851 research outputs found
Jointly Sparse Support Recovery via Deep Auto-encoder with Applications in MIMO-based Grant-Free Random Access for mMTC
In this paper, a data-driven approach is proposed to jointly design the
common sensing (measurement) matrix and jointly support recovery method for
complex signals, using a standard deep auto-encoder for real numbers. The
auto-encoder in the proposed approach includes an encoder that mimics the noisy
linear measurement process for jointly sparse signals with a common sensing
matrix, and a decoder that approximately performs jointly sparse support
recovery based on the empirical covariance matrix of noisy linear measurements.
The proposed approach can effectively utilize the feature of common support and
properties of sparsity patterns to achieve high recovery accuracy, and has
significantly shorter computation time than existing methods. We also study an
application example, i.e., device activity detection in Multiple-Input
Multiple-Output (MIMO)-based grant-free random access for massive machine type
communications (mMTC). The numerical results show that the proposed approach
can provide pilot sequences and device activity detection with better detection
accuracy and substantially shorter computation time than well-known recovery
methods.Comment: 5 pages, 8 figures, to be publised in IEEE SPAWC 2020. arXiv admin
note: text overlap with arXiv:2002.0262
Achieving Energy-Efficient Uplink URLLC with MIMO-Aided Grant-Free Access
The optimal design of the energy-efficient multiple-input multiple-output
(MIMO) aided uplink ultra-reliable low-latency communications (URLLC) system is
an important but unsolved problem. For such a system, we propose a novel
absorbing-Markov-chain-based analysis framework to shed light on the puzzling
relationship between the delay and reliability, as well as to quantify the
system energy efficiency. We derive the transition probabilities of the
absorbing Markov chain considering the Rayleigh fading, the channel estimation
error, the zero-forcing multi-user-detection (ZF-MUD), the grant-free access,
the ACK-enabled retransmissions within the delay bound and the interactions
among these technical ingredients. Then, the delay-constrained reliability and
the system energy efficiency are derived based on the absorbing Markov chain
formulated. Finally, we study the optimal number of user equipments (UEs) and
the optimal number of receiving antennas that maximize the system energy
efficiency, while satisfying the reliability and latency requirements of URLLC
simultaneously. Simulation results demonstrate the accuracy of our theoretical
analysis and the effectiveness of massive MIMO in supporting large-scale URLLC
systems.Comment: 14 pages, 9 figures, accepted to appear on IEEE Transactions on
Wireless Communications, Aug. 202
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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