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Industry 5.0 envisions close cooperation between humans and machines requiring ultra-reliable and low latency communications (URLLC). The Intelligent Reflecting Surface (IRS) has the potential to play a crucial role in realizing wireless URLLC for Industry 5.0. IRS is forecast to be a key enabler of 6G wireless communication networks as it can significantly improve wireless network performance by creating a controllable radio environment. In this paper, we first provide an overview of IRS technology and then conceptualize the potential for IRS implementation in a future smart manufacturing environment to support the emergence of Industry 5.0 with a series of applications. Finally, to stimulate future research in this area, we discuss the strength, open challenges, and opportunities of IRS technology in modern smart manufacturing.

Unmanned aerial vehicles (UAVs) are now beginning to be deployed for enhancing the network performance and coverage in wireless communication. However, due to the limitation of their on-board power and flight time, it is challenging to obtain an optimal resource allocation scheme for the UAV-assisted Internet of Things (IoT). In this paper, we design a new UAV-assisted IoT systems relying on the shortest flight path of the UAVs while maximising the amount of data collected from IoT devices. Then, a deep reinforcement learning-based technique is conceived for finding the optimal trajectory and throughput in a specific coverage area. After training, the UAV has the ability to autonomously collect all the data from user nodes at a significant total sum-rate improvement while minimising the associated resources used. Numerical results are provided to highlight how our techniques strike a balance between the throughput attained, trajectory, and the time spent. More explicitly, we characterise the attainable performance in terms of the UAV trajectory, the expected reward and the total sum-rate., Comment: 30 pages, UAV-assisted wireless network, trajectory, data collection, and deep reinforcement learning

MPTCP is used in Multi-WAN Routers to aggregate multiple WAN/Internet connections using two architectural variants: proxying and tunneling. The proxy variant creates one MPTCP connection for each TCP connection, resulting in a large number of parallel uncoordinated MPTCP connections, which leads to underutilizating the available capacity, suboptimal scheduling, and increased loss rate. The tunnel variant encapsulates TCP over MPTCP, stacking two reliability layers, which leads to large number of spurious retransmissions, an issue known as TCP meltdown. We propose a new solution, BOOST, that eliminates the problems with both variants by multiplexing TCP connections over a single persistent multi-path connection. BOOST also takes a hybrid approach to multi-path scheduling combining load balancing and scheduling by transmitting short flows across a single path, avoiding HoL blocking, and opportunistically transmitting long flows across multiple path, utilizing left-over capacity. Evaluations show that BOOST provides better throughput, lower losses, and retransmissions compared to MPTCP

In addition to high coverage and capacity, localization capabilities are becoming increasingly important for WLAN access points as enabler for additional services. In this paper the joint optimization of coverage and localization is investigated using a switched directional antenna array configuration. It is shown that switched directional antenna arrays can significantly outperform omni-directional antenna arrays in both coverage and angle estimation. In addition, it is shown that optimizing the antenna array for coverage also provides near-optimal localization performance. This is of great practical importance, as coverage optimization does not require manual calibration based on ground truth, and a good joint solution can be autoconfigured easily.

In this paper, we discuss the challenges faced by MPTCP when used to aggregate multiple WAN/Internet connections in Multi-WAN Routers (MWR). We observe that the two architectural variants, proxying and tunneling, used to deploy MPTCP in MWR suffer from key performance problems. First, the proxy variant creates one MPTCP connection for each TCP connection, which results in a large number of parallel uncoordinated MPTCP connections, which we explain leads to underutilization of the available capacity, suboptimal multipath scheduling, and increased loss rate. Second, the tunnel variant, which relies on encapsulating TCP over MPTCP, stacks two reliability layers and leads to a large number of spurious retransmissions, an issue known as TCP meltdown. Instead, we propose a new multi-path solution more suited to MWR, called BOOST. This solution eliminates the problems with both the proxy and tunnel approaches by multiplexing TCP connections over a single persistent multi-path connection. BOOST also takes a novel approach to multi-path scheduling that combines multipath load balancing and scheduling. In particular, short flows are transmitted across a single link to avoid HoL blocking while longer flows are opportunistically transmitted across multiple paths, utilizing left-over capacity. Evaluations show that BOOST provides better throughput, lower losses, and retransmissions while requiring less memory compared to both MPTCP variants

