in the network transmit their data, such that the collected data can guarantee the required estimation accuracy. In this case, a sink needs to selectively wake up sensor nodes whose data can contribute to the improvement of estimation accuracy. In this paper, we propose a wake-up signaling called estimative sampling (ES) that can realize the selective wake-up of desired nodes. The ES method includes a mechanism that dynamically searches the desired nodes over a distribution of sensing data. With numerical results obtained by computer simulations, we show that the distributed estimation with ES method achieves lower energy consumption than that with conventional identity-based wake-up while satisfying the required accuracy.

Wireless networked control systems for the Industrial Internet of Things (IIoT) require low-latency communication techniques that are very reliable and resilient. In this article, we investigate a coding-free control method to achieve ultralow latency communications in single-controller-multiplant networked control systems for both slow- and fast-fading channels. We formulate a power allocation problem to optimize the sum cost functions of multiple plants, subject to the plant stabilization condition and the controller's power limit. Although the optimization problem is a nonconvex one, we derive a closed-form solution, which indicates that the optimal power allocation policy for stabilizing the plants with different channel conditions is reminiscent of the channel-inversion policy. We numerically compare the performance of the proposed coding-free control method and the conventional coding-based control methods in terms of the control performance (i.e., the cost function) of a plant, which shows that the coding-free method is superior in a practical range of signal-to-noise ratios.

One of the central problems in massive Internet-of-Things (IoT) deployments is the monitoring of the status of a massive number of links. The problem is aggravated by the irregularity of the traffic transmitted over the link, as the traffic intermittency can be disguised as a link failure and vice versa. In this article, we present a traffic model for IoT devices running quasiperiodic applications and we present unsupervised, parametric machine learning methods for online monitoring of the network performance of individual devices in IoT deployments with quasiperiodic reporting, such as smart metering, environmental monitoring, and agricultural monitoring. Two clustering methods are based on the Lomb-Scargle periodogram, an approach developed by astronomers for estimating the spectral density of unevenly sampled time series. We present probabilistic performance results for each of the proposed methods based on simulated data and compare the performance to a naïve network monitoring approach. The results show that the proposed methods are more reliable at detecting both hard and soft faults than the naïve-approach, especially, when the network outage is high. Furthermore, we test the methods on real-world data from a smart metering deployment. The methods, in particular the clustering method, are shown to be applicable and useful in a real-world scenario.

This paper presents a finite length analysis of multi-slot type frameless ALOHA based on a dynamic programming approach. The analysis is exact, but its evaluation is only feasible for moderate number of users due to the computational complexity. The analysis is then extended to derive continuous approximations of its key parameters, which, apart from providing an insight into the decoding process, make it possible to estimate the packet error rate with very low computational complexity. Finally, a feedback scheme is presented in which the slot access scheme is dynamically adapted according to the approximate analysis in order to minimize the packet error rate. The results indicate that the introduction of feedback can substantially improve the performance of frameless ALOHA.

A multi-cell Fog-Radio Access Network (F-RAN) architecture is considered in which Internet of Things (IoT) devices periodically make noisy observations of a Quantity of Interest (QoI) and transmit using grant-free access in the uplink. The devices in each cell are connected to an Edge Node (EN), which may also have a finite-capacity fronthaul link to a central processor. In contrast to conventional information-agnostic protocols, the devices transmit using a Type-Based Multiple Access (TBMA) protocol that is tailored to enable the estimate of the field of correlated QoIs in each cell based on the measurements received from IoT devices. In this paper, this form of information-centric radio access is studied for the first time in a multi-cell F-RAN model with edge or cloud detection. Edge and cloud detection are designed and compared for a multi-cell system. Optimal model-based detectors are introduced and the resulting asymptotic behavior of the probability of error at cloud and edge is derived. Then, for the scenario in which a statistical model is not available, data-driven edge and cloud detectors are discussed and evaluated in numerical results.

In this paper, we introduce dynamic time-frequency-division duplex (D-TFDD), which is a novel duplexing scheme that combines time-division duplex (TDD) and frequency-division duplex (FDD). In D-TFDD, a user receives from the base station (BS) on the downlink in one frequency band and transmits to the BS on the uplink in another frequency band, as in FDD. Next, the user shares its uplink transmission (downlink reception) on the corresponding frequency band with the uplink transmission or the downlink reception of another user in a D-TDD fashion. Hence, in a given frequency band, the BS communicates with user 1 (U1) and user 2 (U2) in a D-TDD fashion. The proposed D-TFDD scheme does not require inter-cell interference (ICI) knowledge and only requires channel state information (CSI) of the local BS-U1 and BS-U2 channels. Thereby, it is practical for implementation. The proposed D-TFDD scheme increases the throughput region between the BS and the two users in a given frequency band, and significantly decreases the outage probabilities on the corresponding BS-U1 and BS-U2 channels. Most importantly, the proposed D-TFDD scheme doubles the diversity gain on both the corresponding BS-U1 and the BS-U2 channels compared to the diversity gain of existing duplexing schemes, which results in very large performance gains.

The central challenge in supporting massive IoT connectivity is the uncoordinated, random access by sporadically active devices. The random access protocol and activity detection have been widely studied, while the auxiliary procedures, such as synchronization, channel estimation and equalization, have received much less attention. However, once the protocol is fixed, the access performance can only be improved by a more effective receiver, through more accurate execution of the auxiliary procedures. This motivates the pursuit of joint synchronization and channel estimation, rather than the traditional approach of handling them separately. The prohibitive complexity of the conventional analytical solutions leads us to employ the tools of deep learning in this paper. Specifically, the proposed method is applied to the random access protocol of Narrowband IoT (NB-IoT), preserving its standard preamble structure. We obtain excellent performance in estimating Time-of-Arrival (ToA), Carrier-Frequency Offset (CFO), channel gain and collision multiplicity from a received mixture of transmissions. The proposed estimator achieves a ToA Root-Mean-Square Error (RMSE) of 0.99 us and a CFO RMSE of 1.61 Hz at 10 dB Signal-to-Noise Ratio (SNR), whereas a conventional estimator using two cascaded stages have RMSEs of 15.85 us and 8.05 Hz, respectively.

Wireless networked control systems for Industrial Internet of Things (IIoT) require low latency communication techniques. In this paper, we investigate a coding-free control method to achieve ultra-low latency communications in single-controller-multi-plant networked control systems. We formulate a power allocation problem to optimize the sum cost functions of multiple plants, subject to the plant stabilization condition and the controller's power limit. Although the optimization problem is a non-convex one, we derive a closed-form solution, which indicates that the optimal power allocation policy for stabilizing the plants with different channel conditions is reminiscent of the channel-inversion policy. Also, we numerically compare the performance of the proposed coding-free control method and the conventional coding-based control methods in terms of the cost function of a plant, which shows that the coding-free method is superior in a practical range of SNRs.

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.

The requirement to accommodate ultra-reliable low latency communication (URLLC) is one of the most attractive, yet challenging, new features of upcoming 5G systems. A common way to achieve reliability is retransmission; however, the applicability of this mechanism is hindered by the strict latency requirements. Furthermore, the bandwidth is often limited and shared by multiple connections, which may put the packet into a retransmission queue, leading to even larger latency. We address this problem in an uplink setting by introducing the concept of non-orthogonal multiple access hybrid automatic repeat request (NOMA-HARQ). In essence, NOMA-HARQ allows newly incoming packets to share non-orthogonally the same resource with retransmitted packets. The reliability guarantees are preserved by designing a power optimization procedure that takes into account past transmission attempts as well as the time remaining until the deadline.

Low power wide area networks (LPWANs), such as the ones based on the LoRaWAN protocol, are seen as enablers of large number of IoT applications and services. In this work, we assess the scalability of LoRaWAN by analyzing the frame success probability (FSP) of a LoRa frame while taking into account the capture effect and the number of parallel demodulation paths of the receiving gateway. We have based our model on the commonly used SX1301 gateway chipset, which is capable of demodulating up to eight frames simultaneously; however, the results of the model can be generalized to architectures with arbitrary number of demodulation paths. We have also introduced and investigated three policies for Spreading Factor (SF) allocation. Each policy is evaluated in terms of coverage probability, FSP, and throughput. The overall conclusion is that the presence of multiple demodulation paths introduces a significant change in the analysis and performance of the LoRa random access schemes.

We study multi-base station (BS) preamble detection schemes for the narrow-band Internet of Things (NB-IoT) random access by using stochastic geometry analysis. Specifically, we compare the preamble detection performance of two baseline detection schemes: Quantize-and-Forward (QnF) and Detect-and-Forward (DnF). QnF requires the feedback of quantized received power levels while DnF requires 1-bit feedback of local detection result. Our results show that DnF scheme outperforms QnF scheme when the backhaul capacity is limited or when the minimum distance between user and BSs is less than a threshold. Our results also show that the use of multiple collaborative BSs can lead to a significant improvement of the preamble detection performance, as well as reduction of the total power of the preamble transmission.

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.

We investigate a novel multiple user scalable 8K 360° video mobile virtual reality arcade streaming system that enables high reliability and immersion fidelity, and low interactive latency, by the synergistic integration of scalable 360° content, expected VR user viewport modeling, millimeter wave (mmWave) communication and network edge computation capability. The high data rate mmWave link is used to transmit the video content of the expected user 360 viewport at enhanced quality. To compensate for the dynamic nature of mmWave links and prospective expected viewport characterization error, we integrate a fall back transmission based on Wi-Fi broadcast of a baseline representation of the 360 panorama to all users. In our proposed transmission strategy, the expected viewport content can be sent as raw or encoded at different qualities, which enhances the end-to-end performance, by exploiting effective trade-offs between communication and computation latency at the receiving user. With the aim of maximizing the minimum VR immersion fidelity across all users, we investigate the joint optimization of the mmWave access point (AP) to user association, the data rate for the encoded portion of the 360 viewport content that is to be transmitted, and computation resource allocation. Our experimental results demonstrate that the proposed system can achieve significant improvement in delivered VR user immersion fidelity and quality of experience relative to a state-of-the-art reference method that leverages Wi-Fi transmission only.

In this paper, we propose optimal centralized dynamic-time-division-duplex (D-TDD) scheme for a general network comprised of K half-duplex (HD) nodes. Specifically, for this network, we propose optimal adaptive scheduling of the reception, transmission, and silence at every node in each time slot such that the average sum signal-to-interference-plus-noise-ratio (SINR) of the network is maximized. The numerical results show that the proposed optimal centralized D-TDD scheme achieves significant SINR gains over existing centralized D-TDD schemes, especially in small crowded areas. The proposed scheme can act as an upper bound to distributed D-TDD schemes, but it can also be applicable in small crowded areas where gathering of the channel gains of all links may be practical.

This paper proposes content-based control of wakeup receivers for data collection in wireless sensor networks. The wake-up procedure is designed with a goal of waking up only the subset of the sensor nodes which have the relevant data observations. This prevents the sensors with less relevant data from waking up and wasting energy, which is inevitable when employing conventional ID-based wake-up control. We apply the proposed content-based wake-up scheme to top- k query, where the sink attempts to collect information on the set of nodes that own top- k observations from the sensing field. Assuming medium access based on p-persistent CSMA, we design a content-based wake-up control scheme suited for the data collection of top- k query. We analyze the scheme theoretically in terms of data collection delay and energy-efficiency and compare it to the ID-based wake-up. The numerical results confirm the effectiveness of the proposed content-based wake-up control, especially when the number of sensor nodes is large.

The emerging blockchain protocols provide a decentralized architecture that is suitable of supporting Internet of Things (IoT) interactions. However, keeping a local copy of the blockchain ledger is infeasible for low-power and memory-constrained devices. For this reason, they are equipped with lightweight software implementations that only download the useful data structures, e.g., state of accounts, from the blockchain network, when they are updated. In this paper, we consider and analyze a novel scheme, implemented by the nodes of the blockchain network, which aggregates the blockchain data in periodic updates and further reduces the communication cost of the connected IoT devices. We show that the aggregation period should be selected based on the channel quality, the offered rate, and the statistics of updates of the useful data structures. The results, obtained for the Ethereum protocol, illustrate the benefits of the aggregation scheme in terms of a reduced duty cycle of the device, particularly for low signal-to-noise ratios, and the overall reduction of the amount of information transmitted in downlink from the wireless base station to the IoT device. A potential application of the proposed scheme is to let the IoT device request more information than actually needed, hence increasing its privacy, while keeping the communication cost constant. In the conclusion, this paper is the first to provide rigorous guidelines for the design of lightweight blockchain protocols with wireless connectivity.

Ultra-high reliability and low latency have not been in the mainstream in most wireless networks. Mobile networks have been driven so far by human-centric communications, delay-tolerant content, and non-critical services. The main target have been to boosting data rate and increasing coverage, adopting a rather best-effort networking approach. As wireless connectivity starts to get the status of a commodity, there is an increasing focus on support of services that rely critically on wireless links and therefore the reliability of the wireless connections. Next generation wireless systems, mainly 5G and beyond, are designed to provide wireless connectivity for massive machine-type communications (mMTC) and to support ultra-reliable, low latency communication (URLLC) for mission-critical services. URLLC scenarios impose stringent requirements in terms of latency (ranging from 1 ms and below to few milliseconds end-to-end latency depending on the use cases) and reliability (higher than 99.9999%). This does not mean that there are interesting applications where reliability is of paramount importance, while latency can be in the order of seconds, as in e.g. certain remote healthcare applications. Nevertheless, the coupling of low-latency networking and reliable communication is mainly driven by the need to push the technology boundaries and address a plethora of socially useful services and business domains that could benefit greatly from it. Some of the most challenging use cases are factory automation and industrial control, automated driving/flying, haptic communications, and real-time remote healthcare. URLLC is also expected to revolutionize processes in the areas of smart cities, smart farming, smart grid, remote manufacturing, and algorithmic trading. Although the URLLC constraints and the nature of real-time mission-critical applications imply the predominance of short packets and low-rate transmissions, future evolution of URLLC may also consider rate requirements. The emergence of immersive services, such as augmented and virtual reality (AR/VR), high-definition entertainment and gaming, and consumer robotics, calls for real-time, high-fidelity, broadband networks operating at latencies of few milliseconds.

Mission-critical applications require Ultra-Reliable Low Latency (URLLC) wireless connections, where the packet error rate (PER) goes down to 10-9. Fulfillment of the bold reliability figures becomes meaningful only if it can be related to a statistical model in which the URLLC system operates. However, this model is generally not known and needs to be learned by sampling the wireless environment. In this paper, we treat this fundamental problem in the simplest possible communication-theoretic setting: selecting a transmission rate over a dynamic wireless channel in order to guarantee high transmission reliability. We introduce a novel statistical framework for design and assessment of URLLC systems, consisting of three key components: (i) channel model selection; (ii) learning the model using training; and (iii) selecting the transmission rate to satisfy the required reliability. As it is insufficient to specify the URLLC requirements only through PER, two types of statistical constraints are introduced, Averaged Reliability (AR) and Probably Correct Reliability (PCR). The analysis and the evaluations show that adequate model selection and learning are indispensable for designing consistent physical layer that asymptotically behaves as if the channel was known perfectly, while maintaining the reliability requirements in URLLC systems.

