40 research outputs found
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.Comment: Submitted to Globecom 201
On the Importance of Exploration for Real Life Learned Algorithms
The quality of data driven learning algorithms scales significantly with the
quality of data available. One of the most straight-forward ways to generate
good data is to sample or explore the data source intelligently. Smart sampling
can reduce the cost of gaining samples, reduce computation cost in learning,
and enable the learning algorithm to adapt to unforeseen events. In this paper,
we teach three Deep Q-Networks (DQN) with different exploration strategies to
solve a problem of puncturing ongoing transmissions for URLLC messages. We
demonstrate the efficiency of two adaptive exploration candidates,
variance-based and Maximum Entropy-based exploration, compared to the standard,
simple epsilon-greedy exploration approach
Learning Resource Scheduling with High Priority Users using Deep Deterministic Policy Gradients
Advances in mobile communication capabilities open the door for closer
integration of pre-hospital and in-hospital care processes. For example,
medical specialists can be enabled to guide on-site paramedics and can, in
turn, be supplied with live vitals or visuals. Consolidating such
performance-critical applications with the highly complex workings of mobile
communications requires solutions both reliable and efficient, yet easy to
integrate with existing systems. This paper explores the application of Deep
Deterministic Policy Gradient~(\ddpg) methods for learning a communications
resource scheduling algorithm with special regards to priority users. Unlike
the popular Deep-Q-Network methods, the \ddpg is able to produce
continuous-valued output. With light post-processing, the resulting scheduler
is able to achieve high performance on a flexible sum-utility goal
Towards Massive Connectivity Support for Scalable mMTC Communications in 5G networks
The fifth generation of cellular communication systems is foreseen to enable
a multitude of new applications and use cases with very different requirements.
A new 5G multiservice air interface needs to enhance broadband performance as
well as provide new levels of reliability, latency and supported number of
users. In this paper we focus on the massive Machine Type Communications (mMTC)
service within a multi-service air interface. Specifically, we present an
overview of different physical and medium access techniques to address the
problem of a massive number of access attempts in mMTC and discuss the protocol
performance of these solutions in a common evaluation framework
Semantic Information Retrieval in Wireless Networks
Motivated by recent success of Machine Learning (ML) tools in wireless
communications, the idea of semantic communication by Weaver from 1949 has
received considerable attention. It breaks with the classic design paradigm of
Shannon by aiming to transmit the meaning of a message, i.e., semantics, rather
than its exact copy and thus allows for savings in channel uses or information
rate. In this work, we extend the fundamental approach from Basu et al. for
modeling semantics from logical to probabilistic entailment relations between
meaning and messages. Thus, we model semantics by means of a hidden random
variable and define the task of semantic communication as transmission of
messages over a communication channel such that semantics is best preserved. We
formulate the semantic communication design either as an Information
Maximization or as an Information Bottleneck optimization problem. Finally, we
propose the ML-based semantic communication system SINFONI for a distributed
multipoint scenario: SINFONI communicates the meaning behind multiple messages
that are observed at different senders to a single receiver for semantic
retrieval. We analyze SINFONI by processing images as an example of messages.
Numerical results reveal a tremendous rate normalized SNR shift up to 20 dB
compared to classically designed communication systems.Comment: Submitted for peer revie
Joint activity and data detection for machine to machine communication via bayes risk optimization
Abstract—Performing joint detection of activity and data is a promising approach to reduce management overhead in Machineto-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. I