Autonomous robotic systems are widely deployed in smart factories and operate in dynamic, uncertain, and human-involved environments that require low-latency and robust fault detection and recovery (FDR). To address this, we propose a novel Goal-oriented Semantic Communication (GSC) framework that minimises the FDR time while maximising the robotic task success rate. Our GSC framework defines and extracts the 3D scene graph (3D-SG) as the semantic information for FDR via a semantic extractor, and detects faults by monitoring spatial relationship changes in the 3D-SG. For fault recovery, we fine-tune a small language model (SLM) and enhance its reasoning and generalization capabilities via knowledge distillation to generate recovery motions for robots with low latency. Extensive simulations demonstrate that our GSC framework reduces the average FDR time by 86.1% while achieving a higher task success rate, compared to the State-of-the-Art (SOTA) frameworks that rely on vision-language models (VLMs) for fault detection and large language models (LLMs) for fault recovery. Project website: https://sites.google.com/view/gscfdr.

Semantic communication (SemCom) is an emerging paradigm that leverages semantic-level understanding to improve communication efficiency, particularly in resource-constrained scenarios. However, existing SemCom systems often overlook diverse computational and communication capabilities and requirements among different users. Motivated by the need to adaptively balance semantic accuracy, latency, and energy consumption, this paper presents a reinforcement learning (RL)-driven framework for semantic compression model (SCM) selection and resource allocation in multi-user SemCom systems. To address the challenges of balancing image reconstruction quality and communication performance, a system-level optimization metric called Rate-Distortion Efficiency (RDE) has been defined. The framework considers multiple SCMs with varying complexity and resource requirements. A proximal policy optimization (PPO)-based RL approach is developed to dynamically select SCMs and allocate bandwidth and power under non-convex constraints. Simulations demonstrate that the proposed method outperforms several baseline strategies. This paper also discusses the generalization ability, computational complexity, scalability, and practical implications of the framework for real-world SemCom systems.

Ensuring reliable and low-latency communication in offshore wind farms is critical for efficient monitoring and control, yet remains challenging due to the harsh environment and lack of infrastructure. This paper investigates a flying base station (FBS) approach for wide-area monitoring and control in the UK Hornsea offshore wind farm project. By leveraging mobile, flexible FBS platforms in the remote and harsh offshore environment, the proposed system offers real-time connectivity for turbines without the need for deploying permanent infrastructure at sea. We develop a detailed and practical end-to-end latency model accounting for five key factors: flight duration, connection establishment, turbine state information upload, computational delay, and control transmission, to provide a holistic perspective often missing in prior studies. Furthermore, we combine trajectory planning, beamforming, and resource allocation into a multi-objective optimization framework for the overall latency minimization, specifically designed for large-scale offshore wind farm deployments. Simulation results verify the effectiveness of our proposed method in minimizing latency and enhancing efficiency in FBS-assisted offshore monitoring across various power levels, while consistently outperforming baseline designs.

The disaggregated architecture of advanced Radio Access Networks (RANs) with diverse X-haul latencies, in conjunction with resource-limited multi-access edge computing networks, presents significant challenges in designing a general model in placing baseband and user plane functions to accommodate versatile 5G services. This paper proposes a novel approach, NetMind+, which leverages Deep Reinforcement Learning (DRL) to determine the function placement strategies in diverse and evolving RAN topologies, aiming at minimizing power consumption. NetMind+ resolves the problem with a maze-solving strategy, enabling a Markov Decision Process with standardized action space scales across different networks. Additionally, a Graph Convolutional Network (GCN) based encoding and an incremental learning mechanism are introduced, allowing features from different and dynamic networks to be aggregated into a single DRL agent. This facilitates the generalization capability of DRL and minimizes the negative retraining impact. In an example with three sub-networks, NetMind+ demonstrates a substantial 32.76% improvement in power savings and a 41.67% increase in service stability compared to benchmarks from the existing literature. Compared to traditional methods necessitating a dedicated DRL agent for each network, NetMind+ attains comparable performance with 70% of the training cost savings. Furthermore, it demonstrates robust adaptability during network variations, accelerating training speed by 50%.

This study presents a novel approach for managing out-of-the-box multi-mobile robotic systems designed to optimise area coverage and maintain wireless mesh connectivity. We introduce an area coverage assessment scheme that enables robot swarms to adjust their numbers dynamically, enabling real-time monitoring of dynamically changing environments with sufficient robots. Through decentralized control, the robots adapt to the environment size and geometry without prior environmental knowledge while maintaining ad-hoc connectivity by regulating inter-robot distances. Simulation results validate the system’s scalability and demonstrate its capability to achieve efficient area coverage with a self-regulated swarm size. This technology has broad applications, ranging from real-time inventory management in intralogistics to urban monitoring in smart cities and rapid disaster relief.