The future connectivity landscape, and notably, the 5G wireless systems will feature Ultra-Reliable Low Latency Communication (URLLC). The coupling of high reliability and low latency requirements in URLLC use cases makes the wireless access design very challenging, in terms of both the protocol design and of the associated transmission techniques. This paper aims to provide a broad perspective on the fundamental tradeoffs in URLLC, as well as the principles used in building access protocols. Two specific technologies are considered in the context of URLLC: massive MIMO and multi-connectivity, also termed interface diversity. This paper also touches on the importance of the proper statistical methodology for designing and assessing extremely high-reliability levels.

Millimeter-wave (mmWave) bands will play an important role in 5G wireless systems. The system performance can be assessed by using models from stochastic geometry that cater for the directivity in the desired signal transmissions as well as the interference, and by calculating the signal-to-interference-plus-noise ratio (SINR) coverage. Nonetheless, the accuracy of the existing coverage expressions derived through stochastic geometry may be questioned, as it is not clear whether they would capture the impact of the detailed mmWave channel and antenna features. In this paper, we propose an SINR coverage analysis framework that includes realistic channel model and antenna element radiation patterns. We introduce and estimate two parameters, aligned gain and misaligned gain, associated with the desired signal beam and the interfering signal beam, respectively. The distributions of these gains are used to determine the distribution of the SINR which is compared with the corresponding SINR coverage, calculated through the system-level simulations. The results show that both aligned and misaligned gains can be modeled as exponential-logarithmically distributed random variables with the highest accuracy, and can further be approximated as exponentially distributed random variables with reasonable accuracy. These approximations can be used as a tool to evaluate the system-level performance of various 5G connectivity scenarios in the mmWave band.

Ultra-reliable communication (URC) is one of the distinctive features of the upcoming 5G wireless communication, characterized by packet error rates going down to 10-9. In this paper, we analyze the tail of the cumulative distribution function of block fading channels in the regime of extremely rare events, i.e., the ultra-reliable (UR) regime of operation. Our main contribution consists of providing a unified framework for statistical description of wide range of practically important wireless channel models in the UR regime of operation. Specifically, we show that the wireless channel behavior in this regime can be approximated by a simple power law expression, whose exponent and offset depend on the actual channel model. The unification provides a channel-agnostic tool for analyzing and performance optimization of radio systems that operate in the UR regime. Furthermore, the unified model is particularly useful in the emerging measurement campaigns for empirical characterization of wireless channels in the regime of low outages. Finally, the asymptotic analysis can serve as an underlying building block for designing more elaborate, higher-layer technologies for URC. We showcase this by applying the power law results to analyze the performance of receiver diversity schemes and obtain a new simplified expression for maximum ratio combining.

Narrowband Internet of Things (NB-IoT) is the latest IoT connectivity solution presented by the 3rd generation partnership project (3GPP). NB-IoT introduces coverage classes and offers a significant link budget improvement by allowing repeated transmissions by nodes that experience high path loss. However, those repetitions necessarily increase the energy consumption and the latency in the whole NB-IoT system. The extent to which the whole system is affected depends on the scheduling of the uplink and downlink channels. We address this question, not treated previously, by developing a tractable model of NB-IoT access protocol operation, comprising message exchanges in random-access, control, and data channels, both in the uplink and downlink. The model is then used to analyze the impact of channel scheduling as well as the interaction of coexisting coverage classes, through derivation of the expected latency and battery lifetime for each coverage class. These results are subsequently employed in investigation of latency-energy tradeoff in NB-IoT channel scheduling as well as determining the optimized operation points. Simulations results show validity of the analysis and confirm that channel scheduling and coexistence of coverage classes significantly affect latency and battery lifetime performance of NB-IoT devices.

The performance of orthogonal and non-orthogonal multiple access is studied for the multiplexing of enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) users in the uplink of a multi-cell Cloud Radio Access Network (C-RAN) architecture. While eMBB users can operate over long codewords spread in time and frequency, URLLC users' transmissions are random and localized in time due to their low-latency requirements. These requirements also call for decoding of URLLC packets to be carried out at the edge nodes (ENs), whereas eMBB traffic can leverage the interference management capabilities of centralized decoding at the cloud. Using information-theoretic arguments, the performance tradeoffs between eMBB and URLLC traffic types are investigated in terms of rate for the former, and rate, access latency, and reliability for the latter. The analysis includes non-orthogonal multiple access (NOMA) with different decoding architectures, such as puncturing and successive interference cancellation (SIC). The main results bring insight on effective design choices as a function of inter-cell interference, signal-to-noise ratio levels, and fronthaul capacity constraints.

Machine-type communication requires rethinking of the structure of short packets due to the coding limitations and the significant role of the control information. In ultra-reliable low-latency communication (URLLC), it is crucial to optimally use the limited degrees of freedom (DoFs) to send data and control information. We consider a URLLC model for short packet transmission with acknow 1-edgement (ACK). We compare the detection/decoding performance of two short packet structures: (1) time-multiplexed detection sequence and data; and (2) structure in which both packet detection and data decoding use all DoFs. Specifically, as an instance of the second structure we use superimposed sequences for detection and data. We derive the probabilities of false alarm and misdetection for an AWGN channel and numerically minimize the packet error probability (PER), showing that for delay-constrained data and ACK exchange, there is a tradeoff between the resources spent for detection and decoding. We show that the optimal PER for the superimposed structure is achieved for higher detection overhead. For this reason, the PER is also higher than in the preamble case. However, the superimposed structure is advantageous due to its flexibility to achieve optimal operation without the need to use multiple codebooks.

Blockchain is a technology uniquely suited to support massive number of transactions and smart contracts within the Internet of Things (IoT) ecosystem, thanks to the decentralized accounting mechanism. In a blockchain network, the states of the accounts are stored and updated by the validator nodes, interconnected in a peer-to-peer fashion. IoT devices are characterized by relatively low computing capabilities and low power consumption, as well as sporadic and low-bandwidth wireless connectivity. An IoT device connects to one or more validator nodes to observe or modify the state of the accounts. In order to interact with the most recent state of accounts, a device needs to be synchronized with the blockchain copy stored by the validator nodes. In this work, we describe general architectures and synchronization protocols that enable synchronization of the IoT endpoints to the blockchain, with different communication costs and security levels. We model and analytically characterize the traffic generated by the synchronization protocols, and also investigate the power consumption and synchronization trade-off via numerical simulations. To the best of our knowledge, this is the first study that rigorously models the role of wireless connectivity in blockchain-powered IoT systems.

An important ingredient of the future 5G systems will be ultra-reliable low-latency communication (URLLC). A way to offer URLLC without intervention in the baseband/PHY layer design is to use interface diversity and integrate multiple communication interfaces, each interface based on a different technology. In this paper, we propose to use coding to seamlessly distribute coded payload and redundancy data across multiple available communication interfaces. We formulate an optimization problem to find the payload allocation weights that maximize the reliability at specific target latency values. In order to estimate the performance in terms of latency and reliability of such an integrated communication system, we propose an analysis framework that combines traditional reliability models with technology-specific latency probability distributions. Our model is capable to account for failure correlation among interfaces/technologies. By considering different scenarios, we find that the optimized strategies can in some cases significantly outperform strategies based on k-out-of-n erasure codes, where the latter do not account for the characteristics of the different interfaces. The model has been validated through simulation and is supported by experimental results.

We consider a monitoring application where sensors periodically report data to a common receiver using time division multiplexing. The sensors are constrained by the limited and unpredictable energy availability provided by energy harvesting (EH), and by the channel impairments. To maximize the quality of the reported data, the packets transmitted contain newly generated data blocks together with up to r -1 previously unsuccessfully delivered ones, where r is a design parameter. These data blocks are compressed, concatenated, and encoded with a channel code. The scheme applies lossy compression, such that the fidelity of the individual blocks is traded off with the reliability provided by the channel code. We show that the proposed strategy outperforms the one in which retransmissions are not allowed. We also investigate the tradeoff between the value of r, the compression and the coding rates, under the constraints of the energy availability, and, once r has been decided, use a Markov decision process (MDP) to optimize the compression/coding rates. Finally, we implement a reinforcement learning algorithm, through which devices can learn the optimal transmission policy without knowing a priori the statistics of the EH process, and show that it indeed reaches the performance obtained via MDP.

Industry 4.0 introduces modern communication and computation technologies such as cloud computing and Internet of Things to industrial manufacturing systems. As a result, many devices, machines, and applications will rely on connectivity, while having different requirements to the network, ranging from high reliability and low latency to high data rates. Furthermore, these industrial networks will be highly heterogeneous, as they will feature a number of diverse communication technologies. Current technologies are not well suited for this scenario, which requires that the network is managed at an abstraction level, which is decoupled from the underlying technologies. In this paper, we consider network slicing as a mechanism to handle these challenges. We present methods for slicing deterministic and packet-switched industrial communication protocols, which simplify the manageability of heterogeneous networks with various application requirements. Furthermore, we show how to use network calculus to assess the end-to-end properties of the network slices.

We present a framework for random access that is based on three elements: physical-layer network coding (PLNC), signature codes, and tree splitting. In presence of a collision, physical-layer network coding enables the receiver to decode, i.e., compute, the sum of the packets that were transmitted by the individual users. For each user, the packet consists of the user's signature, as well as the data that the user wants to communicate. As long as no more than K users collide, their identities can be recovered from the sum of their signatures. This framework for creating and transmitting packets can be used as a fundamental building block in random access algorithms, since it helps to deal efficiently with the uncertainty of the set of contending terminals. In this paper, we show how to apply the framework in conjunction with a tree-splitting algorithm, which is required to deal with the case that more than K users collide. We demonstrate that our approach achieves throughput that tends to 1 rapidly as K increases. We also present results on net data-rate of the system, showing the impact of the overheads of the constituent elements of the proposed protocol. We compare the performance of our scheme with an upper bound that is obtained under the assumption that the active users are a priori known. Also, we consider an upper bound on the net data-rate for any PLNC-based strategy in which one linear equation per slot is decoded. We show that already at modest packet lengths, the net data-rate of our scheme becomes close to the second upper bound, i.e., the overhead of the contention resolution algorithm and the signature codes vanishes.

In many wireless systems, the signal-to-interference-and-noise ratio that is applicable to a certain transmission, referred to as channel state information (CSI), can only be learned after the transmission has taken place and is thereby delayed (outdated). In such systems, hybrid automatic repeat request (HARQ) protocols are often used to achieve high throughput with low latency. This paper put forth the family of expandable message space (EMS) protocols, that generalize the HARQ protocol and allow for rate adaptation based on delayed CSI at the transmitter (CSIT). Assuming a block-fading channel, the proposed EMS protocols are analyzed using dynamic programming. When full delayed CSIT is available and there is a constraint on the average decoding time, it is shown that the optimal zero outage EMS protocol has a particularly simple operational interpretation and that the throughput is identical to that of the backtrack retransmission request (BRQ) protocol. We also devise EMS protocols for the case in which CSIT is only available through a finite number of feedback messages. The numerical results demonstrate that BRQ approaches the ergodic capacity quickly compared with HARQ, while EMS protocols with only three and four feedback messages achieve throughputs, that are only slightly worse than that of BRQ.

We investigate the maximum coding rate for a given average blocklength and error probability over a K-user discrete memoryless broadcast channel for the scenario where a common message is transmitted using variable-length stop-feedback codes. For the point-to-point case, Polyanskiy et al. (2011) demonstrated that variable-length coding combined with stop-feedback significantly increases the speed of convergence of the maximum coding rate to capacity. This speed-up manifests itself in the absence of a square-root penalty in the asymptotic expansion of the maximum coding rate for large blocklengths, i.e., zero dispersion. In this paper, we present nonasymptotic achievability and converse bounds on the maximum coding rate of the common-message K-user discrete memoryless broadcast channel, which strengthen and generalize the ones reported in Trillingsgaard et al. (2015) for the two-user case. An asymptotic analysis of these bounds reveals that zero dispersion cannot be achieved for certain common-message broadcast channels (e.g., the binary symmetric broadcast channel). Furthermore, we identify conditions under which our converse and achievability bounds are tight up to the second order. Through numerical evaluations, we illustrate that our second-order expansions approximate accurately the maximum coding rate and that the speed of convergence to capacity is indeed slower than for the point-to-point case.

We investigate the maximum coding rate achievable on a two-user broadcast channel for the case where a common message is transmitted with feedback using either fixed-blocklength codes or variable-length codes. For the fixed-blocklength-code setup, we establish nonasymptotic converse and achievability bounds. An asymptotic analysis of these bounds reveals that feedback improves the second-order term compared to the no-feedback case. In particular, for a certain class of antisymmetric broadcast channels, we show that the dispersion is halved. For the variable-length-code setup, we demonstrate that the channel dispersion is zero.

We present a novel access protocol for crowd scenarios in massive multiple-input multiple-output (MIMO) systems. Crowd scenarios are characterized by a large number of users with intermittent access behavior, whereas orthogonal scheduling is infeasible. In such scenarios, random access is a natural choice. The proposed access protocol relies on two essential properties of a massive MIMO system, namely, asymptotic orthogonality between user channels and asymptotic invariance of channel powers. Signal processing techniques that take advantage of these properties allow us to view a set of contaminated pilot signals as a graph code on which iterative belief propagation can be performed. This makes it possible to decontaminate pilot signals and increase the throughput of the system. Numerical evaluations show that the proposed access protocol increases the throughput by 36%, when there are 400 antennas at the base station, compared to the conventional method of slotted Additive Links On-line Hawaii Area. With 1024 antennas, the throughput is increased by 85%.

Radio access management plays a vital role in delay and energy consumption of connected devices. The radio access in existing cellular networks is unable to efficiently support massive connectivity, due to its signaling overhead. In this paper, we investigate an asynchronous grant-free narrowband data transmission protocol that aims to provide low energy consumption and delay, by relaxing the synchronization/reservation requirement at the cost of sending several packet copies at the transmitter side and more complex signal processing at the receiver side. Specifically, the timing and frequency offsets, as well as sending of multiple replicas of the same packet, are exploited as form of diversities at the receiver-side to trigger successive interference cancellation. The proposed scheme is investigated by deriving closed-form expressions for key performance indicators, including reliability and battery-lifetime. The performance evaluation indicates that the scheme can be tuned to realize long battery lifetime radio access for low-complexity devices. The obtained results indicate existence of traffic load regions, where synchronous access outperforms asynchronous access and vice versa.

A Fog Radio Access Network (F-RAN) is a cellular wireless system that enables content delivery via the caching of popular content at edge nodes (ENs) and cloud processing. The existing information-theoretic analyses of F-RAN systems, and special cases thereof, make the assumption that all requests should be guaranteed the same delivery latency, which results in identical latency for all files in the content library. In practice, however, contents may have heterogeneous timeliness requirements depending on the applications that operate on them. Given per-EN cache capacity constraint, there exists a fundamental trade-off among the delivery latencies of different users' requests, since contents that are allocated more cache space generally enjoy lower delivery latencies. For the case with two ENs and two users, the optimal latency trade-off is characterized in the high-SNR regime in terms of the Normalized Delivery Time (NDT) metric. The main results are illustrated by numerical examples.

An important ingredient of the future 5G systems will be Ultra-Reliable Low-Latency Communication (URLLC). A way to offer URLLC without intervention in the baseband/PHY layer design is to use interface diversity and integrate multiple communication interfaces, each interface based on a different technology. Our approach is to use coding to seamlessly distribute coded payload and redundancy data across multiple available communication interfaces. We formulate an optimization problem to find the payload allocation weights that maximize the reliability at specific target latency values. By considering different scenarios, we find that optimized strategies can significantly outperform k- out-of-n strategies, where the latter do not account for the characteristics of the different interfaces. Our approach is supported by experimental results.

