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 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.

This paper addresses receiver (RX) signal processing in multiuser multiple-input multiple-output (MU-MIMO) systems. We focus on uplink orthogonal frequency-division multiple access (OFDMA)-based MU-MIMO communications under in-phase/quadrature (I/Q) imbalance in the associated radio frequency electronics. It is shown in the existing literature that transceiver I/Q imbalances cause cross-talk of mirror-subcarriers in OFDM systems. As opposed to typically reported single-user studies, we extend the studies to OFDMA-based MU-MIMO communications, with simultaneous user multiplexing in both frequency and spatial domains, and incorporate also external interference from multiple sources at RX input, for modeling challenging conditions in increasingly popular heterogeneous networks. In the signal processing developments, we exploit the augmented subcarrier processing, which processes each subcarrier jointly with its counterpart at the image subcarrier, and jointly across all RX antennas. Furthermore, we derive an optimal augmented linear RX in terms of minimizing the mean-squared error. The novel approach integrates the I/Q imbalance mitigation, external interference suppression, and data stream separation of multiple UEs into a single processing stage, thus avoiding separate transceiver calibration. Extensive analysis and numerical results show the signal-to-interference-plus-noise ratio (SINR) and symbol-error rate (SER) behavior of an arbitrary data stream after RX spatial processing as a function of different system and impairment parameters. Based on the results, the performance of the conventional per-subcarrier processing is heavily limited under transceiver I/Q imbalances, and is particularly sensitive to external interferers, whereas the proposed augmented subcarrier processing provides a high-performance signal processing solution being able to detect the signals of different users as well as suppress the external interference efficiently. Finally, we also extend the studies to massive MIMO framework, with very large antenna systems. It is shown that, despite the huge number of RX antennas, the conventional linear processing methods still suffer heavily from I/Q imbalances while the augmented approach does not have such limitations.

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.

Sectorized antennas are a promising class of antennas for enabling direction-of-arrival (DoA) estimation and successive transmitter localization. In contrast to antenna arrays, sectorized antennas do not require multiple transceiver branches and can be implemented using a single RF front-end only, thus reducing the overall size and cost of the devices. However, for good localization performance the underlying DoA estimator is of uttermost importance. In this paper, we therefore propose a novel high performance DoA estimator for sectorized antennas that does not require cooperation between the transmitter and the localizing network. The proposed DoA estimator is broadly applicable with different sectorized antenna types and signal waveforms, and has low computational complexity. Using computer simulations, we show that our algorithm approaches the respective Cramer-Rao lower bound for DoA estimation variance if the signal-to-noise ratio (SNR) is moderate to large and also outperforms the existing estimators. Moreover, we also derive analytical error models for the underlying DoA estimation principle considering both free space as well as multipath propagation scenarios. Furthermore, we also address the fusion of the individual DoA estimates into a location estimate using the Stansfield algorithm and study the corresponding localization performance in detail. Finally, we show how to implement the localization in practical systems and demonstrate the achievable performance using indoor RF measurements obtained with practical sectorized antenna units.

In this paper, we consider cooperating secondary users (SUs) that are estimating the location and transmit power of an emitter that may be either a primary user (PU) or another SU in the cognitive radio (CR) context. Since SU devices need to be affordable and portable, their capabilities to estimate e.g. the direction of arrival (DOA) are very limited. In addition, the geographical distribution of SUs is random rather than optimized resulting in very stringent conditions for the localization. We therefore analyze possible benefits and performance of hybrid received signal strength (RSS)- DOA based emitter transmit power and position estimation by means of the Cramer-Rao bound (CRB). We show that the RSS-DOA hybrid approach can lead to significantly lower bounds compared to estimation based on only RSS or DOA. Assuming a special case of two SUs at equal distance from the emitter, we derive the CRBs in closed-form and demonstrate that for certain SU-emitter geometries, the RSS-only and DOA-only CRBs become infinite due to the inability to estimate one of the emitter's coordinates. Since those coordinates are orthogonal for RSS and DOA, the hybrid approach does not suffer from these geometries, resulting in a finite CRB. Results from Monte-Carlo simulations are provided which illustrate that significant theoretic gains are possible also for random SU and emitter placements. The results also show that biggest performance gains from joint processing of RSS and DOA are available when the number of cooperating SUs is fairly small, in the order of 2-5.