The dis aggregated and hierarchical architecture of advanced RAN presents significant challenges in efficiently placing baseband functions and user plane functions in conjunction with Multi-Access Edge Computing (MEC) to accommodate diverse 5G services. Therefore, this paper proposes a novel approach NetMind, which leverages Deep Reinforcement Learning (DRL) to determine the function placement strategies in RANs with diverse topologies, aiming at minimizing power consumption. NetMind formulates the function placement problem as a maze-solving task, enabling a Markov Decision Process with standardized action space scales across different networks. Additionally, a Graph Convolutional Network (GCN) based encoding mechanism is introduced, allowing features from different networks to be aggregated into a single RL agent. That facilitates the RL agent's generalization capability and minimizes the negative impact of retraining on power consumption. In an example with three sub-networks, NetMind achieves comparable performance to traditional methods that require a dedicated DRL agent for each network, resulting in a 70 % reduction in training costs. Furthermore, it demonstrates a substantial 32.76% improvement in power savings and a 41.67 % increase in service stability compared to benchmarks from the existing literature.

With the advent of the multimodal immersive communication system, people can interact with each other using multiple devices for sensing, communication and/or application level control either onsite or remotely. As a breakthrough concept, a distributed sensing, computing, communications, and control (DS3C) fabric is introduced in this paper for provisioning 6G services in multi-tenant environments in a unified manner. The DS3C fabric can be further enhanced by natively incorporating intelligent algorithms for network automation and managing networking, computing, and sensing resources efficiently to serve vertical use cases with extreme and/or conflicting requirements. As such, the paper proposes a novel end-to-end 6G system architecture with enhanced intelligence spanning across different network, computing, and business domains, identifies vertical use cases and presents an overview of the relevant standardisation and pre-standardisation landscape.

The Open RAN API and interface standards facilitate the new ecosystems where distinct hardware and software components are brought together to build 5G systems. Key to this concept is the seamless and efficient integration and monetization process among stakeholders. A marketplace serves as a means to realize this collaborative revenue sharing, eliminating the need for intricate proprietary agreements or contracts between each participant. This demo presents the marketplace strategy emphasizing software integration across diverse deployment settings, utilizing the API-centric integration Platform-as-a-Service (iPaas) model aligned with Open RAN standards.

Recently, the concept of digital twins (DTs) has received significant attention within the realm of 5G/6G. This demonstration shows an innovative DT design and implementation framework tailored toward integration within the 5G infrastructure. The proposed DT enables near real-time anomaly detection capability pertaining to user connectivity. It empowers the 5G system to proactively execute decisions for resource control and connection restoration.

This paper provides details of the collaborative robotics testbed platform that has been developed within the UMBRELLA project. The testbed is part of a larger open Industrial IoT testbed which is currently being deployed in the UK. The aim of the testbed is to permit flexible experimentation using different end devices (including smart city sensing and robot nodes) to evaluate algorithms or new practical application scenarios. For the collaborative robotics testbed this relates to warehouse robotics, which can move pallets of different sizes and shapes. The testbed also includes simulator facilities for validation of algorithms prior to deployment on the robot nodes. The nodes support a rich set of sensors, actuators, and wireless communication technologies. The software architecture is based on Docker containers and ROS2 DDS middleware for flexible and extensible evolution to support future sensors or network technologies. This will be provided as an open testbed to support research, experimentation and evaluation of swarm robotics and other Industrial IoT use-cases.

Mobile robots have disrupted the material handling industry which is witnessing radical changes. The requirement for enhanced automation across various industry segments often entails mobile robotic systems operating in logistics facilities with little/no infrastructure. In such environments, out-of-box low-cost robotic solutions are desirable. Wireless connectivity plays a crucial role in successful operation of such mobile robotic systems. A wireless mesh network of mobile robots is an attractive solution; however, a number of system-level challenges create unique and stringent service requirements. The focus of this paper is the role of Bluetooth mesh technology, which is the latest addition to the Internet-of-Things (IoT) connectivity landscape, in addressing the challenges of infrastructure-less connectivity for mobile robotic systems. It articulates the key system-level design challenges from communication, control, cooperation, coverage, security, and navigation/localization perspectives, and explores different capabilities of Bluetooth mesh technology for such challenges. It also provides performance insights through real-world experimental evaluation of Bluetooth mesh while investigating its differentiating features against competing solutions.