Millimeter-wave (mmWave) bands have been attracting growing attention as a possible candidate for next- generation cellular networks, since the available spectrum is orders of magnitude larger than in current cellular allocations. To precisely design mmWave systems, it is important to examine mmWave interference and SIR coverage under large-scale deployments. For this purpose, we apply an accurate mmWave channel model, derived from experiments, into an analytical framework based on stochastic geometry. In this way we obtain an analytical expression for the SIR coverage probability in mmWave cellular networks.

One of the novelties brought by 5G is that wireless system design has increasingly turned its focus on guaranteeing reliability and latency. This shifts the design objective of random access protocols from throughput optimization towards constraints based on reliability and latency. For this purpose, we use frameless ALOHA, which relies on successive interference cancellation (SIC), and derive its exact finite-length analysis of the statistics of the unresolved users (reliability) as a function of the contention period length (latency). The presented analysis can be used to derive the reliability-latency guarantees. We also optimize the scheme parameters in order to maximize the reliability within a given latency. Our approach represents an important step towards the general area of design and analysis of access protocols with reliability-latency guarantees.

We propose a novel framework for secure and reliable authentication of Distributed Energy Resources to the centralized secondary/tertiary control system of a DC MicroGrid (MG), networked using the IEEE 802.11 wireless interface. The key idea is to perform the authentication using power talk - a powerline communication technique executed by the primary control loops of the power electronic converters. In addition, the scheme also promotes direct and active participation of the control system in the authentication process, a feature not commonly encountered in current networked control systems for MicroGrids. The FLECS®-based simulations verifies the viability of the proposed solution.

We present a connection establishment protocol with integrated authentication, suited for Massive Machine-Type Communications (mMTC). The protocol is contention-based and its main feature is that a device contends with a unique signature that also enables the authentication of the device towards the network. The signatures are inspired by Bloom filters and are created based on the output of the MILENAGE authentication and encryption algorithm set, which is used in the authentication and security procedures in the LTE protocol family. We show that our method utilizes the system resources more efficiently, achieves lower latency of connection establishment for Poisson arrivals and allows a 87% signalling overhead reduction. An important conclusion is that the mMTC traffic benefits profoundly from integration of security features into the connection establishment/access protocols, instead of addressing them post-hoc, which has been a common practice.

We investigate the maximum coding rate achievable on a two-user broadcast channel for the case where a common-message is transmitted using fixed-blocklength codes with feedback. Specifically, we focus on a family of broadcast channels composed of two antisymmetric Z-channels. For this setup, we obtain matching upper and lower bounds on the dispersion term in the asymptotic expansion of the maximum coding rate. These bounds reveal that the dispersion is halved compared to the no-feedback case.

Energy harvesting is a promising technology for the Internet of Things (IoT) towards the goal of self-sustainability of the involved devices. However, the intermittent and unreliable nature of the harvested energy demands an intelligent management of devices' operation in order to ensure a sustained performance of the IoT application. In this work, we address the problem of maximizing the quality of the reported data under the constraints of energy harvesting, energy consumption and communication channel impairments. Specifically, we propose an energy-aware joint source-channel coding scheme that minimizes the expected data distortion, for realistic models of energy generation and of the energy spent by the device to process the data, when the communication is performed over a Rayleigh fading channel. The performance of the scheme is optimized by means of a Markov Decision Process framework.

In this paper, we describe CoMP2flex, a user-centric base station (BS) cooperation scheme that provides improvements in reliability of both uplink (UL) and downlink (DL) communications of wireless cellular networks. CoMP2flex supports not only cooperation of two BSs with same direction of traffic but also cooperation of two BSs serving bidirectional traffic. The reliability performance of CoMP2flex is shown with numerical simulations and analytical expressions. We quantify and numerically validate the performance of the greedy BS pairing algorithm by comparing maximum weight matching methods, implemented as the Edmonds matching algorithm for weighted graphs.

One of the goals of 5G wireless systems stated by the NGMN alliance is to provide moderate rates (50+ Mbps) everywhere and with very high reliability. We term this service Ultra-Reliable Ubiquitous-Rate Communication (UR2C). This paper investigates the role of frequency reuse in supporting UR2C in the downlink. To this end, two frequency reuse schemes are considered: user-specific frequency reuse (FRu) and BS-specific frequency reuse (FRb). For a given unit frequency channel, FRureduces the number of serving user equipments (UEs), whereas FRb directly decreases the number of interfering base stations (BSs). This increases the distance from the interfering BSs and the signal-to-interference ratio (SIR) attains ultra-reliability, e.g. 99% SIR coverage at a randomly picked UE. The ultra-reliability is, however, achieved at the cost of the reduced frequency allocation, which may degrade overall downlink rate. To fairly capture this reliability-rate tradeoff, we propose ubiquitous rate defined as the maximum downlink rate whose required SIR can be achieved with ultra-reliability. By using stochastic geometry, we derive closed-form ubiquitous rate as well as the optimal frequency reuse rules for UR2C.

Cloud-radio access network (C-RAN) is characterized by a hierarchical structure, in which the baseband-processing functionalities of remote radio heads (RRHs) are implemented by means of cloud computing at a central unit (CU). A key limitation of C-RANs is given by the capacity constraints of the fronthaul links connecting RRHs to the CU. In this letter, the impact of this architectural constraint is investigated for the fundamental functions of random access and active user equipment (UE) identification in the presence of a potentially massive number of UEs. In particular, the standard C-RAN approach based on quantize-and-forward and centralized detection is compared to a scheme based on an alternative CU-RRH functional split that enables local detection. Both techniques leverage Bayesian sparse detection. Numerical results illustrate the relative merits of the two schemes as a function of the system parameters.

We propose an uncoordinated medium access control (MAC) protocol, called all-to-all broadcast coded slotted ALOHA (B-CSA) for reliable all-to-all broadcast with strict latency constraints. In B-CSA, each user acts as both transmitter and receiver in a half-duplex mode. The half-duplex mode gives rise to a double unequal error protection (DUEP) phenomenon: the more a user repeats its packet, the higher the probability that this packet is decoded by other users, but the lower the probability for this user to decode packets from others. We analyze the performance of B-CSA over the packet erasure channel for a finite frame length. In particular, we provide a general analysis of stopping sets for B-CSA and derive an analytical approximation of the performance in the error floor (EF) region, which captures the DUEP feature of B-CSA. Simulation results reveal that the proposed approximation predicts very well the performance of B-CSA in the EF region. Finally, we consider the application of B-CSA to vehicular communications and compare its performance with that of carrier sense multiple access (CSMA), the current MAC protocol in vehicular networks. The results show that B-CSA is able to support a much larger number of users than CSMA with the same reliability.

Wirelessly powered communications will entail short packets due to naturally small payloads, low-latency requirements, and/or insufficient energy resources to support longer transmissions. In this paper, a wireless-powered communication system is investigated, where an energy harvesting transmitter, charged by power beacons via wireless energy transfer, attempts to communicate with a receiver over a noisy channel. Under a save-then-transmit protocol, the system performance is characterized using metrics, such as the energy supply probability at the transmitter, and the achievable rate at the receiver for the case of short packets. The analytical treatment is provided for two cases: a three-node setup with a single power beacon and a large-scale network with multiple power beacons. Leveraging finite-length information theory, tractable analytical expressions are derived for the considered metrics in terms of the harvest blocklength, the transmit blocklength, the harvested power, the transmit power, and the network density. The analysis provides several useful design guidelines. Though using a small transmit power or a small transmit blocklength helps avoid energy outages, the consequently smaller signal-to-noise ratio or the fewer coding opportunities may cause a data decoding error. Scaling laws are derived to capture this inherent tradeoff between the harvest and transmit blocklengths. Numerical results reveal that power control is essential for improving the achievable rate of the considered system. The asymptotically optimal transmit power yields nearly optimal performance in the finite blocklength regime.

This paper treats a multiuser relay scenario, where multiple user equipments have a two-way communication with a common base station in the presence of a buffer-equipped relay station. Each of the uplink (UL) and downlink (DL) transmission can take place over a direct or over a relayed path. Traditionally, the UL and the DL path of a given two-way link are coupled, that is, either both are direct links or both are relayed links. By removing the restriction for coupling, one opens the design space for a decoupled two-way links. Following this, we devise two protocols: orthogonal decoupled UL/DL buffer-aided (ODBA) relaying protocol and non-ODBA (NODBA) relaying protocol. In NODBA, the receiver can use successive interference cancellation to extract the desired signal from a collision between UL and DL signals. For both protocols, we characterize the transmission decision policies in terms of maximization of the average two-way sum rate of the system. The numerical results show that decoupling association and non-orthogonal radio access lead to significant throughput gains for two-way traffic.

In this paper, we propose a novel splitting receiver, which involves a joint processing of coherently and non-coherently received signals. Using a passive RF power splitter, the received signal at each receiver antenna is split into two streams, which are then processed by a conventional coherent detection (CD) circuit and a power-detection (PD) circuit, respectively. The streams of the signals from all the receiver antennas are then jointly used for information detection. We show that the splitting receiver creates a 3-D received signal space due to the joint coherent and non-coherent processing. We analyze the achievable rate of a splitting receiver, which shows that the splitting receiver provides a rate gain of 3/2 compared with either the conventional (CD-based) coherent receiver or the PD-based non-coherent receiver in the high SNR regime. We also analyze the symbol error rate (SER) for practical modulation schemes, which shows that the splitting receiver achieves asymptotic SER reduction by a factor of at least √M-1 for M-QAM compared with either the conventional (CD-based) coherent receiver or the PD-based non-coherent receiver.

Full-duplex communication enables simultaneous transmission from both ends of a communication link, thereby promising significant performance gains. Generally, it has been shown that the throughput and delay gains of full-duplex communication are somewhat limited in realistic network settings, leading researchers to study other possible applications that can accord higher gains. The potential of full-duplex communication in improving the physical-layer security of a communication link is investigated in this contribution. We specifically present a thorough analysis of the achievable ergodic secrecy rate and the secrecy degrees of freedom with full-duplex communication in the presence of a half-duplex eavesdropper node, with both single-user decoding and multi-user decoding capabilities. For the latter case, an eavesdropper with successive interference cancellation and joint decoding capabilities is assumed. Irrespective of the eavesdropper capabilities and channel strengths, the ergodic secrecy rate with full-duplex communication is found to grow linearly with the log of the direct channel signal-to-noise-ratio (SNR) as opposed to the flattened out secrecy rate with conventional half-duplex communication. Consequently, the secrecy degrees of freedom with full-duplex is shown to be two as opposed to that of zero in half-duplex mode.

Cellular wireless systems rely on frame-based transmissions. The frame design is conventionally based on heuristics, consisting of a frame header and a data part. The frame header contains control information that provides pointers to the messages within the data part. In this paper, we revisit the principles of frame design and show the impact of the new design in scenarios that feature short data packets, which are central to various 5G and Internet of Things applications. We treat framing for downlink transmission in an AWGN broadcast channel with $K$ users, where the sizes of the messages to the users are random variables. Using approximations from finite blocklength information theory, we establish a framework in which a message to a given user is not necessarily encoded as a single packet, but may be grouped with messages to other users and benefit from the improved efficiency of longer codes. This requires changes in the way control information is sent, and it requires that the users need to spend power decoding other messages, thereby increasing the average power consumption. We show that the common heuristic design is only one point on a curve that represents the tradeoff between latency and power consumption.

The massive multiple-input multiple-output (MIMO) technology has great potential to manage the rapid growth of wireless data traffic. Massive MIMO achieves tremendous spectral efficiency by spatial multiplexing many tens of user equipments (UEs). These gains are only achieved in practice if many more UEs can connect efficiently to the network than today. As the number of UEs increases, while each UE intermittently accesses the network, the random access functionality becomes essential to share the limited number of pilots among the UEs. In this paper, we revisit the random access problem in the Massive MIMO context and develop a reengineered protocol, termedstrongest-user collision resolution(SUCRe). An accessing UE asks for a dedicated pilot by sending an uncoordinated random access pilot, with a risk that other UEs send the same pilot. The favorable propagation of massive MIMO channels is utilized to enable distributed collision detection at each UE, thereby determining the strength of the contenders’ signals and deciding to repeat the pilot if the UE judges that its signal at the receiver is the strongest. The SUCRe protocol resolves the vast majority of all pilot collisions in crowded urban scenarios and continues to admit UEs efficiently in overloaded networks.

A massive MIMO system, represented by a base station with hundreds of antennas, is capable of spatially multiplexing many devices and thus naturally suited to serve dense crowds of wireless devices in emerging applications, such as machine-type communications. Crowd scenarios pose new challenges in the pilot-based acquisition of channel state information and call for pilot access protocols that match the intermittent pattern of device activity. A joint pilot assignment and data transmission protocol based on random access is proposed in this paper for the uplink of a massive MIMO system. The protocol relies on the averaging across multiple transmission slots of the pilot collision events that result from the random access process. We derive new uplink sum rate expressions that take pilot collisions, intermittent device activity, and interference into account. Simplified bounds are obtained and used to optimize the device activation probability and pilot length. A performance analysis indicates how performance scales as a function of the number of antennas and the transmission slot duration.

We study a communication framework for nonlinear multibus DC MicroGrids based on a deliberate modification of the parameters of the primary control and termed power talk. We assess the case in which the information is modulated in the deviations of reference voltages of the primary control loops and show that the outputs of the power talk communication channels can be approximated through linear combinations of the respective inputs. We show that the coefficients of the linear combinations, representing equivalent channel gains, depend on the virtual resistances of the primary control loops, implying that they can be modified such that effective received signal-to-noise ratio (SNR) is increased. On the other hand, we investigate the constraints that power talk incurs on the supplied power deviations. We show that these constraints translate into constraints on the reference voltages and virtual resistances that are imposed on all units in the system. In this regard, we develop an optimization approach to find the set of controllable virtual resistances that maximize SNR under the constraints on the supplied power deviations.

We present a random access method inspired on Bloom filters that is suited for Machine-Type Communications (MTC). Each accessing device sends a signature during the contention process. A signature is constructed using the Bloom filtering method and contains information on the device identity and the connection establishment cause. We instantiate the proposed method over the current LTE-A access protocol. However, the method is applicable to a more general class of random access protocols that use preambles or other reservation sequences, as expected to be the case in 5G systems. We show that our method utilizes the system resources more efficiently and achieves significantly lower connection establishment latency in case of synchronous arrivals, compared to the variant of the LTE-A access protocol that is optimized for MTC traffic. A dividend of the proposed method is that it allows the base station (BS) to acquire the device identity and the connection establishment cause already in the initial phase of the connection establishment, thereby enabling their differentiated treatment by the BS.

Massive MIMO systems, where the base stations are equipped with hundreds of antenna elements, are an attractive way to attain unprecedented spectral efficiency in future wireless networks. In the "classical" massive MIMO setting, the terminals are assumed fully loaded and a main impairment to the performance comes from the inter-cell pilot contamination, i.e., interference from terminals in neighboring cells using the same pilots as in the home cell. However, when the terminals are active intermittently, it is viable to avoid inter-cell contamination by pre-allocation of pilots, while same-cell terminals use random access to select the allocated pilot sequences. This leads to the problem of intra-cell pilot contamination. We propose a framework for random access in massive MIMO networks and derive new uplink sum rate expressions that take intra-cell pilot collisions, intermittent terminal activity, and interference into account. We use these expressions to optimize the terminal activation probability and pilot length.

