Janis Werner

Widely-linear beamforming and RF impairment suppression in massive antenna arrays

In this paper, the sensitivity of massive antenna arrays and digital beamforming to radio frequency (RF) chain in-phase quadrature-phase (I/Q) imbalance is studied and analyzed. The analysis shows that massive antenna arrays are increasingly sensitive to such RF chain imperfections, corrupting heavily the radiation pattern and beamforming capabilities. Motivated by this, novel RF-aware digital beamforming methods are then developed for automatically suppressing the unwanted effects of the RF I/Q imbalance without separate calibration loops in all individual receiver branches. More specifically, the paper covers closed-form analysis for signal processing properties as well as the associated radiation and beamforming properties of massive antenna arrays under both systematic and random RF I/Q imbalances. All analysis and derivations in this paper assume ideal signals to be circular. The well-known minimum variance distortionless response (MVDR) beamformer and a widely-linear (WL) extension of it, called WL-MVDR, are analyzed in detail from the RF imperfection perspective, in terms of interference attenuation and beamsteering. The optimum RF-aware WL-MVDR beamforming solution is formulated and shown to efficiently suppress the RF imperfections. Based on the obtained results, the developed solutions and in particular the RF-aware WL-MVDR method can provide efficient beamsteering and interference suppressing characteristics, despite of the imperfections in the RF circuits. This is seen critical especially in the massive antenna array context where the cost-efficiency of individual RF chains is emphasized.

Location Based Beamforming in 5G Ultra-Dense Networks

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 accessnodes (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-interferenceplus- 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.

Analysis and Augmented Spatial Processing for Uplink OFDMA MU-MIMO Receiver With Transceiver I/Q Imbalance and External Interference

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.

Performance and Cramer–Rao Bounds for DoA/RSS Estimation and Transmitter Localization Using Sectorized Antennas

Using collaborative sensors or other observing devices equipped with sectorized antennas provides a practical and low-cost solution to direction-of-arrival (DoA) and received signal strength (RSS) estimation, as well as noncooperative transmitter (TX) localization. In this paper, we study the performance and theoretical bounds of DoA/RSS estimation and localization using sectorized antennas. We first show that the sector-power measurements at an individual sensor form a sufficient statistic for DoA/RSS estimation and TX localization. Motivated by that, we then derive the Cramer-Rao bound (CRB) on DoA/RSS estimation based on sector-powers and study its asymptotic behavior. Moreover, we derive an analytical expression for the mean square error (MSE) of a practical sectorized-antenna-based DoA estimator, compare its performance to the derived CRB, and study its asymptotic properties. Next, we derive the CRB for localization based on sector-powers. The resulting CRB is a lower bound for a localization system where the DoA/RSS estimates, obtained from sector-powers at individual sensors, are fused together into a location estimate. Moreover, the CRB also covers the more general case of a localization system where the sector-powers from individual nodes are directly fused together, without an intermediate DoA/RSS estimation step. We compare the obtained CRB to a localization approach employing an intermediate DoA/RSS estimation step and observe that skipping this intermediate processing step may result in a substantially improved localization performance. Finally, we study the influence of various important system parameters, such as the number of sensors, sectors, and measurement samples, on the achievable estimation and localization performance. Overall, this paper demonstrates and quantifies the achievable DoA/RSS estimation and localization performance of sectorized antennas and provides comprehensive design guidelines for sector-power based low-complexity localization systems.

Joint Device Positioning and Clock Synchronization in 5G Ultra-Dense Networks

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.

Sectorized Antenna-based DoA Estimation and Localization: Advanced Algorithms and Measurements

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.

High-Efficiency Device Localization in 5G Ultra-Dense Networks: Prospects and Enabling Technologies

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.

Primary user localization in cognitive radio networks using sectorized antennas

Information about primary user (PU) location can enable several key capabilities in cognitive radio (CR) networks. In this paper we consider PU localization using received-signal-strength (RSS) and direction-of-arrival (DoA) estimates from sectorized antenna. Abstracting from practical antenna types, we define a sectorized antenna as an antenna that can be set to different operating modes, each of which resulting in a selectivity of those signals that arrive from within a certain, continuous range of angles, i.e. a sector. We propose a low complexity algorithm, the MaxE algorithm, that provides coarse RSS and DoA estimates, and derive the asymptotic bounds for its root mean square error (RMSE) as a function of the antenna parameters. We then propose a modified Stansfield algorithm with a novel RSS-based weighting scheme based on the Stansfield DoA fusion method, which obtains PU location estimates from measurements of the MaxE algorithm. The modified Stansfield algorithm improves the accuracy of the Stansfield algorithm with equal weights. Simulation results studying the impact of various system parameters, such as number of sectors, number of samples and signal-to-noise ratio, on the DoA/RSS estimation and localization accuracy are presented to provide design guidelines for localization systems based on sectorized antennas.

Estimating the primary user location and transmit power in cognitive radio systems using extended Kalman filters

In cognitive radio systems, the primary user location and transmit power are valuable information in order to create an efficient secondary network that uses spatial spectrum holes without introducing interference to the primary users. Since primary users cannot be assumed to cooperate, their locations and transmit powers need to be estimated at the secondary users. Existing localization techniques, based on the received signal strength require a high amount of secondary users in order to achieve a high accuracy while direction of arrival based methods alone suffer from the random secondary user positions and the requirements for making the associated devices portable. In this paper we propose a hybrid solution using an extended Kalman filter that is able to localize a moving primary user and estimate its transmit power under the aforementioned conditions. Its performance is compared to an extended Kalman filter that uses only direction of arrival estimation by means of Monte Carlo simulations, and shown to clearly outperform the DOA-only based processing.

Precoded massive MU-MIMO uplink transmission under transceiver I/Q imbalance

In massive multiple-input multiple-output (MIMO) systems, combined with digital array processing, the amount of the associated radio frequency (RF) front-ends is inevitably high. This paper addresses how imperfections in these RF front-ends affect the overall system performance in precoded massive multiuser MIMO (MU-MIMO) uplink transmission. In particular, we focus on transceiver in-phase/quadrature (I/Q) imbalances and their mitigation with RF-aware spatial processing. We first derive the essential distortion and interference models for OFDMA-based massive MU-MIMO uplink system under I/Q imbalances, and then propose augmented spatial post-processing to be carried out in the uplink receiver (RX) for mitigating the harmful effects efficiently. Numerical examples show that the augmented spatial RX processing clearly outperforms the conventional linear processing, and thus provides significant performance improvements with practical low-cost RF front-ends.