Stephan Sand

Channel estimation for OFDM systems with multiple transmit antennas by exploiting the properties of the discrete Fourier transform

We addresses channel estimation based on the discrete Fourier transform (DFT) applied to OFDM-based MIMO systems. By exploiting the properties of the DFT, channel estimation schemes for MIMO-OFDM system can be simplified. The Fourier transform translates phase shifts in the frequency domain to delays in the time domain. In order to exploit this feature, phase shifted pilot sequences are a perfect match to the Fourier transform in terms of separating the N/sub T/ superimposed signals, corresponding to N/sub T/ transmit antennas, without any further processing. The proposed estimator is shown to be optimum for the sample spaced channel, i.e. the channel tap delays are multiples of the sampling duration. A sub-optimum approximation for the non-sample spaced channel is also suggested.

Particle filter based positioning with 3GPP-LTE in indoor environments

Global navigation satellite systems (GNSSs) can deliver very good position estimates under optimum conditions. However, especially in urban and indoor scenarios with severe multipath propagation and blocking of satellites by buildings the accuracy loss can be very large. Often, a position with GNSS is impossible in these scenarios. On the other hand, cellular wireless communication systems such as the third generation partnership project (3GPP) long-term evolution (LTE) provide excellent coverage in urban and most indoor environments. Thus, this paper researches timing based positioning algorithms, in this case time difference of arrival (TDoA), using 3GPP-LTE measurements. Several approaches and algorithms exist to solve the navigation equation for cellular systems, for instance Bayes filtering methods such as Kalman or particle filter. This paper specifically considers and develops a particle filter for 3GPP-LTE TDoA positioning. To obtain better positioning results, a 3GPP-LTE TDoA error model is derived. This error model is afterwards included in the likelihood function of the particle filter. The last part of this paper, evaluates the positioning performances of the developed particle filter in an indoor scenario. These evaluations show clearly the possibility of using 3GPP-LTE measurements for indoor positioning.

Modeling and analysis of a cellular MC-CDMA downlink system

The concept of a multi-cell environment for an MC-CDMA downlink scenario is presented; it is based on a propagation model taking into account path loss and shadowing. The interference of adjacent cells is studied and the Gaussian approximation (GA) is investigated for simplifying the interference simulation. The major influence of the interfering cells is restricted to the peripheral area of the desired cell. In particular, it is shown, by simulations, that the GA for the impinged interference signals is an appropriate assumption. This drastically decreases the simulation complexity for a high number of interfering cells, e.g., one or two tiers around the desired cell.

Hybrid Data Fusion and Tracking for Positioning with GNSS and 3GPP-LTE

Global navigation satellite systems (GNSSs) can provide reliable positioning information under optimum conditions, where at least four satellites can be accessed with sufficient quality. In critical situations, for example, urban canyons or indoor, due to blocking of satellites by buildings and severe multipath effects, the GNSS performance can be decreased substantially. To overcome this limitation, we propose to exploit additionally information from communications systems for positioning purposes, for example, by using time difference of arrival (TDOA) information. To optimize the performance, hybrid data fusion and tracking algorithms can combine both types of sources and further exploit the mobility of the user. Simulation results for different filter types show the ability of this approach to compensate the lack of satellites by additional TDOA measurements from a future 3GPP-LTE communications system. This paper analyzes the performance in a fairly realistic manner by taking into account ray-tracing simulations to generate a coherent environment for GNSS and 3GPP-LTE.

Interference-Aware Location Estimation in Cellular OFDM Communications Systems

In this paper, we consider location estimation algorithms using cellular orthogonal frequency division multiplexing (OFDM) communications systems. In the classical approach the mobile station (MS) determines time difference of arrival (TDOA) information from the received signals of at least three bases stations (BSs). With these TDOAs the location of the MS is estimated. However, only at the cell edge a good reception of several BSs is ensured. Especially for future systems targeting a frequency re-use of one, interference is a limiting factor. Hence, in the inner cell it is difficult to detect out-of-cell BSs with sufficient quality. Therefore, we propose an interference cancellation scheme to improve the performance in these critical situations for TDOA positioning. Simulation results for a 3GPP- LTE system show the ability of this approach to increase the accuracy and to extend the coverage of the overall location estimation.