Wireless networks with many antennas at the base stations and multiplexing of many users, known as Massive MIMO systems, are key to handle the rapid growth of data traffic. As the number of users increases, the random access in contemporary networks will be flooded by user collisions. In this paper, we propose a reengineered random access protocol, coined strongest-user collision resolution (SUCR). It exploits the channel hardening feature of Massive MIMO channels to enable each user to detect collisions, determine how strong the contenders' channels are, and only keep transmitting if it has the strongest channel gain. The proposed SUCR protocol can quickly and distributively resolve the vast majority of all pilot collisions.

In this paper, we investigate the performance of simultaneous wireless information and power transfer (SWIPT) in a point-to-point system, adopting practical M-ary modulation. We take into account the fact that the receiver's radio-frequency (RF) energy harvesting circuit can only harvest energy when the received signal power is greater than a certain sensitivity level. For both power-splitting (PS) and time-switching (TS) schemes, we derive the energy harvesting performance as well as the information decoding performance for the Nakagami-m fading channel. We also analyze the performance tradeoff between energy harvesting and information decoding by studying an optimization problem, which maximizes the information decoding performance and satisfies a constraint on the minimum harvested energy. Our analysis shows that (i) for the PS scheme, modulations with high peak-to-average power ratio achieve better energy harvesting performance, (ii) for the TS scheme, it is desirable to concentrate the power for wireless power transfer in order to minimize the non-harvested energy caused by the RF energy harvesting sensitivity level, and (iii) channel fading is beneficial for energy harvesting in both PS and TS schemes.

This paper investigates the maximum coding rate over a K-user discrete memoryless broadcast channel for the scenario where a common message is transmitted using variable-length stop-feedback codes. Specifically, upon decoding the common message, each decoder sends a stop signal to the encoder, which transmits continuously until it receives all K stop signals. We present nonasymptotic achievability and converse bounds for the maximum coding rate, which strengthen and generalize the bounds previously reported in Trillingsgaard et al. (2015) for the two-user case. An asymptotic analysis of these bounds reveal that-contrary to the point-to-point case-the second-order term in the asymptotic expansion of the maximum coding rate decays inversely proportional to the square root of the average blocklength. This holds for certain nontrivial common-message broadcast channels, such as the binary symmetric broadcast channel. Furthermore, we identify conditions under which our converse and achievability bounds are tight up to the second order. Through numerical evaluations, we illustrate that our second-order asymptotic expansion approximates accurately the maximum coding rate and that the speed of convergence to capacity is indeed slower than for the point-to-point case.

IEEE 802.11ah is a novel WiFi-based protocol, aiming to provide an access solution for the machine-to-machine (M2M) communications. In this paper, we propose an adaptive access mechanism that can be seamlessly incorporated into IEEE 802.11ah protocol operation and that supports all potential M2M reporting regimes, which are periodic, on-demand, and alarm reporting. The proposed access method is based on a periodically reoccurring pool of time slots, whose size is proactively determined on the basis of the reporting activity in the cell. We show that it is possible to both efficiently and reliably resolve all reporting stations in the cell, within the limits of the allowed deadlines. As a side result, we also provide a rationale for modeling the interarrival time in alarm events by using the Beta distribution, a model that is considered in the 3GPP standardization.

Power talk is a novel ultra narrow-band powerline communication (UNB-PLC) technique for communication among control units in MicroGrids (MGs). Unlike the existing UNB-PLC solutions, power talk does not require installation of additional dedicated communication hardware and, instead, uses only the power electronic converters through which the control units interface the common bus. This way the communication system has practically the same reliability as the power system. The information is transmitted by modulating the parameters of the primary control, incurring subtle power deviations that can be detected by other units. In this paper, we develop power talk communication strategies for direct-current (DC) MG systems with arbitrary number of control units that carry out all-to-all communication. We investigate two multiple access strategies: time-division multiple access, where only one unit transmits at a time, and full duplex, where all units transmit and receive simultaneously. We apply the concepts of signaling space, where the power talk symbol constellations are constructed, and detection space, where the demodulation of the symbols is performed. The proposed communication technique is challenged by the random changes of the bus parameters due to load variations. To this end, we investigate the performance of power talk when a solution based on training sequences that re-establishes detection spaces is employed. The presented evaluation shows that power talk has a potential to offer an effective and inexpensive solution for reliable communication among units in DC MGs.

While LTE has been widely rolled out for human-type services, it is also a promising solution for cost-efficient connectivity of the smart grid monitoring equipment. This is a type of machine-to-machine (M2M) traffic that consists mainly of sporadic uplink transmissions. In such a setting, the amount of traffic that can be served in a cell is not constrained by the data capacity, but rather by the signaling constraints in the random access channel and control channel. In this paper, we explore these limitations using a detailed simulation of the LTE access reservation protocol (ARP). We find that 1) assigning more random access opportunities may actually worsen performance and 2) the additional signaling that follows the ARP has very large impact on the capacity in terms of the number of supported devices; we observed a reduction in the capacity by almost a factor of 3. This suggests that a lightweight access method, with a reduced number of signaling messages, needs to be considered in standardization for M2M applications. Additionally we propose a tractable analytical model to calculate the outage that can be rapidly implemented and evaluated. The model accounts for the features of the random access, control channel, and uplink and downlink data channels, as well as retransmissions.

Most of the recent advances in the design of high-speed wireless systems are based on information-theoretic principles that demonstrate how to efficiently transmit long data packets. However, the upcoming wireless systems, notably the fifth-generation (5G) system, will need to support novel traffic types that use short packets. For example, short packets represent the most common form of traffic generated by sensors and other devices involved in machine-to-machine (M2M) communications. Furthermore, there are emerging applications in which small packets are expected to carry critical information that should be received with low latency and ultrahigh reliability. Current wireless systems are not designed to support short-packet transmissions. For example, the design of current systems relies on the assumption that the metadata (control information) is of negligible size compared to the actual information payload. Hence, transmitting metadata using heuristic methods does not affect the overall system performance. However, when the packets are short, metadata may be of the same size as the payload, and the conventional methods to transmit it may be highly suboptimal. In this paper, we review recent advances in information theory, which provide the theoretical principles that govern the transmission of short packets. We then apply these principles to three exemplary scenarios (the two-way channel, the downlink broadcast channel, and the uplink random access channel), thereby illustrating how the transmission of control information can be optimized when the packets are short. The insights brought by these examples suggest that new principles are needed for the design of wireless protocols supporting short packets. These principles will have a direct impact on the system design.

This paper introduces a novel technique that enables access by a cognitive secondary user (SU) to a spectrum occupied by an incumbent primary user (PU) that employs Type-I hybrid automatic retransmission request (ARQ). The technique allows the SU to perform selective retransmissions of SU data packets, whose transmission previously failed. The temporal redundancy introduced by the PU ARQ protocol and by the selective retransmission process of the SU can be exploited by the SU receiver to perform interference cancellation (IC) over multiple transmission slots, thus creating a “clean” channel for the decoding of the concurrent SU or PU packets. The chain decoding (CD) technique is initiated by a successful decoding operation of an SU or a PU packet and proceeds by an iterative application of IC as previously buffered packets become decodable and their interference can be removed, thus making it possible to recover the concurrent data packets, and so on, until no more packets are decodable. Based on this scheme, an optimal policy is designed that maximizes the SU throughput under a constraint on the average long-term PU performance. The optimality of the CD protocol is proved, which determines which packet the SU should send at any given time, based on four basic rules. Moreover, a decoupling principle is proved, which establishes the optimality of decoupling the secondary access strategy from the CD protocol. Specifically, first, the SU access policy, optimized via dynamic programming, specifies whether the SU should access the channel or remain idle, based on a compact state representation of the protocol, and second, the CD protocol embeds four basic rules that are used to select the packet transmitted by the SU. It is shown numerically that CD outperforms by up to 35% other schemes considered in the literature, which do not employ retransmissions at the SU pair and thus do not exploit the full potentiality of IC.

In this paper, we propose using a wireless-powered friendly jammer to enable secure communication between a source node and destination node, in the presence of an eavesdropper. We consider a two-phase communication protocol with fixed-rate transmission. In the first phase, wireless power transfer is conducted from the source to the jammer. In the second phase, the source transmits the information-bearing signal under the protection of a jamming signal sent by the jammer using the harvested energy in the first phase. We analytically characterize the long-term behavior of the proposed protocol and derive a closed-form expression for the throughput. We further optimize the rate parameters for maximizing the throughput subject to a secrecy outage probability constraint. Our analytical results show that the throughput performance differs significantly between the single-antenna jammer case and the multiantenna jammer case. For instance, as the source transmit power increases, the throughput quickly reaches an upper bound with single-antenna jammer, while the throughput grows unbounded with multiantenna jammer. Our numerical results also validate the derived analytical results.

We introduce a novel communication strategy for DC Micro Grids (MGs), termed power talk, in which the devices communicate by modulating the power levels in the DC bus. The information is transmitted by varying the parameters that the MG units use to control the level of the common bus voltage, while it is received by processing the bus measurements that units perform. This implies that the communication does not require a dedicated modem, but instead it is piggybacked on top of the power electronics. The communication is challenged by the random fluctuations of the voltage level due to the random load variations in the MG. We develop the corresponding communication model and address the random voltage fluctuations by using coding strategies that transform the MG into some well- known communication channels. The performance analysis shows that it is possible to mitigate the random voltage level variations and communicate reliably over the MG bus.

A canonical scenario in Machine-Type Communications (MTC) is the one featuring a large number of devices, each of them with sporadic traffic. Hence, the number of served devices in a single LTE cell is not determined by the available aggregate rate, but rather by the limitations of the LTE access reservation protocol. Specifically, the limited number of contention preambles and the limited amount of uplink grants per random access response are crucial to consider when dimensioning LTE networks for MTC. We propose a low-complexity model of LTE's access reservation protocol that encompasses these two limitations and allows us to evaluate the outage probability at click-speed. The model is based chiefly on closed-form expressions, except for the part with the feedback impact of retransmissions, which is determined by solving a fixed point equation. Our model overcomes the incompleteness of the existing models that are focusing solely on the preamble collisions. A comparison with the simulated LTE access reservation procedure that follows the 3GPP specifications, confirms that our model provides an accurate estimation of the system outage event and the number of supported MTC devices.

Device-to-Device communications are expected to play an important role in current and future cellular generations, by increasing the spatial reuse of spectrum resources and enabling lower latency communication links. This paradigm has two fundamental building blocks: (i) proximity discovery and (ii) direct communication between proximate devices. While (ii) is treated extensively in the recent literature, (i) has received relatively little attention. In this paper we analyze a network-assisted underlay proximity discovery protocol, where a cellular device can take the role of: announcer (which announces its interest in establishing a D2D connection) or monitor (which listens for the transmissions from the announcers). Traditionally, the announcers transmit their messages over dedicated channel resources. In contrast, inspired by recent advances on receivers with multiuser decoding capabilities, we consider the case where the announcers underlay their messages in the downlink transmissions that are directed towards the monitoring devices. We propose a power control scheme applied to the downlink transmission, which copes with the underlay transmission via additional power expenditure, while guaranteeing both reliable downlink transmissions and underlay proximity discovery.

By means of the emerging technique of dynamic Time Division Duplex (TDD), the switching point between uplink and downlink transmissions can be optimized across a multi-cell system in order to reduce the impact of inter-cell interference. It has been recently recognized that optimizing also the order in which uplink and downlink transmissions, or more generally the two directions of a two-way link, are scheduled can lead to significant benefits in terms of interference reduction. In this work, the optimization of bi-directional scheduling is investigated in conjunction with the design of linear precoding and equalization for a general multi-link MIMO two-way system. A simple algorithm is proposed that performs the joint optimization of the ordering of the transmissions in the two directions of the two-way links and of the linear transceivers, with the aim of minimizing the interference leakage power. Numerical results demonstrate the effectiveness of the proposed strategy.

Machine-to-Machine (M2M) communications are one of the major drivers of the cellular network evolution towards 5G systems. One of the key challenges is on how to provide reliability guarantees to each accessing device in a situation in which there is a massive number of almost-simultaneous arrivals from a large set of M2M devices. The existing solutions take a reactive approach in dealing with massive arrivals, such as non-selective barring when a massive arrival event occurs, which implies that the devices cannot get individual reliability guarantees. In this paper we propose a proactive approach, based on a standard operation of the cellular access. The access procedure is divided into two phases, an estimation phase and a serving phase. In the estimation phase the number of arrivals is estimated and this information is used to tune the amount of resources allocated in the serving phase. Our results show that the proactive approach is instrumental in delivering high access reliability to the M2M devices.

Machine-to-Machine (M2M) communications is one of the key enablers of the Internet of Things (IoT). Billions of devices are expected to be deployed in the near future for novel M2M applications demanding ubiquitous access and global connectivity. In order to cope with the massive number of machines, there is a need for new techniques to coordinate the access and allocate the resources. Although the majority of the proposed solutions are focused on the adaptation of the traditional cellular networks to the M2M traffic patterns, novel approaches based on the direct communication among nearby devices may represent an effective way to avoid access congestion and cell overload. In this paper, we propose a new strategy inspired by the classical Trunked Radio Systems (TRS), exploiting the Device-to-Device (D2D) connectivity between cellular users and Machine-Type Devices (MTDs). The aggregation of the locally generated packets is performed by a user device, which aggregates the machine-type data, supplements it with its own data and transmits all of them to the Base Station. We observe a fundamental trade-off between latency and the transmit power needed to deliver the aggregate traffic, in a sense that lower latency requires increase in the transmit power.

A key practical constraint on the design of Hybrid automatic repeat request (HARQ) schemes is the modem chip area that needs to be allocated to store previously received packets. The fact that, in modern wireless standards, this area can amount to a large fraction of the overall chip has recently highlighted the importance of HARQ buffer management, that is, of the use of advanced compression policies for storage of received data. This work tackles the analysis of the throughput of standard HARQ schemes, namely Type-I, Chase Combining and Incremental Redundancy, under the assumption of a finite-capacity HARQ buffer by taking an information-theoretic standpoint based on random coding. Both coded modulation, via Gaussian signaling, and Bit Interleaved Coded Modulation (BICM) are considered. The analysis sheds light on questions of practical relevance for HARQ buffer management such as on the type of information to be extracted from the received packets and on how to store it.

We investigate the maximum coding rate achievable over a two-user broadcast channel for the scenario where a common message is transmitted using variable-length stop-feedback codes. Specifically, upon decoding the common message, each decoder sends a stop signal to the encoder, which transmits continuously until it receives both stop signals. For the point-to-point case, Polyanskiy, Poor, and Verdú (2011) recently demonstrated that variable-length coding combined with stop feedback significantly increases the speed at which the maximum coding rate converges to capacity. This speed-up manifests itself in the absence of a square-root penalty in the asymptotic expansion of the maximum coding rate for large blocklengths, a result a.k.a. zero dispersion. In this paper, we show that this speed-up does not necessarily occur for the broadcast channel with common message. Specifically, there exist scenarios for which variable-length stop-feedback codes yield a positive dispersion.

