688 research outputs found
Communication Theoretic Data Analytics
Widespread use of the Internet and social networks invokes the generation of
big data, which is proving to be useful in a number of applications. To deal
with explosively growing amounts of data, data analytics has emerged as a
critical technology related to computing, signal processing, and information
networking. In this paper, a formalism is considered in which data is modeled
as a generalized social network and communication theory and information theory
are thereby extended to data analytics. First, the creation of an equalizer to
optimize information transfer between two data variables is considered, and
financial data is used to demonstrate the advantages. Then, an information
coupling approach based on information geometry is applied for dimensionality
reduction, with a pattern recognition example to illustrate the effectiveness.
These initial trials suggest the potential of communication theoretic data
analytics for a wide range of applications.Comment: Published in IEEE Journal on Selected Areas in Communications, Jan.
201
Single-Carrier Delay Alignment Modulation for Multi-IRS Aided Communication
Delay alignment modulation (DAM) is a promising technology to achieve
ISI-free wideband communication, by leveraging delay compensation and
path-based beamforming, rather than the conventional channel equalization or
multi-carrier transmission. In particular, when there exist a few strong
time-dispersive channel paths, DAM can effectively align different propagation
delays and achieve their constructive superposition, thus especially appealing
for intelligent reflecting surfaces (IRSs)-aided communications with
controllable multi-paths. In this paper, we apply DAM to multi-IRS aided
wideband communication and study its practical design and achievable
performance. We first provide an asymptotic analysis showing that when the
number of base station (BS) antennas is much larger than that of IRSs, an
ISI-free channel can be established with appropriate delay pre-compensation and
the simple path-based MRT beamforming. We then consider the general system
setup and study the problem of joint path-based beamforming and phase shifts
design for DAM transmission, by considering the three classical beamforming
techniques on a per-path basis, namely the low-complexity path-based MRT
beamforming, the path-based ZF beamforming for ISI-free DAM communication, and
the optimal path-based MMSE beamforming. As a comparison, OFDM-based multi-IRS
aided communication is considered. Simulation results demonstrate that DAM
outperforms OFDM in terms of spectral efficiency, BER, and PAPR.Comment: 16 pages, 10 figure
Minimum BER Criterion Based Robust Blind Separation for MIMO Systems
In this paper, a robust blind source separation (BSS) algorithm is investigated based on a new cost function for noise suppression. This new cost function is established according to the criterion of minimum bit error rate (BER) incorporated into maximum likelihood (ML) principle based independent component analysis (ICA). With the help of natural gradient search, the blind separation work is carried out through optimizing this constructed cost function. Simulation results and analysis corroborate that the proposed blind separation algorithm can realize better performance in speed of convergence and separation accuracy as opposed to the conventional ML-based BSS
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
Semi-supervised MIMO Detection Using Cycle-consistent Generative Adversarial Network
In this paper, a new semi-supervised deep multiple-input multiple-output
(MIMO) detection approach using a cycle-consistent generative adversarial
network (CycleGAN) is proposed for communication systems without any prior
knowledge of underlying channel distributions. Specifically, we propose the
CycleGAN detector by constructing a bidirectional loop of two modified least
squares generative adversarial networks (LS-GAN). The forward LS-GAN learns to
model the transmission process, while the backward LS-GAN learns to detect the
received signals. By optimizing the cycle-consistency of the transmitted and
received signals through this loop, the proposed method is trained online and
semi-supervisedly using both the pilots and the received payload data. As such,
the demand on labelled training dataset is considerably controlled, and thus
the overhead is effectively reduced. Numerical results show that the proposed
CycleGAN detector achieves better performance in terms of both bit error-rate
(BER) and achievable rate than existing semi-blind deep learning (DL) detection
methods as well as conventional linear detectors, especially when considering
signal distortion due to the nonlinearity of power amplifiers (PA) at the
transmitter
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