Data-Aided Location Estimation in Cellular OFDM Communications Systems

In this paper, location estimation algorithms for cellular communications systems employing orthogonal frequency division multiplexing (OFDM) are investigated. To determine the location of a mobile station, usually time difference of arrival (TDOA) measurements are performed with at least three base stations using pilot symbols included in the signal streams. With these TDOAs then the location is estimated. However, the timing estimation process is faced by different effects that restrict the performance. For instance, limited number of pilot symbols, inter-cell interference, and multipath propagation decrease the accuracy of the timing estimates, and hence, position estimates. Therefore, we propose to feed back decided data symbols where particularly data-aided timing estimation and interference cancellation are considered in the location estimation context. Simulation results for a 3GPP-LTE system show the ability of these algorithms to increase the overall accuracy and reliability of location estimates.

Swarm exploration and navigation on mars

We propose autonomous robotic swarm exploration to search for extra-terrestrial life in the Valles Marineris canyon system on Mars. The swarm consists of unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). Key technologies are robust flight and swarm control algorithms as well as infrastructure-less swarm navigation. The swarm navigation uses inertial navigation, laser scanners, cameras, and relative radio positioning systems. The later one employs hybrid time-division multiple access (TDMA) - frequency division multiple access (FDMA) and interleaved round-trip delay ranging measurements. For TDMA, an autonomous distributed slot synchronization algorithm is presented.We present a swarm scenario with initially ten elements, adding one element after 6.25 s, until 25 elements are active. The synchronization algorithm is stable transmitting only five out of eight possible symbols per TDMA slot for 25 swarm elements, but is only stable for 17 swarm elements transmitting all eight symbols per slot. Nevertheless, a distributed swarm navigation particle filter achieves an accuracy of 1m or better for 21 swarm elements in the later case.

Analysis of Cellular Interference for MC-CDMA and its Impact on Channel Estimation

We address the downlink of a cellular multi-carrier CDMA (MC-CDMA) system taking into account channel estimation. The system performance in presence of a synchronization mismatch between two interfering base stations (BS) is analyzed in the way that a mobile terminal receives the perfectly synchronized signal from the desired BS as well as the signal from one interfering BS with a synchronization offset. It is demonstrated through simulations that spreading and cell specific random subcarrier interleaving effectively decorrelates the interfering signal, independent of the synchronization offset. Furthermore, the robustness of the channel estimator to cellular interference is examined

A non-linear precoding technique for downlink MC-CDMA

Multiple-access interference is a major burden for MC-CDMA schemes. The possible complexity in the hardware of the mobile terminal is another major limitation. Both burdens can be answered in the downlink by preceding the signal with the knowledge of the channel state information. In this paper, we introduce a non-linear precoder to cancel the multiple-access interference motivated by the dirty-paper approach and by taking into account a non-linear modulo operation. The results show for a SISO system a significant improvement over recent proposed precoding schemes.

System-level performance analysis for Bayesian cooperative positioning: From global to local

Cooperative positioning (CP) can be used either to calibrate the accumulated error from inertial navigation or as a stand-alone navigation system. Though intensive research has been conducted on CP, there is a need to further investigate the joint impact from the system level on the accuracy. We derive a posterior Cramér-Rao bound (PCRB) considering both the physical layer (PHY) signal structure and the asynchronous latency from the multiple access control layer (MAC). The PCRB shows an immediate relationship between the theoretical accuracy limit and the effective factors, e.g. geometry, node dynamic, latency, signal structure, power, etc. which is useful to assess a cooperative system. However, for a large-scale decentralized cooperation network, calculating the PCRB becomes difficult due to the high state dimension and the absence of global information. We propose an equivalent ranging variance (ERV) scheme which projects the neighbors positioning uncertainty to the distance measurement inaccuracy. With this scheme, the interaction among the mobile terminals (MTs), e.g. measurement and communication can be decoupled. We use the ERV to derive a local PCRB (L-PCRB) which approximates the PCRB locally at each MT with low complexity. We further propose combining the ERV and L-PCRB together to improve the precision of the Bayesian localization algorithms. Simulation with an L-PCRB-aided distributed particle filter (DPF) in two typical cooperative positioning scenarios show a significant improvement comparing with the non-cooperative or standard DPF.