Wireless backhaul communication and power provision to fifth-generation small cells (SCs) is expected to decrease their installation cost significantly. In this paper, hybrid solar/laser-based energy harvesting and storage are investigated for the self-sufficient year-round operation of outdoor SCs. The required electrical power for a SC is assumed to be 10 W according to state-of-the-art millimeter-wave phased-array transceivers. Here we show through simulations that hybrid solar/laser-based energy harvesting with storage and exclusive laser-based energy harvesting enable the SC operation not only in sunnier parts of the world but also in ‘darker’ places. Since the highest monthly power required from the laser is shown to be 10.1 W, a highly directive optical wireless link is designed using the simulation software Zemax. The operation wavelength of 1310 nm is selected because of the highest maximum permissible exposure of 1 W/cm2. The designed system is shown to be capable of harvesting 10.4 W at the distance of 100 m with transmitter-receiver dimensions of the order of cm3 and meeting eye-safety regulations for Class 1M.

In this paper we consider the problem of computing the capacity of multi-user Gaussian MIMO systems under multiple linear transmit covariance constraints (LTCCs). These LTCCs are general enough to include many transmit power constraints such as sum power constraint (SPC) or per-antenna power constraint (PAPC) as special cases. For the considered MIMO systems with multiple LTCCs, existing solutions are based on subgradient or gradient descent methods, which are known to have slow convergence in general and are therefore not applicable to massive MIMO systems. In contrast, we propose a low-complexity semi-closed-form approach to computing the MIMO capacity for the system of interest. To this end, the considered problem in the broadcast channel is transformed into an equivalent minimax problem in the multiple access channel. The special structure of the minimax problem allows us to derive water-filling-like algorithms based on a novel combination of alternating optimization and concave-convex procedure. For the important case of joint SPC and PAPC, we also propose analytical expressions to find the optimal covariance matrix. Extensive analytical and numerical results are provided to demonstrate the effectiveness of our approach under various massive MIMO system settings.

In this demo, we propose a software defined transport-layer proxy architecture as a video streaming solution, that combines multicast and unicast transmissions to provide a seamless video experience. The proposed model employs zero-touch deployment to the handsets and the operator, and allows the combination of different wireless links, e.g., 4G, 5G, WiFi, in a simple and backwards compatible manner. We showcase in a real setup and emulated networks a mobile scenario, where a user migrates from a home DSL network to multicast 5G, and experiences a continuous decline in channel conditions as he moves from the cell center towards the edge. The multicast service is supplemented by different radio technologies (e.g., 4G, WiFi) through unicast and multicast transmissions, by traffic splitting and by provisioning application layer forward error correction (FEC). The proposed Augmented Multicast mUltipath ServicE (AMUSE) is compared against the state-of-the-art, i.e., single radio access multicast service. As the user channel conditions gradually deteriorate, we demonstrate a seamless video experience for AMUSE clients, whereas the typical client suffers from frequent re-buffer events, and eventually a service breakdown.

Grammar-Guided Genetic Programming is already outperforming humans at creating efficient transmission schedulers for large heterogeneous communications networks. We have previously proposed a multi-level grammar approach which achieved significantly better results than the canonical Grammar-Guided Genetic Programming approach. Initially, a restricted ‘small’ grammar is utilised in order to discover suitable structures. A full grammar is then adopted after this initial phase. Hence, evolution can focus on maximising performance, by fine-tuning the well-structured models. In this work, we propose to use a hierarchical approach by employing multiple small grammars instead of a unique small grammar at the lower level, in conjunction with the full grammar at the upper level. To use multiple small grammars while maintaining the same computational budget, we have to use either (i) reduce the number of generations, or (ii) reduce the size of the population for the evolution with each of the small grammars. In this work, we confirm that the hierarchical grammar approach using the division of number of generations strategy achieves significantly better results than the multi-level approach, but requires defining an ideal number of small grammars to achieve the best performance. We also show that the hierarchical grammar approach using the division of population size strategy achieves significantly better results than the multi-level approach. However the division of population size strategy is less sensitive to the number of small grammars.

