Recently, wake-up scheme has been proposed to enhance the energy-efficiency of 5G mobile devices and prolong its battery lifetime while reducing the buffering delay. The existing wake-up optimization mechanisms use off-line methods and are tied to specific traffic models. In this paper, a novel concept of wake-up scheduling is introduced to further improve the energy-efficiency of mobile devices and to deal with realistic traffic. The main idea is to use a fixed configuration of the wake-up scheme and adjust the scheduling of the wake-up signals dynamically. For this, a proactive wake-up scheduler is proposed to take online decisions based on traffic prediction. Towards this end, a framework to predict packet arrivals based on recurrent neural networks is developed. Numerical results show that for given delay requirements of video, audio streaming, and mixed traffic flow, the proactive wake-up scheduler reduces the power consumption of the baseline wake-up scheme without scheduler by up to 36%, 28% and 9%, respectively.

Recently, the concept of wake-up radio based access has been considered as an effective power saving mechanism for 5G mobile devices. In this article, the average power consumption of a wake-up radio enabled mobile device is analyzed and modeled by using a semi-Markov process. Building on this, a delay-constrained optimization problem is then formulated, to maximize the device energy-efficiency under given latency requirements, allowing the optimal parameters of the wake-up scheme to be obtained in closed form. The provided numerical results show that, for a given delay requirement, the proposed solution is able to reduce the power consumption by up to 40% compared with an optimized discontinuous reception (DRX) based reference scheme.

Recently, discontinuous reception mechanisms (DRX) and wake-up schemes (WuS) have been proposed to enhance the energy efficiency of 5G mobile devices and prolong the battery lifetime. The existing DRX and WuS use commonly pre-configured parameters that cannot be adjusted dynamically. In this paper, a novel wake-up scheduling (WuSched) concept is introduced to further improve the energy efficiency of WuS-enabled mobile devices while controlling the buffering delay in a dynamic manner. The main idea of WuSched is to use a fixed configuration of the wake-up scheme and adjust the scheduling of the wake-up signals dynamically based on actual traffic arrivals. For this purpose, two different optimization approaches of the wake-up scheduling concept are proposed, analyzed, and compared, namely offline and online wake-up schedulers (WuSched-Offline and WuSched-Online). First, the WuSched-Offline is analyzed analytically for Poisson traffic arrivals and optimized (offline) to balance the average delay and power consumption. Second, the WuSched-Online is proposed to take online decisions based on traffic prediction, which is able to deal with general and more complex traffic models. Towards this end, we develop a framework for the prediction of packet arrivals based on recurrent neural networks. Numerical results show that both wake-up schedulers outperform the ordinary WuS-based system where wake-up scheduler is not deployed. In particular, for predefined delay requirements of video streaming, audio streaming, and mixed traffic flow, the WuSched-Online reduces the power consumption of the baseline WuS by up to 36%, 28% and 9%, respectively. Results also show that the WuSched-Offline has slightly better energy efficiency than the WuSched-Online in the case of Poisson packet arrivals, as it is optimized for that, while its power consumption is slightly higher than that of the WuSched-Online scheduler for realistic traffic scenarios.

The limitations of state-of-the-art cellular modems prevent achieving low-power and low-latency Machine Type Communications (MTC) based on current power saving mechanisms alone. Recently, the concept of wake-up scheme has been proposed to enhance battery lifetime of 5G devices, while reducing the buffering delay. The existing wake-up algorithms use static operational parameters that are determined by the radio access network at the start of the user's session. In this paper, the average power consumption of the wake-up enabled MTC TIE is modeled by using a semi-Markov process and then optimized through a delay-constrained optimization problem, by which the optimal wake-up cycle is obtained in closed form. Numerical results show that the proposed solution reduces the power consumption of an optimized Discontinuous Reception (DRX) scheme by up to 40% for a given delay requirement.

In this paper, we exploit the 3D-beamforming features of multiantenna equipment employed in fifth generation (5G) networks, operating in the millimeter wave (mmW) band, for accurate positioning and tracking of users. We consider sequential estimation of users' positions, and propose a two-stage extended Kalman filter (EKF) that is based on reference signal received power (RSRP) measurements. In particular, beamformed downlink (DL) reference signals (RSs) are transmitted by multiple base stations (BSs) and measured by user equipments (UEs) employing receive beamforming. The so-obtained beam-RSRP (BRSRP) measurements are reported to the BSs where the corresponding directions of departure (DoDs) are sequentially estimated by a novel EKF. Such angle estimates from multiple BSs are subsequently fused on a central entity into 3D position estimates of UEs by means of another (second-stage) EKF. The proposed positioning scheme is scalable since the computational burden is shared among different network entities, namely transmission/reception points (TRPs) and 5G-NR Node B (gNB), and may be accomplished with the signalling currently specified for 5G. We assess the performance of the proposed algorithm on a realistic outdoor 5G deployment with a detailed ray tracing propagation model based on the METIS Madrid map. Numerical results with a system operating at 39 GHz show that sub-meter 3D positioning accuracy is achievable in future mmW 5G networks.