Various legacy and emerging industrial control ap-plications create the requirement of periodic and time-sensitive communication (TSC) for 5G/6G networks. State-of-the-art semi-persistent scheduling (SPS) techniques fall short of meeting the requirements of this type of critical traffic due to periodicity misalignment between assignments and arriving packets that lead to significant waiting delays. To tackle this challenge, we develop a novel recursive periodicity shifting (RPS)-SPS scheme that provides an optimal scheduling policy by recursively aligning the period of assignments until the timing mismatch is minimized. RPS can be realized in 5G wireless networks with minimal modifications to the scheduling framework. Performance evaluation shows the effectiveness of the proposed scheme in terms of minimizing misalignment delay with arbitrary traffic periodicity.

The fifth-generation (5G) mobile/cellular technology is a game changer for industrial systems. Private 5G deployments are promising to address the challenges faced by industrial networks. Programmability and open-source are two key aspects which bring unprecedented flexibility and customizability to private 5G networks. Recent regulatory initiatives are removing barriers for industrial stakeholders to deploy their own local 5G networks with dedicated equipment. To this end, this demonstration showcases an open and programmable 5G network-in-a-box solution for private deployments. The network-in-a-box provides an integrated solution, based on open-source software stack and general-purpose hardware, for operation in 5G non-standalone (NSA) as well as 4G long-term evolution (LTE) modes. The demonstration also shows the capability of operation in different sub-6 GHz frequency bands, some of which are specifically available for private networks. Performance results, in terms of end-to-end latency and data rates, with a commercial off-the-shelf (COTS) 5G device are shown as well.

In state-of-the-art wireless networks, the radio environment is beyond the control of the operators which leads to several issues. For example, signal attenuation limits radio connectivity, multi-path propagation results in fading phenomena and reflections/refractions from large objects are the main sources of uncontrollable interference. To overcome these issues, a new technology referred to as reconfigurable intelligent surfaces (RISs) has been brought to light. RISs, whose interactions with the electromagnetic waves are reconfigurable, are at the heart of the smart radio environments for beyond 5G (or 6G) wireless systems. Research on design of smart radio environments is still in infancy. To this end, this paper provides performance insights for design of RIS-assisted smart radio environments. By extending a recent system-level simulator and through extensive simulations, it investigates the impact of positioning/placement of RISs, the number of reflecting elements and the tilt/rotation of RISs in both single and multi-RIS environments. Results reveal that these aspects are crucial to achieving capacity and reliability enhancements in smart radio environments.

High-performance real-time wireless connectivity is at the heart of the Industrial Internet of Things (IIoT). Realizing wireless closed-loop control is crucial for various mission-critical IIoT systems. The existing wireless technologies fall short of meeting the stringent performance requirements of the closed-loop control. This article presents a novel wireless solution, called generalized closed-loop control of processes (GALLOP), for realizing closed-loop control over multihop or single-hop networks. GALLOP adopts a pragmatic design approach for addressing the challenges of wireless closed-loop control. The key design aspects of GALLOP include control-aware bidirectional scheduling for cyclic exchange, robust retransmission techniques based on cooperative multiuser diversity, and low-overhead signaling for scalable operation. GALLOP has been specifically designed for control loops that are closed over the whole network with dynamics on the order of a few milliseconds. The performance evaluation based on extensive system-level simulations and hardware implementation on a Bluetooth 5 testbed demonstrates that GALLOP provides high-performance connectivity with very low and deterministic latency, very high reliability and high scalability, to meet the stringent requirements of wireless closed-loop control for versatile IIoT applications.

The term Tactile Internet broadly refers to a communication network that is capable of delivering control, touch, and sensing/actuation information in real-time. The Tactile Internet is currently a topic of interest for various standardization bodies. The emerging IEEE P1918.1 standards working group is focusing on defining a framework for the Tactile Internet. The main objective of this work is to present the IEEE P1918.1 reference architecture framework for the Tactile Internet. The paper provides an indepth survey of various architectural aspects including the key entities, the interfaces, the functional capabilities, and the protocol stack. A case study has been presented as a manifestation of the architecture. Performance evaluation demonstrates the impact of functional capabilities and the underlying enablers on user-level utility pertaining to a generic Tactile Internet application.

Private deployments of 4G and 5G networks in industrial environments are beneficial from various aspects. Private 4G/5G networks typically face the challenge of supporting heterogeneous industrial applications. This technology demonstration highlights the importance of network slicing in private 4G/5G networks. It shows that network slicing is crucial for performance guarantees in multiservice co-existence scenarios. With network slicing, our private 4G/5G network successfully supports closed-loop control, event-driven control and video streaming applications.