In this paper we consider the task of scheduling packet transmissions amongst multiple paths with uncertain, time-varying delay. We make the observation that the requirement is usually to transmit application layer objects (web pages, images, video frames etc) with low latency, and so it is the object delay rather than the per packet delay which is important. This has fundamental implications for multipath scheduler design. We introduce SOS (Stochastic Object-aware Scheduler), the first multipath scheduler that considers application layer object sizes and their relationship to link uncertainty. We show how to interface SOS with cwnd-based/ack-clocked congestion control (as usually used in TCP) and show up to 150% improvement in the 95% percentile and mean delay vs MPTCP/minRTT.

Indoor areas, such as offices and shopping malls, are a natural environment for initial millimeter-wave (mmWave) deployments. While we already have the technology that enables us to realize indoor mmWave deployments, there are many remaining challenges associated with system-level design and planning for such. The objective of this article is to bring together multiple strands of research to provide a comprehensive and integrated framework for the design and performance evaluation of indoor mmWave systems. The paper introduces the framework with a status update on mmWave technology, including ongoing fifth generation (5G) wireless standardization efforts, and then moves on to experimentally-validated channel models that inform performance evaluation and deployment planning. Together these yield insights on indoor mmWave deployment strategies and system configurations, from feasible deployment densities to beam management strategies and necessary capacity extensions., Comment: Proceedings of the IEEE

Interactive cloud-based applications such as virtual reality telepresence require flawless data delivery well within interactivity deadlines. These can range from sub-ms intervals for machines-type communications in industrial settings (e.g., multi-view obstacle detection for mobile vehicles) to 100 ms for basic human-human interactivity (e.g., virtual office, remote cockpit). Yet in wireless networks serving mobile users, the support of any quality-of-service (QoS) constraints is challenging due to their inherent instability and best-effort nature. In this context, we propose a novel multi-connectivity framework that allows federating multiple wireless links such as 4G LTE, 5G New Radio, Wi-Fi and WiGig in a simple and backward-compatible manner, and use them for multi-path data delivery with controlled end-to-end throughput, latency and reliability. The proposal is based on software-defined transport-layer proxies that can be deployed instantaneously and upgraded on-demand with no need for any handset or network modifications. As a proof of concept, we evaluate several innovative protocols for user-centric bandwidth and latency control in real-life LTE and Wi-Fi networks.

The emerging research field of functional micro devices and microbots aims at developing remote-controlled microsensors and actuators that can operate inside living organisms for biomedical applications. However, a significant gap remains in controlling with high accuracy such microdevices in complex environments. This is primarily due to the lack of high spatial and temporal resolution techniques. Here we demonstrate the detection of synthetic microstructures with sizes between 10 and 50 µm size, in deep tissue-mimicking phantoms, using multispectral optoacoustic tomography (MSOT). When referring to guidance/propulsion, cargo-delivery, and imaging capabilities, these microstructures represent key components of the physical, chemical and biohybrid microbots. They are conceived for diverse in vivo medical applications, including non-invasive diagnosis and therapy.

Social networks are more than just simple communication portals between people; they are also used for company marketing, brands or products. Likewise, they are used by government entities to disclose issues related to their managements, interact with citizens and answer their concerns. In this carried out analysis, two (2) major problems were identified: the lack of human and technological resources with the capacity of carrying out the construction of an infrastructure that is able to support a large social network; and, on the other hand, the overload of information generated, specifically, during the hours of greater interaction of users with the social network. In this assignment we propose a distributable, scalable, and highly available technological architecture that aims to expedite the implementation of the infrastructure that will support the social network and, therefore, help solve the problems of information overload at specific times.