This paper considers direction-finding in millimeter wave (mmW) fifth generation (5G) networks by means of beam-based downlink (DL) reference signal received power (RSRP) measurements and subsequent reporting. In particular, we propose two methods that allow user equipments (UEs) to select, in an independent and dynamic manner, the most-relevant beam-RSRP (BRSRP) measurements as a trade-off between angle-related information and load of the feedback channel. A likelihood ratio (LR)-test is derived in which the hypothesis for “noise-only” BRSRP measurement is compared to that of “reference signal (RS)-plus-noise” observations, under a given significance level. A power threshold based method is also proposed in which the BRSRP measurements are compared to a threshold proportional to the noise power. Such a noise variance is estimated at each UE independently. The performance of the proposed beam selection schemes is assessed by means of an extended Kalman filter (EKF) tracking the direction of departure (DoD) of the line-of-sight (LoS) path between base stations (BSs) and a UE. Extensive numerical results are provided on a realistic mmW 5G outdoor deployment scenario operating at 39 GHz and with a ray-tracing propagation model based on the METIS Madrid grid.

Millimeter wave (mmW) communication has been viewed as a way of offloading users from lower frequencies, where currently deployed cellular networks operate. The short wavelength of mmW frequencies allows for electrically large arrays to be deployed in small mobile devices. Electrically large arrays (in terms of wavelength) are needed in order to compensate for the high signal attenuation in mmW frequencies by means of transmit-receive beamforming. Two beamforming architectures are typically considered in practice, namely a fully digital solution in which every antenna has a dedicated radio frequency (RF) chain, and a so-called hybrid solution that consists of an analog beamforming stage prior to digital beamforming. Hybrid beamforming trades-off the versatility of a fully digital architecture for lower power-consumption and cost. In this paper we focus on fully analog beamforming architectures for circular arrays comprised of patch antennas. In particular, we propose two architectures for analog beamforming with circular arrays, and provide a detailed study regarding the number of simultaneously active beams (i.e, number of RF chains), the amount of antennas per beam, as well as the number of required phase-shifters. The proposed analog beamforming scheme is compared to a fully digital beamforming approach on a realistic 5G system level platform simulating an urban environment. Results show that the performance of the proposed analog beamforming architecture is comparable to that of a fully digital beamforming scheme in terms of user throughput.

The expected fifth generation (5G) networks allow for highly accurate direction of arrival and time of arrival (ToA) estimation, thus providing a convenient environment for device positioning, if designed properly. However, utilizing ToA measurements for positioning requires a tight synchronization not only between the target devices but also among the network elements. In this paper, we propose a joint positioning and synchronization solution building on the premises of the envisioned cmWave-based 5G ultra-dense networks and time-varying clock models. In addition to device location estimates, also relative clock offsets and skews are estimated and tracked within the proposed extended Kalman filter based solutions, which can be further used by a network operator in synchronizing the active network elements and devices within the network. Based on extensive simulations and numerical evaluations, accurate positioning performance can be achieved while tracking the clock parameters under time-varying clock errors.

This paper proposes an approach for receiving small uplink data transmissions from a massive amount of machine type communication (MTC) devices in ultra-dense networks (UDNs). In particular, we propose using a beamforming based solution for distributing (broadcasting) uplink grants for small data transmissions. Receive beamforming is also employed in order to receive the subsequent uplink small data transmissions. The proposed approach is particularly useful in reaching high transmission success probability of MTC uplink traffic with low consumption of physical resources. Extensive numerical results are provided based on detailed system level simulations in an ultra-dense 5G context. Results show that low collision and decoding failure probabilities can be achieved by employing beamforming and orthogonal codes for connectionless uplink grant distribution. Additionally, it is shown that uplink transmissions can be received efficiently by utilizing receive filters designed to maximize the received power towards the directions where grants were broadcasted. The performance of the proposed scheme is analyzed with a typical uniformly distributed massive MTC (mMTC) traffic as well as with a peak access profile as an extreme case.