Cooperation among mobile robotic platforms is beneficial for various industrial applications, particularly in the warehouse and logistics sector. Formation control of mobile robots for collaborative transportation of an object, also known as collective transport, is a key challenge for cooperative robotic systems. This paper investigates communications-based formation control which is a promising approach. Based on an analytic model for wirelessly controlled leader-follower formation, the impact of connectivity imperfections on path-tracking performance has been investigated for two different types of mobile robots. Performance evaluation provides insights for wireless-centric system design.

Millimeter wave (mmWave) communication has recently attracted significant attention from both industrial and academic communities. The large bandwidth availability as well as low interference nature of mmWave spectrum is particularly attractive for industrial communication. However, inherent challenges such as coverage and blockage of mmWave communication cause highly fluctuated channel quality. This paper explores wireless medium access control (MAC) schedulers for mmWave-based industrial wireless applications. Our objective is to design a high-performance and enhanced fairness MAC scheduling algorithm that responds rapidly to channel variations. The key contribution of our work is a method to modify the standard proportional fair (SPF) scheduler. It introduces more flexibility and dynamic properties. Compared to the SPF, our enhanced proportional fair (EPF) scheduler not only improves the priority for users in poor channel conditions but also accelerates the reaction time in fluctuated channel conditions. By providing higher fairness for all users and enhancing system robustness, it particularly adapts to the scatter-rich industrial mmWave communication environment. Through extensive performance evaluation based on the widely accepted network simulator (ns-3), we show that the new scheduler achieves better performance in terms of delivering ultra-low latency and reliable services over mmWave-based industrial communication.

Offloading among multiple radio access technologies (RATs) is an effective solution to tackle some of the key challenges faced by 5G wireless networks. While making an offloading decision, in user-centric offloading schemes, the network context is not taken into account, which limits the full utilization of the potential offered by these techniques. Therefore, in this work, we develop a data offloading framework wherein a user defines a tuple, which includes a request for data service and the associated delay limit, and the user equipment (UE) translates this tuple into an optimal offloading policy by taking into account the network context. The data offloading problem is formulated by assuming a finite horizon Markov decision process (MDP), and an analytical data offloading model is derived by using stochastic geometry for modeling the spatial domain of a multi-RAT wireless network, and by employing Markov process for capturing mobility of users. In order to validate the performance of derived model, we also solve the offloading problem by using policy iteration (PI) algorithm. The results show that the analytical algorithm outperforms the iterative algorithm. We also benchmark the proposed framework against standard data offloading methods.

One of the key challenges in realizing ultra-reliable low-latency communications (uRLLC) for factories-of-the-future (FoF) applications is to enhance the cellular random access channel (RACH) procedure. The state-of-the-art LTE RACH procedure does not fulfil the latency requirements for envisioned FoF applications. Moreover, it becomes challenging due to congestion and overloading from massive machine type communication (mMTC) devices leading to collisions especially in a densely populated factory scenarios. The main objective of this paper is to conduct a comprehensive performance evaluation of different random access (RA) enhancements for uRLLC over 5G wireless networks. Our performance evaluation is based on a realistic system-level simulator. The core enhancements considered in this work include early data transmission (EDT), reserved preambles and the use of flexible physical (PHY) layer numerology. We also propose three new RA enhancements for uRLLC. Performance evaluation demonstrates that the proposed RA enhancements can fulfil the 3GPP control plane target of less than 10 ms latency with 99.99% reliability in factory environments.

The term Tactile Internet broadly refers to a communication network that is capable of delivering real-time control, touch, and sensing/actuation information through sufficiently reliable, responsive, and intelligent connectivity. Envisioned to enable unprecedented applications, the Tactile Internet provides a promising opportunity to reshape industrial communication and transform the operation of many existing industrial systems. This paper investigates the role of Tactile Internet in current and future industrial systems and reviews the technological trends from legacy industrial networks to emerging industrial wireless networks, and beyond. To this end, this paper begins with an overview of the Tactile Internet. Complementing prior efforts, it presents a taxonomy of the key Tactile Internet applications. This paper covers the relevant background on the emergence of industrial communication. Then, it discusses the role of the Tactile Internet for various industrial systems and identifies the key use cases with respective connectivity requirements. This paper provides a technology landscape for the Tactile Internet to enable high-performance industrial wireless communication. Specifically, it provides insights into the recent Third Generation Partnership Project developments for enabling ultrareliable and low-latency communications over 5G mobile/cellular networks. It also examines the potential of the IEEE 802.11 and IEEE 802.15 standards for industrial control applications. Besides, it reviews the role of artificial intelligence and edge-computing platforms in overcoming the imperfections of wireless environments. Finally, this paper provides a roadmap for future, investigating the role of Tactile Internet in next generation industrial systems along with some directions and challenges for future research.

