Fabian Monsees

Sparsity Aware Multiuser detection for Machine to Machine communication

With the expected growth of Machine-to-Machine (M2M) communication, new requirements for future communication systems have to be considered. Traffic patterns in M2M communication fundamentally differ from human based communication. Especially packets in M2M are rather small and transmitted sporadically only. Moreover, nodes for M2M communication are often of reduced functionality which makes complex control overhead or resource management infeasible for such devices. Assuming a star-topology with a central aggregation node that processes all node information one possibility to reduce control signaling is to shift the activity detection fully to the central aggregation node. The methodology of a joint activity and data detection differs strongly from common communication scenarios since errors during the activity detection are fundamentally different from errors made at data detection. In this paper we introduce a non-linear joint activity and data detector for M2M communication. The performance regarding data and activity errors is assessed and compared to a scenario where node activity is known by the aggregation node.

Compressive Sensing Multi-User Detection for Multicarrier Systems in Sporadic Machine Type Communication

Massive Machine Type Communication is seen as one major driver for the research of new physical layer technologies for future communication systems. To handle massive access, the main challenges are avoiding control signaling overhead, low complexity data processing per sensor, supporting of diverse but rather low data rates and a flexible and scalable access. To address all these challenges, we propose a combination of compressed sensing based detection known as Compressed Sensing based Multi User Detection (CS-MUD) with multicarrier access schemes. We name this novel combination Multicarrier CS-MUD (MCSM). Previous investigations on CS-MUD facilitates massive direct random access by exploiting the signal sparsity caused by sporadic sensor activity. The new combined scheme MCSM with its flexibility in accessing time frequency resources additionally allows for either reducing the number of subcarriers or shortening the multicarrier symbol duration, i.e., we gain a high spectral efficiency. Simulation results are given to show the performance of the proposed scheme.

Compressed Sensing Bayes Risk Minimization for Under-Determined Systems via Sphere Detection

The application of Compresses Sensing is a promising physical layer technology for the joint activity and data detection of signals. Detecting the activity pattern correctly has severe impact on the system performance and is therefore of major concern. In contrast to previous work, in this paper we optimize joint activity and data detection in under-determined systems by minimizing the Bayes-Risk for erroneous activity detection. We formulate a new Compressed Sensing Bayes-Risk detector which directly allows to influence error rates at the activity detection dynamically by a parameter that can be controlled at higher layers. We derive the detector for a general linear system and show that our detector outperforms classical Compressed Sensing approaches by investigating an overloaded CDMA system.

Reliable activity detection for massive machine to machine communication via multiple measurement vector compressed sensing

Compressed sensing based multiuser detection is a novel research field in massive machine to machine communication. Mainly focusing at decreasing signaling overhead, this approach implements sophisticated detection algorithms at the physical layer that jointly estimate activity and data. As a consequence, the reliability of the activity detection is crucial for the system performance as data is lost if users are erroneously classified as inactive. This paper introduces a novel approach to estimate node activity on a per frame basis by Multiple Measurement Vector Compressed Sensing approaches. This approach allows for reliable activity detection with complexity invariant of the length of the transmitted frame. Moreover, we are able to show that this approach works with only a few measurements available to the detector. In particular we demonstrate that reliable activity detection is possible if the number of observations is larger than the square root of the number of nodes in the system.

Joint activity and data detection for machine to machine communication via Bayes Risk optimization

Performing joint detection of activity and data is a promising approach to reduce management overhead in Machine-to-Machine communication. However, erroneous activity detection has severe impacts on the system performance. Estimating an active node or user erroneously to be inactive results in a loss of data. To optimally balance activity and data detection, we derive a novel joint activity and data detector that bases on the minimization of the Bayes Risk. The Bayes Risk detector allows to control error rates with respect to the activity detection dynamically by a parameter that can be controlled by higher layers. In this paper we derive the Bayes Risk detector for a general linear system and present exemplary results for a specific Machine-to-Machine communication scenario.

Compressed Sensing soft activity processing for sparse multi-user systems

Performing joint activity and data detection has recently gained ubiquitous attention for reducing signaling overhead in multi-user Machine-to-Machine Communication systems. To this end investigations on the application of Compressed Sensing have focused on estimating data and node activity jointly in scenarios where the per node activity is very low. The focus of this paper is to enhance the performance of state of the art detectors by the utilization of soft information. In particular we provide a formulation of activity Log-Likelihood ratios, that we utilize to improve the activity detection.

On the Impact of Low-Rank Interference on the Post-Equalizer SINR in LTE

The standardization of the fourth generation of mobile communication systems was mainly driven by the demands for higher data-rates and improved Quality of Service. To reach these goals interference coordination has been identified as a promising research field for better exploitation of the time and frequency resources. This paradigm shift from interference avoidance to interference coordination is also reflected in the ongoing enhancement of the 4th generation of mobile communication systems such as 3GPP Long Term Evolution. In this context, numerous investigations have focused on the allocation of precoding matrices that are part of the link adaptation process by some form of base station (eNB) coordination. Within this work we develop a non-centralized interference coordination scheme by noticing that the re-allocation of a precoding matrix can lead to an uncontrolled change of the interference level at users located in neighboring cells, especially at the edge. To this end, we provide a fully closed form mathematical framework describing these changes. Based on this, we derive a simple metric that improves the precoding matrix selection process in the User Equipment with the result that interference changes can be reduced without having any standard impact. This novel scheme can also be seen as an extension to previous inter-cell interference coordination schemes without the need of base-station cooperation.

On the SINR distribution of codebook-based precoding in LTE in case of inter-cell interference

Interference is an important performance limiting factor in cell-based mobile communication scenarios. Especially entities located at cell edges significantly suffer from inter-cell interference caused by base stations serving users in neighboring cells. The standard of LTE considers a frequency reuse factor of one, which makes inter-cell interference coordination a widely discussed topic. However, most of these techniques lead to a reduction of inter-cell interference instead of a full suppression. Hence, inter-cell interference remains an unpredictable random effect that has to be classified statistically. Within this paper, we derive the probability density function of the inter-cell interference and the resulting SINR PDF, which is given as a non-linear transformation of the interference. Unitary matrix precoding as applied in LTE Rel. 8 is taken into regard. The results are verified by simulative investigations.

A potential solution for MTC: Multi-Carrier Compressed Sensing Multi-User Detection

Compressed Sensing Multi-User Detection is a recently developed physical layer method to decrease signaling in massive Machine communications by using means from the field of Compressed Sensing and sparse signal processing. Within this work we use the advances of recent research and present a non-coherent CS-MUD system concept basing on a combination of multi-carrier modulation and CDMA. This so called Multi- Carrier Compressed Sensing Multi-User Detection (MCSM) concept aims at multiplexing machine-to-machine traffic in narrow band transmissions over the radio resources. Using non-coherent modulation schemes further decreases the needs for pilot symbols and increases robustness with respect to carrier frequency offsets.

Compressed sensing Bayes-risk detection for frame based multi-user systems

Performing joint activity and data detection has recently gained attention for reducing signaling overhead in multi-user Machine-to-Machine Communication systems. In this context, Compressed Sensing has been identified as a good candidate for joint activity and data detection especially in scenarios where the activity probability is very low. This paper augments activity and data detection for frame based multi-user uplink scenarios where nodes are (in)active for the duration of a frame. We propose a two stage detector which first estimates the set of active nodes followed by a data detector. Our detector outperforms symbol-by-symbol Maximum a posteriori detection.