The millimeter-wave (mmWave) frequency band is seen as a key enabler of multigigabit wireless access in future cellular networks. In order to overcome the propagation challenges, mmWave systems use a large number of antenna elements both at the base station and at the user equipment, which leads to high directivity gains, fully directional communications, and possible noise-limited operations. The fundamental differences between mmWave networks and traditional ones challenge the classical design constraints, objectives, and available degrees of freedom. This paper addresses the implications that highly directional communication has on the design of an efficient medium access control (MAC) layer. The paper discusses key MAC layer issues, such as synchronization, random access, handover, channelization, interference management, scheduling, and association. This paper provides an integrated view on MAC layer issues for cellular networks, identifies new challenges and tradeoffs, and provides novel insights and solution approaches.

A key practical constraint on the design of hybrid automatic repeat request (HARQ) schemes is the size of the on-chip buffer that is available at the receiver to store previously received packets. In fact, in modern wireless standards such as LTE and LTE-A, the HARQ buffer size is one of the main drivers of the modem area and power consumption. This has recently highlighted the importance of HARQ buffer management, that is, of the use of buffer-aware transmission schemes and of advanced compression policies for the storage of received data. This work investigates HARQ buffer management by leveraging information-theoretic achievability arguments based on random coding. Specifically, standard HARQ schemes, namely Type-I, Chase Combining, and Incremental Redundancy, are first studied under the assumption of a finite-capacity HARQ buffer by considering both coded modulation, via Gaussian signaling, and Bit Interleaved Coded Modulation (BICM). The analysis sheds light on the impact of different compression strategies, namely the conventional compression log-likelihood ratios and the direct digitization of baseband signals, on the throughput. The optimization of coding blocklength is also investigated, highlighting the benefits of HARQ buffer-aware transmission scheme.

Two of the emerging trends in wireless cellular systems are device-to-device (D2D) and machine-to-machine (M2M) communications. D2D enables efficient reuse of the licensed spectrum to support localized transmissions, while M2M connections are often characterized by fixed and low transmission rates. D2D connections can be instrumental in localized aggregation of uplink M2M traffic to a more capable cellular device, before being finally delivered to the base station (BS). In this paper we show that a fixed M2M rate is an enabler of efficient machine-type D2D underlay operation taking place simultaneously with another downlink cellular transmission. In the considered scenario, aBS B transmits to a user U, while there are NMmachine-type devices (MTDs) attached to U, all sending simultaneously to U and each using the same rate RM. While assuming that B knows the channel B - U, but not the interfering channels from the MTDs to U, we prove that there is a positive downlink rate that can always be decoded by U, leading to zero-outage of the downlink signal. This is a rather surprising consequence of the features of the multiple access channel and the fixed rate RM. We also consider the case of a simpler, single-user decoder at U with successive interference cancellation. However, with single-user decoder, a positive zero-outage rate exists only when NM= 1 and is zero when NM> 1. This implies that joint decoding is instrumental in enabling fixed-rate underlay operation.

The emergence of Machine-to-Machine (M2M) communication requires new Medium Access Control (MAC) schemes and physical (PHY) layer concepts to support a massive number of access requests. The concept of coded random access, introduced recently, greatly outperforms other random access methods and is inherently capable to take advantage of the capture effect from the PHY layer. Furthermore, at the PHY layer, compressive sensing based multi-user detection (CS-MUD) is a novel technique that exploits sparsity in multi-user detection to achieve a joint activity and data detection. In this paper, we combine coded random access with CS-MUD on the PHY layer and show very promising results for the resulting protocol.

In many practical wireless systems, the Signal-to-Interference-and-Noise Ratio (SINR) that is applicable to a certain transmission, referred to as Channel State Information (CSI), can only be learned after the transmission has taken place and is thereby outdated (delayed). For example, this occurs under intermittent interference. We devise the backward retransmission (BRQ) scheme, which uses the delayed CSIT to send the optimal amount of incremental redundancy (IR). BRQ uses fixed-length packets, fixed-rate R transmission codebook, and operates as Markov block coding, where the correlation between the adjacent packets depends on the amount of IR parity bits. When the delayed CSIT is full and R grows asymptotically, the average throughput of BRQ becomes equal to the value achieved with prior CSIT and a fixed-power transmitter; however, at the expense of increased delay. The second contribution is a method for employing BRQ when a limited number of feedback bits is available to report the delayed CSIT. The main novelty is the idea to assemble multiple feedback opportunities and report multiple SINRs through vector quantization. This challenges the conventional wisdom in ARQ protocols where feedback bits are used to only quantize the CSIT of the immediate previous transmission.

Energy harvesting is becoming a viable option for powering small wireless devices. Energy for data transmission is supplied by the nature, such that when a transmission is about to take place in an arbitrary instant, the amount of available energy is a random quantity. The arrived energy is stored in a battery and transmissions are interrupted if the battery runs out of energy. We address communication in slot-based energy harvesting systems, where the transmitter communicates with ON-OFF signaling: in each slot it can either choose to transmit (ON) or stay silent (OFF). Two different models of harvesting and communication are addressed. In the first model an energy quantum can arrive, with a certain probability, in each slot. The second model is based on a frame of size F: energy arrives periodically over F slots, in batches containing a random number of energy quanta. We devise achievable strategies and compare the slot- with the frame-based model in the case of an errorless transmission channel. Additionally, for the slot-based model and channel with errors, we provide a new proof of the capacity achieved by the save-and-transmit scheme.

In many wireless systems, the channel state information at the transmitter (CSIT) can not be learned until after a transmission has taken place and is thereby outdated. In this paper, we study the benefits of delayed CSIT on a block-fading channel at finite blocklength. First, the achievable rates of a family of codes that allows the number of codewords to expand during transmission, based on delayed CSIT, are characterized. A fixed-length and a variable-length characterization of the rates are provided using the dependency testing bound and the variable-length setting introduced by Polyanskiy et al. Next, a communication protocol based on codes with expandable message space is put forth, and numerically, it is shown that higher rates are achievable compared to coding strategies that do not benefit from delayed CSIT.

The ideal successive interference cancellation paradigm helps to achieve the capacity of some multiuser channels, such as the Gaussian multiple access and broadcast channels. However, its performance is much more modest under realistic constraint on the decodability of the interference signal. In this paper, we rely on basic stochastic geometry models to analytically evaluate the performance of interference cancellation receivers in a local area network scenario, under `realistic' rate-constraints on the decodability of the interference signal. Analytical findings are validated by extensive Monte Carlo experiments. Alongside, complementary system level simulations results are presented to demonstrate the performance in a `practical'-like system. Our findings explicitly quantify how the gains from interference cancellation techniques depend on the spatial density of the active interferers in the network, and their respective data rates. The findings further highlight the importance of properly dimensioning the system in order to fully benefit from such interference cancellation techniques.

The analogies between successive interference cancellation (SIC) in slotted ALOHA framework and iterative belief-propagation erasure-decoding, established recently, enabled the application of the erasure-coding theory and tools to design random access schemes. This approach leads to throughput substantially higher than the one offered by the traditional slotted ALOHA. In the simplest setting, SIC progresses when a successful decoding occurs for a single user transmission. In this paper we consider a more general setting of a channel with capture and explore how such physical model affects the design of the coded random access protocol. Specifically, we assess the impact of capture effect in Rayleigh fading scenario on the design of SIC-enabled slotted ALOHA schemes. We provide analytical treatment of frameless ALOHA, which is a special case of SIC-enabled ALOHA scheme. We demonstrate both through analytical and simulation results that the capture effect can be very beneficial in terms of achieved throughput.

This paper presents a novel framework, which enables a design of rateless codes for binary input additive white Gaussian noise (BIAWGN) channels, using the ripple-based approach known from the works for the binary erasure channel (BEC). We reveal that several aspects of the analytical results from the BEC also hold in BIAWGN channels. The presented framework is applied in a code design example, which shows promising results compared to existing work. In particular it shows a great robustness towards variations in the signal-to-noise power ratio (SNR), contrary to existing codes.

We analyze the problem of zero-error communication through timing channels that can be interpreted as discrete-time queues with bounded waiting times. The channel model includes the following assumptions: 1) time is slotted; 2) at most N particles are sent in each time slot; 3) every particle is delayed in the channel for a number of slots chosen randomly from the set {0, 1, ... , K}; and 4) the particles are identical. It is shown that the zero-error capacity of this channel is log r, where r is the unique positive real root of the polynomial xK+1-xK-N. Capacity-achieving codes are explicitly constructed, and a linear-time decoding algorithm for these codes devised. In the particular case N = 1, K = 1, the capacity is equal to φ, where φ = (1 + √5)/2 is the golden ratio, and constructed codes give another interpretation of the Fibonacci sequence.

Stationary collectors reading wireless, battery powered smart meters, often operate in harsh channel conditions to cut network installation cost to a minimum, challenging the individual link to each meter. The desired performance measure is reliable reception of at least some data from as many as possible meters, rather than maximizing the number of received packets from one meter. We consider a method for improving the reliable reception in a metering system that operates under the constraints of the popular Wireless M-Bus protocol. We develop a framework for reliable reception in which we use the deterministic protocol structure to obtain side information and group the packets from the same meter. We derive the probability of falsely pairing packets from different senders in the simple case of no channel errors, and show through simulation and data from an experimental deployment the probability of false pairing with channel errors. The pairing is an essential step towards recovery of metering data from as many as possible meters under harsh channel conditions. From the experiment we find that more than 15% of all conducted pairings are between two erroneous packets, which sets an upper bound on the number of additional meters that can be reliably recovered.

Two-way relaying in wireless systems has initiated a large research effort during the past few years. In particular, structured codes and lattices are instrumental for achieving high rates when using Physical Layer Network Coding (PLNC). In an attempt to bring the gains of PLNC beyond the classical traffic pattern of two-way relaying, in this paper we consider a scenario with four-way relaying, where each of the two Mobile Stations (MSs) has a two-way connection to the same Base Station (BS), while each connection is through a dedicated Relay Station (RS). The two RSs are in the range of the BS, but they are at antipodal positions within the cell and do not interfere with each other, i. e. achieve a perfect spatial reuse. We introduce communication schemes for serving the four communication flows in two transmission phases. Each phase consists of combined broadcast and multiple access. The main design ingredients are dirty paper coding nested lattice code codes. We compare the performance with a reference scheme that utilizes Decode-and-Forward (DF). The results show that the usage of structured codes in the four-way relaying scenario can significantly increase the achievable rate region.

In the commons model for the spectrum sharing, cognitive users can access the spectrum as long as the target performance in the legitimate primary system is not violated. In this paper, we consider a downlink primary multiple-input-single-output (MISO) system which operates under a controlled interference from the downlink MISO cognitive radio, also called secondary system. We derive exact expressions for outage probability of the primary user under Rayleigh fading, when the primary system is exposed to interference from a secondary base station. Moreover, in high-SNR scenario, a closed-form asymptotic formula for the outage probability is derived, which shows that the primary receiver achieves full spatial diversity under given interference from the secondary user. Next, the optimum transmit power in the secondary system is investigated for maximizing the ergodic capacity when there is an outage constraint at the primary system, and a simple solution is proposed. Finally, the analytical results are confirmed by simulations, in which we analyze the impact of different parameters, such as the number of antennas and the amount of the interference on the system performance; these could be used as system design guidelines.

We propose a contention-based random-access protocol, designed for wireless networks where the number of users is not a priori known. The protocol operates in rounds divided into equal-duration slots, performing at the same time estimation of the number of users and resolution of their transmissions. The users independently access the wireless link on a slot basis with a predefined probability, resulting in a distribution of user transmissions over slots, based on which the estimation and contention resolution are performed. Specifically, the contention resolution is performed using successive interference cancellation which, coupled with the use of the optimized access probabilities, enables throughputs that are substantially higher than the traditional slotted ALOHA-like protocols. The key feature of the proposed protocol is that the round durations are not a priori set and they are terminated when the estimation/contention-resolution performance reach the satisfactory levels.

This paper presents a novel approach to multiple access control called coded splitting tree protocol. The approach builds on the known tree splitting protocols, code structure and successive interference cancellation (SIC). Several instances of the tree splitting protocol are initiated, each instance is terminated prematurely and subsequently iterated. The combined set of leaves from all the tree instances can then be viewed as a graph code, which is decodable using belief propagation. The main design problem is determining the order of splitting, which enables successful decoding as early as possible. Evaluations show that the proposed protocol provides considerable gains over the standard tree splitting protocol applying SIC. The improvement comes at the expense of an increased feedback and receiver complexity.

The source coding problem with action-dependent side information at the decoder has recently been introduced to model data acquisition in resource-constrained systems. In this paper, an efficient Blahut-Arimoto-type algorithm for the numerical computation of the rate-distortion-cost function for this problem is proposed. Moreover, a simplified two-stage code structure based on multiplexing is put forth, whereby the first stage encodes the actions and the second stage is composed of an array of classical Wyner-Ziv codes, one for each action. Leveraging this structure, specific coding/decoding strategies are designed based on LDGM codes and message passing. Through numerical examples, the proposed code design is shown to achieve performance close to the rate-distortion-cost function.

This work considers a cognitive radio (secondary system) that operates under the interference of a WiMAX-like legacy (primary) system. The secondary terminals have knowledge of the codebooks used in the primary system and can apply Opportunistic Interference Cancellation (OIC): if the channel conditions allow, the secondary system can decode and subsequently cancel the interference from the primary system. Contrary to the previous works that utilize the concept of OIC, in this paper we consider practical packet coding, rather than optimal random codebooks in an information-theoretic setting. The key contribution is the mechanism for power control, whose objective is to protect the primary users from a harmful secondary interference. As a dividend, it is seen that in certain regions the proposed power control creates channel conditions that enable the secondary receiver to take advantage of the OIC mechanism. Several power control algorithms have been considered and evaluated in a single and multi-channel scenario. The results clearly indicate the advantage of using power control in conjunction with the OIC concept for achieving spectrally-efficient secondary operation.

Next generation wireless networks aim at a significant improvement of the spectral efficiency in order to meet the dramatic increase in data service demand. In local area scenarios user- deployed base stations are expected to take place, thus making the centralized planning of frequency resources among the cells, a non-viable solution. Cognitive Radio (CR) and Dynamic Spectrum Access (DSA) are the research paradigms which are expected to provide the network nodes the capabilities for an autonomous and efficient selection of the spectrum resources. In this paper we present the first experimental activities with the Autonomous Component Carrier Selection (ACCS) algorithm, a distributed solution for interference management among small neighboring cells. A preliminary evaluation of the algorithm performance is provided considering its live execution on a software defined radio network testbed. The obtained experimental results confirm the performance trends obtained from prior simulation studies. The analysis in dynamic environment conditions also allowed identifying the utilization of static thresholds in the decision making process, as a critical aspect for the optimization of network capacity.

This paper introduces a novel technique for access by a cognitive Secondary User (SU) using best-effort transmission to a spectrum with an incumbent Primary User (PU), which uses Type-I Hybrid ARQ. The technique leverages the primary ARQ protocol to perform Interference Cancellation (IC) at the SU receiver (SUrx). Two IC mechanisms that work in concert are introduced: Forward IC, where SUrx, after decoding the PU message, cancels its interference in the (possible) following PU retransmissions of the same message, to improve the SU throughput; Backward IC, where SUrx performs IC on previous SU transmissions, whose decoding failed due to severe PU interference. Secondary access policies are designed that determine the secondary access probability in each state of the network so as to maximize the average long-term SU throughput by opportunistically leveraging IC, while causing bounded average long-term PU throughput degradation and SU power expenditure. It is proved that the optimal policy prescribes that the SU prioritizes its access in the states where SUrx knows the PU message, thus enabling IC. An algorithm is provided to optimally allocate additional secondary access opportunities in the states where the PU message is unknown. Numerical results are shown to assess the throughput gain provided by the proposed techniques.