The IEEE “Tactile Internet” (TI) Standards working group (WG), designated the numbering IEEE 1918.1, undertakes pioneering work on the development of standards for the TI. This paper describes the WG, its intentions, and its developing baseline standard and the associated reasoning behind that and touches on a further standard already initiated under its scope: IEEE 1918.1.1 on “Haptic Codecs for the TI.” IEEE 1918.1 and its baseline standard aim to set the framework and act as the foundations for the TI, thereby also serving as a basis for further standards developed on TI within the WG. This paper discusses the aspects of the framework such as its created TI architecture, including the elements, functions, interfaces, and other considerations therein, as well as the novel aspects and differentiating factors compared with, e.g., 5G Ultra-Reliable Low-Latency Communication, where it is noted that the TI will likely operate as an overlay on other networks or combinations of networks. Key foundations of the WG and its baseline standard are also highlighted, including the intended use cases and associated requirements that the standard must serve, and the TI's fundamental definition and assumptions as understood by the WG, among other aspects.

The increased penetration of different radio access technologies (RATs) and the growing trend towards their convergence necessitates the investigation of wireless heterogeneous networks (HetNets) from coverage and capacity perspective. This paper develops a unified framework for signal-to-interference-plus-noise ratio and rate coverage analysis of multi-RAT HetNets, with each RAT employing either a contention-free or a contention-based channel access strategy. The proposed framework adopts tools from stochastic geometry, with the location of APs and mobile users modeled through independent Poisson point processes (PPPs). We specifically focus on a two-RAT scenario (i.e., cellular and Wi-Fi), where for multi-tier Wi-Fi RAT, with contention-based channel access like CSMA/CA, the location dependent distribution of interfering APs has been approximated through a homogeneous PPP. Moreover, by using some simple yet realistic set of assumptions, the distance to nearest active AP has been defined which results in simplified expressions. The medium access probability for a random and a tagged AP under a multi-tier Wi-Fi RAT has also been derived and discussed. By keeping in view the tremendous effect of temporal domain on overall network performance, the stable queue probability has been derived by assuming a non-saturated traffic model. The results have been validated through extensive simulations and compared with existing approaches. Some useful insights have also been presented that shed light on design and analysis of multi-RAT HetNets and provide motivation for further research in this direction.

Wireless technologies are increasingly gaining popularity for many industrial automation applications. State-of-the-art wireless technologies mainly support monitoring and open-loop control applications at the lowest level of the automation pyramid. The use of wireless technologies for closed-loop control applications is still in infancy. This paper proposes a novel wireless solution, termed as ENCLOSE, for closed-loop control applications at the sensor/actuator level of factory automation. At the Physical layer, ENCLOSE adopts IEEE 802.15.1 specifications with an enhanced data rate feature. The medium access control layer of ENCLOSE is based on time-division multiple access and frequency-division duplexing. ENCLOSE is based on an optimized frame structure for achieving low latency while accounting for the peculiarities of closed-loop control. Moreover, ENCLOSE implements two different cooperative multi-user diversity techniques for achieving high reliability. Performance evaluation, based on comprehensive analytical modeling and system-level simulation studies, demonstrates that ENCLOSE provides a practical solution, that fulfils the stringent requirements of closed-loop control applications in factory automation environments.

The exchange of haptic information in remote operation systems allows humans to physically interact in remote environments with complete immersion. Such human-in-the-loop networks have been realized through wired solutions in the past. It is expected that the emerging 5G mobile communications networks will be able to meet the stringent requirements of real-time haptic interaction. By enabling haptic communications over 5G networks, human-in-the-loop mobile networks will emerge, that will support unprecedented applications. The main objective of this paper is to investigate the problem of radio resource allocation for haptic communications over 5G networks. The key requirements of haptic communications from a radio resource allocation perspective have been identified. These requirements have been translated into two distinct optimization problems for radio resource allocation, pertaining to perceptual coding and symmetric design cases. To reduce complexity, a two-step solution has been provided after decomposing the original optimization problems. In both cases, low-complexity greedy heuristic algorithms and near-optimal solutions have been proposed for resource allocation. Performance evaluation has been conducted based on a 5G air-interface design.

