Carsten Bockelmann

Compressive sensing based multi‐user detection for machine‐to‐machine communication

With the expected growth of machine-to-machine communication, new requirements for future communication systems have to be considered. More specifically, the sporadic nature of machine-to-machine communication, low data rates, small packets and a large number of nodes necessitate low overhead communication schemes that do not require extended control signaling for resource allocation and management. Assuming a star topology with a central aggregation node that processes all sensor information, one possibility to reduce control signaling is the estimation of sensor node activity. In this paper, we discuss the application of greedy algorithms from the field of compressive sensing in a channel coded code division multiple access context to facilitate a joint detection of sensor node activity and transmitted data. To this end, a short introduction to compressive sensing theory and algorithms will be given. The main focus, however, will be on implications of this new approach. Especially, we consider the activity detection, which strongly determines the performance of the overall system. We show that the performance on a system level is dominated by the missed detection rate in comparison with the false alarm rate. Furthermore, we will discuss the incorporation of activity-aware channel coding into this setup to extend the physical layer detection capabilities to code-aided joint detection of data and activity. Copyright

Coping with CDMA Asynchronicity in Compressive Sensing Multi-User Detection

The growing field of Machine-to-Machine communication requires new physical layer concepts to meet future requirements. In previous works it has been shown for a synchronous CDMA transmission that Compressive Sensing (CS) detectors are capable of jointly detecting both activity and data in multi-user detection (MUD). However, many practical applications show some degree of asynchronicity. In order to reduce transmitter complexity, we propose an enhanced CS MUD that detects the delay in addition to activity and data. This solves synchronicity issues for scenarios with a known maximum delay, without requiring signaling or pre-compensation of asynchronicity.

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.

Characterization of coded random access with compressive sensing based multi-user detection

The emergence of Machine-to-Machine (M2M) communication requires new Medium Access Control (MAC) schemes and physical (PHY) layer concepts to support a massive number of access requests. The concept of coded random access, introduced recently, greatly outperforms other random access methods and is inherently capable to take advantage of the capture effect from the PHY layer. Furthermore, at the PHY layer, compressive sensing based multi-user detection (CS-MUD) is a novel technique that exploits sparsity in multi-user detection to achieve a joint activity and data detection. In this paper, we combine coded random access with CS-MUD on the PHY layer and show very promising results for the resulting protocol.

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.

A closed power allocation solution for outage restricted distributed MIMO multi-hop networks

Power consumption and Quality-of-Service are the critical factors when developing resource allocation strategies for wireless networks. In order to minimize total transmission power while meeting the end-to-end outage probability requirement in a distributed MIMO multi-hop network, we will formulate the power allocation task as a convex optimization problem. By using some approximations to the optimization problem, we derive a novel near-optimal power allocation solution with lower complexity for distributed MIMO multi-hop networks. For the network with a large number of relaying nodes per virtual antenna array even a simple closed-form solution can be obtained. The simulation results show that our solution achieves a near-optimal performance.

Compressive sensing for MTC in new LTE uplink multi-user random access channel

In LTE, establishing a connection requires a relatively complex handshaking procedure. Such an approach is suitable for a system serving only a few high activity users, but it becomes very cumbersome for machine to machine (M2M) traffic, where large amounts of low activity users intermittently transmit a small number of packets. To avoiding excessive signaling overhead, each packet has to facilitate user detection, channel estimation, and data decoding. Even in the case of limited network activity, users may transmit simultaneously, resulting in packet collisions. It has been shown that such traffic can be best served by a Compressive Sensing (CS) detector. However, most of the CS-based multi-user detection (CS-MUD) research deals with Code Division Multiple Access (CDMA) type systems. In this work we propose a CS-MUD algorithm that is designed for single carrier OFDM (SC-OFDM) systems and, as such, can be integrated into LTE uplink subframes. Each packet contains both a user identification code (ID) and data. The CS algorithm uses the ID not only for user detection, but also for channel estimation. We investigate random and structured ID code generation and report system performance in both cases.

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.

Improving Greedy Compressive Sensing Based Multi-User Detection with Iterative Feedback

Machine-to-Machine communication requires new physical layer concepts to meet future requirements. In previous works it has already been shown that Compressive Sensing (CS) detectors are capable of jointly detecting both activity and data in multi-user detection (MUD). For this detection we propose a new generalized Group Orthogonal Matching Pursuit algorithm that allows the use of additional side information regarding the sparsity structure. As a specific example, we exploit the information of a sparsity-aware Viterbi decoder in an iterative feedback loop to improve the activity detection. Here, a significant improvement of the activity detection is already achieved by executing only a single additional detection and decoding step.

Efficient Detectors for Joint Compressed Sensing Detection and Channel Decoding

In slotted random access of many nodes, multi-user detection (MUD) can be applied to handle collisions. One novel PHY layer approach for jointly detecting activity and data in such a setting is Compressed Sensing based Multi-User Detection (CS-MUD). In this paper, we first summarize previous investigations on CS-MUD and subsequently propose two novel solutions for problems which have not yet been fully addressed: Firstly, we improve on previous results, by introducing a new approach which incorporates the channel decoder into the Compressed Sensing (CS) detector. Secondly, we analyze the resource efficiency of CS-MUD by adapting phase diagrams known from CS literature to the application of sporadic communication.