In this paper, we consider a simple network consisting of a source, a half-duplex decode-and-forward relay, and a destination. We propose a new relaying protocol employing adaptive link selection, i.e., in any given time slot, based on the channel state information of the source-relay and the relay-destination link a decision is made whether the source or the relay transmits. In order to avoid data loss at the relay, adaptive link selection requires the relay to be equipped with a buffer such that data can be queued until the relay-destination link is selected for transmission. We study both delay-constrained and delay-unconstrained transmission. For the delay-unconstrained case, we characterize the optimal link selection policy, derive the corresponding throughput, and develop an optimal power allocation scheme. For the delay-constrained case, we propose to starve the buffer of the relay by choosing the decision threshold of the link selection policy smaller than the optimal one and derive a corresponding upper bound on the average delay. Furthermore, we propose a modified link selection protocol which avoids buffer overflow by limiting the queue size. Our analytical and numerical results show that buffer-aided relaying with adaptive link selection achieves significant throughput gains compared to conventional relaying protocols with and without buffers where the relay employs a fixed schedule for reception and transmission.

In some communication networks, such as passive RFID systems, the energy used to transfer information between a sender and a recipient can be reused for successive communication tasks. In fact, from known results in physics, any system that exchanges information via the transfer of given physical resources, such as radio waves, particles and qubits, can conceivably reuse, at least part, of the received resources. This paper aims at illustrating some of the new challenges that arise in the design of communication networks in which the signals exchanged by the nodes carry both information and energy. To this end, a baseline two-way communication system is considered in which two nodes communicate in an interactive fashion. In the system, a node can either send an "onquotedblright symbol (or "1quotedblright), which costs one unit of energy, or an "offquotedblright signal (or "0quotedblright), which does not require any energy expenditure. Upon reception of a "1quotedblright signal, the recipient node "harvestsquotedblright, with some probability, the energy contained in the signal and stores it for future communication tasks. Inner and outer bounds on the achievable rates are derived. Numerical results demonstrate the effectiveness of the proposed strategies and illustrate some key design insights.

This paper introduces a communication model inspired by two practical scenarios. The first scenario is related to the concept of protocol coding, where information is encoded in the actions taken by an existing communication protocol. We investigate strategies for protocol coding via combinatorial reordering of the labelled user resources (packets, channels) in an existing, primary system. However, the degrees of freedom of the reordering are constrained by the operation of the primary system. The second scenario is related to communication systems with energy harvesting, where the transmitted signals are constrained by the energy that is available through the harvesting process. We have introduced a communication model that covers both scenarios and elicits their key feature, namely the constraints of the primary system or the harvesting process. We have shown how to compute the capacity of the channels pertaining to the communication model when the resources that can be reordered have binary values. The capacity result is valid under arbitrary error model in which errors in each resource (packet) occur independently. Inspired by the information-theoretic analysis, we have shown how to design practical error-correcting codes suited for the communication model. It turns out that the information-theoretic insights are instrumental for devising superior design of error-control codes.

Various applications of wireless Machine-to-Machine (M2M) communications have rekindled the research interest in random access protocols, suitable to support a large number of connected devices. Slotted ALOHA and its derivatives represent a simple solution for distributed random access in wireless networks. Recently, a framed version of slotted ALOHA gained renewed interest due to the incorporation of successive interference cancellation (SIC) in the scheme, which resulted in substantially higher throughputs. Based on similar principles and inspired by the rateless coding paradigm, a frameless approach for distributed random access in the slotted ALOHA framework is described in this paper. The proposed approach shares an operational analogy with rateless coding, expressed both through the user access strategy and the adaptive length of the contention period, with the objective to end the contention when the instantaneous throughput is maximized. The paper presents the related analysis, providing heuristic criteria for terminating the contention period and showing that very high throughputs can be achieved, even for a low number for contending users. The demonstrated results potentially have more direct practical implications compared to the approaches for coded random access that lead to high throughputs only asymptotically.

There are two basic principles used in wireless network coding to design throughput-efficient schemes: (1) aggregation of communication flows and (2) interference is embraced and subsequently cancelled or mitigated. These principles inspire design of many novel multi-flow transmission (MFT) schemes. Such are the Coordinated Direct/Relay (CDR) schemes, where each basic transmission involves two flows to a direct and a relayed user. Usage of MFT schemes as building blocks of more complex transmission schemes essentially changes the problem of scheduling, since some of the flows to be scheduled are coupled in a signal domain and they need to be assigned a communication resource simultaneously. In this paper we define a novel framework that can be used to analyze MFT schemes and assess the system-level gains. The framework is based on cellular wireless users with two-way traffic and it sets the basis for devising composite time-multiplexed MFT schemes, tailored to particular optimization criteria. Those criteria can be formulated by adapting well-known schedulers in order to incorporate MFT schemes. The results show rate advantages brought by the CDR schemes in pertinent scenarios. Another key contribution is the proposed framework, which can be used to evaluate any future multi-flow transmission scheme.

The recent years have brought a significant body of research on wireless Two-Way Relaying (TWR), where the use of network coding brings an evident advantage in terms of data rates. Yet, TWR scenarios represent only a special case and it is of interest to devise similar techniques in more general multi-flow scenarios. Such techniques can leverage on the two principles used in Wireless Network Coding to design throughput-efficient schemes: (1) aggregation of communication flows and (2) embracing and subsequently cancel/mitigate the interference. Using these principles, we investigate Coordinated Direct/Relay (CDR) schemes, which involve two flows, of a direct and a relayed user. In this paper we characterize a CDR scheme by deriving/bounding the Diversity-Multiplexing Trade-off (DMT) function. Two cases are considered. In the first case a transmitter knows the Channel State Information (CSI) of all the links in the network, while in the second case each node knows only CSI of the links towards its neighbors. The results show that the new CDR scheme outperforms the reference scheme in terms of DMT characterization. Several interesting features are identified with respect to the impact of the CSI knowledge to the improvement in diversity or multiplexing brought by the CDR scheme.

Recently, the ideas of wireless network coding (NC) has significantly enriched the area of wireless cooperation/relaying. They bring substantial gains in spectral efficiency mainly in scenarios with two-way relaying. Inspired by the ideas of wireless NC, recently we have proposed techniques for coordinated direct/relay (CDR) transmissions. Leveraging on the fact that the interference can be subsequently canceled, these techniques embrace the interference among the communication flows to/from direct and relayed users. Hence, by allowing simultaneous transmissions, spectral efficiency is increased. In our prior work, we have proposed CDR with Decode-and-Forward (DF) relay in two scenarios. In this paper, we extend the two existing regenerative CDR schemes and proposed for the other two scenarios such that all schemes benefit from the aforementioned principle of containing the interference. The parameters in the schemes are optimized to have the largest rate region or the highest sum-rate. Numerical results show that DF CDR is better than the reference scheme and almost better than AF CDR.

In this paper, we present an analytical framework for designing LT codes in additive white Gaussian noise (AWGN) channels. We show that some of analytical results from binary erasure channels (BEC) also hold in AWGN channels with slight modifications. This enables us to apply a ripple-based design approach, which until now has only been used in the BEC. LT codes designed by this way show promising performance which is near the Shannon limit even with short codewords.

The conventional assumption made in the design of communication systems is that the energy used to transfer information between a sender and a recipient cannot be reused for future communication tasks. A notable exception to this norm is given by passive RFID systems, in which a reader can transfer both information and energy via the transmitted radio signal. Conceivably, any system that exchanges information via the transfer of given physical resources (radio waves, particles, qubits) can potentially reuse, at least part, of the received resources for communication later on. In this paper, a two-way communication system is considered that operates with a given initial number of physical resources, referred to as energy units. The energy units are not replenished from outside the system, and are assumed, for simplicity, to be constant over time. A node can either send an “on” symbol (or “1”), which costs one unit of energy, or an “off” signal (or “0”), which does not require any energy expenditure. Upon reception of a “1” signal, the recipient node “harvests” the energy contained in the signal and stores it for future communication tasks. Inner and outer bounds on the achievable rates are derived, and shown via numerical results to coincide if the number of energy units is large enough.

In this paper feedback in LT codes is investigated. The considered type of feedback is acknowledgments, where information on which symbols have been decoded is given to the transmitter. Our analysis reveals that acknowledgments has a very low potential in LT codes with standard degree distributions. Motivated by this, we analyze the impact of acknowledgments on multi-layer LT codes. In this case, feedback proves advantageous. By using only a single feedback message, it is possible to achieve a significant performance improvement compared to traditional LT codes.

Joint processing of multiple communication flows in wireless systems has given rise to a number of novel transmission techniques, notably the two-way relaying, but also more general traffic scenarios, such as coordinated direct and relay (CDR) transmissions. In a CDR scheme the relay has a central role in managing the interference and boosting the overall system performance. In this letter we consider the case in which an amplify-and-forward relay has multiple antennas and can use beamforming to support the coordinated transmissions. We focus on one representative traffic type with one uplink user and one downlink user. Two different criteria for relay beamforming are analyzed: maximal weighted sum-rate and maximization of the worst-case weighted SNR. We propose iterative optimal solutions, as well as low-complexity near-optimal solutions.

In this paper we propose a new design of LT codes, which decreases the amount of necessary overhead in comparison to existing designs. The design focuses on a parameter of the LT decoding process called the ripple size. This parameter was also a key element in the design proposed in the original work by Luby. Specifically, Luby argued that an LT code should provide a constant ripple size during decoding. In this work we show that the ripple size should decrease during decoding, in order to reduce the necessary overhead. Initially we motivate this claim by analytical results related to the redundancy within an LT code. We then propose a new design procedure, which can provide any desired achievable decreasing ripple size. The new design procedure is evaluated and compared to the current state of the art through simulations. This reveals a significant increase in performance with respect to both average overhead and error probability at any fixed overhead.

The technology of Radio Frequency IDentification (RFID) enables many applications that rely on passive, battery-less wireless devices. If a RFID reader needs to gather the ID from multiple tags in its range, then it needs to run an anticollision protocol. Due to errors on the wireless link, a single reader session, which contains one full execution of the anticollision protocol, may not be sufficient to retrieve the ID of all tags. This problem can be mitigated by running multiple, redundant reader sessions and use the statistical relationship between these sessions. On the other hand, each session is time consuming and therefore the number of sessions should be kept minimal. We optimize the process of running multiple reader sessions, by allowing only some of the tags already discovered to reply in subsequent reader sessions. The estimation procedure is integrated with an actual tree-based anticollision protocol, and numerical results show that the reliable tag resolution algorithm attain high speed of protocol execution, while not sacrificing the reliability of the estimators used to assess the probability of missing tags.

In this paper, we consider a simple network consisting of a source, a half-duplex decode-and- forward relay, and a destination. In contrast to most of the existing literature, we assume that the relay is equipped with a buffer and show that this can lead to substantial performance gains. We propose a simple protocol which chooses either the source-relay or the relay-destination link for transmission depending on the instantaneous channel state information. For this simple protocol, we derive the throughput for adaptive rate transmission and the outage probability for fixed rate transmission. Our results show that, unlike conventional relaying, buffer-aided relaying yields a diversity gain of two. In addition, throughput gains of up to 100 % compared to conventional relaying are possible.

In this paper we present a novel method for estimating the inclination of passive UHF RFID tags, for use in supply chains to monitor the handling of tagged items. Based on observations of the polarization, a Bayesian estimator of the tag inclination is constructed. The Bayesian estimator has been analyzed and evaluated in a experimental setup. The results shows great potential as the estimator is very robust when determining the inclination.

The ideas of wireless network coding at the physical layer promise high throughput gains in wireless systems with relays and multi--way traffic flows. This gain can be ascribed to two principles: (1) joint transmission of multiple communication flows and (2) usage of a priori information to cancel the interference. In this paper we use these principles to devise new transmission schemes in wireless cellular systems that feature both users served directly by the base stations (direct users) and users served through relays (relayed users). We present four different schemes for coordinated transmission of uplink and downlink traffic in which one direct and one relayed user are served. These schemes are then used as building blocks in multi--user scenarios, where we present several schemes for scheduling pairs of users for coordinated transmissions. The optimal scheme involves exhaustive search of the best user pair in terms of overall rate. We propose several suboptimal scheduling schemes, which perform closely to the optimal scheme. The numerical results show a substantial increase in the system--level rate with respect to the systems with non--coordinated transmissions.

Optimized resource allocation of the Downlink (DL) in wireless systems utilizing Multi-Carrier (MC) transmission requires Channel State Information (CSI) feedback for each user/subchannel to the Base Station (BS), consuming a high amount of Uplink (UL) radio resources. To alleviate this problem, several works have considered contention-based CSI feedback in the UL control channel. We propose such a feedback scheme for a generic MC system, based on the idea of variable collision protection, where the probability that a feedback information experiences a collision depends on its importance. By partitioning the CSI into orthogonal layers of priority, and allocating different numbers of feedback slots to each layer, this scheme ensures that the feedback success probability is higher for the CSI with better quality, which is more likely to be used by the scheduler. Furthermore, we present a theoretical performance analysis of the proposed scheme, assuming Maximum CSI (Max CSI) and normalized Proportional Fair Scheduler (PFS), where a tight approximation of the achievable throughput is obtained assuming discrete Adaptive Modulation (AM) and CSI feedback which are relevant for the practical systems. Analytical and simulation results show that our proposed scheme provides an excellent trade-off between system performance and feedback overhead.

This paper investigates the problem of spectrally efficient operation of a multiuser uplink cognitive radio system in the presence of a single primary link. The secondary system applies opportunistic interference cancelation (OIC) and decodes the primary signal when such an opportunity is created. We derive the achievable rate in the secondary system when OIC is used. This scheme has a practical significance, since it enables rate adaptation without requiring any action from the primary system. The exact expressions for outage probability of the primary user are derived, when the primary system is exposed to interference from secondary users. Moreover, approximated formulas and tight lower and upper bounds for the ergodic sum-rate capacity of the secondary network are found. Next, the power allocation is investigated in the secondary system for maximizing the sum-rate under an outage constraint at the primary system. We formulate the power optimization problem in various scenarios depending on the availability of channel state information and the type of power constraints, and propose a set of simple solutions. Finally, the analytical results are confirmed by simulations, indicating both the accuracy of the analysis, and the fact that the spectral-efficient, low-complexity, flexible, and high-performing cognitive radio can be designed based on the proposed schemes.

This paper considers secondary usage of spectrum resources that are allocated to frequency division duplexing (FDD)-based cellular systems. We propose an interference cancellation technique using the concept of distributed antenna array. We show that the interference from the primary cellular system can be cancelled at a secondary node equipped with single antenna by introducing a helper that decodes the interference and transfers it to the secondary node. Interference is forwarded during the time periods in which training signals are transmitted within the primary cellular system. Since the duration of those intervals is short relative to periods for data transmission in the primary system, we introduce a scheme by which the helper compresses the interference before forwarding it. The performance advantage of our scheme is verified by computer simulation, where both analytical and measured channel models are used. The measurement data used in evaluations have been obtained through outdoor experiments while assuming a short-range secondary system. The results indicate that the proposed method for distributed interference cancellation leads to performance benefit, in particular under heterogeneous path loss conditions between the helper and the primary/secondary nodes.