TV White Spaces (TVWS) has taken a major step forward with the UK regulator Ofcom initiating a pilot of the technology and associated procedures in the UK, and TVWS rules being harmonized at the European level. This paper considers some aspects of our trial within the Ofcom TV White Spaces Pilot, namely, investigation of aggregation in TVWS and the deployment and testing of IEEE 802.11 white space devices that are capable of aggregating up to 4 TV channels, contiguously or non-contiguously. Our trial is the only deployment of these devices, developed at InterDigital in the USA, within the Ofcom TV White Spaces Pilot. This paper discusses the specifics of the InterDigital devices, including their design and capabilities. Further, results are presented highlighting what is possible in TVWS through contiguous aggregation. These results are achieved through per-channel allowed EIRP and capacity analyses based on feedback from a TVWS database within the Ofcom Pilot, using TVWS functionality developed at King's College London. Additional results outline the added benefit of optimal non-contiguous aggregation. Finally, this paper performs a like-for-like comparison of the performance of the InterDigital devices against what is theoretically possible in TVWS through capacity analyses using feedback from the TVWS database.

The Tactile Internet will be able to transport touch and actuation in real-time. The primary application running over the Tactile Internet will be haptic communications. Design efforts for both the Tactile Internet and the haptic communications are at a nascent stage. It is expected that the next generation (5G) wireless networks will play a key role in realizing the Tactile Internet. On the other hand, Long Term Evolution Advanced (LTE-A) networks would be an integral component of the 5G ecosystem. Therefore, exploring the potential of LTE-A networks for haptic communications would be an important step towards realizing the Tactile Internet. To this end, the main objective of this paper is to investigate radio resource allocation for haptic communications in LTE-A networks. The radio resource requirements of haptic communications have been translated into a unique resource allocation problem which becomes particularly challenging due to the specific constraints of multiple access schemes in LTE-A networks. Novel heuristic algorithm has been developed to solve the resource allocation problem. Performance evaluation has been conducted using simulation studies for a recently proposed 5G air-interface design.

Powered by next generation mobile networking capabilities, the Tactile Internet will be able to transport touch and actuation in real-time. Enabled by suitable robotics and haptics equipment at the edges, and an unprecedented communications network, the Tactile Internet will provide a true paradigm in creating skill-set delivery networks. The fifth generation (5G) mobile communications systems will underpin this emerging Internet at the wireless edge. This paper presents the most important technology concepts which lay at the intersection of the larger Tactile Internet and the emerging 5G systems. Specifically, the paper presents some of the most important Tactile Internet applications, outlines the key technical requirements, and covers end-to-end architectural aspects of the Tactile Internet.

The long-term ambition of the Tactile Internet is to enable a democratization of skill, and how it is being delivered globally. An integral part of this is to be able to transmit touch in perceived real-time, which is enabled by suitable robotics and haptics equipment at the edges, along with an unprecedented communications network. The fifth generation (5G) mobile communications systems will underpin this emerging Internet at the wireless edge. This paper presents the most important technology concepts, which lay at the intersection of the larger Tactile Internet and the emerging 5G systems. The paper outlines the key technical requirements and architectural approaches for the Tactile Internet, pertaining to wireless access protocols, radio resource management aspects, next generation core networking capabilities, edge-cloud, and edge-AI capabilities. The paper also highlights the economic impact of the Tactile Internet as well as a major shift in business models for the traditional telecommunications ecosystem.

In this paper we investigate power-efficient resource allocation for transmission of perceptual quality guaranteed video over LTE downlink under delay quality of service (QoS) constraints. We formulate the resource allocation problem as the minimization of sum power in the downlink under user perceived quality and statistical delay QoS provisioning. We solve the problem using dual decomposition and employ the ellipsoid method to update dual variables. Experimental results have shown significant performance enhancement of the proposed system in terms of power efficiency while satisfying the statistical delay-bounded QoS and perceptual quality requirements.

TV White Spaces (TVWS) technology allows wireless devices to opportunistically use locally-available TV channels enabled by a geolocation database. The UK regulator Ofcom has initiated a pilot of TVWS technology in the UK. This paper concerns a large- scale series of trials under that pilot. The purposes are to test aspects of white space technology, including the white space device and geolocation database interactions, the validity of the channel availability/powers calculations by the database and associated interference effects on primary services, and the performances of the white space devices, among others. An additional key purpose is to perform research investigations such as on aggregation of TVWS resources with conventional resources and also aggregation solely within TVWS, secondary coexistence issues and means to mitigate such issues, and primary coexistence issues under challenging deployment geometries, among others. This paper provides an update on the trials, giving an overview of their objectives and characteristics, some aspects that have been covered, and some early results and observations.

