Software-Defined Radios (SDRs) enable more flexible connectivity solutions than traditional systems, but still face several challenges hindering their widespread adoption. General-Purpose Processor (GPP) based SDRs have generally been too slow for low-latency protocols. Meanwhile Field Programmable Gate Array (FPGA)-based SDR setups suffer from high prices and a steep learning curve for developers. This paper investigates the feasibility of implementing Bluetooth Low Energy (BLE) in a GPP based Peripheral Component Interconnect Express (PCIe) connected SDR. In particular, we focus on adhering to the timing requirements of BLE in a practical SDR implementation. For this, we propose a multi-threaded implementation based on a subset of the open-source BLE library BTLE. Using a signal generator and oscilloscope, we show that the SDR is able to achieve a response time down to 105 μs and can accurately respond in the required$150\pm 2\ \upmu\mathrm{s}$Inter Frame Space (IFS) time window. Furthermore, we also validate that channel hopping is supported by the SDR-based platform. To the best of our knowledge, this is the first SDR implementation able to meet the IFS requirements of BLE, hereby leading the way for more complete fully software based BLE protocol stacks.

The recent rise of the Cloud Virtual Reality (VR) paradigm, in which VR frames are streamed from a remote server to the user's Head-Mounted Display (HMD), poses some interesting challenges from a networking perspective. VR flows are high-throughput and have strict latency requirements. In this paper, we analyze on-the-fly edge transcoding, which follows the opposite philosophy from the more common slicing approach: instead of adapting resource allocation to the content, we compress the frames to fit into the allocated bandwidth, guaranteeing limited latency. Our results show that this strategy is effective in maintaining low latency, but the picture quality is highly dependent on the computing power of the Base Station (BS).

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.

Achieving cyber-physical control over a wireless channel requires satisfying both the timeliness of a single packet and preserving the latency reliability across several consecutive packets. To satisfy those requirements as an ubiquitous service requires big infrastructural developments, or flexible on-demand equipment such as UAVs. To avoid the upfront cost in terms of finance and energy, this paper analyzes the capability of UAV access points (UAVAPs) to satisfy the requirements for cyber-physical traffic. To investigate this, we perform a Gilbert-Eliott burst-error analysis that is analytically derived as a combination of two separate latency measurement campaigns and provide an upper-bound analysis of the UAVAP system. The analysis is centered around a UAVAP that uses its LTE connection to reach the backhaul, while providing service to ground nodes (GNs) with a Wi-Fi access point (AP). Thus, we combine both measurement campaigns to analyze the plausibility of the described setup in casual, crowded or mixed network settings.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

For WLAN systems in which relaying is used to improve throughput performance for nodes located at the cell edge, node mobility and information collection delays can have a significant impact on the performance of a relay selection scheme. In this paper we extend our existing Markov Chain modeling framework for relay selection to allow for efficient calculation of relay policies given either mean throughput or kth throughput percentile as optimization criterium. In a scenario with static access point, static relay, and a mobile destination node, the kth throughput percentile optimization is furthermore shown to be beneficial for improving throughput among weak node positions that will otherwise achieve zero throughput.

For centralized selection of communication relays, the necessary decision information needs to be collected from the mobile nodes by the access point (centralized decision point). In mobile scenarios, the required information collection and forwarding delays will affect the reliability of the collected information and hence will influence the performance of the relay selection method. This paper analyzes this influence in the decision process for the example of a mobile location-based relay selection approach using a continuous time Markov chain model. The model is used to obtain optimal relay policies via a heuristically reduced brute-force search. Numerical results show how forwarding delays affect these optimal policies.

This paper considers the impact of node mobility on the selection of the lowest bit error rate (BER) relay path from access point to destination node in a downlink data transmission scenario. The access point uses link signal to noise ratio (SNR) measurements provided by the mobile nodes to select the path either as direct or two-hop via a mobile relay. The impact of delays in the measurement collection procedure in this scheme is analyzed in comparison to an ideal selection and an always direct scheme. An ns-2 and matlab based simulation framework is used to investigate effects of varying scenario parameters on the achieved average BER of data transmissions. The SNR measurement collection is based on periodic hello broadcasts. Results show that increasing node speed can lead to performance that is worse than always transmitting directly. The hello broadcast rate can mitigate this effect, however, at the cost of an increase in signaling overhead. We find that discarding old measurements can significantly improve the BER performance without increasing the signaling overhead. This result establishes the importance of joint tuning of the hello broadcast rate and the measurement storage time.

In this paper we propose a relay selection scheme which uses collected location information together with a path loss model for relay selection, and analyze the performance impact of mobility and different error causes on this scheme. Performance is evaluated in terms of bit error rate by simulations. The SNR measurement based relay selection scheme proposed earlier is unsuitable for use with fast moving users in e.g. vehicular scenarios due to a large signaling overhead. The proposed location based scheme is shown to work well with fast moving users due to a lower signaling overhead. The required location accuracy was found to be comparable to the accuracy of standard GPS. As the scheme was found to be highly sensitive to NLOS situations with unknown attenuation, knowledge of obstacle locations obtained either by sensing online or from a map of obstacles, was identified as a prerequisite in these situations. As the location-based scheme relies on a path loss model to estimate link qualities and select relays, the sensitivity with respect to inaccurate estimates of the unknown path loss model parameters is investigated. The parameter ranges that result in useful performance were found to be wide enough to allow them to be estimated in practical systems.

Multipoint-to-multipoint message broadcast is a demanding application scenario in ad-hoc networks. Adaptive management of wireless resources is necessary to support such applications in a safety critical context. In this work we study adaptation of transmission rate and power to varying densities of ad-hoc nodes. Our approach is to construct a cross-layer model building on existing models for physical and link layers. To enable optimization in relation to metrics of end-to-end delay and message reception probability a model of flooding broadcast is proposed as a part of the cross-layer model. In a simulation study we show that adaptation of transmission power and rate can be necessary to achieve delay requirements and maximize message reception probability. Compared to simulation our cross-layer model based optimization approach generates slightly more conservative parameter settings. It is further shown how correlated losses have a significant impact on the robustness of the broadcast technique.

The wireless hybrid enhanced mobile radio estimators (WHERE) consortium researches radio positioning techniques to improve various aspects of communications systems. In order to provide the benefits of position information available to communications systems, hybrid data fusion (HDF) techniques estimate reliable position information. Within this paper, we first present the scenarios and radio technologies evaluated by the WHERE consortium for wireless positioning. We compare conventional HDF approaches with two novel approaches developed within the framework of WHERE. Yet, HDF may still provide insufficient localization accuracy and reliability. Hence, we will research and develop new cooperative positioning algorithms, which exploit the available communications links among mobile terminals of heterogeneous wireless networks, to further enhance the positioning accuracy and reliability.