In this paper, we investigate the problem of spectrally efficient operation of a cognitive radio, also called secondary spectrum user, under an interference from the primary system. A secondary receiver observes a multiple access channel of two users, the secondary and the primary transmitter, respectively. The secondary receiver applies Opportunistic Interference Cancelation (OIC) and Suboptimal Opportunistic Interference Cancelation (S-OIC) thus decoding the primary signal when such an opportunity is created by the rate selected at the primary transmitter and the power received from the primary transmitter. First, we investigate how the secondary transmitter, when using OIC and S-OIC for fixed transmitting power, should select its rate in order to meet its target outage probability under different assumptions about the channel-state-information available at the secondary transmitter. We study three different cases and for each of them identify the region of achievable primary and secondary rates. Second, we determine how the secondary transmitter should select its transmitting power not to violate the target outage probability at the primary terminals. Our numerical results show that the best secondary performance is always obtained when the secondary transmitter knows the instantaneous channel-state-information toward the intended receiver. We also evaluate the degradation in terms of achievable rate at the secondary receiver when it uses suboptimal decoding (S-OIC rather than OIC) and the interplay between the allowed power at the secondary transmitter (which depends on the target outage probability at the primary receiver) and the decodability at the secondary receiver.

The operation of communication protocols is conventionally independent of the information being transmitted. This paper puts forth the notion of protocol coding to refer to transmission strategies in which data can be encoded by modulating the protocol actions according to the information message. We focus on communication in the presence of half-duplex constraints, where the task of the protocol is to schedule the transmission/reception times of different nodes. Such a schedule is conventionally decided a priori in the form of time-sharing. While previous work has focused on protocol coding for standard relay channels, this paper tackles two-way communications aided by a relay. Since the techniques developed for standard relay channels cannot be applied to the scenario at hand, a novel simple strategy is proposed that is tailored to two-way communications. The proposed scheme is shown to significantly outperform conventional time-sharing for a deterministic two-way relay channel.

This paper concerns multi path video streaming using adaptive multiple description coding. The adaptation leverages on the fact that multiple descriptions are correlated. Thus if an intermediate node gets feedback telling that another path is likely to deliver a description, this node can compress its description and forward it. Such a compression can also be done already at the source node; however, the feedback arrives more timely and reliably to intermediate nodes that are closer to the final receiver. In this paper we investigate the performance of such adaptation at the source node and an intermediate node, respectively. A trade-off exists between reducing the delay of the feedback by adapting in the vicinity of the receiver and increasing the gain from compression by adapting close to the source. The analysis shows that adaptation in the network provides a better trade-off than adaptation at the source. Schemes which provide simple solutions to adaptation both at the source and in the network are proposed, analyzed, simulated and compared to non-adaptive reference schemes in scenarios that involve last hop that is wireless. The results reveal that the proposed compression schemes offer significant benefits in streaming scenarios.

In this paper, a primary (licensed) user leases part of its resources to independent secondary (unlicensed) terminals in exchange for a tariff in dollars per bit, under the constraint that secondary transmissions do not cause excessive interference at the primary receiver (PRX). The PRX selects a power allocation (PA) for the secondary user that maximizes the secondary rate (and thus its revenue) and enforces it by the following mechanism: Upon violation of a predefined interference level, PRX keeps silencing randomly selected secondary users, until the aggregate secondary interference is below the required threshold. This mechanism ensures that secondary users may not be willing to deviate from the allocated PA. Specifically, the scenario gives rise to a Stackelberg game, in which the primary determines the PA and a Nash equilibrium (NE) constraint is imposed on the PA to ensure that secondary users do not have incentives to deviate, given their knowledge of the silencing mechanism run at the PRX. In principle, the primary should find the set of all PAs that are NE and among them choose the one that maximizes the aggregate secondary utility, and thereby the revenue of the primary. For the most general setting of channel gains, we investigate the conditions for NE for a subset of PAs. When the scenario is symmetric in the sense that all secondary users have the same channel gains in the direct/interfering links, we prove that only two optimal power allocations exist. Finally, for the case of general channel gains with strong interference, we show that there is a unique NE of the game.

We propose a precoding strategy which controls amplitude and phase of receiving signals to improve throughput for two-stage bidirectional relaying. We consider the case when the nodes know channel state information (CSI) and can adopt adaptive modulation techniques. We introduce a novel scheme termed adaptive modulation and network coding (AMNC), which jointly optimizes modulations and network coding based on the CSI. For dynamic bit loading and power allocation, we propose a practical time-sharing method called the segmented precoding, in which a packet is split into several sub-packets, and a set of modulation and network coding is optimized in conjunction with amplitude and phase controls for each sub-packet. It is demonstrated that our proposed scheme can offer a significant improvement of achievable throughput for two-way relaying.

In this paper we consider secondary communication channels, defined via utilization of the protocol overhead information in legacy communication systems, here referred to as primary systems. The secondary communication is carried out by rearranging the order of the packets which are put forward for transmission by the primary system. To motivate the models, we provide several scenarios with wireless primary communication systems. The secondary communication can be seen as a special case of cognitive radio, since additional information is sent by using the same spectrum as the primary system and without affecting the performance of the primary system. The primary system is frame-based and it determines a set of packets that need to be delivered within a frame. The arrangements of the packets within the frame is irrelevant for the primary system and that is creating the opportunity for secondary communication. We define two generic communication models, the permutation model and the combinatorial model, respectively. For both models, we define suitable discrete communication channels, where the error models are obtained by suitably transforming the packet erasures in the primary system. The capacity of these channels is analyzed. It turns out that the capacity results for the combinatorial communication model are related to the fundamental result by Shannon regarding channels with causal side information at the transmitter. The ideas presented in this paper set several interesting directions for future work.

In this paper, we present a forwarding strategy for two-stage bidirectional relaying in which trellis-coded modulation (TCM) is employed. We reveal that adaptive network coding cannot resolve distance shortening occurred at specific channel conditions when a certain TCM is used. To overcome this issue, we introduce an improved amplify-and-forward (AF) scheme termed pseudo AF (PAF). The proposed strategy adaptively switches network coding and PAF according to the channel information. Computer simulations demonstrate that the proposed approach can improve throughput performance.

Packet retransmission strategies are presented for MIMO eigenmode transmission where adaptive modulation and coding (AMC) is implemented. The retransmission design is based on weighted linear MMSE. It includes the transmit and receiver filter, the power and eigenmode allocation and AMC level when new packets are transmitted. The weight matrix of WMMSE is used to appropriately weight streams with different AMC levels in order to maximize the system throughput. Simulations show that the choice of the weight factors has a major impact on the performance.

We investigate modulation schemes optimized for two-way wireless relaying systems, for which network coding is employed at the physical layer. We consider network coding based on denoise-and-forward (DNF) protocol, which consists of two stages: multiple access (MA) stage, where two terminals transmit simultaneously towards a relay, and broadcast (BC) stage, where the relay transmits towards the both terminals. We introduce a design principle of modulation and network coding, considering the superposed constellations during the MA stage. For the case of QPSK modulations at the MA stage, we show that QPSK constellations with an exclusive-or (XOR) network coding do not always offer the best transmission for the BC stage, and that there are several channel conditions in which unconventional 5-ary constellations lead to a better throughput performance. Through the use of sphere packing, we optimize the constellation for such an irregular network coding. We further discuss the design issue of the modulation in the case when the relay exploits diversity receptions such as multiple-antenna diversity and path diversity in frequency-selective fading. In addition, we apply our design strategy to a relaying system using higher-level modulations of 16QAM in the MA stage. Performance evaluations confirm that the proposed scheme can significantly improve end-to-end throughput for two-way relaying systems.

In cellular systems, confidentiality of uplink transmission with respect to eavesdropping terminals can be ensured by creating intentional interference via scheduling of concurrent downlink transmissions. In this paper, this basic idea is explored from an information-theoretic standpoint by focusing on a two-cell scenario where the involved base stations (BSs) are connected via a finite-capacity backbone link. A number of transmission strategies are considered that aim at improving uplink confidentiality under constraints on the downlink rate that acts as an interfering signal. The strategies differ mainly in the way the backbone link is exploited by the cooperating downlink to the uplink-operated BSs. Achievable rates are derived for both the Gaussian (unfaded) and the fading cases, under different assumptions on the channel state information available at different nodes. Numerical results are also provided to corroborate the analysis. Extensions to scenarios with more than two cells are briefly discussed as well. Overall, the analysis reveals that a combination of scheduling and base-station cooperation is a promising means to improve transmission confidentiality in cellular systems.

This letter addresses a scheduling problem for wireless network coding (WNC). In our previous work, we have theoretically shown that the optimum number of nodes to be included into a network coded packet as well as its transmission rate depends on time varying link condition between a transmitting node and receiving nodes [1]. Based on this observation, this letter designs practical scheme which opportunistically selects scheduled nodes, packets to be coded and an employed modulation level according to time varying channel conditions and packet length. The numerical results show that the proposed opportunistic scheduling can improve the overall throughput as compared with non-opportunistic approach.

We investigate on the design of modulation schemes suited for two-way wireless relaying systems that apply network coding at the physical layer. We consider network coding based on denoise-and-forward (DNF), which consists of two stages: multiple access (MA) stage and broadcast (BC) stage. For the case of QPSK constellation in the MA stage, we introduce the modulation-related problems in DNF. We propose two approaches to solve those issues. One uses only QPSK constellations at the BC stage. The other allows the use of unconventional 5-ary modulations, optimized according to the channel condition. The performance evaluation shows that a significant improvement in end-to-end throughput can be achieved, in particular for Nakagami-Rice fading channels.

This paper proposes a novel interference cancellation technique using the concept of a distributed array, for the secondary use of the frequency resources in frequency division duplexing (FDD)-based cellular systems. In order to achieve efficient distributed interference cancellation, the secondary system exploit intervals for periodic training signals transmitted by FDD-time division multiple access (TDMA)-based cellular systems. We show that an efficient interference cancellation can be achieved by introducing helpers that decode the interference and transfer it to the secondary nodes. We take advantage of the high-speed links within the secondary system and devise a data compressing scheme that reduces time for transferring the decoded interference. We clarify the effectiveness of the proposed scheme by using computer simulation. As a parameter, the transmission quality between the helper and secondary nodes is varied. The results indicate that the proposed distributed interference cancellation is effective compared to a conventional adaptive array using a zero forcing algorithm, when the signal to noise ratio between the helper and the target secondary node is greater than 20 dB.

A novel approach for ensuring confidential communications over infrastructure-based wireless networks is proposed and analyzed from an information-theoretic standpoint. The considered techniques leverage the finite-capacity backbone connecting the base stations and the possibility to schedule uplink/downlink transmissions in order to create intentional interference. Two different methods are studied, one based on source coding and one on channel coding arguments, and corresponding rates achievable with perfect secrecy are derived.

Media content distribution constitutes a growing share of the services on the Internet. Two distinct distribution approaches used today are layered coding (LC) and multiple description coding (MDC). Current wireless connection technologies, e.g. Wimax, have properties which make them unsuitable for media distribution using traditional approaches. In particular, the asymmetric relationship between the uplink and the downlink bandwidth makes the cooperative distribution difficult. A promising concept, termed MDC with Conditional Compression (MDC-CC), has been proposed [11], which essentially acts as an adaptive hybrid between LC and MDC. In order to facilitate the use of MDC-CC, a new overlay network approach is proposed, using tree of meshes. A control system for managing description distribution and compression in a small mesh is implemented in the discrete event simulator NS-2. The two traditional approaches, MDC and LC, are used as references for the performance evaluation of the proposed scheme. The system is simulated in a heterogeneous network environment, where packet errors are introduced. Moreover, a test is performed at different network loads. Performance gain is shown over both LC and MDC.

The dynamic scheduling of ultra-reliable and low-latency traffic (URLLC) in the uplink can significantly enhance the efficiency of coexisting services, such as enhanced mobile broadband (eMBB) devices, by only allocating resources when necessary. The main challenge is posed by the uncertainty in the process of URLLC packet generation, which mandates the use of predictors for URLLC traffic in the coming frames. In practice, such prediction may overestimate or underestimate the amount of URLLC data to be generated, yielding either an excessive or an insufficient amount of resources to be pre-emptively allocated for URLLC packets. In this paper, we introduce a novel scheduler for URLLC packets that provides formal guarantees on reliability and latencyirrespective of the quality of the URLLC traffic predictor. The proposed method leverages recent advances inonline conformal prediction (CP), and follows the principle of dynamically adjusting the amount of allocated resources so as to meet reliability and latency requirements set by the designer.

We propose generic relay and subcarrier allocation schemes for multicarrier (MC) system with amplify-and-forward (AF) relays. The outage probability bounds are derived analytically for each scheme. Simulation results show that these bounds are very tight and better than the bounds obtained straightforwardly from the analysis in the Single-Carrier (SC) case. This is because in our analysis we reckon with the increased degree of freedom brought by the parallel channels. One of the proposed schemes, the Average Best Relay Selection scheme, is best suited for practical implementation since it approaches the best performance while minimizing the required amount of signaling.

Optimal Proportional Fair Scheduling (PFS) in a multi-carrier system is a prohibitively complex combinatorial problem. In this paper we consider practical time frames with multiple time slots, where this optimal allocation becomes even more complex. Therefore, we derive bounds for the optimal proportional fair allocation, by means of convex optimization, and propose approximation algorithms where several users can be time-multiplexed on a same subchannel. With a much lower complexity than the optimal allocation, these algorithms achieve an excellent tradeoff between throughput and proportional fairness, even with the increased signaling overhead.

We introduce a new two-step relaying scheme based on superposition coding, SC-relaying. In Step 1, the source ASbroadcasts a message, created by superposition coding, to the relay ARand the destination AD. After decoding the information from AS, ARrelays a part of this message in Step 2, by using a codebook that is adapted to the link AR- AD. The information-theoretic design shows that the proposed scheme can improve the spectral efficiency, attaining almost optimal value. We demonstrate that the SC-relaying can be ported even to the case of uncoded systems, where it is shown how to select the transmission scheme in order to maximize the throughput between the source and the destination.

This paper proposes a physical-layer security scheme for wireless networks, aiming to achieve communication secrecy by making the eavesdropper incapable of decoding the secret wireless message. The considered scenario features one user within the range of two access points, API and AP2. The APs are assumed to be connected through an alternative secure (e.g. wired) connection. The goal is to secure the wireless link between the user and API. While the user transmits to API, AP2 simultaneously transmits an interfering signal, which is a priori provided to API, such that API is likely the only node capable of decoding the user's entire transmission. Evaluation is done through simulation by measuring the upper bound of the information-theoretic secrecy and error performance. In the latter case it is shown that the eavesdropper experiences significantly higher error rates than the intended receiver, thus providing evidence of practical security.

We address the problem of radio resource allocation in the Downlink (DL) of relay-aided cellular system, based on OFDMA transmission technology. There has been little work on specific resource allocation algorithms for this system in the literature, although these are the key elements for realizing the potential capacity and coverage increase offered by the relay. Therefore, we propose two resource allocation algorithms which improve the overall throughput and coverage compared to a system without relay. The advantage of our algorithms is that they perform well while minimizing the complexity and the required amount of Channel State Information (CSI), making them suitable for practical use.