Machine-to-machine (M2M) communications enables networked devices to exchange information among each other as well as with business application servers and therefore creates what is known as the Internet-of-Things (IoT). The research community has a consensus for the need of a standardized protocol stack for M2M communications. On the other hand, cognitive radio technology is very promising for M2M communications due to a number of factors. It is expected that cognitive M2M communications will be indispensable in order to realize the vision of IoT. However cognitive M2M communications requires a cognitive radio-enabled protocol stack in addition to the fundamental requirements of energy efficiency, reliability, and Internet connectivity. The main objective of this paper is to provide the state of the art in cognitive M2M communications from a protocol stack perspective. This paper covers the emerging standardization efforts and the latest developments on protocols for cognitive M2M networks. In addition, this paper also presents the authors' recent work in this area, which includes a centralized cognitive medium access control (MAC) protocol, a distributed cognitive MAC protocol, and a specially designed routing protocol for cognitive M2M networks. These protocols explicitly account for the peculiarities of cognitive radio environments. Performance evaluation demonstrates that the proposed protocols not only ensure protection to the primary users (PUs) but also fulfil the utility requirements of the secondary M2M networks.

Cooperative routing and spectrum aggregation are two promising techniques for Cognitive Radio Ad-Hoc Networks (CRAHNs). In this paper, we propose a spectrum aggregation-based cooperative routing protocol, termed as SACRP, for CRAHNs. To the best of our knowledge, this is the first contribution on spectrum aggregation-based cooperative routing for CRAHNs. The primary objective of SACRP is to provide higher energy efficiency, improve throughput, and reduce network delay for CRAHNs. In this regard, we design the MAC and Physical (PHY) layer, and proposed different spectrum aggregation algorithms for cognitive radio (CR) users. We propose two different classes of routing protocols;Class Afor achieving higher energy efficiency and throughput, andClass Bfor reducing end-to-end latency. Based on stochastic geometry approach, we build a comprehensive analytical model for the proposed protocol. Besides, the proposed protocol is compared with the state of the art cooperative and non-cooperative routing algorithms with spectrum aggregation. Performance evaluation demonstrates the effectiveness of SACRP in terms of energy efficiency, throughput, and end-to-end delay.

In this paper, we investigate power efficient resource allocation for the uplink of LTE networks under delay Quality-of-Service (QoS) constraints. We formulate the resource allocation problem as the minimization of sum power in the uplink under statistical delay QoS provisioning, which is complicated due to the specific constraints of SC-FDMA (uplink air interface in LTE networks). We solve the problem using Canonical duality theory. Numerical results which are obtained using the Invasive Weed Optimization algorithm, show that the proposed resource allocation algorithm not only outperforms classical algorithms in terms of power efficiency while satisfying the QoS requirements, but also performs closer to the optimal solution.

It is expected that cognitive Machine-to-Machine (M2M) communication will be indispensable in near future. Moreover, M2M networks must be Internet Protocol (IP) enabled for ubiquitous connectivity. Recently, IETF has standardized RPL (Routing Protocol for Low Power and Lossy Networks), which is expected to be the standard routing protocol for majority of M2M applications. Our objective in this paper is to enhance RPL for cognitive radio enabled M2M networks. Our enhanced protocol provides novel modifications to RPL in order to address the routing challenges in cognitive radio environments along with protecting the primary users as well as meeting the utility requirements of secondary network. The proposed protocol is evaluated through system level simulation studies.

This paper investigates energy and interference aware Cooperative Routing algorithms in Cognitive Radio Ad-Hoc Networks. Two routing algorithms have been proposed. In Algorithm I, we propose to minimize transmission power based on the rate required, and investigate the total transmission power consumption of the shortest path from source to destination whereby each link on the path is given the option of selecting a relay to further reduce transmission power. In Algorithm II, we consider Primary User (PU) receiver protection by reducing the fractional overlap area of each secondary node with the primary coverage area, and use the average interference probability to optimize route choice again allowing a cooperative relay to be chosen for each link on each possible path. Simulation results show that the proposed Algorithm I can achieve a transmission power saving of up to 30%, while Algorithm II can reduce interference probability for PU receivers by up to 6%.

Recently, energy efficiency in wireless networks has become an important objective. Aside from the growing proliferation of smartphones and other high-end devices in conventional human-to-human (H2H) communication, the introduction of machine-to-machine (M2M) communication or machine-type communication into cellular networks is another contributing factor. In this paper, we investigate quality-of-service (QoS)-driven energy-efficient design for the uplink of long term evolution (LTE) networks in M2M/H2H co-existence scenarios. We formulate the resource allocation problem as a maximization of effective capacity-based bits-per-joule capacity under statistical QoS provisioning. The specific constraints of single carrier frequency division multiple access (uplink air interface in LTE networks) pertaining to power and resource block allocation not only complicate the resource allocation problem, but also render the standard Lagrangian duality techniques inapplicable. We overcome the analytical and computational intractability by first transforming the original problem into a mixed integer programming (MIP) problem and then formulating its dual problem using the canonical duality theory. The proposed energy-efficient design is compared with the spectral efficient design along with round robin (RR) and best channel quality indicator (BCQI) algorithms. Numerical results, which are obtained using the invasive weed optimization (IWO) algorithm, show that the proposed energy-efficient uplink design not only outperforms other algorithms in terms of energy efficiency while satisfying the QoS requirements, but also performs closer to the optimal design.

