Caching-Aided Collaborative D2D Operation for Predictive Data Dissemination in Industrial IoT

Industrial automation deployments constitute challenging environments where moving IoT machines may produce high-definition video and other heavy sensor data during surveying and inspection operations. Transporting massive contents to the edge network infrastructure and then eventually to the remote human operator requires reliable and high-rate radio links supported by intelligent data caching and delivery mechanisms. In this work, we address the challenges of contents dissemination in characteristic factory automation scenarios by proposing to engage moving industrial machines as device-to-device (D2D) caching helpers. With the goal to improve reliability of high-rate millimeter-wave (mmWave) data connections, we introduce the alternative contents dissemination modes and then construct a novel mobility-aware methodology that helps develop predictive mode selection strategies based on the anticipated radio link conditions. We also conduct a thorough system-level evaluation of representative data dissemination strategies to confirm the benefits of predictive solutions that employ D2D-enabled collaborative caching at the wireless edge to lower contents delivery latency and improve data acquisition reliability

Capacity and Outage of Terahertz Communications with User Micro-mobility and Beam Misalignment

User equipment mobility is one of the primary challenges for the design of reliable and efficient wireless links over millimeter-wave and terahertz bands. These high-rate communication systems use directional antennas and therefore have to constantly maintain alignment between transmitter and receiver beams. For terahertz links, envisioned to employ radiation patterns of no more than few degrees wide, not only the macro-scale user mobility (human walking, car driving, etc.) but also the micro-scale mobility - spontaneous shakes and rotations of the device - becomes a severe issue. In this paper, we propose a mathematical framework for the first-order analysis of the effects caused by micro-mobility on the capacity and outage in terahertz communications. The performance of terahertz communications is compared with and without micro-mobility illustrating the difference of up to 1 Tbit/s or 75%. In response to this gap, it is finally shown how the negative effects of the micro-mobility can be partially addressed by a proper adjustment of the terahertz antenna arrays and the period of beam realignment procedure.Comment: Accepted to IEEE Transactions on Vehicular Technology on April 9, 2020. Copyright may be transferred without further notice after which this version may become non-availabl

Analysis of Intelligent Vehicular Relaying in Urban 5G+ Millimeter-Wave Cellular Deployments

The capability of smarter networked devices to dynamically select appropriate radio connectivity options is especially important in the emerging millimeter-wave (mmWave) systems to mitigate abrupt link blockage in complex environments. To enrich the levels of diversity, mobile mmWave relays can be employed for improved connection reliability. These are considered by 3GPP for on-demand densification on top of the static mmWave infrastructure. However, performance dynamics of mobile mmWave relaying is not nearly well explored, especially in realistic conditions, such as urban vehicular scenarios. In this paper, we develop a mathematical framework for the performance evaluation of mmWave vehicular relaying in a typical street deployment. We analyze and compare alternative connectivity strategies by quantifying the performance gains made available to smart devices in the presence of mmWave relays. We identify situations where the use of mmWave vehicular relaying is particularly beneficial. Our methodology and results can support further standardization and deployment of mmWave relaying in more intelligent 5G+ "all-mmWave" cellular networks.Comment: 6 pages, 8 figures. The paper has been accepted for IEEE GLOBECOM 2019. Copyright may be transferred without notice, after which this version may no longer be accessibl

Counter Waves Link Activation Policy for Latency Control in In-Band IAB Systems

3GPP’s Integrated Access and Backhaul (IAB) architecture is expected to deliver a cost-efficient option for deploying 5G New Radio (NR) systems. However, IAB relies on multi-hop wireless communications, and packet latency therefore becomes a critical metric in such systems. Latency minimization in the in-band backhauling regime involves dynamical scheduling of active transmission links so as to avoid half-duplex conflicts, which brings significant control overheads. In this paper, by using the formalism of Markov decision processes (MDP), we identify a general fixed link activation policy and the associated policy design algorithm for tree-shaped in-band IAB systems with half-duplex constraints. The proposed policy, named “counter waves”, does not require signalling between the IAB donor and nodes and provides stable low latency for low-to-medium traffic conditions spanning up to 60% of the capacity region of the system.Peer reviewe

Hover or Perch: Comparing Capacity of Airborne and Landed Millimeter-Wave UAV Cells

On-demand deployments of millimeter-wave (mmWave) access points (APs) carried by unmanned aerial vehicles (UAVs) are considered today as a potential solution to enhance the performance of 5G+ networks. The battery lifetime of modern UAVs, though, limits the flight times in such systems. In this letter, we evaluate a feasible deployment alternative for temporary capacity boost in the areas with highly fluctuating user demands. The approach is to land UAV-based mmWave APs on the nearby buildings instead of hovering over the area. Within the developed mathematical framework, we compare the system-level performance of airborne and landed deployments by taking into account the full operation cycle of the employed drones. Our numerical results demonstrate that the choice of the UAV deployment option is determined by an interplay of the separation distance between the service area and the UAV charging station, drone battery lifetime, and the number of aerial APs in use. The presented methodology and results can support efficient on-demand deployments of UAV-based mmWave APs in prospective 5G+ networks.Comment: Accepted to IEEE Wireless Communications Letters on July 20, 2020. Copyright may be transferred without further notice after which this version may become non-availabl

An Accurate Approximation of Resource Request Distributions in Millimeter Wave 3GPP New Radio Systems

The recently standardized millimeter wave-based 3GPP New Radio technology is expected to become an enabler for both enhanced Mobile Broadband (eMBB) and ultra-reliable low latency communication (URLLC) services specified to future 5G systems. One of the first steps in mathematical modeling of such systems is the characterization of the session resource request probability mass function (pmf) as a function of the channel conditions, cell size, application demands, user location and system parameters including modulation and coding schemes employed at the air interface. Unfortunately, this pmf cannot be expressed via elementary functions. In this paper, we develop an accurate approximation of the sought pmf. First, we show that Normal distribution provides a fairly accurate approximation to the cumulative distribution function (CDF) of the signal-to-noise ratio for communication systems operating in the millimeter frequency band, further allowing evaluating the resource request pmf via error function. We also investigate the impact of shadow fading on the resource request pmf.Comment: The 19th International Conference on Next Generation Wired/Wireless Networks and Systems (New2An 2019

Performance Assessment of an ITU-T Compliant Machine Learning Enhancements for 5 G RAN Network Slicing

Network slicing is a technique introduced by 3GPP to enable multi-tenant operation in 5 G systems. However, the support of slicing at the air interface requires not only efficient optimization algorithms operating in real time but also its tight integration into the 5 G control plane. In this paper, we first present a priority-based mechanism enabling defined performance isolation among slices competing for resources. Then, to speed up the resource arbitration process, we propose and compare several supervised machine learning (ML) techniques. We show how to embed the proposed approach into the ITU-T standardized ML architecture. The proposed ML enhancement is evaluated under realistic traffic conditions with respect to the performance criteria defined by GSMA while explicitly accounting for 5 G millimeter wave channel conditions. Our results show that ML techniques are able to provide suitable approximations for the resource allocation process ensuring slice performance isolation, efficient resource use, and fairness. Among the considered algorithms, polynomial regressions show the best results outperforming the exact solution algorithm by 5–6 orders of magnitude in terms of execution time and both neural network and random forest algorithms in terms of accuracy (by 20–40 %), sensitiveness to workload variations and training sample size. Finally, ML algorithms are generally prone to service level agreements (SLA) violation under high load and time-varying channel conditions, implying that an SLA enforcement system is needed in ITU-T's 5 G ML framework.publishedVersionPeer reviewe