Jussi Turkka

A Novel Radio Frame Structure for 5G Dense Outdoor Radio Access 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.

Borderless Mobility in 5G Outdoor Ultra-Dense Networks

This paper considers borderless 5G ultra-dense networks (UDNs). In particular, a novel scheduling algorithm is proposed that achieves a more uniform distribution of user-throughput than that of the state-of-the-art maximum-throughput (MT) schedulers. The proposed scheduling algorithm also takes the coherence time of the channel into account as well as the impact to the acquired channel state information. A novel radio frame structure that is appropriate for achieving a 1-ms round trip time latency is also proposed. Such a low latency allows one to employ multiuser as well as cooperative multiple input multiple output schemes for mobile users. An evaluation of matched-filter and zero-forcing precoding for mobile users in UDNs is included. The performance of the proposed 5G UDN concept is assessed using a system-level simulator. Extensive numerical results show that the proposed borderless scheduling concept achieves ~77% higher median user-throughput than that of the MT scheduler at the cost of ~17% lower area-throughput. Such results are obtained for a high density of mobile users at velocities of ~50 km/h.

Supporting mobility in 5G: A comparison between massive MIMO and continuous ultra dense networks

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.

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.

Coverage optimization for Minimization of Drive Tests in LTE with extended RLF reporting

In this article we have studied the coverage optimization for 3G Long Term Evolution (LTE) according to Minimization of Drive Tests (MDT) work item description in Third Generation Partnership Project (3GPP). Target for the MDT work item is to define a set of measurements and measurement reporting procedures, so that operators are able to collect measurement data from users and thereby reducing need for manual drive tests. The main focus of MDT is coverage optimization. We propose an extended Radio Link Failure (RLF) reporting for both coverage and mobility optimization use cases for MDT. In this paper the MDT studies and especially the benefits of extended RLF reporting and classification are analyzed by means of dynamic system level simulations. The RLFs are classified into three categories based on the content in extended RLF reports: coverage, handover parameterization and interference problem. It can be concluded that the coverage as well as other problems may be identified with quite high probability with only a minimal standardization effort.

An efficient grid-based RF fingerprint positioning algorithm for user location estimation in heterogeneous small cell networks

This paper proposes a novel technique to enhance the performance of grid-based Radio Frequency (RF) fingerprint position estimation framework. First enhancement is an introduction of two overlapping grids of training signatures. As the second enhancement, the location of the testing signature is estimated to be a weighted geometric center of a set of nearest grid units whereas in a traditional grid-based RF fingerprinting only the center point of the nearest grid unit is used for determining the user location. By using the weighting-based location estimation, the accuracy of the location estimation can be improved. The performance evaluation of the enhanced RF fingerprinting algorithm was conducted by analyzing the positioning accuracy of the RF fingerprint signatures obtained from a dynamic system simulation in a heterogeneous LTE small cell environment. The performance evaluation indicates that if the interpolation is based on two nearest grid units, then a maximum of 18.8% improvement in positioning accuracy can be achieved over the conventional approach.

Performance evaluation of LTE radio fingerprinting using field measurements

This paper evaluates positioning accuracy of radio fingerprinting algorithm in commercially deployed LTE (Long Term Evolution) network operating on 800 MHz, 1800 MHz and 2600 MHz frequency bands. Training database required for fingerprint positioning is constructed from field measurements that resemble LTE Release 10 Minimization of Drive Test (MDT) trace records. Performance is compared between LTE and LTE+WLAN grid-based RF fingerprint measurements utilizing partial fingerprint matching. Results indicate that partial fingerprints that consist of LTE and WLAN radio measurements decrease positioning errors at least by a factor of 3.5x while the percentage of discarded samples is kept low. This emphasizes the importance of having generic MDT functionality for automating the collection and correlation of radio measurements in heterogeneous LTE and WLAN networks.

Genetic algorithm optimized grid-based RF fingerprint positioning in heterogeneous small cell networks

In this paper we propose a novel optimization algorithm for grid-based RF fingerprinting to improve user equipment (UE) positioning accuracy. For this purpose we have used Multi-objective Genetic Algorithm (MOGA) which enables autonomous calibration of grid-cell layout (GCL) for better UE positioning as compared to that of the conventional fingerprinting approach. Performance evaluations were carried out using two different training data-sets consisting of Minimization of Drive Testing measurements obtained from a dynamic system simulation in a heterogeneous LTE small cell environment. The robustness of the proposed method has been tested analyzing positioning results from two different areas of interest. Optimization of GCL is performed in two ways: (1) array-wise calibration of the grid-cell units using non-overlapping GCL and (2) creating an overlapping GCL to cover of whole simulation area with different rectangular grid-cell units. Simulation results show that if sufficient amount of training data is available then the proposed method can improve positioning accuracy of 56.74% over the conventional grid-based RF fingerprinting.

On the spatial consistency of stochastic and map-based 5G channel models

The 5th Generation (5G) of wireless mobile communication systems is currently the focus of many research projects and standardization bodies, such as the 3rd Generation Partnership Project (3GPP). Accurate modeling of the radio propagation channel is important for evaluating the performance of candidate technologies for 5G. However, the state-of-the-art radio channel models, such as the 3GPP 3D channel model, are not appropriate for analyzing all use-cases and scenarios commonly considered for 5G mainly due to its inherent stochastic nature. In particular, the difficulty in dealing with long-term spatial consistency and lack of correlation of small-scale parameters motivates using channel models based on ray-tracing. In fact, channel models that provide spatially consistent observations are of fundamental importance in assessing the performance of multiuser and massive MIMO schemes, by taking into account the spatial correlation of small-scale parameters, as well as of continuous ultra-dense networks (UDNs). This paper shows, by means of extensive numerical results, that the 3GPP 3D channel model consistently underestimates the achievable SIR in LoS multiuser MISO settings. However, in NLoS scenarios and urban micro-cell deployments, the 3GPP 3D channel model significantly overestimates the SIR experienced by the users. Hence, the achievable performance of massive MIMO schemes and continuous UDNs can only be assessed with channel models that take into account the spatial correlation of small-scale parameters.

Generic Architecture for Minimizing Drive Tests in Heterogeneous Networks

This paper proposes a generic measurement architecture for automating the collection of User Equipment (UE) radio measurements to minimize the need of manual drive-tests in heterogeneous small cell networks consisting of LTE (Long Term Evolution) and WLAN (Wireless Local Area Network) access nodes. The proposed architecture relies on Minimization of Drive Tests (MDT) functionality specified in Third Generation Partnership Project (3GPP) and consists of user plane solution with trace assistance. In the proposed solution, trace assistance information, which is signaled to UE as part of MDT configuration, is provided to UE WLAN measurement unit allowing exchanging and correlating measurements later between LTE and WLAN network management systems. In addition, the prospects and the constraints of the architecture are discussed highlighting the importance in various operator use cases. Finally, the RF fingerprint positioning performance improvement of the proposed concept is analyzed by means of field measurements.