This conference paper presents the latest results of the in-vitro and in-vivo longevity evaluation of silicon-stiffened neural probes designed for a free-floating implantation into the cortex to reduce the foreign body response and thus enable a chronic application. The polyimide-based electrical insulation of the flexible gold conductive paths revealed a long-term stability of up to 10 months immersed in Ringer’s solution at 37°C using the current microfabrication process. In-vivo impedance measurement results show a stabilization of the electrode impedance after approximately 3 weeks post implantation. Recording of spontaneous neural activity in rat visual cortex using chronically implanted probes was done to evaluate the long-term signal quality. Spikes were observable on many channels after at least 55 days post implantation. These results indicate the applicability of the presented probes for chronic neural interfacing.

Small Cells are being deployed alongside pre-existing Macro Cells in order to satisfy demand during the current era of exponential growth in mobile traffic. Heterogeneous networks are economical because both cell tiers share the same scarce and expensive spectrum. However, customers at cell edges experience severe cross-tier interference in channel sharing Het-Nets, resulting in poor service quality. Techniques for improving fairness globally have been developed in previous works. In this paper, a novel method for service differentiation at the level of individual customers is proposed. The proposed algorithm redistributes spectrum on a millisecond timescale, so that premium customers experience minimum downlink rates exceeding a target threshold. System level simulations indicate that downlink rate targets of at least 1 [Mbps] are always satisfied under the proposed scheme. By contrast, naive scheduling achieves the 1 [Mbps] target only 83% of the time. Quality of service can be improved for premium customers without significantly impacting global fairness metrics. Flexible service differentiation will be key to effectively monetizing the next generation of 5G wireless communications networks.

Providing consistently high wireless capacity is becoming increasingly important to support the applications required by future digital enterprises. In this paper, we propose Eigen-direction-aware ZF (EDA-ZF) with partial coordination among base stations (BSs) and distributed interference suppression as a practical approach to achieve this objective. We compare our solution with Zero Forcing (ZF), entailing neither BS coordination or inter-cell interference mitigation, and Network MIMO (NeMIMO), where full BS coordination enables centralized inter-cell interference management. We also evaluate the performance of said schemes for three sub-6 GHz deployments with varying BS densities -- sparse, intermediate, and dense -- all with fixed total number of antennas and radiated power. Extensive simulations show that: (i) indoor massive MIMO implementing the proposed EDA-ZF provides uniformly good rates for all users; (ii) indoor network densification is detrimental unless full coordination is implemented; (iii) deploying NeMIMO pays off under strong outdoor interference, especially for cell-edge users.

This paper presents an efficient approach to computing the capacity of multiple-input multiple-output (MIMO) channels under multiple linear transmit covariance constraints (LTCCs). LTCCs are general enough to include several special types of power constraints as special cases such as the sum power constraint (SPC), per-antenna power constraint (PAPC), or a combination thereof. Despite its importance and generality, most of the existing literature considers either SPC or PAPC independently. Efficient solutions to the computation of the MIMO capacity with a combination of SPC and PAPC have been recently reported, but were only dedicated to multiple-input single-output (MISO) systems. For the general case of LTCCs, we propose a low-complexity semi-closed-form approach to the computation of the MIMO capacity. Specifically, a modified minimax duality is first invoked to transform the considered problem in the broadcast channel into an equivalent minimax problem in the dual multiple access channel. Then alternating optimization and concave-convex procedure are utilized to derive water-filling-based algorithms to find a saddle point of the minimax problem. This is different from the state-of-the-art solutions to the considered problem, which are based on interior-point or subgradient methods. Analytical and numerical results are provided to demonstrate the effectiveness of the proposed low-complexity solution under various MIMO scenarios.