The use of cognitive radio technology for M2M communications is expected to be indispensable in near future. In the meanwhile, the unique features of M2M communications create various challenges, especially at the MAC layer. We aim to design a cognitive MAC protocol for generic M2M communications that sufficiently fulfils the service requirements of M2M devices. For this purpose, we propose PRMA, carry out its feasibility study, adapt, and significantly enhance it with modifications especially tailored for M2M communications. By combining PRMA and cognitive radio technology, a frame structure is developed for supporting co-existence of cognitive M2M network with the primary network. This frame structure is further optimized considering different tradeoffs. The proposed protocol is evaluated using both analytical and simulation results.

M2M communications enables devices with an ability to communicate autonomously and thus acts as the enabling technology for Internet-of-Things. It is expected that a multitude of connected devices will exist in near future. In order to avoid any spectrum scarcity issues, there is a need to explore alternate spectrum opportunities. Thus the need of cognitive radio technology for M2M communications will be indispensable. On the other hand, the unique features of M2M communications create a number of challenges for existing communication networks, especially at the MAC layer. We aim to design a MAC protocol for generic M2M communications that uses cognitive radio technology at the physical layer. For this purpose, we propose PRMA, carry out its feasibility study, adapt and significantly enhance it with modifications especially tailored for M2M communications. Analytical and simulation results show a promising combination for application in practical M2M scenarios.

This paper investigates the green or energy efficient operation of cellular access networks through dynamic spectrum and traffic load management techniques. Different energy saving techniques are discussed along with applications in realistic multi-cellular scenarios. The use of base station cooperation is discussed. The outage probability analysis is also discussed for different energy saving techniques in order to satisfy a particular Quality-of-Service (QoS) threshold for different services. The analysis is supported by different base station power consumption models, traffic models, and realistic statistics of different base stations in London, UK. Results show a considerable power saving potential of up to 65% or more in base station power.

The introduction of Machine-to-Machine (M2M) communications in cellular networks creates a whole new set of challenges due to the unique service requirements and features of M2M devices. One such challenge is the radio resource management, particularly on the uplink. The requirements of high energy efficiency coupled with diverse QoS requirements of M2M devices and conventional Human-to-Human (H2H) users complicate the resource allocation problem. In this paper, we address the issue of energy efficient radio resource allocation for M2M/H2H co-existence scenarios in LTE networks, while meeting the QoS requirements for different users. Our proposed algorithm shows encouraging performance in achieving the desired objectives, compared to existing algorithms in literature.

The rapidly increasing demand for mobile data services urges operators to explore alternative technologies to handle the voluminous data traffic on their networks. In this regard, mobile data offloading is expected to become a key industry segment in near future. In this paper, we propose a policy based offloading framework for cellular network which is based on a cost function approach. We discuss user centric, network centric and hybrid policies where the decision making is shared between the user and the network. We also present a novel mechanism for the decision sharing between user and network which is based on principles of autonomic networking and where policies are chosen dynamically according to the variation of network conditions and the operator strategies. Detailed simulation studies are conducted to validate the effectiveness of these policies in real networks.

This paper investigates the energy savings a cellular operator can achieve in the access network by dynamically offloading users or traffic loads to Wi-Fi networks. The method used to analyze energy savings is presented, followed by detailed simulations using different traffic types. Results show that significant savings of up to 65-70% can be achieved by opportunistically powering down cellular radio network equipment as facilitated by the offloading of users to Wi-Fi.

Dynamic load and spectrum usage management techniques can significantly improve the energy efficiency of mobile communications systems. This paper considers: (i) the opportunistic reallocation of traffic loads between bands to allow radio network equipment in the bands that the traffic is originated from to be powered down, and (ii) the opportunistic selection of more appropriate spectrum based on propagation characteristics to minimize necessary transmission power through improving propagation and/or reducing power leaking into co-channel cells in frequency reuse cases. This paper addresses the simulation of video, FTP and HTTP (web browsing) traffic sources for configurations representing LTE and HSDPA telecommunications networks, and shows that the opportunistic reallocation of users between bands to power down radio equipment achieves a significant saving of 50% or more in from-the-socket power. Furthermore, it shows that the opportunistic reallocation of users/links to minimize transmission power through using more appropriate propagation spectrum leads to a further modest reduction in from-the-socket power consumption.