A5042872699- Sparse and Compressive Sensing Techniques
- Advanced MIMO Systems Optimization
- Distributed Sensor Networks and Detection Algorithms
- IoT Networks and Protocols
- Advanced Wireless Communication Techniques
- Advanced Wireless Communication Technologies
- Advanced Wireless Network Optimization
- Indoor and Outdoor Localization Technologies
- Age of Information Optimization
- Satellite Communication Systems
- Blind Source Separation Techniques
- Wireless Body Area Networks
- Wireless Signal Modulation Classification
- Power Line Communications and Noise
- Telecommunications and Broadcasting Technologies
- Wireless Communication Networks Research
- Wireless Communication Security Techniques
- Cooperative Communication and Network Coding
- Advanced Photonic Communication Systems
- PAPR reduction in OFDM
- Energy Efficient Wireless Sensor Networks
- IoT and Edge/Fog Computing
- Full-Duplex Wireless Communications
- Analog and Mixed-Signal Circuit Design
- Interconnection Networks and Systems
University of Bremen
2016-2025
MTC are expected to play an essential role within future 5G systems. In the FP7 project METIS, has been further classified into mMTC and uMTC. While is about wireless connectivity tens of billions machinetype terminals, uMTC availability, low latency, high reliability. The main challenge in scalable efficient for a massive number devices sending very short packets, which not done adequately cellular systems designed human-type communications. Furthermore, solutions need enable wide area...
The fifth generation of cellular communication systems is foreseen to enable a multitude new applications and use cases with very different requirements. A 5G multi-service air interface needs enhance broadband performance as well provide levels reliability, latency, supported number users. In this paper, we focus on the massive Machine Type Communications (mMTC) service within interface. Specifically, present an overview physical medium access techniques address problem attempts in mMTC...
ABSTRACT With the expected growth of machine‐to‐machine communication, new requirements for future communication systems have to be considered. More specifically, sporadic nature low data rates, small packets and a large number nodes necessitate overhead schemes that do not require extended control signaling resource allocation management. Assuming star topology with central aggregation node processes all sensor information, one possibility reduce is estimation activity. In this paper, we...
Massive Machine Type Communication is seen as one major driver for the research of new physical layer technologies future communication systems. To handle massive access, main challenges are avoiding control signaling overhead, low complexity data processing per sensor, supporting diverse but rather rates and a flexible scalable access. address all these challenges, we propose combination compressed sensing based detection known Compressed Sensing Multi User Detection (CS-MUD) with...
Motivated by the recent success of Machine Learning (ML) tools in wireless communications, idea semantic communication Weaver from 1949 has gained attention. It breaks with Shannon's classic design paradigm aiming to transmit meaning a message, i.e., semantics, rather than its exact version and, thus, enables savings information rate. In this work, we extend fundamental approach Basu et al. for modeling semantics complete communications Markov chain. Thus, model means hidden random variables...
In order to make the Internet of Things a reality, ubiquitous coverage and low-complexity connectivity are required. Cellular networks hence most straightforward realistic solution enable massive deployment always connected Machines around globe. Nevertheless, paradigm shift in conception design future cellular is called for. Massive access attempts, cheap machines, sporadic transmission correlated signals among main properties this new whose consequence disruption development current...
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). this paper, we first summarize previous investigations on CS-MUD subsequently propose two solutions problems which have not yet been fully addressed: Firstly, improve results, by introducing new incorporates the channel decoder into (CS)...
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 access requests. concept coded random access, introduced recently, greatly outperforms other methods is inherently capable take advantage the capture effect from PHY layer. Furthermore, at layer, compressive sensing based multi-user detection (CS-MUD) novel technique that exploits sparsity in achieve joint activity data...
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 jointly detecting both activity and data in multi-user detection (MUD). However, many practical applications show some degree asynchronicity. order reduce transmitter complexity, we propose an enhanced CS MUD detects the delay addition data. This...
Massive machine-type communications (mMTC) is one of the key application scenarios fifth generation (5G) and beyond cellular networks. Bringing unique technical challenge supporting a huge number MTC devices (MTCD) in networks, how to efficiently estimate channel, detect active users data this scenario an open research topic. In regard, paper aims present overview different techniques address problem channel estimation, activity detection specifically for mMTC scenario. order highlight...
With the expected growth of Machine-to-Machine (M2M) communication, new requirements for future communication systems have to be considered. Traffic patterns in M2M fundamentally differ from human based communication. Especially packets are rather small and transmitted sporadically only. Moreover, nodes often reduced functionality which makes complex control overhead or resource management infeasible such devices. Assuming a star-topology with central aggregation node that processes all...
This work investigates the potential of employing approach Compressed Sensing Dynamic Mode Decomposition (CS-DMD) in context time-varying wireless channels. To best authors' knowledge, this marks first instance utilizing CS-DMD for pilot-based channel estimation Orthogonal Frequency Division Multiplexing (OFDM) systems. The effectiveness method is compared with two advanced deep learning-based techniques: Interpolation-ResNet and Learned Approximate Message Passing (LAMP). Furthermore, we...
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 we propose a generalized Group Orthogonal Matching Pursuit algorithm allows the use additional side information regarding sparsity structure. As specific example, exploit sparsity-aware Viterbi decoder an iterative...
In LTE, establishing a connection requires relatively complex handshaking procedure. Such an approach is suitable for system serving only few high activity users, but it becomes very cumbersome machine to (M2M) traffic, where large amounts of low users intermittently transmit small number packets. To avoiding excessive signaling overhead, each packet has facilitate user detection, channel estimation, and data decoding. Even in the case limited network activity, may simultaneously, resulting...
Compressed Sensing based Multi-User Detection (CS-MUD) is a novel MUD approach applied in sporadic Machine Type Communication (MTC) to identify actively transmitting sensors nodes at the same time as transmitted data. In this context, different reconstruction algorithms from CS well detection concepts established communications have been adapted, but either are not exploiting finite alphabets or highly complex. paper, we focus on an iterative soft interference cancellation scheme that...
Compressed sensing based multiuser detection is a novel research field in massive machine to communication. Mainly focusing at decreasing signaling overhead, this approach implements sophisticated algorithms the physical layer that jointly estimate activity and data. As consequence, reliability of crucial for system performance as data lost if users are erroneously classified inactive. This paper introduces node on per frame basis by Multiple Measurement Vector Sensing approaches. allows...
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 and sparse signal processing. Within this work we use advances recent research present non-coherent CS-MUD system concept basing on combination multi-carrier modulation CDMA. This so called Multi- Carrier (MCSM) aims at multiplexing machine-to-machine traffic narrow band transmissions over radio resources. Using schemes...
Machine-type communications are quite often of very low data rate and sporadic nature therefore not well-suited for nowadays high cellular communication systems. Since signaling overhead must be reasonable in relation to message size, research towards joint activity estimation was initiated. When the detection multiuser signals is modeled as a sparse vector recovery problem, concerning node can avoided it demonstrated previous works. In this paper we show how well-known K-Best modified...