The upcoming fifth generation (5G) radio networks will be the game changer of future societies. In addition to obvious improvements in wireless communications, 5G enables also highly accurate user equipment (UE) positioning that is carried out on the network side. Such a solution provides ubiquitous positioning services without draining the batteries of the UEs. In this paper, we concentrate on positioning methods that suits the future needs of automotive transportation and intelligent transportation system (ITS). In particular, we demonstrate how the location estimates can be obtained in 5G ultra-dense networks (UDNs) efficiently and even in a proactive manner where the UE locations can be predicted to some extent. Numerical performance analysis will then illustrate that the proposed 5G-based network-centric positioning solutions are well-suited for car and traffic applications, providing even sub-meter range positioning accuracy.

In this paper, we address the prospects and key enabling technologies for highly efficient and accurate device positioning and tracking in fifth generation (5G) radio access networks. Building on the premises of ultra-dense networks as well as on the adoption of multicarrier waveforms and antenna arrays in the access nodes (ANs), we first formulate extended Kalman filter (EKF)-based solutions for computationally efficient joint estimation and tracking of the time of arrival (ToA) and direction of arrival (DoA) of the user nodes (UNs) using uplink reference signals. Then, a second EKF stage is proposed in order to fuse the individual DoA and ToA estimates from one or several ANs into a UN position estimate. Since all the processing takes place at the network side, the computing complexity and energy consumption at the UN side are kept to a minimum. The cascaded EKFs proposed in this article also take into account the unavoidable relative clock offsets between UNs and ANs, such that reliable clock synchronization of the access-link is obtained as a valuable by-product. The proposed cascaded EKF scheme is then revised and extended to more general and challenging scenarios where not only the UNs have clock offsets against the network time, but also the ANs themselves are not mutually synchronized in time. Finally, comprehensive performance evaluations of the proposed solutions on a realistic 5G network setup, building on the METIS project based outdoor Madrid map model together with complete ray tracing based propagation modeling, are provided. The obtained results clearly demonstrate that by using the developed methods, sub-meter scale positioning and tracking accuracy of moving devices is indeed technically feasible in future 5G radio access networks operating at sub-6 GHz frequencies, despite the realistic assumptions related to clock offsets and potentially even under unsynchronized network elements.

This paper proposes an approach for providing 5G services to mobile users that is based on continuous ultra dense networks (C-UDNs). The proposed approach outperforms the widely accepted solution based on macro cells and massive MIMO systems (M-MIMO). In particular, we show that the performance of M-MIMO systems deployed on macro cells is significantly limited by channel aging. The proposed mobility solution based on uplink beacons overcomes the handover problem often linked to UDNs. Dense networks make it possible for users to transmit at a power lower than that of macro cells, thus making C-UDN more robust to pilot contamination and allowing for lower Channel State Information (CSI) latencies due to shorter reuse distance of UL pilots. Extensive numerical results from detailed systemlevel simulations are provided in order to compare and highlight the benefits of the proposed C-UDN mobility solution to that of a macro M-MIMO deployment.

This paper proposes a novel user-transparent mobility concept based on the transmission of uplink reference signals (beacons) using Zadoff-Chu signature sequences. Unlike legacy mobility schemes, the proposed scheme overcomes the need for the user to perform time and energy consuming downlink measurements and reporting them back to the network. This is particularly relevant for ultra dense network (UDN) deployments and high user densities as forecasted in the near future. The system requirements to support uplink beacons are presented and a detailed design for the signature sequence is provided to support a variety of system configurations and deployment cases. Numerical results illustrate the suitability and sufficiency of beacon resources as well as the reliability of the proposed beacon design in order to support a truly user-transparent and border-less mobility concept.

In this paper we consider transmit (Tx) and receive (Rx) beamforming schemes based on the location of the device. In particular, we propose a design methodology for the Tx/Rx beamforming weight-vectors that is based on the departure and arrival angles of the line-of-sight (LoS) path between access-nodes (ANds) and user-nodes (UNds). A network-centric extended Kalman filter (EKF) is also proposed for estimating and tracking the directional parameters needed for designing the Tx and Rx beamforming weights. The proposed approach is particularly useful in 5G ultra-dense networks (UDNs) since the high-probability of LoS condition makes it possible to design geometric beams at both Tx and Rx in order to increase the signal-to-interference-plus-noise ratio (SINR). Moreover, relying on the location of the UNd relative to the ANds makes it possible to replace fullband uplink (UL) reference signals, commonly employed for acquiring the channel-state-information-at-transmitter (CSIT) in time-division-duplex (TDD) systems, by narrowband UL pilots. Also, employing the EKF for tracking the double-directional parameters of the LoS-path allows one to reduce the rate at which UL reference signals are transmitted. Consequently, savings in terms of time-frequency resources are achieved compared to beamforming schemes based on full-band CSI. Extensive numerical results are included using a realistic ray-tracing based system-level simulator in ultra-dense 5G network context. Results show that position based beamforming schemes outperform those based on full-band CSI in terms of mean user-throughput even for highly mobile users.