It has recently been recognized that the wireless networks represent a fertile ground for devising communication modes based on network coding. A particularly suitable application of the network coding arises for the two-way relay channels, where two nodes communicate with each other assisted by using a third, relay node. Such a scenario enables application of physical network coding, where the network coding is either done (a) jointly with the channel coding or (b) through physical combining of the communication flows over the multiple access channel. In this paper we first group the existing schemes for physical network coding into two generic schemes, termed 3-step and 2-step scheme, respectively. We investigate the conditions for maximization of the two-way rate for each individual scheme: (1) the decode-and-forward (DF) 3-step schemes (2) three different schemes with two steps: amplify-and-forward (AF), JDF and denoise-and-forward (DNF). While the DNF scheme has a potential to offer the best two-way rate, the most interesting result of the paper is that, for some SNR configurations of the source - relay links, JDF yields identical maximal two-way rate as the upper bound on the rate for DNF.

This paper addresses a scheduling problem for wireless network coding which has been recently proposed as a novel method to enhance the throughput in wireless networks. The wireless network coding involves broadcast transmission of a network-coded packet which contains unicast data to several receiving nodes. These receiving nodes have time-varying fading links to the transmitting node, which can generate different instantaneous conditions for different links. In this paper, we introduce an opportunistic scheduling for such a wireless network coding, which selects a set of nodes whose packets are network - coded as well as the data rate for the broadcast transmission according to the instantaneous link conditions. We analyze the average capacity of such a scheduling, and discuss the impact of different parameters on the average capacity. We show that the opportunistic scheduling can maximize the average capacity by choosing the appropriate set of network-coded nodes according to the instantaneous link conditions. We also discuss the practical factors which can additionally affect the best scheduling strategy for wireless network coding.

Packet retransmission strategies are presented for 2x2 MIMO spatial multiplexing systems where adaptive modulation and coding (AMC) is implemented based on outdated CSI at the transmitter. Antennas for retransmission are selected to maximize the throughput of the system. A sign change or conjugate operation is allowed for retransmitted packets. The proposed schemes perform antenna selection based on the CSI at the receiver and feed back relevant information to the transmitter. When 2 packets are decoded with errors, retransmission follows a space time block code structure, allowing a seamless switching between spatial multiplexing and space time block coding.

We address the problem of radio resource allocation in the downlink (DL) of a cellular system with relay stations (RS), based on orthogonal frequency division multiple access (OFDMA) transmission technology. There is a need for the design of resource allocation algorithms for this type of system, where practical yet efficient algorithms are required to exploit the potential capacity and coverage increase offered by the relays. We propose several resource allocation algorithms with different options such as time or frequency division. The evaluations give some directions about the suitable allocation schemes. One algorithm offers an overall improvement of throughput and coverage, compared to a system without relays. At the same time, the advantage of our algorithms is that their complexity and amount of information overhead are much reduced compared to an optimal algorithm.

Wireless transmission based on cooperation promises to bring performance improvements in multi-node networks. Several cooperative schemes have appeared in the literature, and in most cases have been assessed under simplified assumptions for the channel model. Experimental data for cooperative transmission are difficult to obtain because they should provide simultaneous characterization of at least three links. In this paper we present measurements that involve two access points (APs) and two user terminals, each of which is equipped with 4 antennas, in an indoor office environment. The data indeed allow for the simultaneous characterization of the multiple input-multiple output (MIMO) links from the APs to the user terminals and between the user terminals. We use these measurements to practically assess several schemes for cooperative transmission.

In this paper, the possibility for a short-range cognitive radio (secondary communication system) to be located within the service area of the primary system is discussed. Although the secondary system interferes with the primary system, there can be certain locations in the service area of the primary system where the cognitive radio can reuse the frequency of the primary system without disturbing it or being disturbed by the primary system. We say that in those locations there is a spatial opportunity for communication in the secondary system. The primary and secondary systems have different features and usage scenarios, which result in different deployment conditions. This gives rise to differences between the path loss for a link in the primary system and the path loss for the interference from the primary to secondary systems. This paper investigates the impact of the heterogeneous path loss conditions on the spatial opportunity for the secondary system. Our approach can be applied as a general way to investigate the coexistence among multiple systems. We clarify that the change of the path loss coefficient due to the different antenna heights of the primary and secondary systems can largely affect the spatial opportunity for frequency reuse by the cognitive radios.

The wireless relay systems represent an important segment in the wireless communication systems. In a simple wireless relay scenario, a source node ASneeds to communicate to a destination AD, with the help of a relay AR. In this paper we introduce a new two-step relaying scheme based on superposition coding, SC-relaying. In step 1, ASbroadcasts a message, created by superposition coding, to ARand AD. After decoding the information from AS, ARrelays a part of this message in step 2, by adapting the transmission rate with respect to the link quality towards AD. The unique point of the proposed scheme is that the signal format in the two steps is chosen in a way that enables a simple combining at AD. The numerical results show that the proposed scheme can bring a notable gain in spectral efficiency over the conventional multi-hop transmission with adaptive modulation, even in the case when the instantaneous SNR about all the links is not fully known at the source. In addition, the proposed scheme performs very closely to the scheme that is optimal in an information-theoretic sense.

Frequency Hopping (FH) technology has been widely used for short-range networks operating in unlicensed band. As the short-range FH networks gain momentum in ubiquitous usage, the interference that collocated FH networks cause to each other, termed self-interference, becomes one of the major sources that degrade the communication performance. This paper proposes the Adaptive Frequency Rolling (AFR), a particular adaptive instance of FH that enables FH networks to cooperate and effectively avoid the self-interference. The AFR uses as input solely the observed packet error rate (PER) and it does not require any exchange of information among the collocated networks. The effect of the AFR over a longer time interval is that the networks use the complete set of disposable channels in an implicit time-division and cooperative manner. The parameter choice is such that a network which uses AFR never occupies the channels in the unlicensed spectrum more than what is permitted by the current regulation. AFR is designed to be robust towards the noise-induced channel errors. We also design AFR with probing (AFR-P), a modified version of AFR, which can also overcome frequency-static interference from collocated non-FH network by introducing channel removal strategy with probing. Our simulation results show that AFR and AFR-P have superior goodput performance to pseudorandom frequency hopping (PFH), and also standard adaptive frequency hopping (AFH), where the latter is designed to exclusively combat frequency-static interference. All these features promote the great potential of AFR as a coexistence mechanism for unlicensed operation

This work addresses radio resource allocation for downlink (DL) transmissions in a cellular system based on Orthogonal Frequency Division Multiple (OFDM). While scheduling based on multi-user diversity increases throughput, it decreases fairness across the users. Fairness is usually provided by Proportional Fair Scheduling (PFS). However, it was shown that optimal PFS in a Multi-Carrier (MC) system is prohibitively complex. In this paper we consider the practical assumption where there are multiple time slots (OFDM symbols) per scheduling frame, for which the optimal allocation becomes even more complex. Therefore, we derive the upper and lower proportional fairness limits which bound the optimal solution, by means of convex optimization. The results show that those derived proportional fairness limits are in fact very close, which gives an excellent bounding of the optimal solution. We also propose two heuristic algorithms which exploit the fact that several users can be multiplexed on one subcarrier in a time-division manner. We investigate their performance in terms of throughput, proportional fairness metric and latency. The additional overhead due to user multiplexing is taken into account. The results show that the proposed algorithms achieve an excellent tradeoff between throughput and proportional fairness.

This paper considers relaying techniques that increase the achievable throughput in multi-hop wireless networks by taking advantage of the bi-directional traffic flow. Such a relaying technique is termed relaying with Bi-directional Amplification of Throughput (BAT-relaying). The BAT-relaying is utilizing the concept of anti-packets, defined for bi-directional traffic flows. The relay node combines the anti-packets that are destined for different nodes and broadcasts the combined packet. Two BAT-relaying techniques have been proposed previously, Decode-and-Forward (DF) BAT-relaying and Amplify-and-Forward (AF) BAT-relaying. While in DF the relay node combines the packets by an XOR operation, AF BAT-relaying utilizes the inherent packet combining provided by the multiple access channel. In an errorless channel, AF has always higher achievable throughput than DF, but in noisy channels the noise amplification can severely degrade the performance of AF. In this paper we introduce a new scheme for BAT-relaying, termed Denoise-And-Forward (DNF) BAT-Relaying. The DNF BAT-relaying also makes use of the combining provided by the multiple access channel, but it removes the noise from the combined anti-packets before broadcasting to the destinations. While in the noiseless channel DNF and AF offer the same throughput performance which is superior to DF BAT-relaying, in large regions of the lower SNR values DNF BAT-relaying has the best throughput performance of all three schemes. Due to the unconventional nature of the BAT-relaying schemes, there are many open issues for further investigation. The design of a practical DNF scheme concerns several protocol layers, including modulation and coding.

We address the problem of channel state information (CSI) feedback for scheduling the downlink (DL) data transmissions in a cellular system based on orthogonal frequency division multiple access (OFDMA). In order to benefit from multi-user diversity, the scheduler needs to know each user's CSI for each subchannel and time frame. However, this feedback information can become very high when the number of subchannels and/or users increase. Therefore, we have designed an adaptive feedback encoding method which can optimize the amount of feedback according to the variable amount of CSI requested by the base station (BS). The amount of useful CSI depends not only on the number of users, but also on the scheduling made at the BS. The simulation results show that with our adaptive encoding scheme, the performance of maximum CSI and proportional fair scheduling (PFS) with full CSI can be approached with a considerably reduced amount of feedback

In wireless networks, the shared broadcast medium enables interactions among nodes and thus introduction of novel communication modes. This paper introduces and analyzes relaying techniques that increase the achievable throughput in multi-hop wireless networks by taking advantage of the bi-directional traffic flow. Such a relaying technique is termed relaying with bi-directional amplification of throughput (BAT-relaying). The BAT-relaying is utilizing the concept of anti-packets, defined for bi-directional traffic flows. The relay node combines the packets (anti-packets) that are destined for different nodes and broadcasts the combined packet. The first variant, termed decode-and-forward (DF) BAT-relaying, has been proposed before in the literature. It combines the packets by using the XOR operation, which makes such proposal closely related to the network coding approaches. We proposed another type of BAT-relaying based on amplify-and-forward (AF), which utilizes the inherent packet combining that emerges from simultaneous utilization of a multiple access channel. We analyze the achievable throughput of the DF and AF BAT-relaying, regarding the impact of the traffic asymmetry and the channel errors. The unconventionality of this relaying, in particular AF BAT-relaying, opens many possibilities for further research

As the wireless personal area network (WPAN) gets utilized by more individuals, the interference that collocated WPANs cause to each other, termed self-interference, will be one of the major sources that degrade WPAN's communication performance. The conventional adaptive frequency hopping (AFH) strategies avoid frequency-static interference by reducing the hopset, but this deteriorates the performance if there is also self-interference. In this paper, we propose dynamic AFH (DAFH) mechanisms that are concurrently employed by collocated WPANs in order to avoid the self-interference. With DAFH, WPAN adaptively self-allocates a subset of frequency channels to be hopped, such as to minimize the experienced interference. The packet error rate is the only input to the proposed mechanisms, which enables DAFH to also avoid interference from frequency-static interferer. The optimization of the throughput should not be the sole target of the DAFH because WPAN operates in unlicensed spectrum and arbitrary adaptation of the FH pattern may be harmful to proximate non-WPAN devices. Therefore, we define and adopt an etiquette rule to characterize the behavior of the collocated WPANs with DAFH as a single collective entity that produces interference. The operation of DAFH is robust and adaptive to the dynamic changes in the environment and to the noise errors in the channel. Simulation results show that DAFH significantly increases the throughput of the WPANs in presence of both self-interference and frequency-static interference, while the WPANs employ best effort to minimize changes in the overall interference pattern

In this paper we introduce a novel header compression technique for parallel channels as they are found in meshed networks. The approach introduced advocates the cooperative behavior of parallel channels to maintain the compression state on sender and receiver side. The general concept as well as one particular implementation are shown. Our approach is characterized by no need of a feedback channel and a low complexity of a compression strategy. By means of analytical study combined with our testbed results we show the performance of the introduced approach. The designing goals were to have a low complex, highly efficient and also robust header compression dealing with the characteristics of the wireless channel. As a first result we can show that for independent error pattern a number of three cooperative channels achieves both robustness and efficiency for a wide range of errors.

In this paper we advocate the use of multiple base stations for providing wireless link to an OFDM-based terminal and thus obtain macro diversity, in particular site diversity. The wireless link to a terminal is defined through a subset of sub-carriers that is optimized in a greedy manner over the union of base stations. The wireless terminal is unaware of how many and which base stations are providing its allocated set of sub-carrier, which simplifies the terminal design. We give an analytical comparison of our approach to the conventional solutions. The results show that the proposed schemes with multiple base stations can outperform the single base station case, while keeping the complexity of the wireless terminal unchanged. We further evaluate our schemes by considering minimize signalling over the air interface.

4G wireless communication networks are characterized by the need to support heterogenous terminals differing in size, display, battery, computational power, etc. For efficient usage of the wireless spectrum all devices should be served by the same spectrum instead of allocating spectra dedicated to the different terminal classes. This feature is naturally supported when the source coding of the traffic is done as a multiple description coding. The restoration quality of the information source is proportional to the quantity of the descriptors used in the restoration process. Hence, terminals with less capabilities may simply discard or not receive some of the descriptors, while high class terminals try to receive all information. In the case of partial reception of descriptors the performance can be improved by selection strategies for the descriptors. We advocate the usage of new descriptor selection schemes for multiple description coded services. The proposed schemes differ with respect to the availability of a feedback channel. All solutions are terminal oriented and are beneficial in the design of terminals that have robust and high quality services. Furthermore, our approaches inherently achieve fairness among different terminals. As an example of our results for video communication, we can show gains of 4 dB when the proposed approach is utilized in the system.

A Wireless Personal Area Network (WPAN) provides wireless links among proximate devices,usually carried by an individual.As WPAN gains momentum in ubiquitous usage, the interference that collocated WPANs cause to each other, termed self-interference, becomes one of the major sources that degrade the communication performance of WPAN. This paper in roduces the Frequency Rolling (FR), a particular instance of frequency hopping (FH) that enables he collocated WPANs to cooperate and avoid the self-interference. The FR uses as input solely the observed packet error rate (PER) and it does not require any exchange of information among he collocated WPANs. The effect of the FR over a longer time in erval is hat the WPANs use he complete set of disposable channels in an implicit time-division and cooperative manner. The parameters of he FR are chosen such that a WPAN which uses FR never occupies the channels in the unlicensed spectrum more than what is permitted by the current regulation. We compare the goodput offered by FR to the goodput of the collocated piconets when conventional pseudorandom FH is used. Our simulation results show that FR has superior goodput performance. In addition, he design of FR is made robust owards the errors due to the channel noise. Some guidance for practical fault-toleran design and future extensions of FR are given. All hese features promote the great potential of FR as a coexistence mechanism for unlicensed operation.

This paper proposes a QoS-based strategy for call admission in mobile cellular networks, based on the past system's behavior. The algorithm is an extension of the conventional guard scheme (CGC) and demonstrates improvements compared to CGC. It reduces the new call blocking probability, increases the total carried traffic and keeps the forced call termination probability almost unchanged. The proposed policy falls into the class of fractional guard policies. Each cell builds cumulative statistics from its "experience" and accepts a new call only when predetermined handoff failure probability is not exceeded. The generic form of the algorithm is independent of cellular network topology.