It is commonly expected that network densification will play an important role in achieving the capacity demands of 5G communication networks. While densification is introduced to improve the spectral efficiency and area-capacity, it also results in an infrastructure that is perfectly suitable for user node (UN) positioning. However, so far this compelling opportunity has not been clearly recognized in the literature. In this paper, we therefore propose to make "always on" positioning an integral part of 5G networks such that highly accurate user location estimates are available at any given moment but without draining the UN batteries. We furthermore propose an extended Kalman filter (EKF) that tracks the user location based on the fusion of direction of arrival (DoA) and time of arrival (ToA) estimates obtained at the access nodes (ANs) of the 5G network. Since ToA estimates are typically not useful for positioning unless the UN is synchronized with the network, we include a realistic clock model within the DoA/ToA EKF. This addition makes it possible to estimate the offset of the imperfect UN clock, along with the UN position. In an extensive analysis that is based on specific 5G simulation models, we then quantify the enormous potential of high accuracy positioning in 5G networks, in general, and the proposed DoA/ToA EKF, in particular. Moreover, we demonstrate that the proposed DoA/ToA EKF substantially outperforms the classical DoA-only EKF and is furthermore also able to handle practically extremely relevant situations where the DoA-only EKF fails to position the UN.

The deployment of future 5G ultra-dense small cell networks provides unprecedented opportunities to create an advanced localization system that meets the demands of future location-based services and functionalities. In this paper, we present technical enablers for obtaining location information of user nodes (UNs) in a network-centric manner. More specifically, we focus on signal properties, access node (AN) hardware and AN deployments in the envisioned 5G systems. Moreover, we provide illustrative examples of the expected localization performance and indicate how to efficiently predict the UN location. Finally, we offer insights into the utilization of location-awareness and location prediction, and show that it provides substantial benefits compared to existing radio networks.

This paper proposes a novel frame structure for the radio access interface of the next generation of mobile networks. The proposed frame structure has been designed to support multiuser spatial multiplexing, short latencies on the radio access interface, as well as mobility and small packet transmissions. The focus is on ultra dense small cell networks deployed in outdoor environments. This paper also highlights the various prospects and constraints of the proposed dense outdoor system in comparison with alternative system designs. Numerical results are included and a comparison to the Long Term Evolution (LTE) system is provided. Results show that the proposed radio frame structure leads to an improvement of the area spectral efficiency by a factor of ≈ 2.4 as well as a reduction of the average air interface latency by a factor of 5, thus remaining shorter than 1 millisecond.

This letter considers the problem of estimating the directional probability distribution of wavefields observed by sensor arrays. In particular, the angular distributions of wavefields are assumed to be mixtures of von-Mises distributions. Mixture models facilitate estimating multimodal and skewed angular distributions. The von-Mises distribution is fully defined with two parameters, namely the mean direction (circular mean) and the concentration parameter. The widely-employed Gaussian distribution is not appropriate in directional statistics since its support is the entire real-line instead of the$[- \pi,\pi)$angular domain. A covariance-matching based estimator is proposed for the parameters of a mixture of von-Mises distributions and the corresponding Cramér-Rao lower bound is derived. A closed-form expression for the covariance matrix of the array response due to scattering is also derived based on the wavefield modeling principle. These results remain valid even for real-world conformal arrays with nonidealities including mutual coupling, mounting platform reflections, and array elements with individual directional beampatterns.

In this paper we propose azimuth and elevation angle of arrival estimation algorithms for arbitrary array configurations. The proposed algorithms extend the Polynomial Rooting Intersection for Multidimensional Estimation (PRIME) and statistically efficient Modified Variable Projection (MVP) algorithms to arbitrary sensor array configurations without explicit knowledge of the steering vector. The proposed algorithms exploit the concept of Manifold Separation Technique (MST). Thus, the data are processed in the element-space domain and are not subject to mapping errors. Moreover, closed-form derivatives of the Weighted Subspace Fitting (WSF) cost function are obtained, even for real-world arrays with imperfections, making the proposed MVP computationally attractive. The obtained estimates for both elevation and azimuth show an error variance close to the Cramer-Rao Lower Bound (CRLB).